A power transmission line path determination method, device, equipment and storage medium

Abstract

The application discloses a power transmission line path determination method, device and equipment and a storage medium. The method comprises the following steps: obtaining a comprehensive influence value matrix corresponding to a power transmission line area grid based on a grid table method and a plurality of types of influence factor data influencing power transmission line planning; generating N groups of to-be-processed paths in the power transmission line area grid according to a preset path tendency standard and a path search quantity; inputting the to-be-processed paths into a path search model constructed by an improved multi-peak optimization genetic algorithm, screening and determining an optimal target path corresponding to each group of paths, and obtaining the first N optimal paths; and determining an optimal path planning result of the current power transmission line by using each optimal target path. The application comprehensively processes multi-source heterogeneous data, quantifies various influence factors, comprehensively considers influences of multiple aspects such as geography, economy and environment in path selection, uses an improved multi-peak optimization genetic algorithm to obtain the first N optimal paths, and compares and optimizes each path according to statistical indexes.

Description

Technical Field

[0001] This invention relates to the field of power transmission line route planning technology, and in particular to a method, apparatus, equipment and storage medium for determining power transmission line routes. Background Technology

[0002] With the development of my country's social economy and power grid construction, the corridor resources for power grid projects are becoming increasingly strained. Many power grid projects involve large land acquisition areas and span wide regions, and are influenced by many factors such as the ecological environment and urban planning, leading to a continuous increase in the complexity of power grid project route selection.

[0003] Currently, due to the fragmented and opaque management of information related to environmentally sensitive factors and urban planning, relying solely on traditional methods such as manual data collection and on-site surveys is not only time-consuming and labor-intensive, but also fails to meet the requirements for comprehensiveness and accuracy of information acquisition. Existing high-voltage transmission line route planning methods generally only plan a single transmission route. In actual engineering projects, when different influencing factors have varying inclinations, alternative routes are needed, and existing high-voltage transmission line route planning methods suffer from insufficient comprehensiveness in considering these influencing factors.

[0004] Therefore, how to comprehensively plan the route of power transmission lines has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and storage medium for determining transmission line routes, so as to rationally plan the routes of transmission lines based on influencing factors and demand trends. To solve the above-mentioned technical problems, embodiments of this invention provide a method for determining transmission line routes, including: Based on the raster table method, the data of several types of influencing factors affecting the planning of transmission lines are optimized to obtain a comprehensive influence value matrix corresponding to the grid of the transmission line area. The comprehensive influence value matrix is ​​used to determine the attribute values ​​in the grid of the transmission line area.

[0006] Based on the preset path preference criteria and the number of paths to be searched, N sets of paths to be processed are generated in the power transmission line area grid.

[0007] Each group of paths to be processed is input into a path search model constructed by an improved multi-peak optimization genetic algorithm. The optimal target path corresponding to each group of paths is selected and determined to obtain the top N optimal paths. In the process of processing, the path search model is designed to analyze the area enclosed by two paths to redetermine the optimal target path corresponding to each group.

[0008] Based on the previous N optimal target paths, determine the optimal path planning result for the current transmission line.

[0009] Furthermore, the grid-based method is used to optimize the data of several types of influencing factors affecting transmission line planning, resulting in a comprehensive influence value matrix corresponding to the transmission line area grid, including: The data on several types of influencing factors affecting transmission line planning are classified into levels, and weights are assigned to each of the influencing factors based on the classification results.

[0010] The planned transmission line area is divided into grids using the grid table method. The weights of each grid are calculated based on the type and weight of the influencing factor data corresponding to each grid area to obtain the comprehensive influence value matrix corresponding to the transmission line area.

[0011] Furthermore, the influencing factor data includes at least land spatial planning data, power resource distribution data, and geographic information system data.

[0012] Furthermore, the construction of the path search model includes: Input a preset path start and end point, and randomly generate several sets of initial paths, each set of initial paths being represented by a population.

[0013] Symbolic coding is used to encode the transmission line paths in various groups.

[0014] The fitness function and genetic operators are designed to perform genetic iteration on the encoded populations in order to complete the construction of the path search model.

[0015] Furthermore, the design fitness function and genetic operator include: The fitness function is designed based on actual planning requirements, which include path cost, corridor clearing, line intersections, and line turns.

