Method and device for selecting anti-falling mounting point of power transmission tower

Abstract

The invention discloses a method and a device for selecting an anti-falling mounting point of a transmission tower. The method comprises the following steps: acquiring three-dimensional point cloud data of a transmission tower; generating a three-dimensional model of the transmission tower based on the three-dimensional point cloud data; selecting part of point cloud data of the three-dimensional model as initial mounting points to form an initial selection scheme; performing optimization iteration on the initial selection scheme by utilizing a self-adaptive genetic algorithm to obtain an optimal selection scheme; and evaluating the optimal selection scheme, and outputting the mounting points meeting the evaluation standard as optimal candidate mounting points. According to the invention, the technical problems of low selection efficiency and inaccurate selection of the installation point of the anti-falling device in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of aerial work safety, and in particular to a method and device for selecting anti-fall installation points of a transmission tower. Background Art

[0002] In the construction and maintenance process of the power industry, high-altitude work is an indispensable part, especially in the installation, inspection and maintenance of transmission lines. One of the main risks faced by workers working at heights is falling, which not only endangers the life safety of workers, but also may cause the interruption of the power system and cause huge economic losses. Therefore, taking effective anti-fall measures to ensure the safety of workers is an important task in the power industry.

[0003] At present, the selection of fall protection installation points mainly depends on on-site experience and manual judgment. In the high-altitude operation of the power system, the working environment is complex and changeable, covering high-voltage lines, substation facilities, etc., and the selection of installation points needs to comprehensively consider the structural characteristics, bearing capacity and stability of the power facilities, as well as the range of activities of the operators and the work requirements. The traditional manual selection method has significant limitations, including:

[0004] 1) Subjectivity of site selection: Although experienced on-site operators can provide certain guidance, their judgments are often limited by personal experience and conditions and lack scientificity and objectivity.

[0005] 2) Inefficiency: Without the assistance of automated tools, manual search for the optimal installation point is time-consuming and inefficient, especially on transmission towers with complex structures.

[0006] 3) Incomplete risk assessment: Manual selection of installation points makes it difficult to comprehensively consider all factors that may affect safety, such as wind load, weather conditions, scope of work activities, etc.

[0007] Therefore, in the field of high-altitude work safety protection in the prior art, how to quickly and accurately select the installation point of the anti-fall device has become a key issue at present. In view of the above problems, no effective solution has been proposed yet. Summary of the invention

[0008] The embodiments of the present invention provide a method and device for selecting an anti-fall installation point for a transmission tower, so as to at least solve the technical problems of low efficiency and inaccurate selection of installation points for anti-fall devices in related technologies.

[0009] According to one aspect of an embodiment of the present invention, a method for selecting anti-fall installation points for a transmission tower is provided, the method comprising the following steps: collecting three-dimensional point cloud data of the transmission tower; generating a three-dimensional model of the transmission tower based on the three-dimensional point cloud data; selecting part of the point cloud data of the three-dimensional model as initial installation points to form an initial selection plan; optimizing and iterating the initial selection plan using an adaptive genetic algorithm to obtain an optimal selection plan; evaluating the optimal selection plan, and outputting installation points that meet the evaluation criteria as optimal candidate installation points.

[0010] Optionally, an adaptive genetic algorithm is used to optimize and iterate the initial selection plan to obtain the best selection plan, including the following steps: obtaining constraint conditions, the constraint conditions at least include point selection distance conditions and point selection safety conditions; based on the constraint conditions, an adaptive genetic algorithm is used to optimize and iterate the initial selection plan to obtain the best selection plan.

[0011] Optionally, the distance condition for selecting a point is D ≥ d min , where D is the distance between selected points, d min is the minimum safety distance, and the safety condition for selecting a point is S≤S max , where S is the safety index of the selected point, S max The maximum safety index.

[0012] Optionally, before obtaining the safety conditions for selecting a point, the method also includes: obtaining point selection parameters, the point selection parameters including at least one of the following: bearing capacity parameter, height parameter, dynamic pressure parameter, windward area parameter, drag coefficient, maximum ambient air pressure, design safety air pressure, maximum ambient wind speed, and design safety wind speed; inputting the point selection parameters into a point selection safety assessment model to obtain a point selection safety index.

[0013] Optionally, the point selection parameters are input into the point selection safety assessment model to obtain the point selection safety index, including the following steps: inputting the bearing capacity parameter and the height parameter into the first construction model of the point selection safety assessment model to obtain the construction level parameter; inputting the dynamic pressure parameter, the windward area parameter, and the resistance coefficient into the second construction model of the point selection safety assessment model to obtain the wind load parameter; inputting the maximum ambient air pressure, the design safety air pressure, the maximum ambient wind speed, and the design safety wind speed into the third construction model of the point selection safety assessment model to obtain the weather impact parameter; inputting the construction level parameter, the wind load parameter, and the weather impact parameter into the fourth construction model of the point selection safety assessment model to obtain the point selection safety index.

[0014] Optionally, an adaptive genetic algorithm is used to optimize and iterate the initial selection plan to obtain the best selection plan, including the following steps: obtaining a point selection quality evaluation model, the point selection quality evaluation model is used to evaluate the point selection quality, and output the point selection quality evaluation result; based on the point selection quality evaluation model, an adaptive genetic algorithm is used to optimize and iterate the initial selection plan until the stagnation condition is met to obtain the best selection plan.

[0015] Optionally, an adaptive genetic algorithm is used to optimize and iterate the initial selection plan until a stagnation condition is met to obtain the best selection plan, including the following steps: based on the initial selection plan, an adaptive local search algorithm is used for optimization to find a better solution in the neighborhood, and the initial selection plan is updated based on the better solution; crossover and mutation operations are performed on the updated initial selection plan to generate a new candidate plan; based on a point selection quality evaluation model, the point selection quality evaluation results of each candidate plan are obtained, and individuals that meet the quality evaluation conditions are selected as parent individuals to participate in the generation of the next generation; the candidate plans are continuously iterated until the stagnation condition is met; and the candidate plan that meets the stagnation condition is output as the best selection plan.

[0016] Optionally, generating a three-dimensional model of a transmission tower based on the three-dimensional point cloud data comprises the following steps: performing data processing on the three-dimensional point cloud data to obtain key point cloud data, wherein the data processing comprises at least one of the following: data cleaning and data filtering; obtaining an initial model corresponding to the transmission tower from a three-dimensional model library; and generating a three-dimensional model of the transmission tower based on the initial model and the key point cloud data.

