A method for generating power tower erection plans based on drones

Abstract

The present invention discloses a method for generating a tower erection plan based on a drone, comprising the following steps: S1. Determining the number N of towers required for construction across the entire region and establishing a global map model using acquired survey data; S2. Determining the tower construction locations using optimal search rules; S3. Inputting standard operating condition parameters into the operating condition model to guide the construction of simulated towers; S4. Performing a robustness test on the constructed simulated towers using a disaster model, guiding the modification of the simulated towers based on the degree of tower loss, and generating correction parameters; S5. Inputting the standard operating condition parameters and correction parameters into a tool library model to generate a tool list. The scheme establishes a virtual map and determines the tower construction locations. After the constructed simulated towers are constructed, robustness tests are performed on the disaster-resistant capabilities of the simulated towers, further modifying the tower operating condition parameters to improve the towers' actual disaster resistance. Finally, a tool list is generated using the tool library model, making the construction process safer and more efficient.

Description

Technical Field

[0001] The present invention relates to the field of power construction planning and design, and in particular to a method for generating an electric tower erection plan based on an unmanned aerial vehicle (UAV). Background Art

[0002] Since many power line project routes are often located far from residential areas, they often run through remote, mountainous, and densely forested areas. Minimizing impacts on the human environment while avoiding significant increases in project costs and time due to land acquisition and relocation creates real challenges, such as complex terrain and challenging survey and design. Traditional transmission line survey methods would require extensive clearing of pathways, which is neither environmentally friendly nor efficient, and does not meet government regulations for addressing the "whitewashed mountains" problem. Furthermore, due to the non-aerial survey approach, route plans must be finalized only after obtaining on-site measurement data, resulting in high on-site labor intensity and low efficiency. These established route plans lack a holistic and integrated approach, and as the line progresses, new obstacles often disrupt previously selected options. This ultimately leads to significant duplication of work, limited efficiency gains, and delayed project completion. Therefore, the use of aerial surveys to assist in the survey and design of power line projects has become a growing trend.

[0003] With recent advances in drone aerial photography technology, drones are increasingly being used to capture image data in surveying and mapping projects. Drones are compact, maneuverable, efficient, and low-cost, offering significant advantages for quickly acquiring high-resolution imagery in small areas and difficult-to-fly areas. However, due to the difficulty and significant impact of topography on the erection of power towers, their safety and stability, along with the impact of topography, make it difficult for engineers to factor these factors into their overall design. Furthermore, existing experience makes it difficult to assess the disaster resistance of power towers. Consequently, the construction of power towers is long and expensive. Furthermore, the long construction period and the large number of components involved make it difficult for construction engineers to estimate the required component list. Therefore, accurate planning and layout during the initial phase of tower construction are crucial for ensuring the ease and cost-effectiveness of subsequent on-site construction. Summary of the Invention

[0004] The purpose of the present invention is to propose a method for generating a tower erection plan based on a drone. By establishing a virtual map and finding the optimal installation position of the tower on the virtual map, a simulated tower is built in a working condition model. After the simulated tower is built, the robustness test of the disaster resistance capability is performed on the simulated tower, and the working condition parameters of the tower are further corrected to improve the actual disaster resistance capability of the tower. Finally, a tool list is generated through a tool library model, making the construction process safer and more efficient.

[0005] To achieve the above technical objectives, the present invention provides a technical solution that is a method for generating a tower erection plan based on a drone, comprising the following steps:

[0006] S1. Determine the number of towers N required for the entire area and build a global map model using the acquired survey data.

[0007] S2. Determine the location of the tower using the optimal search rule;

[0008] S3. Inputting standard operating condition parameters into the operating condition model to guide the construction of the simulated power tower;

[0009] S4. Conduct robustness testing on the constructed simulated power tower using a disaster model, guide the modification of the simulated power tower based on the degree of tower loss, and generate modification parameters;

[0010] S5. Input the standard working condition parameters and the correction parameters into the tool library model to generate a tool list.

[0011] In this solution, first, a virtual map is created based on the number of towers to be built and the optimal installation location of the towers is found on the virtual map. Then, a simulated tower is built in the working condition model according to the standard working condition parameters. After the simulated tower is built, its robustness to disaster resistance is tested, and the working condition parameters of the tower are further revised to improve the actual disaster resistance of the tower. Finally, a tool list is generated through the tool library model, making the construction process safer and more efficient.

