Optimal selection method for construction mode of overhead transmission line

Abstract

The invention relates to an optimal selection method for construction modes of an overhead transmission line. The optimal selection method comprises the following steps: defining a plurality of construction indexes for each construction process of the overhead transmission line; construction features are extracted based on historical construction data and a mechanical construction report and constructed into an initial construction feature set, and iteration is performed on the initial construction feature set through a genetic algorithm to obtain an optimized construction feature set; adjusting the weight of each construction index through a fuzzy analytic hierarchy process; and constructing an initial construction scheme set based on the optimized construction feature set, and iterating the initial construction scheme set through a multi-objective optimized particle swarm model based on the adjusted weight of each construction index to obtain an optimal construction scheme.

Description

Technical Field

[0001] The invention relates to an optimization method for an overhead power transmission line construction mode, belonging to the technical field of electric power engineering. Background Art

[0002] Traditional overhead transmission line construction methods, due to their high reliance on manual labor, often exhibit problems such as low process efficiency, long construction period, prominent safety hazards, and serious environmental damage, making it difficult to meet the comprehensive requirements of modern power engineering for efficient, safe, and green construction. At the same time, with the increase in engineering complexity and the intensification of the diversity of construction environments, the adaptability of traditional models in complex terrain and harsh climatic conditions has significantly decreased, further restricting the overall optimization and scientific decision-making of construction. For this reason, mechanized construction technology has gradually attracted attention. With its advantages of high construction precision, significant efficiency, strong personnel safety, and low environmental impact, it has shown great potential in the construction of transmission lines. However, the introduction and promotion of mechanized construction technology still requires systematic theoretical guidance and scientific decision-making support to solve the problem of optimizing conventional construction and mechanized construction under different terrain conditions and project scales. Summary of the invention

[0003] In order to solve the above problems existing in the prior art, the present invention proposes a preferred method for the construction of overhead power transmission lines.

[0004] The technical solution of the present invention is as follows:

[0005] On the one hand, the present invention provides a method for optimizing the construction mode of an overhead power transmission line, comprising the following steps:

[0006] Define several construction indicators for each construction process of overhead transmission lines;

[0007] Based on historical construction data and mechanized construction reports, construction features are extracted and constructed as an initial construction feature set. The initial construction feature set is iterated through a genetic algorithm to obtain an optimized construction feature set.

[0008] The weight of each construction index is adjusted through fuzzy analytic hierarchy process;

[0009] An initial set of construction schemes is constructed based on the optimized construction feature set. The initial set of construction schemes is iterated through a multi-objective optimization particle swarm model based on the adjusted weights of each construction indicator to obtain the optimal construction scheme.

[0010] As a preferred embodiment of the present invention, the construction process of the overhead power transmission line includes temporary road construction, material transportation, foundation pit excavation, foundation concrete pouring, tower assembly construction and ground wire installation;

[0011] The construction indicators include cost indicators, progress indicators, quality indicators, safety indicators and environmental indicators.

[0012] As a preferred embodiment of the present invention, the specific steps of iterating the initial construction feature set by genetic algorithm are:

[0013] The initial construction feature set is constructed based on historical construction data and mechanized construction reports, and the initial construction feature set is regarded as the initial population {x1,x2,…,x m}, where x m represents the mth construction feature in the initial construction feature set;

[0014] Calculate the fitness of each individual in the population, as shown in the following formula:

[0015]

[0016] Where: F(x m ) represents individual x m The fitness of i represents the initial weight of the i-th construction index; f i (x m ) represents individual x m The score of the i-th construction indicator; n represents the total number of construction indicators;

[0017] The adaptive individuals are selected based on the roulette wheel selection algorithm, as shown in the following formula:

[0018]

[0019] Where: P k Represents individual x k The probability of being selected;

[0020] Several individuals are randomly selected through the roulette algorithm, and the selected individuals are cross-operated to generate new individuals, as shown in the following formula:

[0021] x new =θ·x p +(1-θ)·x q

[0022] Where: x new represents the generated new individual; θ represents the crossover coefficient; x p 、x q They represent the selected individuals respectively;

[0023] Randomly select the genes of several new individuals for mutation, as shown in the following formula:

[0024] x′ new =x new +δ·(xmax -x min )·rand

[0025] Where: x′ new represents the new individual after mutation; x max represents the individual with the highest fitness in the population; x min Represents the individual with the smallest fitness in the population;

[0026] Generate a new population based on the mutated new individuals, the new individuals generated by the crossover operation, and the individuals whose fitness is greater than the threshold;

[0027] The above steps are repeated until the preset number of iterations is reached to obtain the final population, which is the optimized construction feature set.

