Immersive overhead line virtual survey design method and system based on oblique photography

Abstract

The invention provides an immersive overhead line virtual survey design method and system based on oblique photography. The scheme comprises the following steps: acquiring oblique photography data of a to-be-laid overhead line area; partitioning the obtained oblique photography data through neighborhood expansion and a dynamic merging strategy according to digital elevation data characteristics; modeling an overhead line area to be laid on the basis of the partitioned oblique photography data to obtain a virtual scene model; pre-designed overhead line power grid information is imported into the virtual scene model, optimization of an overhead line is carried out in a virtual scene through man-machine interaction design, and design of the overhead line is achieved; wherein in the optimization of the overhead line, a strategy based on adaptive discrete point solution and polynomial approximation is adopted to carry out shape-finding optimization of the catenary.

Description

Technical Field

[0001] The invention belongs to the technical field of overhead line design, and in particular relates to an immersive overhead line virtual survey and design method and system based on oblique photography. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The survey and design of overhead lines is a key link in the construction of power projects. The traditional survey and design methods of overhead lines have the following problems: the traditional survey and design methods mainly rely on field surveys, which have low on-site efficiency. Moreover, due to the on-site perspective and conditions, it is difficult to select a reasonable plan in time, and the line is often changed repeatedly, which takes a long time; field surveys face various safety hazards and require a lot of manpower and material resources for field surveying and mapping, which has poor safety and requires high costs; due to on-site conditions and time constraints, it is difficult to conduct comprehensive and in-depth analysis and optimization; all professionals must be on site at the same time for collaborative design and review.

[0004] Based on the problems of traditional overhead line survey and design methods, some digital tools have been developed to assist survey and design, such as satellite images, geographic information systems (GIS) and 3D design platforms. However, these tools have the following limitations: Insufficient scene accuracy: The scale of satellite images or aerial images such as orthophoto maps (DOM, Digital Orthophoto Map) currently used in engineering generally does not exceed 1:1000, and the surface details are not adequately expressed, and they cannot replace field surveys; Lack of realism: Digital elevation models / digital terrain models (DEM / DOM, Digital Elevation Model / Digital Terrain Model) commonly used in engineering are not accurate enough. The scene constructed by the 3D model only has the original height information of the surface, but lacks the 3D height information of the objects. For example, the height information of various buildings (structures) such as power lines, bridges or houses are filtered out in the above models. The scene is very different from the site, which makes it impossible to reach the depth of field survey. Poor interactivity: the current 3D design platform generally drives the design plan through data files. When optimizing the design plan, it is necessary to modify the data file in the 2D design environment first, then regenerate the technical plan, and finally generate the 3D model. It is out of touch with the 3D integration and cannot realize the working mode of simulating the field final survey. Data fragmentation: Since the design of the original data to the design of the 3D model includes many technical gaps such as the regeneration of the power grid information model in the virtual scene and the dynamic model modification after modeling, it is difficult for the original design data to directly generate the final visualization technical plan in the virtual scene. The use of massive high-precision surveying and mapping data for oblique photography modeling consumes a lot of computing and storage resources. The traditional solution lacks consideration of the data modeling and overhead line optimization calculation efficiency, resulting in low efficiency of overhead line optimization design. Summary of the invention

[0005] The present invention provides an immersive overhead line virtual survey and design method and system based on oblique photography to solve the problem that the existing solutions lack consideration of data modeling and overhead line optimization calculation efficiency, resulting in low efficiency of overhead line optimization design; and the existing solutions have poor interactivity and usually adopt a method of data file-driven design solution, which cannot intuitively display the design solution in a three-dimensional scene.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0007] In a first aspect, the present invention provides an immersive overhead line virtual survey and design method based on oblique photography, comprising:

[0008] Obtain oblique photography data of the area where the overhead line is to be laid;

[0009] The obtained oblique photography data is divided into blocks according to the characteristics of digital elevation data through neighborhood expansion and dynamic merging strategies; and the area where the overhead line is to be laid is modeled based on the divided oblique photography data to obtain a virtual scene model;

[0010] The pre-designed overhead line power grid information is imported into the virtual scene model, and the overhead line is optimized in the virtual scene through human-computer interactive design to realize the design of the overhead line; wherein, in the optimization of the overhead line, a strategy based on adaptive discrete point solution and polynomial approximation is adopted to perform catenary form optimization.

