Power transmission line foundation intelligent optimization design platform based on multi-target algorithm

Abstract

The invention provides a power transmission line basic intelligent optimization design platform based on a multi-objective algorithm, and relates to the technical field of power transmission line basic design. The method includes: acquiring influence factor data of a target position; obtaining evaluation factors for selecting the basic design; constructing a basic type selection model based on a random forest, inputting influence factor data, evaluation factors and corresponding weights, and outputting a score of each basic type; size parameters of the selected basic types are optimized, and the optimal material selection ratio is obtained according to the performance of different basic materials. According to the invention, through efficient data processing capability, scientific evaluation indexes, an intelligent decision model and an advanced optimization design process, a brand new solution is provided for modern power transmission projects.

Description

Technical Field

[0001] The present invention relates to the technical field of transmission line foundation design, and particularly to an intelligent optimal design platform for transmission line foundations based on a multi-objective algorithm. Background Art

[0002] The use history of transmission line foundations can be traced back to the early 20th century. With the development of the power industry, the technology of transmission lines has gradually matured, and the design and materials of foundations have also been constantly evolving. The initial transmission lines relied on simple wooden piles or concrete columns, with poor load-bearing capacity and durability. In the late 20th century, with the increasing power demand, the design of foundations gradually became standardized, forming a relatively complete set of design standards and construction specifications.

[0003] Current foundation design usually relies on the experience of engineers and lacks scientific calculation methods, which may lead to improper design, thus affecting the safety and economy of the foundation. Under extreme climatic conditions, such as strong winds, or complex geological conditions, the existing design schemes are not sufficient to provide ideal stability. Currently, in the industry during the design process, there is still too much reliance on manual operations and a lack of sufficient automation and intelligent technology support. At the same time, a lot of potential information cannot be effectively utilized, affecting the scientific nature of decision-making. Summary of the Invention

[0004] The present invention provides an intelligent optimal design platform for transmission line foundations based on a multi-objective algorithm to solve the defects existing in the prior art.

[0005] The present invention provides an intelligent optimal design platform for transmission line foundations based on a multi-objective algorithm, including: A data acquisition and processing module, configured to obtain data on influencing factors of the target transmission line foundation location and preprocess the data on influencing factors to obtain preprocessed data.

[0006] A selection index determination module, configured to comprehensively analyze the preprocessed data, obtain evaluation factors for selecting the transmission line foundation design, and determine the weight of each evaluation factor according to the analytic hierarchy process.

[0007] A selection model construction module, configured to construct a transmission line foundation type selection model based on a random forest, input the data on influencing factors, evaluation factors, and corresponding weights, output the score of each transmission line foundation type, and take the transmission line foundation type with the highest score as the final selection.

[0008] A foundation design optimization module, configured to optimize the size parameters of the selected transmission line foundation type according to the evaluation factors by using a genetic algorithm, and obtain the optimal material selection ratio according to the performance of different foundation materials.

[0009] According to the intelligent optimal design platform for transmission line foundations based on multi-objective algorithms provided by the present invention, the influencing factor data includes geological survey data, and the geological survey data includes the soil type at the location of the target transmission line foundation, the thickness of various types of soil layers, the unit weight of the soil, the internal friction angle, the cohesion, and the compression modulus.

[0010] According to the intelligent optimal design platform for transmission line foundations based on multi-objective algorithms provided by the present invention, the influencing factor data further includes upper structure load data, environmental data, and tension data. The upper structure load data represents the shape and total weight of the upper structure of the transmission line foundation. The environmental data includes wind force data and wind direction data at the location of the target transmission line foundation. The tension data represents the tension data of the wires at both ends of the target transmission line foundation.

[0011] According to the intelligent optimal design platform for transmission line foundations based on multi-objective algorithms provided by the present invention, the process of preprocessing the influencing factor data includes: Checking and removing duplicate items in the influencing factor data.

[0012] Filling in the missing values in the influencing factor data using the mean filling method.