[0012] Preferably, S1 includes the following steps:

[0013] S11. Determine the size of the global virtual map based on the number N of power towers and the direction of the power lines.

[0014] S12, dividing the global virtual map into N independent local maps evenly, and editing and imaging each independent local map separately;

[0015] S13, acquiring image data of each independent local image through a drone, filling the impact data into the independent local image, and performing 3D rendering;

[0016] S14. After several independent local maps are spliced and their boundary contours are processed, a global 3D virtual map is obtained.

[0017] Preferably, S11 includes the following steps:

[0018] Determine the positions of the first and Nth towers, connect them with arrowed lines to determine the direction and distance S of the wiring, and create a global virtual map with distance S as the length and S / N as the width.

[0019] Preferably, the UAV searches using a local map as a search unit, and S13 includes the following steps:

[0020] S131, the local image is a square with a side length of S / N, which is cut into M small squares of equal length;

[0021] S132. Determine the center point of each small square and use the center point of each small square as the drone camera detection point; S133. Set the drone's flight altitude based on the terrain and altitude; and determine the image scaling ratio based on the flight altitude; S134. Acquire image data of the M small square areas and scale the images to a uniform standard size based on the drone's flight altitude;

[0022] S135 , performing boundary cutting on the image using the boundary formed by the small squares, and rotating and splicing the cut images of the same size to obtain independent partial images.

[0023] Preferably, S2 includes the following steps:

[0024] Obtain topographic survey data within each small square area, and further render and mark the overall map of the small square area; after processing several small square areas, obtain independent local images. Through analyzing the topographic survey data within the independent local images, obtain the optimal tower installation point with the minimum foundation settlement value and away from water.

[0025] Preferably, the topographic survey data includes the type and attributes of the terrain, wherein the type includes at least one of high mountains, water areas, plains, and deserts, and the topographic attributes include the type and thickness of geological rock layers and the historical water level in the water area; the basic settlement value is calculated according to the type and thickness of the geological rock layers.

[0026] As a preferred embodiment, in S3, after the locations of all the tower erection points are determined, the standard operating condition parameters in the operating condition model are adjusted to meet the tower construction specifications;

[0027] The working condition model includes a tower model and a tower base model. The standard working condition data includes tower body attributes, connection medium attributes and adjacent attributes. The tower body attributes include: tower base elevation, tower positioning height, and hardware string length. The connection medium attributes include conductor density and line turning angle. The adjacent attributes include: span, span elevation and crossing angle. The tower base model determines the pouring thickness of the tower base according to the geological foundation settlement value of the tower construction location.

[0028] Preferably, S4 includes the following steps:

[0029] The disaster model includes earthquake disasters, rain and snow disasters, and typhoon disasters. Different disaster types are divided into T1, T2, and T3 according to the disaster level. The disaster level is T3>T2>T1 from large to small.

[0030] Disaster levels are set according to different disaster types to simulate disasters on the prepared virtual towers. The loss data of each construction site is extracted and compared with the loss values of each construction site in the standard library at different disaster levels of different disaster types. The correction values under the current disaster simulation are generated, and the working condition parameters of unqualified construction sites are further corrected and the correction parameters are output.

[0031] Preferably, in S5, the standard working condition parameters and the correction parameters are input into the tool library model, and the tool library model exports a construction data list, which includes the types and quantities of tools required to build all working conditions.

[0032] Beneficial effects of the present invention: The drone-based tower erection plan generation method designed by the present invention establishes a virtual map and finds the optimal installation position of the tower on the virtual map, builds a simulated tower in the working condition model, and conducts a robustness test of the simulated tower's ability to resist disasters after construction. The tower's working condition parameters are further corrected to improve the tower's actual disaster resistance. Finally, a tool list is generated through a tool library model, making the construction process safer and more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The figure is a flow chart of the method for generating a UAV-based tower erection plan according to the present invention. DETAILED DESCRIPTION

[0034] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0035] Example: Figure 1 As shown in FIG, a flowchart of a method for generating a tower erection plan based on a drone includes the following steps: S1, determining the number N of towers that need to be built in the entire area, and establishing a global map model through the acquired survey data; modeling the collected data through aerial triangulation data processing software to generate a panoramic map with a three-dimensional scene. This technology is a routine modeling skill for general technicians in this field and will not be repeated here.