[0017] Optionally, the best selection scheme is evaluated, and the installation points that meet the evaluation criteria are output as the best candidate installation points, including the following steps: using the hierarchical analysis method to construct an evaluation model; using the evaluation model to evaluate the best selection scheme, and outputting the installation points that meet the evaluation criteria as the best candidate installation points.

[0018] According to another aspect of an embodiment of the present invention, there is also provided a device for selecting anti-fall installation points for a transmission tower, comprising: an acquisition module, the acquisition module being used to acquire three-dimensional point cloud data of the transmission tower; a generation module, the generation module being used to generate a three-dimensional model of the transmission tower based on the three-dimensional point cloud data; a selection module, the selection module being used to select part of the point cloud data of the three-dimensional model as initial installation points to form an initial selection scheme; an optimization iteration module, the optimization iteration module being used to optimize and iterate the initial selection scheme using an adaptive genetic algorithm to obtain an optimal selection scheme; and an evaluation module, the evaluation module being used to evaluate the optimal selection scheme and output installation points that meet the evaluation criteria as optimal candidate installation points.

[0019] In an embodiment of the present invention, three-dimensional point cloud data of a transmission tower is collected; a three-dimensional model of the transmission tower is generated based on the three-dimensional point cloud data; part of the point cloud data of the three-dimensional model is selected as the initial installation point to form an initial selection scheme; the initial selection scheme is optimized and iterated using an adaptive genetic algorithm to obtain an optimal selection scheme; the optimal selection scheme is evaluated, and the installation point that meets the evaluation criteria is output as the optimal candidate installation point. The method in this embodiment achieves the purpose of quickly, accurately and safely selecting the installation point of the anti-fall device through point cloud data collection, three-dimensional model construction, initial selection scheme determination, optimization iteration of the initial selection scheme and evaluation of the optimal selection scheme, and comprehensively uses modern data collection technology, three-dimensional modeling, adaptive genetic algorithm optimization and a comprehensive evaluation mechanism, so as to achieve the technical effect of improving the installation efficiency and accuracy of the anti-fall device, solve the technical problem of low efficiency and inaccurate selection of the installation point of the anti-fall device in the related technology, and is suitable for transmission towers with various complex terrains and structures, especially in areas that are difficult to measure manually, such as mountains and canyons, and significantly improves work efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0021] Figure 1 is a hardware structure block diagram of a computer terminal according to one embodiment of the present invention;

[0022] Figure 2 is a flow chart of a method for selecting an anti-fall installation point for a transmission tower according to one of the optional embodiments of the present invention;

[0023] Figure 3 is a structural block diagram of a device for selecting an anti-fall installation point for a transmission tower according to one optional embodiment of the present invention;

[0024] Figure 4 It is a flow chart of a method for selecting an anti-fall installation point of a transmission tower according to one optional embodiment of the present invention;

[0025] Figure 5 It is a schematic diagram of the principle of a transmission tower anti-fall installation point selection system according to one of the optional embodiments of the present invention;

[0026] Figure 6 It is a schematic diagram of a system for selecting a fall prevention installation point for a transmission tower according to one optional embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] According to one embodiment of the present invention, an embodiment of a method for selecting a fall protection installation point for a transmission tower is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] The method embodiment can be executed in a computer terminal or a similar computing device. For example, if the method is executed in a computer terminal, Figure 1 As shown, the computer terminal may include one or more processors 102 (the processor may include but is not limited to a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microprocessor (MCU), a programmable logic device (FPGA), a neural network processor (NPU), a tensor processor (TPU), an artificial intelligence (AI) type processor, etc.) and a memory 104 for storing data. Optionally, the electronic device of the above vehicle may also include a transmission device 106 for communication functions, an input and output device 108, and a display 110. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. For example, the computer terminal may also include more or fewer components than the above-mentioned structure description, or have a configuration different from the above-mentioned structure description.

[0031] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for selecting the anti-fall installation point of the transmission tower in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above-mentioned method for selecting the anti-fall installation point of the transmission tower is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0032] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one embodiment, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0033] The display 110 may be, for example, a touch screen liquid crystal display (LCD). The liquid crystal display may enable a user to interact with a user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), and a user may interact with the GUI by finger contact and / or gestures on a touch-sensitive surface, wherein the human-computer interaction functions here may optionally include the following interactions: creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital videos, playing digital music, and / or web browsing, etc. The executable instructions for executing the above human-computer interaction functions are configured / stored in a computer program product or a readable storage medium executable by one or more processors.

[0034] This embodiment provides a method for selecting an anti-fall installation point of a transmission tower running on the above-mentioned computer terminal. Figure 2 FIG. 1 is a flow chart of a method for selecting an anti-fall installation point for a transmission tower according to one embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0035] Step S21, collecting three-dimensional point cloud data of the transmission tower;

[0036] Specifically, in step S21, a drone and a high-precision scanning device can be used to perform a comprehensive scan of the transmission tower. The drone flies at a low altitude to ensure that all angles and details are covered, and a large amount of high-resolution images and laser ranging data are collected to generate three-dimensional point cloud data of the transmission tower. The point cloud data can provide detailed information on the surface of the transmission tower, providing raw data support for subsequent three-dimensional model construction and selection of fall prevention installation points.

[0037] Step S22, generating a three-dimensional model of the transmission tower based on the three-dimensional point cloud data;

[0038] Specifically, in step S22, a three-dimensional model of a transmission tower can be generated by auxiliary tools such as computer-aided design (CAD) software, and the model can be deeply analyzed through three-dimensional visualization technology to identify its performance under different conditions, so as to prepare for the subsequent selection of the best installation point. The construction of these models ensures a high degree of matching with the actual geometric features of the transmission tower, providing an accurate reference for subsequent analysis and optimization.

[0039] Step S23, selecting part of the point cloud data of the three-dimensional model as the initial installation point to form an initial selection plan;

[0040] Specifically, in step S23, part of the point cloud data can be randomly selected from the point cloud data as the initial installation point of the anti-fall installation point, or the key point information captured in the early stage can be used to select points within a certain range from the key structural point and with higher structural reliability and safety as the initial installation point.

[0041] Step S24, using an adaptive genetic algorithm to iterate the initial selection scheme to obtain the best selection scheme;

[0042] Specifically, in step S24, the initial selection scheme is optimized and iterated using an adaptive genetic algorithm, which can combine the advantages of local search and global search to quickly screen out anti-fall installation points that meet the safety and stability requirements. The adaptive characteristics of the genetic algorithm enable the algorithm to continuously adjust parameters during the search process, such as the crossover rate and mutation rate, to adapt to different types of transmission towers and complex and changeable working environments, while improving the accuracy and efficiency of installation point selection and ensuring safety, while maximizing the satisfaction of work requirements and environmental constraints, thereby improving the safety and economy of high-altitude operations.