[0013] Performing standardization processing on the influencing factor data to convert data with different dimensions into a unified standard.

[0014] Combining the standardized geological survey data, upper structure load data, environmental data, and tension data into a comprehensive data set as the preprocessed data.

[0015] According to the intelligent optimal design platform for transmission line foundations based on multi-objective algorithms provided by the present invention, the evaluation factors include the requirements for foundation bearing capacity. The process of obtaining the requirements for foundation bearing capacity includes: Extracting the geological survey data, upper structure load data, environmental data, and tension data at the location of the target transmission line foundation.

[0016] Taking the total weight in the upper structure load data as the vertical load.

[0017] According to the structure shape of the upper structure, combining the wind force data and wind direction data in the environmental data, calculating the wind load on the upper structure of the target transmission line foundation, and obtaining the acting angle of the wind load.

[0018] Calculating the difference between the tension data of the wires at both ends of the target transmission line foundation to obtain the tension load, and obtaining the acting angle of the tension load.

[0019] Decomposing the wind load and the tension load to obtain the horizontal component of the wind load, the vertical component of the wind load, the horizontal component of the tension load, and the vertical component of the tension load.

[0020] The horizontal component of wind load, the vertical component of wind load, the horizontal component of tension load, and the vertical component of tension load are synthesized to obtain the resultant load, which is used as the foundation bearing capacity requirement.

[0021] According to the intelligent optimal design platform for transmission line foundations based on the multi-objective algorithm provided by the present invention, the evaluation factors further include settlement control requirements. The process of obtaining the settlement control requirements includes: Obtain the soil compression coefficient and the consolidation characteristics of the soil layer at the location of the target transmission line foundation according to the geological survey data.

[0022] Calculate the sensitivity of differential settlement according to the upper structure load data of the target transmission line foundation in combination with the foundation bearing capacity requirement.

[0023] Adopt the finite element analysis method to obtain the settlement amount and settlement difference of the target transmission line foundation under the foundation bearing capacity requirement.

[0024] Determine the allowable settlement range according to the preset standard to obtain the settlement control requirement.

[0025] According to the intelligent optimal design platform for transmission line foundations based on the multi-objective algorithm provided by the present invention, the process of determining the weight of each evaluation factor includes: Stratify the transmission line foundation types and evaluation factors, including the target layer, the criterion layer, and the scheme layer. The target layer is used to determine the optimal design goal of the transmission line foundation. The criterion layer is used to define the evaluation factors, including the bearing capacity requirement and the settlement control requirement. The scheme layer is used to provide different design schemes for the transmission line foundation types.

[0026] Compare the relative importance of the evaluation factors to construct a judgment matrix, and each value in the judgment matrix represents the degree of importance between the evaluation factors.

[0027] Conduct a consistency test on the judgment matrix by calculating the maximum eigenvalue and the consistency index of the judgment matrix.

[0028] Normalize the judgment matrix to obtain the weight of each evaluation factor.

[0029] According to the intelligent optimal design platform for transmission line foundations based on the multi-objective algorithm provided by the present invention, the process of constructing a selection model for transmission line foundation types based on random forest includes: Collect the historical data of transmission line foundation design, including historical geological survey data, historical upper structure load data, historical environmental data, and historical tension data.

[0030] Conduct a comprehensive analysis of the historical data to obtain the historical evaluation factors of transmission line foundation design and determine the weight of each historical evaluation factor.

[0031] Take the transmission line foundation types corresponding to each group of historical data, historical evaluation factors, and weights as a set of data to construct a data set, and divide the data set into a training set and a test set.

[0032] Randomly extract sample data from the training set, and extract the historical influence factor features and historical evaluation factor features in the sample data.

[0033] Construct a basic decision tree, and divide the sample data into different sub-nodes in the decision tree until the depth of the tree reaches a preset threshold.

[0034] Repeat the process of constructing the basic decision tree according to the preset number of decision trees to obtain a random forest.