[0036] S1 includes the following steps:

[0037] S11. Determine the size of the global virtual map based on the number N of power towers and the direction of the power lines.

[0038] S12, dividing the global virtual map into N independent local maps evenly, and editing and imaging each independent local map separately;

[0039] S13, acquiring image data of each independent local image through a drone, filling the impact data into the independent local image, and performing 3D rendering;

[0040] S14. After several independent local maps are spliced and their boundary contours are processed, a global 3D virtual map is obtained.

[0041] S11 includes the following steps:

[0042] Determine the positions of the first and Nth towers, connect them with arrowed lines to determine the direction and distance S of the wiring, and create a global virtual map with distance S as the length and S / N as the width.

[0043] The drone searches using a local map as a search unit. S13 includes the following steps:

[0044] S131, the local image is a square with a side length of S / N, which is cut into M small squares of equal length;

[0045] S132. Determine the center point of each small square and use the center point of each small square as the drone camera detection point; S133. Set the drone's flight altitude based on the terrain and altitude; and determine the image scaling ratio based on the flight altitude; S134. Acquire image data of the M small square areas and scale the images to a uniform standard size based on the drone's flight altitude;

[0046] S135 , performing boundary cutting on the image using the boundary formed by the small squares, and rotating and splicing the cut images of the same size to obtain independent partial images.

[0047] S2. Determine the location of the tower using the optimal search rule;

[0048] S2 includes the following steps:

[0049] Obtain topographic survey data within each small square area, and further render and mark the overall map of the small square area; after processing several small square areas, obtain independent local images. Through analyzing the topographic survey data within the independent local images, obtain the optimal tower installation point with the minimum foundation settlement value and away from water.

[0050] The topographic survey data includes the type and attributes of the topography, wherein the type includes at least one of high mountains, water areas, plains, and deserts, and the topographic attributes include the type and thickness of geological rock layers and the historical water level in the water area; the basic settlement value is calculated based on the type and thickness of the geological rock layers.

[0051] S3. Inputting standard operating condition parameters into the operating condition model to guide the construction of the simulated power tower;

[0052] In S3, after all tower erection points are determined, the standard operating parameters in the operating condition model are adjusted to meet the tower construction specifications.

[0053] The working condition model includes a tower model and a tower base model (the tower model and the tower base model can be imported through BIM software. This technology is a routine modeling skill for general technicians in this field and will not be repeated here). The standard working condition data includes tower body properties, connection medium properties and adjacent properties; the tower body properties include: tower base elevation, tower positioning height, and hardware string length; the connection medium properties include conductor specific gravity and line turning angle; the adjacent properties include: span, spanned object elevation and crossing angle; the tower base model determines the pouring thickness of the tower base according to the geological foundation settlement value of the tower construction location.

[0054] S4. Conduct robustness testing on the constructed simulated power tower using a disaster model, guide the modification of the simulated power tower based on the degree of tower loss, and generate modification parameters;

[0055] S4 includes the following steps:

[0056] The disaster model includes earthquake disasters, rain and snow disasters, and typhoon disasters. Different disaster types are divided into T1, T2, and T3 according to the disaster level. The disaster level is T3>T2>T1 from large to small.

[0057] Disaster levels are set according to different disaster types to simulate disasters on the prepared virtual towers. The loss data of each construction site is extracted and compared with the loss values of each construction site in the standard library at different disaster levels of different disaster types. The correction values under the current disaster simulation are generated, and the working condition parameters of unqualified construction sites are further corrected and the correction parameters are output.

[0058] S5. Input the standard working condition parameters and the modified parameters into the tool library model to generate a tool list. Based on the standard working condition parameters and the modified parameters input into the tool library model, the tool library model generates a construction data list, which includes the types and quantities of tools required to build all working conditions.

[0059] In this embodiment, first, a virtual map is established according to the number of towers to be built and the optimal installation position of the tower is found on the virtual map. Then, a simulated tower is built in the working condition model according to the standard working condition parameters. After the simulated tower is built, the robustness test of the disaster resistance capability is carried out, and the working condition parameters of the tower are further corrected to improve the actual disaster resistance capability of the tower. Finally, a tool list is generated through the tool library model to make the construction process safer and more efficient.