[0043] Step S25, evaluating the best selection scheme, and outputting the installation point that meets the evaluation criteria as the best candidate installation point.

[0044] Specifically, in step S25, by evaluating the best selection plan, it is further determined whether the installation point is objective and accurate, and whether it meets the safety and efficiency requirements of actual operations, thereby ensuring that the selected installation point is efficient and safe in actual applications.

[0045] Through the above steps, the three-dimensional point cloud data of the transmission tower is collected; based on the three-dimensional point cloud data, a three-dimensional model of the transmission tower is generated; part of the point cloud data of the three-dimensional model is selected as the initial installation point to form an initial selection plan; the initial selection plan is optimized and iterated by an adaptive genetic algorithm to obtain the best selection plan; the best selection plan is evaluated, and the installation point that meets the evaluation criteria is output as the best candidate installation point. The method in this embodiment achieves the purpose of quickly, accurately and safely selecting the installation point of the anti-fall device through point cloud data collection, three-dimensional model construction, initial selection plan determination, optimization iteration of the initial selection plan and evaluation of the best selection plan, and comprehensively uses modern data acquisition technology, three-dimensional modeling, adaptive genetic algorithm optimization and comprehensive evaluation mechanism, so as to achieve the technical effect of improving the installation efficiency and accuracy of the anti-fall device, solve the technical problem of low efficiency and inaccurate selection of the installation point of the anti-fall device in the related technology, and is suitable for transmission towers with various complex terrains and structures, especially in areas that are difficult to measure manually, such as mountains and canyons, and significantly improves work efficiency and safety.

[0046] Optionally, in step S24, the initial selection scheme is optimized and iterated using an adaptive genetic algorithm to obtain the best selection scheme, including the following steps:

[0047] Step S241, obtaining constraint conditions, which at least include a point selection distance condition and a point selection safety condition;

[0048] Specifically, in step S241, constraints are important parameters that guide the search direction and range of the genetic algorithm. The point selection distance condition and the point selection safety condition can ensure that the installation point solution generated by the algorithm not only meets the actual work requirements, but also complies with safety standards. Among them, the point selection distance condition specifies the safe distance threshold for operators to move between the installation point and the work area, ensuring that the risk of falling will not increase due to the long distance when the operator is performing the task. Point selection safety conditions can cover more factors, such as structural bearing capacity, wind load assessment, weather safety indicators, etc., to ensure that the anti-fall installation point has sufficient safety and stability in various possible working environments. The setting of constraints provides a clear boundary for the search of the genetic algorithm and avoids the selection of invalid or unsafe solutions.

[0049] Step S242, based on the constraint conditions, use an adaptive genetic algorithm to iterate the initial selection scheme to obtain the best selection scheme.

[0050] Specifically, in step S242, the adaptive genetic algorithm searches for the optimal installation point solution through continuous iteration and optimization combined with constraint conditions. The adaptive genetic algorithm can automatically adjust during the iterative optimization process, quickly locate the optimal solution in a complex search space, shorten the time for searching for installation points, and improve the efficiency of installation point selection.

[0051] Through steps S241 to S242, reasonable constraints are set to ensure that the installation point of the anti-fall device meets both safety requirements and engineering specifications. The application of the adaptive genetic algorithm realizes the effective optimization of the initial selection plan, ensuring the scientificity, safety and efficiency of the selection of anti-fall installation points, and solving the problem that the installation point selection in the traditional method relies on subjective judgment and low selection efficiency.

[0052] Optionally, in step S241, the distance condition for selecting a point is D ≥ d min , where D is the distance between selected points, d min is the minimum safety distance, and the safety condition for selecting a point is S≤S max , where S is the safety index of the selected point, S max The maximum safety index.

[0053] It should be noted that the selected point distance D represents the maximum distance from the installation point to the working range, that is, the maximum moving distance from the fixing point of the fall protection device to the working area when the operator is performing aerial work. min Indicates the minimum safety distance, ensuring that even under the most unfavorable conditions, operators can safely move within the working range to avoid the risk of falling due to excessive distance. The site selection safety index S combines multiple safety indicators, including but not limited to structural bearing capacity, wind load assessment, weather safety impact and other factors, to quantify the safety level of each installation point. Maximum safety index S max The maximum allowable safety index is an upper limit set according to the severity of the operating environment and the safety standards of power facilities, ensuring that the safety level of the selected installation point does not exceed the acceptable range, thereby avoiding the risk of failure of the installation point under extreme conditions.

[0054] Optionally, in step S241, before obtaining the safety condition for selecting a point, the method further includes:

[0055] Step S2411, obtaining point selection parameters, which include at least one of the following: bearing capacity parameter, height parameter, dynamic pressure parameter, windward area parameter, drag coefficient, maximum ambient air pressure, design safety air pressure, maximum ambient wind speed, and design safety wind speed;

[0056] Specifically, in step S2411, the point selection parameters may also include more types of parameters, such as structural integrity parameters, operation frequency, material property parameters, temperature and humidity parameters, electromagnetic interference parameters, visual visibility parameters, terrain parameters, operator comfort parameters, etc.

[0057] Step S2412, input the site selection parameters into the site selection safety assessment model to obtain the site selection safety index.

[0058] Through steps S2411 to S2412, various point selection parameters are comprehensively considered and the safety of the installation point is comprehensively evaluated, so that the installation point selection method of this embodiment is suitable for the installation of anti-fall devices under various extreme weather conditions, ensuring the personal safety of workers in harsh environments.

[0059] Optionally, in step S2412, the site selection parameters are input into a site selection safety assessment model to obtain a site selection safety index, including the following steps:

[0060] Step S24121, inputting the bearing capacity parameter and the height parameter into the first construction model of the site selection safety assessment model to obtain the construction level parameter;

[0061] In step S24121, the calculation of the construction level parameters is based on the direct correlation between the bearing capacity and the height parameter, reflecting the comprehensive bearing capacity and stability of the structure where the installation point is located. Specifically, the first construction model is as follows:

[0062]

[0063] Among them, F is the bearing capacity of the structure, H is the height of the structure, and L is the building level parameter.