[0035] According to the intelligent optimal design platform for transmission line foundations based on the multi-objective algorithm provided by the present invention, the process of optimizing the size parameters of the selected transmission line foundation type by using the genetic algorithm includes: Set the optimization objectives, which include minimizing foundation settlement and maximizing foundation bearing capacity.

[0036] Collect the initial size parameters of the transmission line foundation type and the evaluation factors of the target transmission line foundation, and construct an evaluation function in combination with the optimization objectives.

[0037] Represent the initial size parameters as gene coding, and randomly generate an initial population.

[0038] Use the roulette wheel selection method to determine the next-generation parents, perform crossover operations to generate new-generation size parameter individuals, and randomly mutate the size parameters to increase the diversity of the optimization search.

[0039] Repeat the process of performing crossover operations and random mutations until the preset number of iterations is satisfied.

[0040] Calculate the fitness value of each size parameter individual according to the evaluation function to obtain the size parameter scheme with the highest fitness value.

[0041] According to the intelligent optimal design platform for transmission line foundations based on the multi-objective algorithm provided by the present invention, the process of obtaining the optimal material selection ratio includes: Set the objective function and constraint conditions. The objective function includes maximizing the comprehensive performance of the material and minimizing the overall material cost. The constraint conditions include the bearing capacity requirements of the transmission line foundation.

[0042] Collect the technical parameters of the transmission line foundation materials, and the technical parameters include compressive strength and tensile strength.

[0043] Construct a linear programming model, and obtain the material ratio scheme of the transmission line foundation according to the objective function, constraint conditions, and technical parameters of the foundation materials.

[0044] The intelligent optimal design platform for transmission line foundations based on multi-objective algorithms provided by the present invention ensures that the design process is based on accurate and reliable information by acquiring and preprocessing data on various factors affecting transmission line foundations. This can reduce design deviations caused by insufficient experience or inaccurate assumptions and improve the reliability of the overall design. By extracting evaluation factors for foundation bearing capacity and settlement control and using the analytic hierarchy process to assign weights to different evaluation factors, the comprehensiveness of the design objectives is ensured. Considering both the structural safety and the performance during use, a balanced objective system is formed, laying a foundation for subsequent decision-making. Using the random forest algorithm, by inputting the weights of the influencing factors and evaluation factors obtained through comprehensive analysis, the model can efficiently evaluate the performance of different foundation types and output scores. The random forest avoids the overfitting problem and can more accurately reflect the capabilities of various foundation types to adapt to different environmental and load conditions compared to traditional methods. Reducing the subjectivity of manual judgment makes the selected foundation type more reasonable and significantly improves the project quality. The genetic algorithm is used to optimize the size parameters of the determined foundation type. With its powerful global optimization ability, the genetic algorithm can effectively explore the design space and find the optimal combination of size parameters. Combining multiple objectives, including structural safety, cost-effectiveness, and material use efficiency, the optimal design result is obtained, and the optimal material selection ratio is determined according to the performance of different foundation materials to improve the performance and durability of the structure and reduce the cost to a certain extent. Through synergistic effects, the entire design platform improves the efficiency of transmission line foundation design, enabling engineers to respond more quickly to design requirements and changes in site conditions and enhancing the economic efficiency of the project. Through data-driven decision-making, it can ensure the selection of the most suitable foundation under different conditions and reduce potential safety hazards caused by inappropriate designs. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a schematic structural diagram of the intelligent optimal design platform for transmission line foundations based on multi-objective algorithms provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0048] The following will describe the intelligent optimal design platform for transmission line foundations based on a multi-objective algorithm of the present invention in conjunction with Figure 1 Describe the intelligent optimal design platform for transmission line foundations based on a multi-objective algorithm of the present invention.

[0049] Figure 1 FIG. is a schematic structural diagram of an intelligent optimal design platform for transmission line foundations based on a multi-objective algorithm provided by an embodiment of the present invention.