[0028] As a preferred embodiment of the present invention, the specific steps of adjusting the weights of various construction indicators by fuzzy analytic hierarchy process are as follows:

[0029] Construct the judgment matrix A = [a ij ], where a ij Indicates the relative importance of the i-th construction index and the j-th construction index;

[0030] Calculate the maximum eigenvalue λ of the judgment matrix A max And the weight vector W = (w1,w2,…,w n );

[0031] The consistency of the judgment matrix is ​​verified as shown in the following formula:

[0032]

[0033] Where: CI represents the consistency index; RI represents the random consistency index; CR represents the consistency verification result. If CR<0.1, the consistency verification of the judgment matrix passes, and its corresponding weight vector is used as the weight of each construction indicator;

[0034] If the judgment matrix consistency verification fails, the judgment matrix is ​​regenerated and a one-time verification is performed again until the consistency verification passes.

[0035] As a preferred embodiment of the present invention, the specific steps of iterating the initial construction plan set through a multi-objective optimized particle swarm model based on the adjusted weights of each construction indicator are as follows:

[0036] The initial construction plan set is used as the initial population, and each particle in each population is assigned an initial position and an initial speed. Each sample in the initial construction plan set is a construction plan composed of the construction features in the optimized construction feature set, and the position of the particle represents the feature value setting of the construction plan.

[0037] Start the iteration, and calculate the fitness of each individual through the fitness function in each iteration, as shown in the following formula:

[0038] F p (X) = α·S(X) - β·R(X)

[0039]

[0040] Among them: F p (X) represents the fitness of individual X in the population; S(X) represents the comprehensive benefit value of individual X; R(X) represents the risk value of individual X; α and β represent the weights of the comprehensive benefit value and risk value respectively; w t,i Represents the adjusted weight of the i-th construction indicator; I i (X) represents the score of individual X for indicator i; U represents the risk amount; P u represents the probability of occurrence of risk u; r u Indicates the severity of risk u;

[0041] After each iteration, the individual optimal position and the global optimal position are updated according to the fitness of each individual, and the particle position and speed of the next iteration are updated according to the following formula:

[0042]

[0043] in: represents the velocity of particle i in the t+1th iteration; represents the speed of particle i in the tth iteration; ω represents the inertia weight; c1 and c2 represent the acceleration factors; r1 and r2 represent random numbers; represents the optimal position of an individual; represents the global optimal position; represents the position of particle i in iteration t+1; represents the position of particle i at the tth iteration;

[0044] When the preset number of iterations is reached, the global optimal position is selected as the optimal construction plan.

[0045] On the other hand, the present invention also provides a system for optimizing the construction method of an overhead power transmission line, comprising the following steps: an indicator setting module, a construction plan optimization module, an indicator weight adjustment module, and a construction plan confirmation module;

[0046] The construction index definition module is used to define a number of construction indexes for each construction process of the overhead transmission line;

[0047] The construction scheme optimization module is used to extract construction features based on historical construction data and mechanized construction reports and construct an initial construction feature set, and iterate the initial construction feature set through a genetic algorithm to obtain an optimized construction feature set;

[0048] The indicator weight adjustment module is used to adjust the weight of each construction indicator through fuzzy hierarchical analysis method;

[0049] The construction plan confirmation module is used to construct an initial construction plan set based on the optimized construction feature set, and iterate the initial construction plan set through a multi-objective optimized particle swarm model based on the adjusted weights of each construction indicator to obtain the optimal construction plan.

[0050] On the other hand, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0051] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0052] The present invention has the following beneficial effects:

[0053] 1. Based on the integrated framework of genetic algorithm, fuzzy analytic hierarchy process and improved particle swarm optimization algorithm, the present invention proposes a method for scientific optimization of conventional construction and mechanized construction of overhead transmission lines, which can effectively reduce construction costs, improve construction efficiency, ensure construction safety and reduce damage to the ecological environment. It is of great significance to promote the intelligent upgrading and sustainable development of transmission line construction technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0056] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0057] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0058] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0059] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0060] Embodiment 1:

[0061] See also Figure 1 , a method for optimizing the construction mode of an overhead transmission line, comprising the following steps:

[0062] Define several construction indicators for each construction process of overhead transmission lines;

[0063] Based on historical construction data and mechanized construction reports, construction features are extracted and constructed as an initial construction feature set. The initial construction feature set is iterated through a genetic algorithm to obtain an optimized construction feature set.