[0064] Step S24122, inputting the dynamic pressure parameter, the windward area parameter, and the drag coefficient into the second construction model of the point selection safety assessment model to obtain the wind load parameter;

[0065] In step S24122, the evaluation of wind load parameters mainly focuses on the stress of the installation point under the action of wind force, ensuring that the installation point can remain stable under the worst wind conditions and will not fail due to excessive wind load. Specifically, the second construction model is as follows:

[0066] W=q×A×C d

[0067] Among them, W is the wind load parameter, q is the dynamic pressure, A is the windward area, and Cd is the drag coefficient.

[0068] Step S24123, inputting the maximum ambient air pressure, the designed safe air pressure, the maximum ambient wind speed, and the designed safe wind speed into the third construction model of the site selection safety assessment model to obtain weather impact parameters;

[0069] In step S24123, the evaluation of weather impact parameters aims to predict the safety margin of the installation point under extreme weather conditions, to ensure that the fall protection system can operate normally even under severe weather conditions and to protect the safety of the operators. Specifically, the third construction model is as follows:

[0070]

[0071] Among them, P max is the maximum air pressure or maximum wind speed that may occur in the environment, P safe It is the safe air pressure or safe wind speed considered during design, and T is the weather impact parameter.

[0072] Step S24124, input the construction level parameters, wind load parameters, and weather impact parameters into the fourth construction model of the site selection safety assessment model to obtain the site selection safety index.

[0073] In step S24124, the site safety index S is a comprehensive consideration of all evaluation parameters to ensure the overall safety of the installation point under physical construction, wind load and extreme weather conditions. Specifically, the fourth construction model is as follows:

[0074] S=f(L,W,T)

[0075] Among them, T is the weather impact parameter, W is the wind load parameter, and L is the building level parameter.

[0076] Through steps S24121 to S24124, based on detailed evaluation of building level parameters, wind load parameters, and weather impact parameters, it is ensured that the point selection safety index S can fully reflect the safety status of the installation point, thereby improving the accuracy of point selection; by considering complex and changeable weather conditions and wind loads, the selection of the installation point of the anti-fall device can better adapt to various working environments and reduce the risk of falling; by integrating multiple parameters into a point selection safety index S, the subsequent adaptive genetic algorithm iteration can quickly screen out the best installation point in a data-driven manner, realizing intelligent decision-making and automated optimization. This solution effectively improves the accuracy of safety assessment and the adaptability of the anti-fall system, realizes a comprehensive evaluation of the safety of the installation point, reduces the subjectivity and uncertainty of human judgment, and ensures that the installation point can provide stable and reliable anti-fall protection under various conditions.

[0077] Optionally, in step S242, the initial selection scheme is optimized and iterated using an adaptive genetic algorithm to obtain the best selection scheme, including the following steps:

[0078] Step S2421, obtaining a site selection quality evaluation model, the site selection quality evaluation model is used to evaluate site selection quality, and output a site selection quality evaluation result;

[0079] Specifically, in step S2421, the site selection quality evaluation result can be a value or a grade, which reflects the comprehensive situation of the installation point in terms of safety, efficiency and economy. The site selection quality evaluation model is used to quantitatively evaluate the quality of each installation point to ensure that the installation point can meet the safety requirements and efficiency standards of the operation. The construction of the site selection quality evaluation model can consider the following factors: safety assessment, operation efficiency analysis, and economic considerations.

[0080] Step S2422, based on the site selection quality evaluation model, the initial selection scheme is optimized and iterated using an adaptive genetic algorithm until the stagnation condition is met, thereby obtaining the best selection scheme.

[0081] Specifically, in step S2422, the adaptive genetic algorithm can, based on the initial selection scheme, determine a best anti-fall installation point scheme under the selection point quality evaluation model through continuous iterative optimization. This scheme takes into account the safety, stability, convenience and economy of the selection point, and improves the accuracy and efficiency of the selection of the anti-fall installation point of the transmission tower.

[0082] Through steps S2421 to S2422, the scientific nature and safety of the scheme are ensured through modeling and quantitative decision-making. The initial selection scheme is optimized and iterated using an adaptive genetic algorithm, which can continuously improve the selection of installation points until the optimal solution is found. This is suitable for the installation of transmission tower fall protection devices that have strict requirements on the quality of the installation points, ensuring that each installation point can provide the best safety protection.

[0083] Optionally, in step S2422, the initial selection scheme is optimized and iterated using an adaptive genetic algorithm until a stagnation condition is met to obtain the best selection scheme, including the following steps:

[0084] Step S24221, based on the initial selection scheme, use an adaptive local search algorithm to optimize to find a better solution in the neighborhood, and update the initial selection scheme based on the better solution;

[0085] Step S24222, performing crossover and mutation operations on the updated initial selected solution to generate a new candidate solution;

[0086] Step S24223, based on the site selection quality evaluation model, obtain the site selection quality evaluation results of each candidate solution, select individuals that meet the quality evaluation conditions as parent individuals to participate in the generation of the next generation;

[0087] Step S24224, continuously iterating the candidate solutions until a stagnation condition is met;

[0088] Step S24225, output the candidate solution that meets the stagnation condition as the best selected solution.

[0089] Through steps S24221 to S24225, based on the strict screening of the site selection quality evaluation model, it is ensured that the optimal solution meets the requirements in terms of safety, operational convenience and economy. The adaptive local search algorithm can quickly locate the better solution in the neighborhood, while the crossover and mutation operations of the genetic algorithm accelerate the exploration of the global optimal solution, and overall speed up the optimization iteration. The adaptive genetic algorithm combines local search with global search to ensure that in the process of searching for the optimal solution, it not only takes into account the fine-tuning in the neighborhood, but also covers a wider solution space, effectively improving the accuracy of the selection of fall protection installation points. Through a series of iterative optimization steps, the installation points that meet the requirements are searched quickly and accurately, and the efficient selection of installation points is achieved.

[0090] It should be noted that in step S24224, the stopping condition can be any one of the following: 1) In several consecutive iterations, the site quality evaluation results of the best installation point have not changed significantly or the change is less than the set threshold, indicating that it is difficult for the algorithm to find a better installation point; 2) Iteration number limit: when the predetermined maximum number of iterations is reached, even if the current solution may not be absolutely optimal, the algorithm is considered to have explored sufficiently and further iterations are stopped; 3) The diversity of individuals in the population (i.e., installation point solutions) is reduced to a certain extent, indicating that the algorithm has fallen into a local optimal solution, and continued iteration may not be able to jump out of the current solution space.