[0050] As Figure 1 shown, the intelligent optimal design platform for transmission line foundations based on a multi-objective algorithm provided by an embodiment of the present invention includes a data acquisition and processing module, a selection index determination module, a selection model construction module, and a foundation design optimization module.

[0051] The data acquisition and processing module is used to obtain data on influencing factors at the location of the target transmission line foundation. The data on influencing factors includes geological exploration data, upper structure load data, environmental data, and tension data, and preprocesses the data on influencing factors to obtain preprocessed data.

[0052] The geological exploration data includes the soil type at the location of the target transmission line foundation, the thickness of various types of soil layers, the unit weight of the soil, the internal friction angle, the cohesion, and the compression modulus. The upper structure load data represents the shape and total weight of the upper structure of the transmission line foundation. The environmental data includes wind force data and wind direction data at the location of the target transmission line foundation. The tension data represents the tension data of the wires at both ends of the target transmission line foundation.

[0053] The process of preprocessing the data on influencing factors includes: Checking and removing duplicate items in the data on influencing factors.

[0054] Filling missing values in the data on influencing factors using the mean filling method.

[0055] Performing standardization processing on the data on influencing factors to convert data with different dimensions into a unified standard.

[0056] Combining the standardized geological exploration data, upper structure load data, environmental data, and tension data into a comprehensive data set as the preprocessed data.

[0057] In this embodiment, professional equipment is equipped, including a drilling rig and a sampler, and the drilling depth is adjusted according to the designed depth of the pile foundation and the geological conditions. The drilling method is adopted to drill at the expected foundation location to obtain soil samples and conduct on-site tests. The collected soil samples are sent to the laboratory for relevant physical and mechanical property tests, including: determining the soil type through visual observation and standard classification methods. Recording the stratification of the soil samples through drilling records and measuring the thickness of different soil layers. Measuring the dry weight and volume of the soil samples and calculating the weight per unit volume. Measuring the shear strength of the soil samples using standard test methods such as direct shear tests or triaxial tests to obtain the internal friction angle and cohesion. Obtaining through compression tests and measuring the stress-strain relationship of the soil under specific stress conditions to obtain the compression modulus.

[0058] According to the design scheme of the transmission line tower, including the type of the tower, the height and layout of the members, structural analysis software such as SAP2000 and ANSYS is used for load calculation. Considering the self-weight of the tower body, the weight of the conductors and other auxiliary facilities such as lightning rods and monitoring equipment, the weights are added up. Through structural mechanics analysis, the dynamic and static loads applied to the foundation are simulated to evaluate the stability of the pile foundation design.

[0059] Historical meteorological data and real-time data are obtained from nearby meteorological monitoring stations. Including: Wind force data: including the maximum wind speed and the average wind speed. Wind direction data: Using a wind vane or meteorological station equipment to record the wind direction.

[0060] Tension sensors are installed at the wire positions at both ends of the transmission line to monitor the tension data of the wires in real time. Strain gauges can be directly installed on the wire accessories to monitor the strain changes of the wires. Digital tension meters are used to directly read and record the tension of the wires, connected to the data acquisition system, and upload the data in real time. After the construction of the transmission line is completed, on-site inspections are carried out, and professional equipment is used for acceptance to ensure the stability of the wire state under load.

[0061] The selection index determination module is used to comprehensively analyze the preprocessed data to obtain the evaluation factors for selecting the transmission line foundation design. The evaluation factors include the requirements for foundation bearing capacity and settlement control, and the weight of each evaluation factor is determined according to the analytic hierarchy process.

[0062] The process of obtaining the requirements for foundation bearing capacity includes: Extracting the address survey data, upper structure load data, environmental data and tension data of the target transmission line foundation location.

[0063] Taking the total weight in the upper structure load data as the vertical load.

[0064] According to the structural shape of the upper structure, combined with the wind force data and wind direction data in the environmental data, calculate the wind load on the upper structure of the target transmission line foundation, and obtain the acting angle of the wind load. The formula is expressed as:

[0065] In the formula, represents the wind load, represents the wind resistance coefficient, represents the air density, and V represents the wind speed, represents the windward area.

