Power transmission line selection method, device, equipment and medium

Abstract

The invention relates to the technical field of power transmission line engineering, in particular to a power transmission line selection method and device, equipment and a medium, and the method comprises the steps: obtaining the multi-source geographic information of a target region; performing classification processing on the multi-source geographic information to obtain a target classification result, and calculating a path geographic comprehensive cost value weight of each power transmission line path in the target area according to the target classification result; the route geographical comprehensive cost value weight of each route is input into a route selection model, the route selection model outputs a target power transmission line route of a target area, the route selection model performs route selection based on a target grey wolf algorithm, and the target grey wolf algorithm is a grey wolf algorithm introducing crossover and mutation operation of a genetic algorithm. Therefore, the problems of high power transmission line planning cost, low efficiency, poor precision, high subjectivity and the like in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the technical field of power transmission line engineering, and in particular to a power transmission line selection method, device, equipment and medium. Background Art

[0002] The selection of transmission lines, as a key link in power grid construction, is an important basis for transmission line design. Its reliability and stability have a significant impact on the safety and operating efficiency of the power grid.

[0003] However, traditional transmission line planning methods rely too much on field surveys and personal experience of power grid researchers. This model has exposed significant limitations in practice: high survey costs, relatively low work efficiency, lack of accuracy, and difficult-to-get- rid of subjective biases, all of which pose significant constraints to the quality of planning results. Summary of the invention

[0004] The present application provides a transmission line selection method, device, equipment and medium to solve the problems of high cost, low efficiency, poor accuracy and strong subjectivity in transmission line planning in the prior art.

[0005] A first aspect of the present application provides a method for selecting a transmission line, comprising the following steps: acquiring multi-source geographic information of a target area; classifying the multi-source geographic information to obtain a target classification result, and calculating the path geographic comprehensive cost value weight of each transmission line path in the target area according to the target classification result; inputting the path geographic comprehensive cost value weight of each path into a line selection model, and the line selection model outputs a target transmission line path in the target area, wherein the line selection model performs line selection based on a target gray wolf algorithm, and the target gray wolf algorithm is a gray wolf algorithm that introduces crossover and mutation operations of a genetic algorithm.

[0006] Optionally, multi-source geographic information of the target area is obtained, including: obtaining remote sensing images of the target area; using a random forest method to preliminarily classify the remote sensing images to obtain preliminary classification results; and using GIS technology to reclassify the preliminary classification results to obtain target classification results.

[0007] Optionally, the path geographic comprehensive cost value weight of each transmission line path in the target area is calculated according to the target classification result, including: determining the target layer, criterion layer and scheme layer of the hierarchical model according to the target classification result; performing pairwise comparisons on the elements in the criterion layer and the scheme layer, and using triangular fuzzy numbers to represent the comparison results; constructing a fuzzy judgment matrix according to the comparison results, if the fuzzy judgment matrix does not meet the consistency requirements, then readjusting the fuzzy judgment matrix, otherwise calculating the evaluation factor matrix and weight results of the fuzzy number according to the fuzzy judgment matrix; and calculating the path geographic comprehensive cost value weight according to the evaluation factor matrix and the weight results.

[0008] Optionally, the fuzzy judgment matrix is:

[0009]

[0010] Among them, n is the number of types of geographic information factors, a ij is a triangular fuzzy number.

[0011] Optionally, the fuzzy evaluation factor matrix is:

[0012]

[0013] Among them, l ij is the lower bound, u ij is the upper bound; m ij Yes ij and u ij The median value of .

[0014] Optionally, the line selection model performs line selection based on a target gray wolf algorithm, including: initializing the populations of the target gray wolf algorithm and the genetic algorithm; setting a target fitness function, and calculating the fitness value using the target fitness function and the weight of the path geographical comprehensive cost value; using the position information of the gray wolf in the target gray wolf algorithm to guide other wolves to approach the optimal solution, using the two-point crossover in the genetic algorithm to generate a new solution in the wolf pack, using the mutation operator in the genetic algorithm to perform a local search for the new solution, and selecting the new solution to join the wolf pack according to the fitness value; until the target transmission line path is obtained when the iteration stop condition is met; and selecting the newly generated solution according to the fitness value to determine whether it should be added to the wolf pack.