[0064] The weight of each construction index is adjusted through fuzzy analytic hierarchy process;

[0065] An initial set of construction schemes is constructed based on the optimized construction feature set. The initial set of construction schemes is iterated through a multi-objective optimization particle swarm model based on the adjusted weights of each construction indicator to obtain the optimal construction scheme.

[0066] As a preferred implementation of this embodiment, the construction process of the overhead transmission line includes temporary road construction, material transportation, foundation pit excavation, foundation concrete pouring, tower assembly construction and ground wire installation;

[0067] During the construction of temporary roads, the adaptability of construction machinery and equipment under complex terrain conditions must be considered, especially for steep slopes and soft foundations, and the construction path must be optimized to reduce unnecessary waste of resources and improve equipment utilization;

[0068] The material transportation process involves long-distance transportation, adverse weather conditions, and irregular route planning. Based on the single transportation capacity and operating efficiency of mechanized equipment, the transportation tasks are reasonably allocated to ensure the efficient flow of construction resources;

[0069] During the excavation process, mechanical equipment needs to adapt to different geological conditions, such as soft soil, rock and sandy soil; standardized operating procedures can reduce manual intervention, improve excavation efficiency, and ensure slope stability;

[0070] The foundation concrete pouring process emphasizes construction uniformity and quality stability. Mechanized equipment needs to accurately control the mixing, conveying and pouring processes to avoid cold joints and adapt to construction needs under different weather conditions;

[0071] The tower assembly construction process requires mechanized equipment to have accurate positioning and lifting capabilities during high-altitude operations, especially for changes in tower weight and construction height, to ensure safety and construction efficiency;

[0072] The installation of ground wires faces challenges such as large spans, high tension, and complex terrain conditions. Mechanized equipment optimizes the wire laying path and reduces losses during the installation process through dynamic tension adjustment and multi-point control;

[0073] The construction indicators include cost indicators, progress indicators, quality indicators, safety indicators and environmental indicators.

[0074] As a preferred implementation of this embodiment, the specific steps of iterating the initial construction feature set by using a genetic algorithm are:

[0075] The initial construction feature set is constructed based on historical construction data and mechanized construction reports. When constructing the initial construction feature set, a multi-objective optimization model is defined, key construction indicators are introduced, and feature selection is optimized:

[0076] minZ=α1·C(x)+α2·T(x)+α3·Q(x)

[0077] Where: C(x) represents the cost function; T(x) represents the progress function; Q(x) represents the quality function; α1, α2, α3 represent the weights of each function respectively;

[0078] According to the construction budget C max Define the constraint C(x)≤C max , ensure that the selected features are within the budget;

[0079] Construct the progress risk function R based on the historical data of the construction progress T (x) = T(x)·(1-e -λT ), where λ represents the time risk adjustment coefficient;

[0080] The main features f1 and f2 that affect the construction quality are extracted through genetic algorithm, and a weighted model Q(x) = β1f1 + β2f2 is constructed, where β1 and β2 are the weights of the quality features;

[0081] The initial construction feature set is regarded as the initial population {x1,x2,…,x m}, where x m represents the mth construction feature in the initial construction feature set;

[0082] Calculate the fitness of each individual in the population, as shown in the following formula:

[0083]

[0084] Where: F(x m ) represents individual x m The fitness of i represents the initial weight of the i-th construction index; f i (x m ) represents individual x m The score of the i-th construction indicator; n represents the total number of construction indicators;

[0085] In this embodiment, an environmental impact function is introduced into the fitness function for evaluation, and the environmental impact function is specifically shown in the following formula:

[0086]

[0087] Where: γ k represents the environmental indicator weight of the i-th feature; e k represents the environmental emission value of the i-th feature;

[0088] The adaptive individuals are selected based on the roulette wheel selection algorithm, as shown in the following formula:

[0089]

[0090] Where: P k Represents individual x k The probability of being selected;