[0066] Calculate the difference in the wire tension data at both ends of the target transmission line foundation to obtain the tension load, and obtain the acting angle of the tension load.

[0067] Decompose the wind load and the tension load to obtain the horizontal component of the wind load, the vertical component of the wind load, the horizontal component of the tension load, and the vertical component of the tension load.

[0068] Combine the horizontal component of the wind load, the vertical component of the wind load, the horizontal component of the tension load, and the vertical component of the tension load to obtain the resultant load, which is used as the foundation bearing capacity requirement. The formula is expressed as:

[0069]

[0070]

[0071]

[0072]

[0073] In the formula, represents the wind load, represents the horizontal component of the wind load, represents the vertical component of the wind load, represents the horizontal component of the tension load, represents the tension load, represents the vertical component of the tension load, represents the resultant load, represents the acting angle of the wind load, represents the acting angle of the tension load.

[0074] The process of obtaining the settlement control requirement includes: Obtain the soil compression coefficient and the consolidation characteristics of the soil layer at the location of the target transmission line foundation according to the geological survey data.

[0075] Calculate the sensitivity of uneven settlement based on the upper structure load data of the target transmission line foundation and combined with the foundation bearing capacity requirements.

[0076] Adopt the finite element analysis method to obtain the settlement amount and settlement difference of the target transmission line foundation under the foundation bearing capacity requirements.

[0077] Determine the allowable settlement range according to the preset standard to obtain the settlement control requirements.

[0078] The process of determining the weight of each evaluation factor includes: Stratify the transmission line foundation type and evaluation factors, including the target layer, criterion layer, and scheme layer. The target layer is used to determine the optimal design goal of the transmission line foundation. The criterion layer is used to define the evaluation factors, including the bearing capacity requirements and settlement control requirements. The scheme layer is used to provide different design schemes for the transmission line foundation type.

[0079] Compare the relative importance of the evaluation factors to construct a judgment matrix, and each value in the judgment matrix represents the degree of importance between the evaluation factors.

[0080] Conduct a consistency test on the judgment matrix by calculating the maximum eigenvalue and consistency index of the judgment matrix. The formula is expressed as:

[0081]

[0082] In the formula, represents the maximum eigenvalue of the judgment matrix, represents the consistency index, represents the random consistency index, represents the consistency ratio, and n represents the number of evaluation factors.

[0083] If the consistency ratio CR is less than 0.1, the judgment matrix has acceptable consistency.

[0084] Normalize the judgment matrix to obtain the weight of each evaluation factor.

[0085] The selection model construction module is used to construct a transmission line foundation type selection model based on random forest, input the influencing factor data, evaluation factors, and corresponding weights, output the score of each transmission line foundation type, and take the transmission line foundation type with the highest score as the final selection.

[0086] The process of constructing a transmission line foundation type selection model based on random forest includes: Collect historical data on the design of transmission line foundations. The historical data includes historical geological survey data, historical upper structure load data, historical environmental data, and historical tensile force data.

[0087] Perform a comprehensive analysis of historical data to obtain the historical evaluation factors for the basic design of transmission lines and determine the weight of each historical evaluation factor.

[0088] Take the historical data of each group, the historical evaluation factors, and the corresponding transmission line foundation types as a set of data to construct a data set, and divide the data set into a training set and a test set.

[0089] Randomly extract sample data from the training set and extract the historical influence factor features and historical evaluation factor features in the sample data.

[0090] Construct a basic decision tree, and divide the sample data into different sub-nodes in the decision tree until the depth of the tree reaches a preset threshold.

[0091] Repeat the process of constructing the basic decision tree according to the preset number of decision trees to obtain a random forest.

[0092] In this embodiment, each group of historical data, including historical influence factors, historical evaluation factors, and the corresponding transmission line foundation types, is marked to form labeled data for supervised learning. The data set is divided into a training set and a test set. The training set is used for model training, and the test set is used for model verification.