[0011] Furthermore, the method of dividing the oblique photography data into blocks according to the characteristics of the digital elevation data through neighborhood expansion and dynamic merging strategies is specifically as follows: evaluating the current computing resources to determine the maximum workload of the current computing resources when performing tasks; preliminarily dividing the oblique photography data into blocks according to the characteristics of the digital elevation data; calculating the workload of each block separately; and refining and merging the obtained blocks with the goal of minimizing the difference between the workload of each block and the maximum workload to obtain new blocks.

[0012] Furthermore, the obtained blocks are refined and merged, specifically:

[0013] For blocks whose workload is greater than the maximum workload, recursive refinement or binary division is used to gradually refine the large blocks into smaller blocks, and the workload of each sub-block is recalculated until the preset requirements are met;

[0014] For a block whose workload is less than the preset maximum workload, merge the block with the adjacent blocks;

[0015] For the blocks whose workload is between the preset proportion maximum workload and the maximum workload, they are retained.

[0016] Furthermore, the workload of each block is calculated using the following formula:

[0017] W block =A block ×(α×V slope-norm +(1-α)×R terrain-norm )

[0018] Among them, W block Represents the workload of the block, V slope-norm Represents the normalized slope change rate, R terrain-norm represents the normalized terrain roughness, A block It represents the area corresponding to the block, and α is the weight factor.

[0019] Furthermore, the maximum workload is determined by the following formula:

[0020] W threshold =min(k cpu ×P cpu ,k mem ×P mem ,k gpu ×P gpu_effective )×T unit

[0021] Among them, k cpu ,k mem ,k gpu are the weight coefficients of CPU, memory and GPU respectively, P cpu , P mem , P gpu_effective Respectively, CPU processing power, memory limit and GPU processing power, T unit The expected processing time for a single chunk.

[0022] Furthermore, the form-finding optimization of the catenary is performed by using a strategy based on adaptive discrete point solving and polynomial approximation, specifically: based on the initial information of the catenary, a quadratic polynomial approximation method is used to quickly generate initial discrete points; based on the obtained initial discrete points, the curvature change error between adjacent discrete points is calculated; based on the obtained curvature change error, new discrete points are adaptively inserted for error control, and, in areas where the curvature error is greater than a preset threshold, a hyperbolic equation is used for local correction, or Newton's iteration method is used for local optimization to obtain an optimized discrete point set, thereby achieving form-finding optimization of the catenary.

[0023] Furthermore, the initial information of the catenary includes a pre-designed start point and end point of the catenary, a total length of the catenary, a number of initial discrete points, an adaptive interpolation error threshold, and a curvature change threshold.

[0024] In a second aspect, the present invention provides an immersive overhead line virtual survey and design system based on oblique photography, comprising:

[0025] A data acquisition unit, which is used to acquire oblique photography data of the area where the overhead line is to be laid;

[0026] A virtual scene model building unit is used to divide the acquired oblique photography data into blocks according to the characteristics of digital elevation data through neighborhood expansion and dynamic merging strategies; and to model the area where the overhead line is to be laid based on the divided oblique photography data to obtain a virtual scene model;

[0027] An overhead line optimization design unit is used to import pre-designed overhead line power grid information into a virtual scene model, and optimize the overhead line in the virtual scene through human-computer interactive design to achieve the design of the overhead line; wherein, in the optimization of the overhead line, a strategy based on adaptive discrete point solution and polynomial approximation is used to perform catenary shape optimization.

[0028] In a third aspect, the present invention provides an electronic device comprising a memory, a processor and a computer program stored and running on the memory, wherein when the processor executes the program, the immersive overhead line virtual survey and design method based on oblique photography is implemented.

[0029] In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the immersive overhead line virtual survey and design method based on oblique photography.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] (1) The present invention provides an immersive overhead line virtual survey and design method and system based on oblique photography. The scheme closely combines the design process with the virtual scene by modeling the virtual scene of the area where the overhead line is to be laid, so that designers can grasp the real environment of the site and realize the optimal design of the overhead line in the immersive virtual scene.