[0091] Optionally, in step S22, generating a three-dimensional model of a transmission tower based on the three-dimensional point cloud data includes the following steps:

[0092] Step S221, processing the three-dimensional point cloud data to obtain key point cloud data, wherein the data processing includes at least one of the following: data cleaning and data filtering;

[0093] Specifically, in step S221, data cleaning and data filtering can remove noise and redundant information and improve the quality of point cloud data. The goal of data processing is to extract point cloud data that is critical to building a model, namely key point cloud data. Data cleaning generally includes removing outliers (i.e., points that are significantly different from most points), duplicate points, and abnormal points caused by measurement errors. Data filtering smoothes point cloud data through algorithms (such as mean filtering, median filtering, or Gaussian filtering) to further reduce the impact of noise. The data processing step ensures the accuracy and reliability of subsequent three-dimensional modeling. It should be understood that more processing methods can be used for data processing of three-dimensional point cloud data, such as feature point extraction and matching, segmentation and merging of point cloud data, and intelligent analysis and processing of point cloud data using deep learning and machine learning techniques (such as convolutional neural networks (CNN) or point cloud classification networks).

[0094] Step S222, obtaining an initial model corresponding to the transmission tower from a three-dimensional model library;

[0095] Specifically, in step S222, based on the type of transmission tower (such as drum tower, goat tower, variable diameter tower, multi-leg tower, etc.), an initial model matching it is selected from the predefined 3D model library. This step utilizes the existing model library resources and provides a basic framework for subsequent personalized modeling. The initial model contains the basic geometric features and structural information of the transmission tower, which helps to quickly generate a 3D model that matches the actual point cloud data. It should be understood that the initial model in the 3D model library can also be subdivided according to the tower material type to provide more diverse model selection.

[0096] Step S223, generating a three-dimensional model of the transmission tower based on the initial model and key point cloud data.

[0097] Specifically, in step S223, generating a three-dimensional model of a transmission tower may include steps such as point cloud registration, feature point matching, surface reconstruction, and detail optimization. Among them, registering the point cloud data to the model ensures that the spatial position and posture of the two are consistent; feature point matching is to identify key feature points in the point cloud data that match the model, such as the connection points and cross arm positions of the transmission tower; surface reconstruction generates a three-dimensional grid that conforms to the actual tower surface by calculating and fitting the point cloud data; detail optimization is to add more details to the geometric features of the model to make the model more realistic and accurate.

[0098] Through steps S221 to S223, the three-dimensional point cloud data of the transmission tower is effectively utilized, and a high-precision and high-reliability three-dimensional model is generated. Through data cleaning and filtering, the key point cloud data is more accurate, so that the generated model can more realistically reflect the actual structure and geometric characteristics of the transmission tower, providing an accurate reference for the subsequent selection of anti-fall installation points; using the pre-built three-dimensional model library, the initial model that matches the type of transmission tower is quickly selected, which simplifies the modeling process and improves the overall efficiency; the customized modeling method combining point cloud data and initial model can automatically adapt to various types of transmission towers, generate three-dimensional models that meet actual needs, and enhance the flexibility and applicability of the system; based on accurate three-dimensional models, the selection of anti-fall installation points can more comprehensively evaluate the safety of the installation points and ensure the safety and stability of workers when working at high altitudes.

[0099] Optionally, in step S25, the best selection scheme is evaluated, and the installation point that meets the evaluation criteria is output as the best candidate installation point, including the following steps:

[0100] Step S251, constructing an evaluation model using the analytic hierarchy process;

[0101] Specifically, in step S251, the evaluation model considers multiple evaluation dimensions, including but not limited to structural safety, operational convenience, economic cost, environmental adaptability, etc. Preferably, the specific implementation steps are as follows: 1) Determine the criterion hierarchy: First, define the criterion hierarchy for evaluating the selection of fall protection installation points, including primary criteria (such as safety, economy, operation) and secondary criteria (such as load-bearing capacity, wind load, installation cost, etc.); 2) Construct a judgment matrix: Compare the criteria in the same level in pairs to construct a judgment matrix. These judgment matrices are used to quantify the relative importance of different criteria; 3) Calculate weights: Use the mathematical formula of AHP (Analytic Hierarchy Process) to calculate the weight of each criterion from the judgment matrix. This weight reflects the importance of the criterion in the final decision; 4) Consistency test: Use the consistency ratio (CR) to test the consistency of the judgment matrix to ensure that the comparison between the criteria is reasonable and consistent. It should be noted that the weight construction of each criterion can be flexibly adjusted according to the specific project requirements, allowing different evaluation weights to be set in different scenarios to adapt to specific transmission tower environments and operation requirements.

[0102] Step S252: Use the evaluation model to evaluate the best selection solution, and output the installation point that meets the evaluation criteria as the best candidate installation point.

[0103] Specifically, in step S252, the evaluation model is applied to comprehensively evaluate the best selection scheme output by the genetic algorithm. This process involves comparing each scheme with the criteria defined in the model, calculating its score under each criterion, and comprehensively calculating the overall evaluation result based on the weight of each criterion. The specific operations are as follows: 1) Apply model scoring: Input each installation point scheme optimized by the genetic algorithm into the evaluation model, and the model scores the point based on its performance in multiple dimensions such as structural safety and operational convenience; 2) Comprehensive analysis: Use the evaluation model to calculate the weighted score of each scheme under all evaluation criteria to reflect its overall pros and cons; 3) Output the best candidate installation point: Only when the overall evaluation result of a certain installation point scheme meets the preset evaluation criteria, it will be output as the best candidate installation point.

[0104] Through step S251 and step S252, the analytic hierarchy process allows multiple decision criteria to be considered simultaneously, ensuring that the selection of fall protection installation points takes into account multiple aspects such as safety, economy and operational convenience, thus achieving comprehensive optimization. At the same time, converting qualitative evaluation criteria into quantitative scores helps reduce subjectivity and uncertainty in the decision-making process and improve the accuracy and reliability of decision-making.

[0105] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, 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, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0106] In this embodiment, a device for selecting an anti-fall installation point for a transmission tower is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0107] Figure 3 is a structural block diagram of a device for selecting an anti-fall installation point for a transmission tower according to one embodiment of the present invention, such as Figure 3 As shown, the device includes: an acquisition module 30, which is used to acquire three-dimensional point cloud data of a transmission tower; a generation module 32, which is used to generate a three-dimensional model of a transmission tower based on the three-dimensional point cloud data; a selection module 34, which is used to select part of the point cloud data of the three-dimensional model as an initial installation point to form an initial selection plan; an optimization iteration module 36, which is used to optimize and iterate the initial selection plan using an adaptive genetic algorithm to obtain an optimal selection plan; and an evaluation module 38, which is used to evaluate the optimal selection plan and output the installation point that meets the evaluation criteria as the best candidate installation point.