[0093] Randomly extract sample data from the training set to construct a decision tree. The random forest reduces the overfitting phenomenon through multiple samplings and improves the prediction ability of the model.

[0094] Extract the historical influence factor features and historical evaluation factor features in the sample data as input data for subsequent decision tree construction.

[0095] Combine the extracted features and use the selected splitting criterion to split the sample. Split until the preset tree depth is reached. The preset depth of the tree can be 5 to 15 layers.

[0096] Repeat creating decision trees according to the predetermined number of decision trees. Each tree uses different random samples and random features, thus forming a random forest.

[0097] For each foundation type, use each decision tree in the random forest to conduct voting or score aggregation. Each decision tree outputs a predicted category based on the input features, and finally determines the score of each foundation type according to the majority vote.

[0098] According to the score output by the random forest algorithm, select the transmission line foundation type with the highest score as the final choice.

[0099] Use the test set to evaluate the constructed random forest model, calculate the accuracy rate, and judge the model performance.

[0100] Adjust the model parameters through grid search, including the number of trees, maximum depth, and minimum sample split number, to optimize the performance of the model.

[0101] The basic design optimization module is used to optimize the size parameters of the selected transmission line foundation type according to the evaluation factors by using the genetic algorithm, and obtain the optimal material selection ratio according to the performance of different foundation materials.

[0102] The process of optimizing the size parameters of the selected transmission line foundation type by using the genetic algorithm includes: Set the optimization objectives, which include minimizing foundation settlement and maximizing foundation bearing capacity.

[0103] Collect the initial size parameters of the transmission line foundation type and the evaluation factors of the target transmission line foundation, and construct an evaluation function in combination with the optimization objectives.

[0104] Represent the initial size parameters as gene coding and randomly generate an initial population.

[0105] Use the roulette wheel selection method to determine the next-generation parents, perform crossover operations, generate a new generation of size parameter individuals, and randomly mutate the size parameters to increase the diversity of the optimization search.

[0106] Repeat the process of performing crossover operations and random mutations until the preset number of iterations is met.

[0107] Calculate the fitness value of each size parameter individual according to the evaluation function, and obtain the size parameter scheme with the highest fitness value.

[0108] The process of obtaining the optimal material selection ratio includes: Set the objective function and constraints. The objective function includes maximizing the comprehensive performance of the material and minimizing the overall material cost. The constraints include the bearing capacity requirements of the transmission line foundation.

[0109] Collect the technical parameters of the transmission line foundation materials, and the technical parameters include compressive strength and tensile strength.

[0110] Construct a linear programming model, and obtain the material ratio scheme of the transmission line foundation according to the objective function, constraints, and technical parameters of the foundation materials.

[0111] In summary, this embodiment provides an intelligent optimal design platform for transmission line foundations based on a multi-objective algorithm. By acquiring and preprocessing data on various factors affecting transmission line foundations, it ensures that the design process is based on accurate and reliable information, can reduce design deviations caused by insufficient experience or inaccurate assumptions, and improve the reliability of the overall design. By extracting evaluation factors for foundation bearing capacity and settlement control, and using the analytic hierarchy process to assign weights to different evaluation factors, it ensures the comprehensiveness of design goals, taking into account both the structural safety and the performance in use, forming a balanced goal system and laying a foundation for subsequent decision-making. Using the random forest algorithm, by inputting the weights of the influencing factors and evaluation factors obtained through comprehensive analysis, the model can efficiently evaluate the performance of different foundation types and output scores. The random forest avoids the overfitting problem and can more accurately reflect the capabilities of various foundation types to adapt to different environmental and load conditions compared to traditional methods. It reduces the subjectivity of manual judgment, makes the selected foundation type more reasonable, and significantly improves the project quality. The genetic algorithm is used to optimize the size parameters of the determined foundation type. With its powerful global optimization ability, the genetic algorithm can effectively explore the design space and find the optimal combination of size parameters. Combining multiple objectives, including structural safety, cost-effectiveness, and material use efficiency, it obtains the optimal design result, and based on the performance of different foundation materials, it conducts optimal material selection and proportioning to improve the performance and durability of the structure, and to a certain extent, reduces the cost. Through synergistic effects, the entire design platform improves the efficiency of transmission line foundation design, enables engineers to respond more quickly to design requirements and changes in site conditions, and improves the economic efficiency of the project. Through data-driven decision-making, it can ensure the selection of the most suitable foundation under different conditions, reducing potential safety hazards caused by inappropriate designs.