[0032] (2) Considering that both image processing and spatial calculation of oblique photography modeling require a large amount of computing and storage resources, and the line project is located in the field, the terrain undulations and surface roughness of different regions vary greatly. Uneven block division often leads to waste of computing resources and low modeling efficiency. The solution described in the present invention provides a blocking strategy for photographic data based on digital elevation features. Through neighborhood expansion and dynamic merging, oblique photography modeling block areas can be automatically generated, thereby effectively improving the efficiency of oblique photography modeling, achieving balanced computing load, avoiding waste of computing resources, and effectively improving modeling efficiency.

[0033] (3) In view of the limited computing resources in virtual scenes, the solution described in the present invention innovatively proposes a catenary shape optimization algorithm that combines adaptive discrete point solution with polynomial approximation method, which realizes efficient calculation of the catenary, improves the computing efficiency by 30% to 50%, and greatly improves the system performance.

[0034] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0036] Figure 1 It is a schematic diagram of the overall process of an immersive overhead line virtual survey and design method based on oblique photography described in an embodiment of the present invention;

[0037] Figure 2 This is a flowchart of the oblique photography data segmentation described in an embodiment of the present invention;

[0038] Figure 3 It is a flow chart of catenary shape finding optimization described in an embodiment of the present invention;

[0039] Figure 4 It is a schematic diagram of the interactive design process described in an embodiment of the present invention;

[0040] Figure 5 The figure is a schematic diagram showing the comparison of the computational efficiency between the conventional solution described in the embodiment of the present invention and the solution described in the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0042] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0043] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0044] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0045] In one or more embodiments, Figure 1 As shown, this embodiment provides an immersive overhead line virtual survey and design method based on oblique photography, which specifically includes the following processing steps:

[0046] Step 1: Obtain oblique photography data of the area where the overhead line is to be laid;

[0047] In a specific implementation, the acquisition of the oblique photography data specifically includes the following processing steps:

[0048] (1) Use multi-rotor drones and other equipment equipped with high-resolution tilt cameras for aerial photography.

[0049] (2) Design a reasonable flight route to ensure complete oblique photography data coverage.

[0050] (3) Collect control point information for subsequent data processing and accuracy correction.

[0051] Step 2: For the oblique photography data obtained, the oblique photography data is divided into blocks according to the characteristics of the digital elevation data through neighborhood expansion and dynamic merging strategies; and the area where the overhead line is to be laid is modeled based on the divided oblique photography data to obtain a virtual scene model;

[0052] In the specific implementation, Figure 2 As shown, the segmentation of the oblique photography data specifically includes the following processing steps:

[0053] 1) Computing resource evaluation and threshold setting

[0054] Set the maximum workload W for each subtask threshold It is the basis of intelligent partitioning. First, evaluate the computing resources of the system, including CPU cores, GPU units or other computing devices. For each computing unit, define its computing capacity per unit time as C unit , it is hoped that the computation time of each subtask does not exceed T max (Usually set to 1 hour).

[0055] This allows us to set the maximum workload W for each subtask. threshold :

[0056] W threshold =C unit ×T max

[0057] Specific value examples:

[0058] Assume that the workload threshold W threshold The determination is based on the following computing resource parameters:

[0059] (1) Processor resources:

[0060] Number of CPU cores N cpu

[0061] Baseline processing power per core P core (measured in GFLOPS)

[0062] CPU frequency f cpu(GHz)

[0063] (2) Memory resources:

[0064] Available memory capacity M available (GB)

[0065] Memory speed V mem (GB / s)

[0066] (3) Graphics card resources (if GPU acceleration is involved):

[0067] GPU memory capacity M gpu (GB)

[0068] Number of GPU computing units N cuda

[0069] GPU processing power gpu (TFLOPS)

[0070] Based on the above parameters, the calculation method of the workload threshold is as follows:

[0071] CPU processing power evaluation: N cpu =N cpu ×P core ×f cpu_factor where f cpu_factor is the main frequency influence coefficient;

[0072] Memory Limit Assessment: P mem =min(M available ×0.8,V mem ) Reserve 20% of memory as system buffer;

[0073] GPU processing power evaluation (if available): P gpu_effective =P gpu ×mem ratio where mem ratio is the memory occupancy rate influence coefficient;

[0074] Comprehensive workload threshold: W threshold =min(k cpu ×P cpu ,k mem ×P mem ,k gpu ×P gpu_effective )×T unit .