[0091] Several individuals are randomly selected through the roulette algorithm, and the selected individuals are cross-operated to generate new individuals, as shown in the following formula:

[0092] x new =θ·x p +(1-θ)·x q

[0093] Where: x new represents the generated new individual; θ represents the crossover coefficient; x p 、x q They represent the selected individuals respectively;

[0094] Randomly select the genes of several new individuals for mutation, as shown in the following formula:

[0095] x′ new =x new +δ·(x max -x min )·rand

[0096] Where: x′ new represents the new individual after mutation; x max represents the individual with the highest fitness in the population; x min Represents the individual with the smallest fitness in the population;

[0097] In this embodiment, during the mutation process, a feature correlation matrix is ​​generated based on historical data, and redundant features are reduced through sparse regularization constraints, as shown in the following formula:

[0098] min||R·XY|| 2 +λ||X||

[0099] Where: ||X|| represents the sparse regularization term; λ represents the regularization coefficient; R represents the feature correlation matrix;

[0100] Generate a new population based on the mutated new individuals, the new individuals generated by the crossover operation, and the individuals whose fitness is greater than the threshold;

[0101] In this embodiment, a dynamic correction function of global fitness is defined as F′(x)=F′(x)·(1+η·G), where η represents the correction coefficient; G represents the generational growth rate;

[0102] The above steps are repeated until the preset number of iterations is reached to obtain the final population, which is the optimized construction feature set;

[0103] In this embodiment, principal component analysis (PCA) is used to reduce the dimension of the feature selection results generated by the genetic algorithm to extract the feature set with the maximum amount of information;

[0104] Fuzzy mathematics is used to process the uncertainty of historical construction data for quality characteristics. In the feature selection of construction progress, the resource allocation of the construction process is optimized through the time-cost trade-off model. In the process of extracting environmental indicators, the weighted fuzzy clustering algorithm is used to classify environmental impact data. For safety characteristics, the safety threshold is dynamically adjusted to ensure optimal safety performance in high-risk scenarios.

[0105] During the genetic algorithm optimization process, the quality of feature selection of each construction scheme was evaluated by weighted Euclidean distance;

[0106] The feature selection results generated by multiple generations of genetic algorithms are summarized to build a global construction index optimization framework based on the constrained optimization model;

[0107] For individuals with low fitness values ​​in the genetic algorithm, an elimination mechanism is designed to dynamically adjust the population size through the population diversity control function;

[0108] During the evolution process, the weighted coefficient of the fitness function is dynamically updated according to the changing trend of historical construction data.

[0109] As a preferred implementation of this embodiment, the specific steps of adjusting the weights of various construction indicators by fuzzy analytic hierarchy process are as follows:

[0110] The construction process of overhead transmission lines: temporary road construction, material transportation, foundation pit excavation, foundation concrete pouring, tower assembly construction and ground wire erection are divided into criterion layers, and key influencing factors such as equipment utilization, operating efficiency and safety are subdivided into sub-criteria layers;

[0111] Combine expert experience to construct a judgment matrix A = [a ij ], where a ij Indicates the relative importance of the i-th construction index and the j-th construction index;

[0112] Calculate the maximum eigenvalue λ of the judgment matrix A max And the weight vector W = (w1,w2,…,w n );

[0113] The consistency of the judgment matrix is ​​verified as shown in the following formula:

[0114]

[0115]

[0116] Where: CI represents the consistency index; RI represents the random consistency index; CR represents the consistency verification result. If CR<0.1, the consistency verification of the judgment matrix passes, and its corresponding weight vector is used as the weight of each construction indicator;

[0117] If the judgment matrix consistency verification fails, the judgment matrix is ​​regenerated and a one-time verification is performed again until the consistency verification passes.

[0118] As a preferred implementation of this embodiment, the specific steps of iterating the initial construction plan set through a multi-objective optimized particle swarm model based on the adjusted weights of each construction indicator are as follows:

[0119] The initial construction plan set is used as the initial population, and each particle in each population is assigned an initial position and an initial speed. Each sample in the initial construction plan set is a construction plan composed of the construction features in the optimized construction feature set. The position of the particle represents the characteristic value setting of the construction plan, including the key parameters of mechanized construction and conventional construction.