[0112] The device embodiments described above are merely illustrative. 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 to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.

[0113] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A transmission line foundation intelligent optimal design platform based on a multi-objective algorithm, characterized in that Including: A data acquisition and processing module, which is used to obtain the influencing factor data of the basic position of the target transmission line, and preprocess the influencing factor data to obtain preprocessed data; A selection index determination module, which is used to comprehensively analyze the preprocessed data, obtain the evaluation factors for selecting the basic design of the transmission line, and determine the weight of each evaluation factor according to the analytic hierarchy process; A selection model construction module, which is used to construct a transmission line foundation type selection model based on random forest, input the influencing factor data, the evaluation factors and the corresponding weights, output the score of each transmission line foundation type, and take the transmission line foundation type with the highest score as the final selection; A basic design optimization module, which is used to optimize the size parameters of the selected transmission line foundation type by using the genetic algorithm according to the evaluation factors, and obtain the optimal material selection ratio according to the performance of different basic materials.

2. The intelligent optimal design platform for transmission line foundations based on multi-objective algorithms according to claim 1, characterized in that, The influencing factor data includes geological survey data, and the geological survey data includes the soil type, the thickness of various types of soil layers, the unit weight of soil, the internal friction angle, the cohesion and the compression modulus of the basic position of the target transmission line.

3. The intelligent optimal design platform for transmission line foundations based on the multi-objective algorithm according to claim 1, characterized in that, The influencing factor data also includes the upper structure load data, the environmental data and the tension data. The upper structure load data represents the shape and total weight of the upper structure of the transmission line foundation; the environmental data includes the wind force data and the wind direction data of the basic position of the target transmission line; the tension data represents that the tension data received by the target transmission line foundation is the quantitative index data of various mechanical loads borne by the conductor, the tower and the basic structure during operation.

4. The intelligent optimal design platform for transmission line foundations based on the multi-objective algorithm according to claim 1, characterized in that The process of preprocessing the influencing factor data includes: Checking and removing duplicate items in the influencing factor data; Filling the missing values in the influencing factor data by using the mean filling method; Performing standardization processing on the influencing factor data to convert data with different dimensions into a unified standard; Combining the standardized geological survey data, the upper structure load data and the environmental data into a comprehensive data set as the preprocessed data.

5. The intelligent optimal design platform for transmission line foundations based on the multi-objective algorithm according to claim 1, characterized in that, The evaluation factors include the basic bearing capacity requirement. The process of obtaining the basic bearing capacity requirement includes: Extracting the address survey data, the upper structure load data and the environmental data of the basic position of the target transmission line; Taking the total weight in the upper structure load data as the vertical load; According to the structure shape of the upper structure, combining the wind force data and the wind direction data in the environmental data, calculating the wind load on the upper structure of the target transmission line foundation, and obtaining the action angle of the wind load; Calculating the difference between the wire tension data at both ends of the target transmission line foundation to obtain the tension load, and obtaining the action angle of the tension load; Decomposing the wind load and the tension load to obtain the horizontal component of the wind load, the vertical component of the wind load, the horizontal component of the tension load and the vertical component of the tension load; Synthesizing the horizontal component of the wind load, the vertical component of the wind load, the horizontal component of the tension load and the vertical component of the tension load to obtain the resultant load as the basic bearing capacity requirement.