[0016] Genetic operators are designed for the path search model, including: a crossover operator that combines single-point crossover and multi-point crossover; a mutation operator that combines self-searching mutation with segment-type linear connections; and a selection operator that combines the optimal individual method with the roulette wheel method.

[0017] Furthermore, the genetic iteration of the encoded populations includes: Each group of paths represents a population that evolves independently and iteratively according to the fitness function and the genetic operator. During the iterative evolution process, the populations are periodically differentiated, and the differentiation specifically includes: Select a population as the target population, calculate the area enclosed between the optimal target path in each of the other populations and the optimal target path in the target population. If the area is less than a preset population differentiation area threshold, delete the optimal target path of the corresponding population, use the second optimal target path as the new optimal target path for that population, and recalculate the area between the optimal target path of that population and the optimal target path of the target population until the area is not less than the preset population differentiation area threshold.

[0018] Furthermore, the optimal path planning result for the current transmission line, derived from the previous N optimal target paths, includes: Based on the optimal target path in each group of paths, a comprehensive evaluation is conducted to determine the transmission line path; the comprehensive evaluation indicators include at least path length, engineering cost, environmental impact, and construction difficulty.

[0019] Another embodiment of the present invention provides a transmission line path determination device, comprising: The data processing module is used to optimize the data of several types of influencing factors affecting the planning of transmission lines based on the raster table method, and obtain a comprehensive influence value matrix corresponding to the grid of the transmission line area. The comprehensive influence value matrix is ​​used to determine the attribute values ​​in the grid of the transmission line area.

[0020] The path generation module is used to generate N sets of paths to be processed in the transmission line area grid according to the preset path preference criteria and the number of path searches N.

[0021] The genetic iteration module is used to input the paths to be processed in each group into the path search model constructed by the improved multi-peak optimization genetic algorithm, and to screen and determine the optimal target path corresponding to each group of paths to obtain the top N optimal paths. In the process of processing, the path search model is designed to analyze the area enclosed by two paths to redetermine the optimal target path corresponding to each group.

[0022] The path determination module is used to determine the optimal path planning result for the current transmission line based on the previous N optimal target paths.

[0023] Another embodiment of the present invention provides a computer device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the transmission line path determination method as described above.

[0024] In another embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, the transmission line path determination method described above is implemented.

[0025] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: (1) Integrate multi-source heterogeneous data, quantify various influencing factors, and comprehensively consider the impact of geographical, economic and environmental factors in route selection to improve the rationality of transmission line route selection; (2) Each group of paths is independently evolved by the population, and multiple steps are added to distinguish between the populations to prevent the population from converging to a local optimum too early during the evolution process, thus ensuring the diversity of the path search algorithm results and the global search capability. Attached Figure Description

[0026] Figure 1 This is a flowchart of the steps of the transmission line path determination method provided in the embodiments of the present invention; Figure 2 This is a schematic diagram of genetic algorithm line crossover and interchange provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the mutation of the circuit in the genetic algorithm provided in this embodiment of the invention; Figure 4 This is an evolutionary iteration flowchart of the path search model provided in this embodiment of the invention; Figure 5 This is a computational example verification diagram provided in an embodiment of the present invention; Figure 6 This is a structural block diagram of the transmission line path determination device provided in an embodiment of the present invention; Figure 7 A structural diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0028] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0029] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0030] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0031] One embodiment of the present invention provides a method for determining the path of a power transmission line. For details, please refer to [link / reference needed]. Figure 1 , Figure 1 The flowchart shown is a step diagram of a transmission line path determination method according to one embodiment of the present invention, including steps S11 to S14: Step S11: Based on the raster table method, optimize the data of several types of influencing factors affecting the planning of transmission lines to obtain a comprehensive influence value matrix corresponding to the grid of the transmission line area, and use the comprehensive influence value matrix to determine the attribute values ​​in the grid of the transmission line area.

[0032] Collect multi-source heterogeneous data affecting transmission line planning from different sources, such as land spatial planning data, power resource distribution data, and geographic information system data, and process them uniformly to convert them into raster data format to ensure good compatibility and comparability between data. Specific processing methods include data conversion, coordinate alignment, and rasterization.