[0015] Optionally, the target fitness function is:

[0016]

[0017] Among them, a represents the path length weight; b represents the path geographical comprehensive cost value weight; c represents the path continuity weight; f1 is the path length function; f2 is the geographical comprehensive cost function; and f3 is the path continuity function.

[0018] The second aspect of the present application provides a transmission line selection device, including: an acquisition module, used to acquire multi-source geographic information of a target area; a calculation module, used to classify the multi-source geographic information to obtain a target classification result, and calculate the path geographic comprehensive cost value weight of each transmission line path in the target area according to the target classification result; an output module, used to input the path geographic comprehensive cost value weight of each path into a line selection model, and the line selection model outputs a target transmission line path in the target area, wherein the line selection model performs line selection based on a target gray wolf algorithm, and the target gray wolf algorithm is a gray wolf algorithm that introduces crossover and mutation operations of a genetic algorithm.

[0019] The third aspect of the present application 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 executes the program to implement the transmission line selection method as described in the above embodiment.

[0020] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the transmission line selection method as described in the above embodiment.

[0021] Therefore, this application includes the following beneficial effects:

[0022] The embodiment of the present application builds an accurate data analysis framework for transmission line selection by integrating multi-source geographic information and applying advanced classification and processing technology. By calculating the weight of the path geographical comprehensive cost value, the optimal balance between economy and comprehensive benefits is achieved, and a more cost-effective transmission line path is selected. The crossover and mutation operations of the genetic algorithm are introduced into the gray wolf algorithm to form a new target gray wolf algorithm, which not only retains the advantages of the gray wolf algorithm in solving optimization problems, but also enhances the algorithm's local search ability and the ability to jump out of the local optimal solution through the crossover and mutation mechanism of the genetic algorithm, making the transmission line selection process more intelligent and refined. As a result, the technical problems of high cost, low efficiency, poor accuracy and strong subjectivity in transmission line planning in the prior art are solved.

[0023] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0025] Figure 1 A flowchart of a transmission line selection method provided according to an embodiment of the present application;

[0026] Figure 2 A flowchart of a transmission line selection method provided according to an embodiment of the present application;

[0027] Figure 3 This is an example diagram of a transmission line selection device provided according to an embodiment of the present application;

[0028] Figure 4 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0030] The following describes the transmission line selection method, device, equipment and medium of the embodiment of the present application with reference to the accompanying drawings. In view of the fact that the existing manual line selection design technology mentioned in the above background technology cannot accurately handle the complex path selection factors and the problem of strong subjectivity, the present application provides a transmission line selection method, in which a precise data analysis framework is constructed for transmission line selection by integrating multi-source geographic information and applying advanced classification processing technology. By calculating the weight of the path geographical comprehensive cost value, the optimal balance between economy and comprehensive benefits is achieved, and a more cost-effective transmission line path is selected. The crossover and mutation operations of the genetic algorithm are introduced into the gray wolf algorithm to form a new target gray wolf algorithm, which not only retains the advantages of the gray wolf algorithm in solving optimization problems, but also enhances the algorithm's local search ability and the ability to jump out of the local optimal solution through the crossover and mutation mechanism of the genetic algorithm, making the transmission line selection process more intelligent and refined. As a result, the problems of high cost, low efficiency, poor accuracy and strong subjectivity in the prior art of transmission line planning are solved.

[0031] Specifically, Figure 1 A schematic flow chart of a transmission line selection method provided in an embodiment of the present application.

[0032] like Figure 1 As shown, the transmission line selection method includes the following steps:

[0033] In step S101, multi-source geographic information of a target area is obtained.

[0034] Among them, multi-source geographic information can refer to geospatial data from different sources, different sensors or different data collection methods.