[0075] Where: k cpu ,k mem ,k gpu is the weight coefficient of each resource, T unitThe expected processing time for a single block (e.g. 1 hour)

[0076] For example: For the standard configuration (8-core CPU, 16GB memory, 4GB independent graphics card):

[0077] ·N cpu =8,P core =50GFLOPS,f cpu =3.0GHz

[0078] ·M available =16GB,V mem =25.6GB / s

[0079] ·M gpu =4GB,P gpu =5TFLOPS

[0080] Substitute into the calculation and get: W threshold ≈1200 (standardized workload units);

[0081] 2) Preliminary Block Division

[0082] According to the DEM data, the area is initially divided into several blocks, each of which contains the corresponding area A block and the elevation data it contains.

[0083] 3) Calculate the workload of each block

[0084] Define the workload W for each block block , which depends on the area of ​​the block and the terrain complexity. The terrain complexity is usually measured by the slope change rate V slope and terrain roughness R terrain The workload of each block can be expressed as:

[0085] W block =A block ×(α×V slope-norm +(1-α)×R terrain-norm )

[0086] Among them, α is a weight factor, which is between 0 and 1. The selection of roughness and slope change rate can be selected in combination with the terrain conditions, or adjusted with reference to the vegetation conditions to be more suitable for DSM modeling.

[0087] 4) Normalized calculation of slope change rate and terrain roughness

[0088] Normalization of slope change rate:

[0089] Slope change rate V slope Represents the change of terrain elevation in space. Its normalized calculation formula is:

[0090]

[0091] Among them, V slope The slope change rate indicates the degree of undulation of terrain elevation, and is calculated as follows:

[0092]

[0093] Among them, Δh is the elevation difference of the grids, and Δd is the horizontal distance between the grids.

[0094] In the block area, calculate the average slope change rate of all grids as the V of the block. slope .

[0095] Terrain roughness normalization:

[0096] Terrain roughness R terrain Represents the undulation and complexity of the terrain surface. Its normalized calculation formula is:

[0097]

[0098] Among them, R terrain Terrain roughness is used to measure the complexity of the surface of the block area. The specific calculation is as follows:

[0099]

[0100] Among them, h i is the elevation value of the current grid, h avg is the average elevation value of adjacent grids, and N is the total number of all grids in the block.

[0101] The elevation differences of all grids are averaged to obtain the terrain roughness R of the block area. terrain .

[0102] 5) Intelligent block adjustment

[0103] The goal of smart chunking is to ensure that the workload of each chunk is close to W by neighborhood expansion and dynamic merging. threshold , to avoid blocks that are too large or too small.

[0104] (1) Neighborhood expansion (refined block division):

[0105] When the workload of a block W block >W threshold , it means that the workload of the block is too large and needs to be refined. The steps of refinement are as follows:

[0106] Refinement process: Divide the block into several sub-blocks, ensuring that the workload of each sub-block gradually decreases and approaches W threshold .

[0107] Refinement strategy: Recursive refinement or binary division can be used to gradually refine large blocks into smaller blocks and recalculate the workload of each sub-block.

[0108] (2) Dynamic merging (expanding blocks):

[0109] When the workload of a block W block <0.8×W threshold , it means that the workload of this block is too small, which may lead to resource waste. At this time, you can try to merge this block with the adjacent block. Here 0.8 is just an example, which can be combined with T max Adjust the size of T max The larger it is, the smaller the value can be.

[0110] (3) Moderate workload (reasonable division)

[0111] When 0.8×W threshold ≤W block ≤W threshold When , the workload of the block is close to the optimal level and no further adjustment is needed, so the current block is directly retained.

[0112] 6) Termination judgment and final output

[0113] Termination condition: When the workload of all blocks is 0.8×W threshold ≤W block ≤W threshold If the maximum number of iterations is reached, the algorithm stops executing.

[0114] Final output: Output the boundaries and workload distribution results of all blocks that meet the conditions as modeling input.

[0115] Step 3: Import the pre-designed overhead line power grid information into the virtual scene model, and optimize the overhead line in the virtual scene through human-computer interactive design to realize the design of the overhead line; wherein, in the optimization of the overhead line, a strategy based on adaptive discrete point solution and polynomial approximation is adopted to perform catenary form optimization.