[0108] Through the above device, the three-dimensional point cloud data of the transmission tower is collected; based on the three-dimensional point cloud data, a three-dimensional model of the transmission tower is generated; part of the point cloud data of the three-dimensional model is selected as the initial installation point to form an initial selection plan; the initial selection plan is optimized and iterated by an adaptive genetic algorithm to obtain the best selection plan; the best selection plan is evaluated, and the installation point that meets the evaluation criteria is output as the best candidate installation point. The method in this embodiment achieves the purpose of quickly, accurately and safely selecting the installation point of the anti-fall device through point cloud data collection, three-dimensional model construction, initial selection plan determination, optimization iteration of the initial selection plan and evaluation of the best selection plan, and comprehensively uses modern data collection technology, three-dimensional modeling, adaptive genetic algorithm optimization and comprehensive evaluation mechanism, so as to achieve the technical effect of improving the installation efficiency and accuracy of the anti-fall device, solve the technical problems of low efficiency and inaccurate selection of the installation point of the anti-fall device in the related technology, and is suitable for transmission towers with various complex terrains and structures, especially in areas that are difficult to measure manually, such as mountains and canyons, and significantly improves work efficiency and safety.

[0109] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0110] Figure 4 FIG. 1 is a schematic diagram of a flow chart of a method for selecting an anti-fall installation point for a transmission tower according to one of the optional embodiments of the present invention. Figure 4 As shown, the method comprises the following steps:

[0111] S1: Use drones and scanning equipment to conduct a comprehensive scan of the transmission towers to obtain the three-dimensional point cloud data of the transmission towers. Construct a standard three-dimensional model library for different types of transmission towers, such as drum-shaped towers, goat-shaped towers, etc. Classify the point cloud data, capture the key points of the transmission towers, and record and store the captured key point coordinates. Based on the selected model and the captured key points, perform three-dimensional modeling to generate a three-dimensional model of the transmission tower. Based on the three-dimensional model, randomly select part of the point cloud data of the transmission tower, determine it as the best installation point, and use this installation point as the initial best solution;

[0112] S2: After determining the initial optimal solution, determine a series of key site selection parameters such as important construction levels, wind load assessment, and weather safety indicators, and use the work site selection distance and work activity range as strict condition equations and constraints to ensure that the installation point meets the actual operation requirements and does not exceed the safety range;

[0113] S3: Adaptive local search is performed on the initial optimal solution, combined with genetic algorithms, to continuously generate new optimal solutions and continuously update the optimal solution. In this process, the genetic algorithm continuously optimizes the selection of installation points by simulating biological evolution to find the solution that best meets the requirements. After multiple iterations and optimizations, when the optimal solution meets the stagnation condition, that is, when there is no obvious room for improvement, the final optimal fall protection installation point is obtained;

[0114] S4: After obtaining the best selection point, combine expert knowledge and the actual working environment to conduct a comprehensive and integrated evaluation of the best installation point for fall protection. Expert knowledge can provide experience and professional judgment on the best installation point, while the actual working environment takes into account various actual conditions on site, such as terrain, climate, surrounding facilities, etc., to evaluate whether the selected best installation point is objective and accurate, and whether it meets the safety and efficiency requirements of the actual operation.

[0115] In this embodiment, step S1 includes the following specific steps:

[0116] S11: Use drones and high-precision scanning equipment to conduct a comprehensive scan of the transmission towers. The drones fly at low altitudes to ensure coverage of all angles and details, and collect a large amount of high-resolution images and laser ranging data. Generate 3D point cloud data of the transmission towers, classify the point cloud data, capture key points of the transmission towers, and record and store the captured key point coordinates;

[0117] S12: Post-process the acquired 3D point cloud data, perform data cleaning and filtering, and remove noise and redundant information. Through algorithm analysis, convert the effective point cloud data into a usable 3D model to ensure that the geometric features of the model are consistent with the actual transmission tower. In this process, ensure that every detail is fully preserved to provide a basis for the selection of the best installation point. The specific formula is as follows:

[0118] 1) Smooth the point cloud data through mean filtering to remove noise. Suppose there are N points in the neighborhood, and the filtered point P i The coordinates of ′ are:

[0119]

[0120] Among them, P j Represents a point in the neighborhood.

[0121] 2) The covariance matrix of the local points is solved by the PCA method. The eigenvector corresponding to the minimum eigenvalue is the normal vector, which is used to describe the direction of the point cloud surface and is helpful for modeling the surface of the electric tower:

[0122]

[0123] Among them, P iis a point in the point cloud, is the centroid of the points in the region, and C is the covariance matrix.

[0124] S13: Build a standard 3D model library for different types of transmission towers, such as drum towers, goat towers, etc. Perform 3D modeling based on the selected model and captured key points to generate a 3D model of the transmission tower. Use computer-aided design (CAD) software to build and optimize the model to ensure that the model has accurate dimensions and structural features. Use 3D visualization technology to conduct in-depth analysis of the model to identify its performance under different conditions, and prepare for the subsequent selection of the best installation point;

[0125] S14: In the generated 3D model, some point cloud data are randomly selected as the initial best solution to provide a data basis for subsequent screening.

[0126] In this embodiment, step S2 includes the following specific steps:

[0127] S21: After determining the initial optimal solution, first construct a series of safety indicators. These indicators include important construction levels, wind load assessments, and weather safety indicators. The safety index is expressed as: S = f(L, W, T), where S is the safety index, L is the construction level, W is the wind load, and T is the weather impact. Through the systematic evaluation of these indicators, ensure that they fully reflect the safety of the installation point during the site selection process;

[0128] S22: The specific definition of site selection parameters should be clarified. For each key parameter, corresponding quantitative standards should be established.

[0129] The build level L can be quantified as:

[0130]

[0131] Where F is the load-bearing capacity of the structure and H is the height of the structure.

[0132] Wind load can be quantified as:

[0133] W=q×A×C d

[0134] Where W is the wind load, q is the dynamic pressure, A is the windward area, C d is the resistance coefficient.

[0135] The weather safety index T can be expressed by the following formula:

[0136]

[0137] Among them, P max is the maximum air pressure or maximum wind speed that may occur in the environment, P safeIt is the safe air pressure or safe wind speed considered during design.