[0060] The specific implementation described above is a preferred implementation of the method for generating a tower erection plan based on a drone of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. Any equivalent changes made in accordance with the shape and structure of the present invention are within the scope of protection of the present invention.

Claims

1. A method for generating a tower erection plan based on a drone, characterized in that: The steps include: S1. Determine the number of towers N required for the entire area and create a 3D virtual map of the entire area using the acquired survey data. S1 includes the following steps: S11. Determine the size of the global virtual map based on the number N of power towers and the direction of the power lines. S12, dividing the global virtual map into N independent local maps evenly, and editing and imaging each independent local map separately; S13, acquiring image data of each independent local image through a drone, filling the image data into the independent local image, and performing 3D rendering; S14, after several independent local images are stitched and their boundary contours are processed, a global 3D virtual map is obtained; S2. Determine the location of the tower on the global 3D virtual map using the best search rule; S3. Inputting standard operating condition parameters into the operating condition model to guide the construction of the simulated power tower; S4. Conduct robustness testing on the constructed simulated power tower using a disaster model, guide the modification of the simulated power tower based on the degree of tower loss, and generate modification parameters; S5. Input the standard working condition parameters and the correction parameters into the tool library model to generate a tool list.

2. The method for generating a tower erection plan based on a drone according to claim 1, characterized in that: S11 includes the following steps: Determine the positions of the first and Nth towers, connect them with arrowed lines to determine the direction and distance S of the wiring, and create a global virtual map with distance S as the length and S / N as the width.

3. The method for generating a tower erection plan based on a drone according to claim 1, characterized in that: The drone searches using a local map as a search unit. S13 includes the following steps: S131, the local image is a square with a side length of S / N, which is cut into M small squares of equal length; S132, determining the center point of each small square, and using the center point of each small square as the drone camera detection point; S133, setting the flight altitude of the UAV according to the terrain and altitude; and determining the zoom ratio of the captured image according to the flight altitude; S134, obtaining image data of M small square areas, and scaling the images to a uniform standard size according to the flight altitude of the UAV; S135 , performing boundary cutting on the image using the boundary formed by the small squares, and rotating and splicing the cut images of the same size to obtain independent partial images.

4. The method for generating a tower erection plan based on a drone according to claim 3, characterized in that: S2 includes the following steps: Obtain the topographic survey data within each small square area, and further render and mark the map of the small square area; After processing, several small square areas are obtained into independent local images. By analyzing the terrain survey data in the independent local images, the optimal tower installation point with the minimum foundation settlement value and away from the water is obtained.

5. The method for generating a tower erection plan based on a drone according to claim 4, characterized in that: The topographic survey data includes the type and attributes of the topography, wherein the type includes at least one of high mountains, water areas, plains, and deserts, and the topographic attributes include the type and thickness of geological rock layers and the historical water level in the water area; the basic settlement value is calculated based on the type and thickness of the geological rock layers.

6. The method for generating a tower erection plan based on a drone according to claim 1, 4 or 5, characterized in that: In S3, after all tower erection points are determined, the standard operating parameters in the operating condition model are adjusted to meet the tower construction specifications. The operating condition model includes a tower model and a tower base model, and the standard operating condition parameters include tower body properties, connection medium properties, and adjacency properties; The tower body properties include: tower base elevation, tower positioning height, and hardware string length; the connecting medium properties include conductor density and line angle; the adjacent properties include: span, span elevation, and crossing angle; the tower base model determines the pouring thickness of the tower base based on the geological foundation settlement value of the tower construction location.

7. The method for generating a tower erection plan based on a drone according to claim 1, characterized in that: S4 includes the following steps: The disaster model includes earthquake disasters, rain and snow disasters, and typhoon disasters. Different disaster types are divided into T1, T2, and T3 according to the disaster level. The disaster level is T3>T2>T1 from large to small. Disaster levels are set according to different disaster types to simulate disasters on the prepared virtual towers. The loss data of each construction site is extracted and compared with the loss values of each construction site in the standard library at different disaster levels of different disaster types. The correction values under the current disaster simulation are generated, and the working condition parameters of unqualified construction sites are further corrected and the correction parameters are output.

8. The method for generating a tower erection plan based on a drone according to claim 1, characterized in that: S5 includes the following steps: According to the standard working condition parameters and the correction parameters input into the tool library model, the tool library model exports a construction data list, which includes the types and quantities of tools required to build all working conditions.

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