[0035] It can be understood that the embodiments of the present application provide a rich data basis for the selection of transmission lines by acquiring multi-source geographic information of the target area.

[0036] In an embodiment of the present application, multi-source geographic information of a target area is obtained, including: obtaining a remote sensing image of the target area; performing preliminary classification on the remote sensing image using a random forest method to obtain a preliminary classification result; and reclassifying the preliminary classification result using GIS technology to obtain a target classification result.

[0037] The target classification result may be a data set obtained after classification processing, and these data sets have been divided into different categories or groups according to predetermined standards or rules.

[0038] It is understandable that the embodiment of the present application achieves efficient, accurate and practical classification effect in remote sensing image classification by integrating random forest algorithm and GIS technology. The random forest algorithm takes advantage of its ensemble learning to effectively reduce overfitting, improve the accuracy and robustness of classification, and improve classification efficiency through parallel processing. The introduction of GIS technology further optimizes the classification results, and enhances the accuracy and practicality of classification through spatial data analysis and verification of expert knowledge base. The combination of the two not only accelerates the classification process, but also enables the classification results to support multi-dimensional spatial analysis, providing strong data support for urban planning, environmental monitoring, resource management and other fields.

[0039] Specifically, a research area is selected according to actual needs; remote sensing satellite images of the selected research area are obtained, and the random forest classification method is used in ArcMap software to classify the research area. The specific categories include five categories: residential areas, roads, rivers, fields, and trees; for the preliminary classification results obtained, the multi-source geographic information is reclassified according to the five-level standard based on the requirements for the construction level of transmission lines.

[0040] In step S102, the multi-source geographic information is classified to obtain a target classification result, and the path geographic comprehensive cost value weight of each transmission line path in the target area is calculated based on the target classification result.

[0041] Among them, the path geographical comprehensive cost value weight can refer to a comprehensive evaluation index calculated for each transmission line path based on considering multiple factors.

[0042] It is understandable that the embodiment of the present application provides decision makers with an intuitive and scientific basis for selecting the optimal path with lower cost and less risk by calculating the weight of the path geographical comprehensive cost value, which not only effectively reduces the full life cycle cost of the transmission line, especially the construction and operation and maintenance costs under complex and harsh environmental conditions, but also significantly enhances the environmental friendliness and sustainability of the project and reduces damage to the natural environment. At the same time, by avoiding high-risk areas and reducing cross-interference with other infrastructure, the safety and reliability of power grid operation are improved, ensuring the stability and efficiency of power supply.

[0043] In an embodiment of the present application, the path geographic comprehensive cost value weight of each transmission line path in the target area is calculated according to the target classification result, including: determining the target layer, criterion layer and scheme layer of the hierarchical model according to the target classification result; performing pairwise comparisons on the elements in the criterion layer and the scheme layer, and using triangular fuzzy numbers to represent the comparison results; constructing a fuzzy judgment matrix according to the comparison results, if the fuzzy judgment matrix does not meet the consistency requirements, then readjusting the fuzzy judgment matrix, otherwise calculating the evaluation factor matrix and weight results of the fuzzy number according to the fuzzy judgment matrix; and calculating the path geographic comprehensive cost value weight according to the evaluation factor matrix and the weight results.

[0044] Among them, the goal layer can represent the overall goal or ultimate goal of the decision analysis, the criterion layer can be located below the goal layer, and contains a series of standards or criteria for evaluating whether the plan meets the requirements of the goal layer, and the solution layer can contain all possible solutions or alternatives.

[0045] It is understandable that the embodiment of the present application breaks down complex problems into clear, hierarchical sub-problems by constructing a hierarchical model, which significantly improves the systematicness and comprehensiveness of decision-making. The use of triangular fuzzy numbers for quantitative evaluation not only converts subjective judgments into objective values, but also enhances the accuracy and objectivity of the evaluation. At the same time, the process is highly flexible and adaptable, can effectively handle uncertain factors, and ensures the scientificity and rationality of the decision-making process through a dynamic adjustment mechanism.