[0138] Through clear parameter definition, provide accurate basis for subsequent steps and ensure the operability of all parameters in practical applications;

[0139] S23: After completing the parameter definition, establish the relevant conditional equations. Take the work point distance and the scope of operation into consideration to meet safety and practicality. Assume that the conditional equation D≥d min , where D is the distance between selected points, d min is the minimum value of the safety distance. By establishing these conditional equations, it is ensured that various influencing factors can be fully considered when selecting the installation point to ensure the feasibility of safe operation;

[0140] S24: Finally, set strict constraints to ensure the safety and effectiveness of the installation point. The constraints should include specific requirements such as wind load, working range and worker safety distance. Set the constraint S≤S max , where S max The establishment of these constraints provides a guarantee for the final selection of the installation point, ensuring that the selected point remains within the safety range while meeting the operational requirements.

[0141] In this embodiment, step S3 includes the following specific steps:

[0142] S31: Perform an adaptive local search to determine whether the current solution can be adjusted locally to obtain a better solution. The local search is based on the evaluation indicators of the surrounding points and continuously adjusts to improve the solution. If the local search finds a better solution, a local update is performed and the best solution is updated;

[0143] S32: When no significant improvement is found in the local search, the global search phase of the genetic algorithm is entered. The genetic algorithm simulates the biological evolution process and generates new candidate solutions through crossover and mutation operations. In this process, the fitness function is defined as:

[0144] f(S)=αS 安全 +βS 稳定

[0145] Among them, S 安全 is the safety index of the scheme, S 稳定 is the stability index of the scheme, and α and β are weight coefficients.

[0146] S33: At the same time, combine the best candidate point of the local search to generate a new best candidate point solution, continue to perform genetic operations to generate a new best solution algorithm, and continuously screen and retain solutions with higher fitness, so as to obtain a gradually optimized installation point selection. The generation of new solutions is based on the mutation rate P mutateControl, ensuring a balance between exploring new solutions and retaining optimal solutions;

[0147] S34: After multiple iterations, if the current solution has met the stagnation condition, that is, there is no significant room for improvement in multiple iterations (for example: |f(S new )-f(S best )|<ε), the algorithm stops and outputs the final optimal installation point.

[0148] In this embodiment, step S4 includes the following specific steps:

[0149] S41: Use the analytic hierarchy process to construct the evaluation index system. First, construct a judgment matrix, that is, use the analytic hierarchy process to judge the relative importance of each factor in each level to the factors in the previous level, and express this judgment with an appropriate scale to form a judgment matrix; then perform hierarchical single sorting and consistency test, that is, obtain the maximum eigenvalue and eigenvector of the judgment matrix.

[0150] S42: Based on the determined judgment matrix, obtain the characteristic root and perform consistency test. Use the consistency index CI. The smaller the CI, the better the consistency; the larger the CI, the worse the consistency. Considering the influence of randomness, the consistency ratio CR should also be considered. The formula is as follows:

[0151]

[0152] Among them, λ max is the maximum eigenvalue of the judgment matrix.

[0153] S43: Expert manual evaluation, combining professional experience and knowledge, provides evaluation opinions for specific installation points, covering safety risks, operational difficulty and technical applicability. This link can effectively improve the accuracy and practicality of site selection;

[0154] S44: Comprehensively analyze expert opinions and on-site inspection results to evaluate whether the best installation point meets safety requirements and operating efficiency standards, and form a comprehensive evaluation report. Based on the evaluation results, a final decision is made on whether to use the installation point to ensure that the selected installation point is efficient and safe in actual application.

[0155] The embodiment of the present invention also provides a transmission tower anti-fall installation point selection system, such as Figure 6 As shown, the system includes a data acquisition and preprocessing module 10, a model building and data selection module 20, a parameter definition, constraint condition establishment and algorithm implementation module 30 and an optimal fall prevention point installation point evaluation module 40. Figure 5 Schematic diagram of the principle of selecting the installation point of the transmission tower fall protection system, combined with Figure 5 and Figure 6 As shown, the system structure is as follows:

[0156] The data acquisition and preprocessing module 10 uses drones and scanning equipment to perform a comprehensive scan of the transmission tower to obtain the three-dimensional point cloud data of the transmission tower. For different types of transmission towers, a standard three-dimensional model library is constructed, such as drum-shaped towers, goat-shaped towers, etc. The point cloud data is classified, and the key points of the transmission tower are captured, and the captured key point coordinates are recorded and stored.

[0157] The model building and data selection module 20 performs three-dimensional modeling based on the selected model and the captured key points to generate a three-dimensional model of the transmission tower. Based on the model, some point cloud data of the transmission tower is randomly selected and determined as the best installation point, and this installation point is used as the initial best solution.

[0158] The parameter definition, constraint condition establishment and algorithm implementation module 30 is used to quickly and accurately screen the anti-fall installation points, use the adaptive genetic algorithm to perform adaptive local search on the initial optimal solution, combine the genetic algorithm to generate a new optimal solution and continuously update the optimal solution until the optimal solution meets the stagnation condition, obtain the optimal anti-fall installation point, and generate the optimal solution.

[0159] The optimal fall protection installation point evaluation module 40 is used to conduct a comprehensive and integrated evaluation of the optimal fall protection installation point, determine whether the selected optimal installation point is objective and accurate, whether it meets the safety requirements and high efficiency requirements of actual operations, and output the optimal candidate point.

[0160] An embodiment of the present invention further provides a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for selecting an anti-fall installation point for a transmission tower.

[0161] Optionally, in this embodiment, when the computer program is executed by a processor, the following steps are implemented:

[0162] Step S1, collecting three-dimensional point cloud data of transmission towers;

[0163] Step S2, generating a three-dimensional model of the transmission tower based on the three-dimensional point cloud data;

[0164] Step S3, selecting part of the point cloud data of the three-dimensional model as the initial installation point to form an initial selection plan;

[0165] Step S4, using an adaptive genetic algorithm to iterate the initial selection scheme to obtain the best selection scheme;

[0166] Step S5, evaluating the best selection scheme, and outputting the installation point that meets the evaluation criteria as the best candidate installation point.

[0167] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for selecting an anti-fall installation point for a transmission tower.

[0168] Optionally, in this embodiment, the computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0169] Step S1, collecting three-dimensional point cloud data of transmission towers;

[0170] Step S2, generating a three-dimensional model of the transmission tower based on the three-dimensional point cloud data;

[0171] Step S3, selecting part of the point cloud data of the three-dimensional model as the initial installation point to form an initial selection plan;

[0172] Step S4, using an adaptive genetic algorithm to iterate the initial selection scheme to obtain the best selection scheme;

[0173] Step S5, evaluating the best selection scheme, and outputting the installation point that meets the evaluation criteria as the best candidate installation point.