[0120] Start the iteration, and calculate the fitness of each individual through the fitness function in each iteration, as shown in the following formula:

[0121] F p (X) = α·S(X) - β·R(X)

[0122]

[0123] Among them: F p (X) represents the fitness of individual X in the population; S(X) represents the comprehensive benefit value of individual X; R(X) represents the risk value of individual X; α and β represent the weights of the comprehensive benefit value and risk value respectively; w t,i represents the adjusted weight of the i-th construction indicator; I i (X) represents the score of individual X for indicator i; U represents the risk amount; P u represents the probability of occurrence of risk u; r u Indicates the severity of risk u;

[0124] After each iteration, the individual optimal position and the global optimal position are updated according to the fitness of each individual, and the particle position and speed of the next iteration are updated according to the following formula:

[0125]

[0126] in: represents the velocity of particle i in the t+1th iteration; represents the speed of particle i in the tth iteration; ω represents the inertia weight; c1 and c2 represent the acceleration factors; r1 and r2 represent random numbers; represents the optimal position of an individual; represents the global optimal position; represents the position of particle i in iteration t+1; represents the position of particle i at the tth iteration;

[0127] In this implementation, the inertia weight is dynamically adjusted to ω=ω max -(ω max -ω min )·t T , to enhance the convergence of particles in later iterations;

[0128] In this implementation, an improved dynamic acceleration factor is used to balance the search capabilities of individual and global optimality.

[0129] In the iteration of particle swarm optimization, the operating status feedback of mechanized construction equipment, such as tension fluctuation and vibration frequency, is introduced to optimize equipment scheduling;

[0130] When the preset number of iterations is reached, the global optimal position is selected as the optimal construction plan. The global optimal solution of the particle swarm is subsequently verified to ensure the practical applicability of the construction plan in multiple dimensions such as cost, quality and environmental impact.

[0131] Embodiment 2:

[0132] A system for optimizing the construction method of an overhead power transmission line comprises the following steps: an indicator setting module, a construction plan optimization module, an indicator weight adjustment module and a construction plan confirmation module;

[0133] The construction index definition module is used to define a number of construction indexes for each construction process of the overhead transmission line;

[0134] The construction scheme optimization module is used to extract construction features based on historical construction data and mechanized construction reports and construct an initial construction feature set, and iterate the initial construction feature set through a genetic algorithm to obtain an optimized construction feature set;

[0135] The indicator weight adjustment module is used to adjust the weight of each construction indicator through fuzzy hierarchical analysis method;

[0136] The construction plan confirmation module is used to construct an initial construction plan set based on the optimized construction feature set, and iterate the initial construction plan set through a multi-objective optimized particle swarm model based on the adjusted weights of each construction indicator to obtain the optimal construction plan.

[0137] The system is used to implement the method in Example 1, which will not be described in detail here.

[0138] Embodiment three:

[0139] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0140] Embodiment 4:

[0141] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in any embodiment of the present invention is implemented.

[0142] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0143] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0144] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0145] In several embodiments provided in the present application, if any function 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 application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several 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 application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0146] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for optimizing the construction mode of an overhead power transmission line, characterized in that: The following steps are involved: Define several construction indicators for each construction process of overhead transmission lines; Based on historical construction data and mechanized construction reports, construction features are extracted and constructed as an initial construction feature set. The initial construction feature set is iterated through a genetic algorithm to obtain an optimized construction feature set. The weight of each construction index is adjusted through fuzzy analytic hierarchy process; An initial set of construction schemes is constructed based on the optimized construction feature set. The initial set of construction schemes is iterated based on the adjusted weights of each construction indicator through a multi-objective optimization particle swarm model to obtain the optimal construction scheme.

2. The method for optimizing the construction mode of an overhead power transmission line according to claim 1, characterized in that: The construction process of the overhead transmission line includes temporary road construction, material transportation, foundation pit excavation, foundation concrete pouring, tower assembly construction and ground wire installation; The construction indicators include cost indicators, progress indicators, quality indicators, safety indicators and environmental indicators.