[0046] In the embodiment of the present application, the fuzzy judgment matrix is:

[0047]

[0048] Among them, n is the number of types of geographic information factors, a ij is a triangular fuzzy number.

[0049] In the embodiment of the present application, the fuzzy evaluation factor matrix is:

[0050]

[0051] Among them, l ij is the lower bound, u ij is the upper bound; m ij Yes ij and u ij The median value of .

[0052] Specifically, the target layer, criterion layer and solution layer of the problem to be solved are determined according to actual needs, and a hierarchical model is constructed; for the determined criterion layer, a judgment matrix for pairwise comparison of criteria is established; the ratios in the established judgment matrix are converted into triangular fuzzy numbers, and the values ​​are taken according to the 1-9 scaling method; for the obtained triangular fuzzy numbers, three matrices L, M and U are defined, which represent the fuzzy judgment matrices of the lower limit, median and upper limit, respectively, and the fuzzy judgment matrix A is determined:

[0053]

[0054] Among them, n is the number of types of geographic information factors, a ij is a triangular fuzzy number;

[0055] The fuzzy evaluation factor matrix E is constructed as:

[0056]

[0057] Among them, l ij is the lower bound, u ij is the upper bound; m ij Yes ij and u ij The median value of .

[0058] Perform a consistency check on the constructed fuzzy evaluation factor matrix E. If the consistency check does not meet the requirements, it is necessary to recalculate and adjust the judgment matrix Q:

[0059] Q=M×E

[0060] in M is the median of the triangular fuzzy number m ij The matrix composed of

[0061] The calculated adjustment judgment matrix Q is converted into a new judgment matrix P with all diagonals being 1 according to the list, and the consistency condition analysis is performed again;

[0062] After analyzing the consistency conditions and meeting the requirements, determine the weight values ​​of each layer of geographic information elements:

[0063]

[0064]

[0065] The weight values ​​of geographic information elements at each layer are analyzed by raster overlay to calculate the final comprehensive cost value of geographic data.

[0066] In step S103, the path geographical comprehensive cost value weight of each path is input into the line selection model, and the line selection model outputs the target transmission line path of the target area, wherein the line selection model performs line selection based on the target gray wolf algorithm, and the target gray wolf algorithm is a gray wolf algorithm that introduces crossover and mutation operations of the genetic algorithm.

[0067] Among them, the target gray wolf algorithm can be an optimization algorithm based on swarm intelligence, which simulates the social hierarchy and hunting behavior of gray wolves in nature.

[0068] It is understandable that the line selection model of the embodiment of the present application realizes a comprehensive evaluation of multi-dimensional factors such as economy, environmental impact, and technical feasibility by comprehensively considering the weight of the geographical comprehensive cost value of each path, thereby ensuring the overall optimality or suboptimality of the selected transmission line. The target gray wolf algorithm that introduces genetic algorithm crossover and mutation operations not only enhances the efficiency of the algorithm in complex search space, but also significantly improves its global search capability and ability to avoid local optimal solutions, thereby further improving the accuracy and precision of line selection.

[0069] Specifically, the crossover and mutation operators of the genetic algorithm are integrated into the gray wolf algorithm, and the advantages of the genetic algorithm in local search are used to enhance the global search ability of the gray wolf algorithm and avoid it from falling into the local optimal solution. First, the gray wolf population is randomly initialized in the search space. Where a is the impact The convergence factor decreases linearly in the range [2,0], and is the coefficient vector:

[0070]

[0071]

[0072]

[0073] in, and is a random vector, t is the number of current iterations, t max is the maximum number of iterations;

[0074] After initialization, the gray wolf algorithm optimizes the iterative calculation:

[0075]

[0076]

[0077] During the iteration process, each time an iteration is performed, the positions of the three best gray wolves (α, β, δ) in the current population are kept and updated. The update calculation mathematical model is as follows:

[0078]

[0079]

[0080]