[0116] In a specific implementation, the human-computer interaction design specifically includes the following measures:

[0117] (1) Modify the technical solution directly in the virtual space through the VR controller;

[0118] (2) Support real-time adjustment of tower position, tower type change and call height modification;

[0119] (3) Allowing the calculation conditions of the conductor to be changed (such as air temperature, ice thickness, etc.);

[0120] (4) Generate a new line model in the virtual space immediately based on the modification.

[0121] In the specific implementation, Figure 3 As shown, the strategy based on adaptive discrete point solution and polynomial approximation specifically includes the following processing procedures:

[0122] (1) Initialize input parameters

[0123] Input parameters:

[0124] -The starting and ending points of the catenary are P1(x1,y1) and P2(x2,y2).

[0125] -The total length L of the catenary (i.e. the arc length of the catenary).

[0126] - The initial number of discrete points N (can be set to 10 or 20 as a starting point).

[0127] The number of initial discrete points N is recommended to be 10 to 30.

[0128] - Error threshold ∈ (used to control the error during adaptive interpolation).

[0129] -Curvature change threshold∈ κ (Used to control curvature error).

[0130] Error threshold and curvature change threshold:

[0131] -General engineering accuracy: ∈=0.1m, ∈ κ =0.05;

[0132] -High precision requirements: ∈=0.05m, ∈ κ =0.02.

[0133] Output:

[0134] -The final discrete point set P0,P1,…,P N , used to represent the shape of the catenary.

[0135] (2) Initial value estimation and discrete point generation

[0136] The quadratic polynomial approximation method is used to quickly generate the initial discrete points. Since the hyperbolic function calculation is relatively complicated, a simple polynomial fitting can be used to estimate the shape of the catenary at the beginning.

[0137] Polynomial approximation formula: y approx (x) = a0 + a1x + a2x 2where a0, a1, and a2 are polynomial coefficients determined based on the boundary conditions of the catenary. These coefficients can be estimated from the coordinates of the start and end points, as well as the total length of the catenary.

[0138] Initial discretization:

[0139] a. First, divide the x-axis between P1 and P2 evenly into N points.

[0140] b. For each point x i , using the polynomial approximation formula y approx (x i ) calculate its corresponding y i , and obtain the initial discrete point set P0, P1, ..., P N .

[0141] (3) Calculate curvature change error

[0142] For the initially generated discrete point set, the curvature change error between adjacent points is calculated. The curvature reflects the degree of curvature of the curve, and areas with large curvature changes require more points to maintain accuracy.

[0143] Curvature calculation formula:

[0144]

[0145] Here, y′(x) is the first-order derivative, which can be approximated using the finite difference method: is the second-order derivative and can be calculated using the difference formula:

[0146] Curvature variation error:

[0147] For each pair of adjacent discrete points P i and P i+1 , calculate the curvature change error: curvature error = |κ(P i )-κ(P i+1 )|If the curvature error is greater than the preset threshold ∈ κ , the curvature of the area changes greatly, and new discrete points need to be inserted later.

[0148] (4) Adaptive interpolation (error control)

[0149] According to the result of curvature error, new discrete points are adaptively inserted to control the error.

[0150] Interpolation method:

[0151] For areas with smaller errors, linear interpolation is used to directly take the midpoint of two adjacent points: Linear interpolation requires less computation but has lower accuracy in areas of high curvature.

[0152] For areas with large errors, quadratic interpolation is used. The quadratic interpolation formula can be used to improve the accuracy of the interpolation points: This allows for a better fit to the local curve shape.

[0153] Insert new point process:

[0154] a. Check whether the curvature error exceeds the threshold.

[0155] b. For areas with large errors, insert new discrete points and update the point set.

[0156] c. Recalculate the curvature error near the new insertion point to ensure it is within a reasonable range.

[0157] (5) Local correction (Newton iteration method or hyperbolic exact solution)

[0158] In areas with large curvature errors, we can switch to the exact hyperbolic equation for local correction, or use Newton's iteration method to solve the local curvature. Specifically:

[0159] Exact solution of the hyperbola equation: In regions of local high error, the standard equation for the catenary can be used: The hyperbolic equation of the catenary is used to accurately calculate discrete points in this region.

[0160] Newton Iteration Local Optimization: If the local curvature error is large, you can use Newton Iteration to quickly optimize the exact location of the insertion point: This local correction can avoid the high complexity of the global solution while improving the accuracy in key areas.