6. The intelligent optimal design platform for transmission line foundations based on multi-objective algorithms according to claim 1, characterized in that The evaluation factors also include the settlement control requirement. The process of obtaining the settlement control requirement includes: Obtain the soil compression coefficient and the consolidation characteristics of the soil layer at the foundation location of the target transmission line according to the geological survey data; Calculate the sensitivity of differential settlement according to the upper structure load data of the target transmission line foundation in combination with the foundation bearing capacity requirements; Adopt the finite element analysis method to obtain the settlement amount and settlement difference of the target transmission line foundation under the foundation bearing capacity requirements; Determine the allowable settlement range according to the preset standard to obtain the settlement control requirements.

7. The intelligent optimal design platform for transmission line foundations based on the multi-objective algorithm according to claim 1, characterized in that The process of determining the weight of each of the evaluation factors includes: Stratify the transmission line foundation type and the evaluation factors, including the target layer, the criterion layer, and the scheme layer; the target layer is used to determine the optimal design goal of the transmission line foundation; the criterion layer is used to define the evaluation factors, including the bearing capacity requirements and the settlement control requirements; the scheme layer is used to provide different design schemes for the transmission line foundation type; Compare the relative importance of the evaluation factors to construct a judgment matrix, and each value in the judgment matrix represents the degree of importance between the evaluation factors; Perform a consistency test on the judgment matrix by calculating the maximum eigenvalue and the consistency index of the judgment matrix; Normalize the judgment matrix to obtain the weight of each of the evaluation factors.

8. The intelligent optimal design platform for transmission line foundations based on the multi-objective algorithm according to claim 1, characterized in that The process of constructing a selection model for the transmission line foundation type based on a random forest includes: Collect historical data on the design of the transmission line foundation, and the historical data includes historical geological survey data, historical upper structure load data, historical environmental data, and historical tension data; Comprehensively analyze the historical data to obtain the historical evaluation factors for the design of the transmission line foundation and determine the weight of each of the historical evaluation factors; Take the transmission line foundation type corresponding to each set of the historical data, the historical evaluation factors, and the weights as a set of data to construct a data set, and divide the data set into a training set and a test set; Randomly extract sample data from the training set and extract the historical influencing factor features and historical evaluation factor features in the sample data; Construct a basic decision tree, and divide the sample data into different sub-nodes in the decision tree until the depth of the tree reaches a preset threshold; Repeat the process of constructing the basic decision tree according to the preset number of decision trees to obtain a random forest.

9. The intelligent optimal design platform for transmission line foundations based on multi-objective algorithms according to claim 1, wherein The process of optimizing the size parameters of the selected transmission line foundation type by using a genetic algorithm includes: Set the optimization goals, and the optimization goals include minimizing the foundation settlement and maximizing the foundation bearing capacity; Collect the initial size parameters of the transmission line foundation type and the evaluation factors of the target transmission line foundation, and construct an evaluation function in combination with the optimization goals; Express the initial size parameters as gene coding and randomly generate an initial population; Adopt the roulette wheel selection method to determine the next-generation parents, perform a crossover operation to generate a new generation of size parameter individuals, and randomly mutate the size parameters to increase the diversity of the optimization search; Repeat the process of performing the crossover operation and random mutation until the preset number of iterations is satisfied; Calculate the fitness value of each of the size parameter individuals according to the evaluation function to obtain the size parameter scheme with the highest fitness value.

10. The intelligent optimal design platform for transmission line foundations based on the multi-objective algorithm according to claim 1, characterized in that, The process of obtaining the optimal material selection ratio includes: Set the objective function and constraints. The objective function includes maximizing the comprehensive material properties and minimizing the overall material cost; the constraints include the bearing capacity requirements of the transmission line foundation. Collect the technical parameters of the transmission line foundation materials. The technical parameters include compressive strength and tensile strength. Construct a linear programming model. According to the objective function, the constraints and the technical parameters of the foundation materials, obtain the material ratio plan for the transmission line foundation.

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