[0081] in, is the position vector representing α, β, δ, is the position vector at the tth iteration, is the distance between other wolves in the pack and α, β, δ, is the position vector at the t+1th iteration;

[0082] The crossover and mutation operations of the genetic algorithm are integrated into the optimization process of the gray wolf algorithm. The crossover operation uses a two-point crossover operation to randomly generate a crossover point, and the crossover point is within the non-zero length range of the two parent solutions to ensure that the length of the offspring after the crossover is the same as that of the parent. The sequence between the two points is exchanged to generate two new offspring solutions; the mutation operation is a randomly generated number. If it is less than the set mutation rate value, the mutation operation is performed.

[0083] In an embodiment of the present application, the line selection model performs line selection based on a target gray wolf algorithm, including: initializing the populations of the target gray wolf algorithm and the genetic algorithm; setting a target fitness function, and calculating the fitness value using the target fitness function and the weight of the path geographical comprehensive cost value; using the position information of the gray wolf in the target gray wolf algorithm to guide other wolves to approach the optimal solution, using the two-point crossover in the genetic algorithm to generate a new solution in the wolf pack, using the mutation operator in the genetic algorithm to perform a local search for the new solution, and selecting the new solution to join the wolf pack according to the fitness value; until the target transmission line path is obtained when the iteration stop condition is met; and selecting the newly generated solution according to the fitness value to determine whether it should be added to the wolf pack.

[0084] It can be understood that the embodiment of the present application combines the global search capability of the target gray wolf algorithm with the local search and diversity retention advantages of the genetic algorithm. By initializing the population, setting and calculating the fitness value, the gray wolf algorithm guiding the search direction, the genetic algorithm cross-mutation to generate new solutions, and the selection mechanism based on the fitness value, it can quickly and accurately find the overall optimal or suboptimal transmission line path in a complex and changing environment, thereby optimizing the allocation of power resources and improving the efficiency and stability of power grid operation.

[0085] In the embodiment of the present application, the target fitness function is:

[0086]

[0087] Among them, a represents the path length weight; b represents the path geographical comprehensive cost value weight; c represents the path continuity weight; f1 is the path length function; f2 is the geographical comprehensive cost function; and f3 is the path continuity function.

[0088] Specifically, the shortest constraint on the total length of the path is:

[0089]

[0090] Among them, l i is the distance between two adjacent grids in the i-th segment of the path; n is the number of nodes in the path;

[0091] l i for:

[0092]

[0093] The minimum constraint of geographical comprehensive cost is:

[0094]

[0095] Among them, w i is the geographical integrated cost value of the i-th grid in the path;

[0096] The continuity constraint of the path can be determined by calculating the angle:

[0097] f3=|θ i+1 -θ i |

[0098]

[0099] Among them, θ i is the angle between the i-th node and the planned path; θ i+1 is the angle between the i+1th node and the planned path;

[0100] The target fitness function is:

[0101]

[0102] Among them, a represents the path length weight; b represents the path geographical comprehensive cost value weight; c represents the path continuity weight; f1 is the path length function; f2 is the geographical comprehensive cost function; and f3 is the path continuity function.

[0103] According to the transmission line selection method proposed in the embodiment of the present application, an accurate data analysis framework is constructed for transmission line selection by integrating multi-source geographic information and applying advanced classification processing technology. By calculating the weight of the path geographical comprehensive cost value, the optimal balance between economy and comprehensive benefits is achieved, and a more cost-effective transmission line path is selected. The crossover and mutation operations of the genetic algorithm are introduced into the gray wolf algorithm to form a new target gray wolf algorithm, which not only retains the advantages of the gray wolf algorithm in solving optimization problems, but also enhances the algorithm's local search ability and the ability to jump out of the local optimal solution through the crossover and mutation mechanism of the genetic algorithm, making the transmission line selection process more intelligent and refined. As a result, the problems of high cost, low efficiency, poor accuracy and strong subjectivity in transmission line planning in the prior art are solved.