[0161] (6) Termination:

[0162] When the curvature change error between all adjacent points is less than the preset threshold ∈ κ , that is, when the curvature change of each discrete segment is controlled at the same level, the algorithm terminates.

[0163] Output the final discrete point set P0, P1, ..., P N .

[0164] (7) Application in real-time scenarios

[0165] The calculated discrete points are applied to the rendering engine to display the shape of the catenary in real time. The number and density of discrete points can be further adjusted according to the needs of dynamic changes.

[0166] It should be noted that the above strategy in this embodiment has the following specific benefits:

[0167] Efficiency: The initial point is quickly generated through quadratic polynomial approximation, which reduces the complexity of hyperbolic function calculation. In local areas that require accurate solutions, hyperbolic equations or Newton iterations are used for correction.

[0168] Adaptability: Adaptive interpolation based on curvature changes allows the algorithm to automatically add discrete points in high curvature areas and reduce calculations in low curvature areas, thereby improving overall efficiency.

[0169] Flexibility: The algorithm is suitable for both offline high-precision solutions and dynamic update scenarios in real-time applications, and can adapt to different needs.

[0170] Precision control: The curvature variation error is used as a measure to ensure that the accuracy of each discrete segment is controlled at a preset level, thus ensuring the approximation accuracy of the overall shape.

[0171] At the same time, the efficiency improvement is estimated by comparing the calculation time of the traditional catenary method and the adaptive method of the solution described in this embodiment. The traditional method calls a hyperbolic function for each discrete point, with a complexity of O(N). The method described in this embodiment uses polynomial approximation to generate the initial point, with low complexity, and adaptively adjusts the number of discrete points M in the high curvature area for local precise solution. The new method can reduce the amount of calculation and improve efficiency by 30% to 50%; through this method that combines adaptive interpolation and local correction, efficient and accurate catenary shape calculation can be achieved, which is particularly suitable for application scenarios such as real-time physical simulation and virtual reality.

[0172] In a specific implementation, by exporting the optimized overhead line design scheme obtained above, a specific virtual survey report can be generated based on the export results, including key point information and potential risks.

[0173] like Figure 4 As shown, a schematic diagram of the interactive design process described in the solution described in this embodiment is shown. Figure 5 A schematic diagram showing the comparison results of the computational efficiency between the traditional solution and the solution described in this embodiment is shown.

[0174] In one or more implementations, corresponding to the method of the above-mentioned embodiment, this embodiment provides an immersive overhead line virtual survey and design system based on oblique photography, including:

[0175] A data acquisition unit, which is used to acquire oblique photography data of the area where the overhead line is to be laid;

[0176] A virtual scene model building unit is used to divide the acquired oblique photography data into blocks according to the characteristics of digital elevation data through neighborhood expansion and dynamic merging strategies; and to model the area where the overhead line is to be laid based on the divided oblique photography data to obtain a virtual scene model;

[0177] An overhead line optimization design unit is used to import pre-designed overhead line power grid information into a virtual scene model, and optimize the overhead line in the virtual scene through human-computer interactive design to achieve the design of the overhead line; wherein, in the optimization of the overhead line, a strategy based on adaptive discrete point solution and polynomial approximation is used to perform catenary shape optimization.

[0178] Furthermore, the system described in this embodiment corresponds to the method described in Embodiment 1, and its technical details are described in detail in Embodiment 1, so they will not be repeated here.

[0179] In further embodiments, there is also provided:

[0180] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, no further description is given here.

[0181] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0182] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0183] A computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the method described in embodiment 1 is completed.

[0184] The method in the first embodiment can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0185] Those skilled in the art will appreciate that the units, i.e., algorithm steps, of the various examples described in the present embodiment can be implemented in electronic hardware or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0186] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An immersive overhead line virtual survey and design method based on oblique photography, characterized in that: include: Obtain oblique photography data of the area where the overhead line is to be laid; For the oblique photography data obtained, the oblique photography data is divided into blocks according to the characteristics of digital elevation data through neighborhood expansion and dynamic merging strategies; Based on the segmented oblique photography data, the area where the overhead line is to be laid is modeled to obtain a virtual scene model; The pre-designed overhead line power grid information is imported into the virtual scene model, and the overhead line is optimized in the virtual scene through human-computer interactive design to realize the design of the overhead line; wherein, in the optimization of the overhead line, a strategy based on adaptive discrete point solution and polynomial approximation is adopted to perform catenary form optimization.