[0174] An embodiment of the present invention further provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the computer-readable storage medium is controlled, and the device where it is located executes the above-mentioned method for selecting the anti-fall installation point of the transmission tower.

[0175] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0176] Step S1, collecting three-dimensional point cloud data of transmission towers;

[0177] Step S2, generating a three-dimensional model of the transmission tower based on the three-dimensional point cloud data;

[0178] Step S3, selecting part of the point cloud data of the three-dimensional model as the initial installation point to form an initial selection plan;

[0179] Step S4, using an adaptive genetic algorithm to iterate the initial selection scheme to obtain the best selection scheme;

[0180] Step S5, evaluating the best selection scheme, and outputting the installation point that meets the evaluation criteria as the best candidate installation point.

[0181] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0182] An embodiment of the present invention further provides a processor, which is configured to run a computer program to execute the steps in any of the above method embodiments.

[0183] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0184] Step S1, collecting three-dimensional point cloud data of transmission towers;

[0185] Step S2, generating a three-dimensional model of the transmission tower based on the three-dimensional point cloud data;

[0186] Step S3, selecting part of the point cloud data of the three-dimensional model as the initial installation point to form an initial selection plan;

[0187] Step S4, using an adaptive genetic algorithm to iterate the initial selection scheme to obtain the best selection scheme;

[0188] Step S5, evaluating the best selection scheme, and outputting the installation point that meets the evaluation criteria as the best candidate installation point.

[0189] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0190] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0191] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0192] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0193] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0194] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for selecting anti-fall installation points for transmission towers, characterized in that: The method comprises the following steps: Collecting three-dimensional point cloud data of the transmission tower; Based on the three-dimensional point cloud data, generating a three-dimensional model of the transmission tower; Selecting part of the point cloud data of the three-dimensional model as initial installation points to form an initial selection plan; Using an adaptive genetic algorithm to iterate the initial selection scheme to obtain an optimal selection scheme; The best selection scheme is evaluated, and the installation point that meets the evaluation criteria is output as the best candidate installation point.

2. The method according to claim 1, characterized in that The initial selection scheme is optimized and iterated by using an adaptive genetic algorithm to obtain the best selection scheme, including the following steps: Obtaining constraint conditions, wherein the constraint conditions at least include a point selection distance condition and a point selection safety condition; Based on the constraint conditions, the adaptive genetic algorithm is used to iterate the initial selection scheme to obtain the best selection scheme.

3. The method according to claim 2, characterized in that The point selection distance condition is D ≥ d min , where D is the distance between selected points, d min is the minimum safety distance, and the safety condition for selecting the point is S≤S max , where S is the safety index of the selected point, S max The maximum safety index.

4. The method according to claim 3, characterized in that Before obtaining the safety condition for selecting a point, the method further includes: Obtaining point selection parameters, wherein the point selection parameters include at least one of the following: bearing capacity parameter, height parameter, dynamic pressure parameter, windward area parameter, drag coefficient, maximum ambient air pressure, design safety air pressure, maximum ambient wind speed, and design safety wind speed; The site selection parameters are input into a site selection safety assessment model to obtain the site selection safety index.

5. The method according to claim 4, characterized in that Inputting the site selection parameters into a site selection safety assessment model to obtain the site selection safety index comprises the following steps: Inputting the bearing capacity parameter and the height parameter into the first construction model of the site selection safety assessment model to obtain the construction level parameter; Inputting the dynamic pressure parameter, the windward area parameter, and the drag coefficient into the second construction model of the point selection safety assessment model to obtain the wind load parameter; Inputting the maximum ambient air pressure, the designed safe air pressure, the maximum ambient wind speed, and the designed safe wind speed into the third construction model of the site selection safety assessment model to obtain weather impact parameters; The construction level parameter, the wind load parameter, and the weather impact parameter are input into a fourth construction model of the site selection safety assessment model to obtain the site selection safety index.

6. The method according to claim 1, characterized in that The adaptive genetic algorithm is used to optimize and iterate the initial selection scheme to obtain the best selection scheme, including the following steps: Acquire a site selection quality evaluation model, wherein the site selection quality evaluation model is used to evaluate site selection quality and output a site selection quality evaluation result; Based on the site selection quality evaluation model, the adaptive genetic algorithm is used to optimize and iterate the initial selection plan until the stagnation condition is met, thereby obtaining the optimal selection plan.

7. The method according to claim 6, characterized in that The initial selection scheme is optimized and iterated by using the adaptive genetic algorithm until the stagnation condition is met to obtain the optimal selection scheme, including the following steps: Based on the initial selection scheme, an adaptive local search algorithm is used for optimization to find a better solution in the neighborhood, and the initial selection scheme is updated based on the better solution; Performing crossover and mutation operations on the updated initial selected solution to generate new candidate solutions; Based on the site selection quality evaluation model, the site selection quality evaluation results of each candidate solution are obtained, and individuals that meet the quality evaluation conditions are selected as parent individuals to participate in the generation of the next generation; Continuously iterating the candidate solutions until the stagnation condition is met; The candidate solution that satisfies the stagnation condition is output as the best selected solution.

8. The method according to claim 1, characterized in that: Generating a three-dimensional model of the transmission tower based on the three-dimensional point cloud data comprises the following steps: Processing the three-dimensional point cloud data to obtain key point cloud data, wherein the data processing includes at least one of the following: data cleaning and data filtering; Acquire an initial model corresponding to the transmission tower from a three-dimensional model library; Based on the initial model and the key point cloud data, a three-dimensional model of the transmission tower is generated.

9. The method according to claim 1, characterized in that: Evaluating the best selection scheme and outputting the installation point that meets the evaluation criteria as the best candidate installation point includes the following steps: The evaluation model was constructed using the analytic hierarchy process; The optimal selection scheme is evaluated using the evaluation model, and the installation point that meets the evaluation criteria is output as the optimal candidate installation point.

10. A device for selecting installation points for preventing a transmission tower from falling, characterized in that: include: A collection module, the collection module is used to collect three-dimensional point cloud data of the transmission tower; A generating module, the generating module is used to generate a three-dimensional model of the transmission tower based on the three-dimensional point cloud data; A selection module, the selection module is used to select part of the point cloud data of the three-dimensional model as an initial installation point to form an initial selection plan; An optimization and iteration module, wherein the optimization and iteration module is used to optimize and iterate the initial selection scheme using an adaptive genetic algorithm to obtain an optimal selection scheme; An evaluation module is used to evaluate the optimal selection scheme and output the installation point that meets the evaluation criteria as the optimal candidate installation point.

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