[0104] The following will be combined Figure 2 The transmission line selection method is specifically described as follows:

[0105] S1. Select the research area and use GIS to process geographic information data;

[0106] Specifically, a certain area in Jiaxing City, Zhejiang Province was selected as the study area and its remote sensing satellite images were obtained; the random forest classification method was used in ArcMap software to classify the study area, and the specific categories included five categories: residential areas, roads, rivers, fields, and trees; based on the preliminary classification results, the multi-source geographic information was reclassified according to the five-level standard according to the requirements of the transmission line construction level.

[0107] S2, using triangular fuzzy number analytic hierarchy process to determine weights and cost values, and integrating crossover and mutation operations of genetic algorithm into the target grey wolf algorithm;

[0108] S3, design objective function;

[0109] Among them, the target fitness function is:

[0110]

[0111] Among them, a represents the path length weight; b represents the path geographical comprehensive cost value weight; c represents the path continuity weight; f1 is the path length function; f2 is the geographical comprehensive cost function; and f3 is the path continuity function.

[0112] S4, using the improved grey wolf algorithm to find the optimal path in the map data;

[0113] The comprehensive cost value of the obtained geographic information is extracted into a table, and the data in the table is converted into binary data for easy calculation; the improved gray wolf algorithm is used for path planning to find the optimal path.

[0114] In summary, the embodiments of the present application can more comprehensively consider the factors affecting path planning by combining multi-source geographic information, thereby improving the accuracy of path planning. The improved gray wolf algorithm can better adapt to complex terrain and changing environmental conditions, making the line selection results more in line with actual needs. At the same time, the gray wolf algorithm after the crossover and mutation operations in the genetic algorithm is integrated, which is not easy to fall into the local optimal solution, and improves the ability of global search.

[0115] Next, the transmission line selection device proposed according to the embodiment of the present application is described with reference to the accompanying drawings.

[0116] Figure 3 It is a block diagram of a transmission line selection device according to an embodiment of the present application.

[0117] like Figure 3 As shown, the transmission line selection device 10 includes: an acquisition module 100, a calculation module 200 and an output module 300.

[0118] Among them, the acquisition module 100 is used to acquire multi-source geographic information of the target area; the calculation module 200 is used to classify the multi-source geographic information to obtain the target classification result, and calculate the path geographic comprehensive cost value weight of each transmission line path in the target area according to the target classification result; the output module 300 is used to input the path geographic comprehensive cost value weight of each path into the line selection model, and the line selection model outputs the target transmission line path of the target area, wherein the line selection model performs line selection based on the target gray wolf algorithm, and the target gray wolf algorithm is a gray wolf algorithm that introduces crossover and mutation operations of the genetic algorithm.

[0119] It should be noted that the above explanation of the embodiment of the transmission line selection method is also applicable to the transmission line selection device of this embodiment, and will not be repeated here.

[0120] According to the transmission line selection device proposed in the embodiment of the present application, an accurate data analysis framework is constructed for transmission line selection by integrating multi-source geographic information and applying advanced classification processing technology. By calculating the weight of the path geographical comprehensive cost value, the optimal balance between economy and comprehensive benefits is achieved, and a more cost-effective transmission line path is selected. The crossover and mutation operations of the genetic algorithm are introduced into the gray wolf algorithm to form a new target gray wolf algorithm, which not only retains the advantages of the gray wolf algorithm in solving optimization problems, but also enhances the algorithm's local search ability and the ability to jump out of the local optimal solution through the crossover and mutation mechanism of the genetic algorithm, making the transmission line selection process more intelligent and refined. As a result, the problems of high cost, low efficiency, poor accuracy and strong subjectivity in transmission line planning in the prior art are solved.

[0121] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0122] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0123] When the processor 402 executes the program, the transmission line selection method provided in the above embodiment is implemented.

[0124] Furthermore, the electronic device further comprises:

[0125] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0126] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0127] The memory 401 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0128] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0129] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0130] The processor 402 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0131] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned transmission line selection method when executed by a processor.