3. The method for optimizing the construction mode of an overhead power transmission line according to claim 1, characterized in that: The specific steps of iterating the initial construction feature set by genetic algorithm are: The initial construction feature set is constructed based on historical construction data and mechanized construction reports, and the initial construction feature set is regarded as the initial population {x1,x2,…,x m }, where x m represents the mth construction feature in the initial construction feature set; Calculate the fitness of each individual in the population, as shown in the following formula: Where: F(x m ) represents individual x m The fitness of i represents the initial weight of the i-th construction index; f i (x m ) represents individual x m The score of the i-th construction indicator; n represents the total number of construction indicators; The adaptive individuals are selected based on the roulette wheel selection algorithm, as shown in the following formula: Where: P k Represents individual x k The probability of being selected; Several individuals are randomly selected through the roulette algorithm, and the selected individuals are cross-operated to generate new individuals, as shown in the following formula: x new =θ·x p +(1-θ)·x q Where: x new represents the generated new individual; θ represents the crossover coefficient; x p 、x q They represent the selected individuals respectively; Randomly select the genes of several new individuals for mutation, as shown in the following formula: x′ new =x new +δ·(x max -x min )·rand Where: x′ new represents the new individual after mutation; x max represents the individual with the highest fitness in the population; x min Represents the individual with the smallest fitness in the population; Generate a new population based on the mutated new individuals, the new individuals generated by the crossover operation, and the individuals whose fitness is greater than the threshold; The above steps are repeated until the preset number of iterations is reached to obtain the final population, which is the optimized construction feature set.

4. The method for optimizing the construction mode of an overhead power transmission line according to claim 1, characterized in that: The specific steps of adjusting the weights of each construction index through fuzzy analytic hierarchy process are as follows: Construct the judgment matrix A = [a ij ], where a ij Indicates the relative importance of the i-th construction index and the j-th construction index; Calculate the maximum eigenvalue λ of the judgment matrix A max And the weight vector W = (w1,w2,…,w n ); The consistency of the judgment matrix is ​​verified as shown in the following formula: Where: CI represents the consistency index; RI represents the random consistency index; CR represents the consistency verification result. If CR<0.1, the consistency verification of the judgment matrix passes, and its corresponding weight vector is used as the weight of each construction indicator; If the judgment matrix consistency verification fails, the judgment matrix is ​​regenerated and a one-time verification is performed again until the consistency verification passes.

5. The method for optimizing the construction mode of an overhead power transmission line according to claim 1, characterized in that: The specific steps of iterating the initial construction plan set based on the adjusted weights of each construction indicator through the multi-objective optimization particle swarm model are as follows: The initial construction plan set is used as the initial population, and each particle in each population is assigned an initial position and an initial speed. Each sample in the initial construction plan set is a construction plan composed of the construction features in the optimized construction feature set, and the position of the particle represents the feature value setting of the construction plan. Start the iteration, and calculate the fitness of each individual through the fitness function in each iteration, as shown in the following formula: F p (X)=α·S(X)-β·R(X) Among them: F p (X) represents the fitness of individual X in the population; S(X) represents the comprehensive benefit value of individual X; R(X) represents the risk value of individual X; α and β represent the weights of the comprehensive benefit value and risk value respectively; w t,i represents the adjusted weight of the i-th construction indicator; I i (X) represents the score of individual X for indicator i; U represents the risk amount; P u represents the probability of occurrence of risk u; r u Indicates the severity of risk u; After each iteration, the individual optimal position and the global optimal position are updated according to the fitness of each individual, and the particle position and speed of the next iteration are updated according to the following formula: in: represents the velocity of particle i in the t+1th iteration; represents the speed of particle i in the tth iteration; ω represents the inertia weight; c1 and c2 represent the acceleration factors; r1 and r2 represent random numbers; represents the optimal position of an individual; represents the global optimal position; represents the position of particle i in iteration t+1; represents the position of particle i at the tth iteration; When the preset number of iterations is reached, the global optimal position is selected as the optimal construction plan.

6. A system for optimizing the construction method of an overhead power transmission line, characterized in that: The following steps are involved: Indicator setting module, construction plan optimization module, indicator weight adjustment module and construction plan confirmation module; The construction index definition module is used to define a number of construction indexes for each construction process of the overhead transmission line; The construction scheme optimization module is used to extract construction features based on historical construction data and mechanized construction reports and construct an initial construction feature set, and iterate the initial construction feature set through a genetic algorithm to obtain an optimized construction feature set; The indicator weight adjustment module is used to adjust the weight of each construction indicator through fuzzy hierarchical analysis method; The construction plan confirmation module is used to construct an initial construction plan set based on the optimized construction feature set, and iterate the initial construction plan set through a multi-objective optimized particle swarm model based on the adjusted weights of each construction indicator to obtain the optimal construction plan.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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