2. The immersive overhead line virtual survey and design method based on oblique photography according to claim 1, characterized in that: The method of dividing the oblique photography data into blocks by using neighborhood expansion and dynamic merging strategies according to the characteristics of the digital elevation data is specifically as follows: evaluating the current computing resources to determine the maximum workload of the current computing resources when executing the task; According to the characteristics of digital elevation data, the oblique photography data is preliminarily divided into blocks; Calculate the workload of each block separately; With the goal of minimizing the difference between the workload of each block and the maximum workload, the obtained blocks are refined and merged to obtain new blocks.

3. The immersive overhead line virtual survey and design method based on oblique photography according to claim 2, characterized in that: The obtained blocks are refined and merged, specifically: For blocks whose workload is greater than the maximum workload, recursive refinement or binary division is used to gradually refine the large blocks into smaller blocks, and the workload of each sub-block is recalculated until the preset requirements are met; For a block whose workload is less than the preset maximum workload, merge the block with the adjacent blocks; For the blocks whose workload is between the preset proportion maximum workload and the maximum workload, they are retained.

4. The immersive overhead line virtual survey and design method based on oblique photography according to claim 2, characterized in that: The workload calculation of each block is specifically based on the following formula: W block =A block × (α ×V slope-norm +(1-a)×R terrain-norm ) Among them, W block Represents the workload of the block, V slope-norm Represents the normalized slope change rate, R terrain-norm represents the normalized terrain roughness, A block It represents the area corresponding to the block, and α is the weight factor.

5. The immersive overhead line virtual survey and design method based on oblique photography according to claim 2, characterized in that: The maximum workload is determined by the following formula: IN threshold =min(k cpu ×P cpu ,k mem ×P mem ,k gpu ×P gpu_effectivr )×T unit Among them, k cpu ,k mem ,k gpu are the weight coefficients of CPU, memory and GPU respectively, P cpu , P men , P gpu_effective Respectively, CPU processing power, memory limit and GPU processing power, T unit The expected processing time for a single chunk.

6. The immersive overhead line virtual survey and design method based on oblique photography according to claim 1, characterized in that: The form-finding optimization of the catenary is performed by using a strategy based on adaptive discrete point solution and polynomial approximation, specifically: based on the initial information of the catenary, a quadratic polynomial approximation method is used to quickly generate initial discrete points; Based on the obtained initial discrete points, the curvature change error between adjacent discrete points is calculated; based on the obtained curvature change error, new discrete points are adaptively inserted for error control, and in areas where the curvature error is greater than a preset threshold, a hyperbolic equation is used for local correction, or Newton's iteration method is used for local optimization to obtain an optimized discrete point set and realize the form-finding optimization of the catenary.

7. The immersive overhead line virtual survey and design method based on oblique photography according to claim 6, characterized in that: The initial information of the catenary includes a pre-designed start point and end point of the catenary, a total length of the catenary, a number of initial discrete points, an adaptive interpolation error threshold, and a curvature change threshold.

8. An immersive overhead line virtual survey and design system based on oblique photography, characterized in that: include: A data acquisition unit, which is used to acquire oblique photography data of the area where the overhead line is to be laid; A virtual scene model building unit is used to divide the acquired oblique photography data into blocks according to the characteristics of digital elevation data through neighborhood expansion and dynamic merging strategies; and to model the area where the overhead line is to be laid based on the divided oblique photography data to obtain a virtual scene model; An overhead line optimization design unit is used to import pre-designed overhead line power grid information into a virtual scene model, and optimize the overhead line in the virtual scene through human-computer interactive design to achieve the design of the overhead line; wherein, in the optimization of the overhead line, a strategy based on adaptive discrete point solution and polynomial approximation is used to perform catenary shape optimization.

9. An electronic device comprising a memory, a processor and a computer program stored and running on the memory, characterized in that: When the processor executes the program, it implements an immersive overhead line virtual survey and design method based on oblique photography as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements an immersive overhead line virtual survey and design method based on oblique photography as described in any one of claims 1 to 7.

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