[0132] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0133] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0134] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0135] It should be understood that the various parts of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, the steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0136] A person of ordinary skill in the art may understand that all or part of the steps carried by the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the above-mentioned program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0137] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for selecting a transmission line, characterized in that: The following steps are involved: Obtain multi-source geographic information of the target area; Classify the multi-source geographic information to obtain a target classification result, and calculate the path geographic comprehensive cost value weight of each transmission line path in the target area according to the target classification result; The path geographical comprehensive cost value weight of each path is input into the line selection model, and the line selection model outputs the target transmission line path of the target area, wherein the line selection model performs line selection based on a target gray wolf algorithm, and the target gray wolf algorithm is a gray wolf algorithm that introduces crossover and mutation operations of a genetic algorithm.

2. The transmission line selection method according to claim 1, characterized in that: The step of obtaining multi-source geographic information of the target area includes: Acquiring a remote sensing image of the target area; Using random forest method to preliminarily classify the remote sensing image to obtain a preliminary classification result; The preliminary classification results are reclassified using GIS technology to obtain target classification results.

3. The transmission line selection method according to claim 1, characterized in that: The step of calculating the path geographical comprehensive cost value weight of each transmission line path in the target area according to the target classification result includes: Determine the target layer, criterion layer and scheme layer of the hierarchical structure model according to the target classification result; Perform pairwise comparisons on the elements in the criterion layer and the solution layer, and use triangular fuzzy numbers to represent the comparison results; Constructing a fuzzy judgment matrix according to the comparison result, if the fuzzy judgment matrix does not meet the consistency requirement, readjusting the fuzzy judgment matrix, otherwise calculating the evaluation factor matrix and weight result of the fuzzy number according to the fuzzy judgment matrix; The path geographical comprehensive cost value weight is calculated based on the evaluation factor matrix and the weight result.

4. The transmission line selection method according to claim 3, characterized in that: The fuzzy judgment matrix is: Among them, n is the number of types of geographic information factors, a ij is a triangular fuzzy number.

5. The transmission line selection method according to claim 3, characterized in that: The fuzzy evaluation factor matrix is: Among them, l ij is the lower bound, u ij is the upper bound; m ij Yes ij and u ij The median value of .

6. The transmission line selection method according to claim 1, characterized in that: The line selection model performs line selection based on the target grey wolf algorithm, including: Initializing the target grey wolf algorithm and the population of the genetic algorithm; Setting a target fitness function, and calculating a fitness value using the target fitness function and the path geographical comprehensive cost value weight; Using the position information of the gray wolf in the target gray wolf algorithm to guide other wolves to approach the optimal solution, using the two-point crossover in the genetic algorithm to generate a new solution in the wolf pack, using the mutation operator in the genetic algorithm to perform local search on the new solution, and selecting the new solution added to the wolf pack according to the fitness value; Until the iteration stop condition is met, the target transmission line path is obtained; The newly generated solution is selected according to the fitness value to decide whether to join the wolf pack.

7. The transmission line selection method according to claim 4, characterized in that: The target fitness function is: Among them, a represents the path length weight; b represents the path geographical comprehensive cost value weight; c represents the path continuity weight; f1 is the path length function; f2 is the geographical comprehensive cost function; and f3 is the path continuity function.

8. A transmission line selection device, characterized in that: include: An acquisition module is used to acquire multi-source geographic information of a target area; A calculation module, used for classifying the multi-source geographic information to obtain a target classification result, and calculating the path geographic comprehensive cost value weight of each transmission line path in the target area according to the target classification result; An output module is used to input the path geographical comprehensive cost value weight of each path into a line selection model, and the line selection model outputs the target transmission line path of the target area, wherein the line selection model selects the line based on a target gray wolf algorithm, and the target gray wolf algorithm is a gray wolf algorithm that introduces crossover and mutation operations of a genetic algorithm.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the transmission line selection method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed, the transmission line selection method described in any one of claims 1-7 is implemented.

Images