[0033] Different influencing factors are classified into different levels, and different weights are assigned to each level of each influencing factor to quantify their comprehensive impact on path selection. Since these data come from different sources, have different data structures, and use different evaluation indicators, they need to be pre-classified and weighted. For example, wind speeds in different ranges are divided into different wind zone levels and weights are assigned to each level, with the grid value set as the weight value corresponding to the wind zone level.

[0034] The influencing factors corresponding to each multi-source heterogeneous data are classified in detail, and each level of each influencing factor is assigned a corresponding weight. The area of ​​the transmission line to be planned is divided into grids according to the grid table method. The comprehensive influence weight of the grid is obtained by adding the weights of each influencing factor in the area corresponding to each grid.

[0035] Specifically, Figure 2 The diagram shown is a schematic diagram of the crossover and interchange of genetic algorithm lines provided in an embodiment of the present invention. Figure 3 The diagram shown is a schematic representation of the mutation of a circuit in a genetic algorithm provided in an embodiment of the present invention. Figure 2 and Figure 3 As shown, the value of each grid in the matrix is ​​the total weight of that grid. In subsequent steps, the combined influence weights of all grids along the entire path are added together to obtain the total influence weight of that path.

[0036] Based on the calculation results of the comprehensive influence weight of each grid in the raster table in the above process, the comprehensive influence value matrix is ​​obtained.

[0037] Using the raster table method to divide the complex area of ​​the planned transmission line into pixel-level grids, and calculating the comprehensive influence weight of each grid according to its influencing factors, can provide a data foundation for subsequent path selection and evolution.

[0038] Step S12: Based on the preset path preference criteria and the number of path searches N, generate N sets of paths to be processed in the transmission line area grid.

[0039] Multiple paths are generated based on preset path preference criteria, and each path in each path group is coded. The preset path preference criteria can be the shortest path length, the shortest construction time, the most suitable power supply allocation, or other preference criteria. In the actual construction process, the path is generated based on the preset path preference criteria according to the actual situation.

[0040] Symbolic encoding is used to encode transmission line paths, with each path represented as a symbol string, and each symbol in the string corresponding to a node on the path. This encoding method facilitates genetic operations such as crossover and mutation.

[0041] Specifically, this embodiment uses real values ​​for encoding, in order to Figure 2 Taking the left subgraph as an example, assuming the starting point of the path is the bottom left corner and the ending point is the top right corner, then the path represented by the yellow line is encoded as: {(10, 1), (9, 1), (8, 2), (7, 3), (6, 3), (6, 4), (6, 5), (6, 6), (6, 7), (5, 8), (4, 9), (3, 9), (2,10), (1, 10)}.

[0042] Given a starting point and an ending point, assuming we want to search for N routes, we randomly generate N initial paths, each represented by a population. The number of routes k in each population is calculated using the following formula: Where W and H represent the number of pixels in the length and height directions of the rectangle formed by the start and end points, respectively. Each pixel corresponds to a grid in the raster table. round represents the rounding operation, and α represents a coefficient greater than 1. In actual power transmission line planning, the coefficients are flexibly selected according to the actual situation.

[0043] By randomly generating multiple paths and encoding each pixel node in the path, the total weight of the entire path can be calculated at any time during the subsequent evolution process to influence the selection of the optimal path, and the transmission path and source of data can be easily tracked.

[0044] Step S13: Input the paths to be processed in each group into the path search model constructed by the improved multi-peak optimization genetic algorithm, screen and determine the optimal target path corresponding to each group of paths, and obtain N optimal paths, which are the top N optimal paths in the entire algorithm generated. In the process of processing, the path search model is designed to analyze the area enclosed by two paths to redetermine the optimal target path corresponding to each group.

[0045] Path searching is performed using an improved multi-peaked optimization genetic algorithm. This multi-peaked genetic algorithm is a type of multi-population genetic algorithm. In multi-peaked optimization problems, multiple local optima exist, meaning the algorithm's results have multiple peak points. Traditional optimization algorithms are prone to getting trapped in these local optima and failing to find the global optimum. By introducing multiple populations, the multi-population genetic algorithm can explore different regions of the solution space, thus avoiding premature convergence to local optima and generating multiple distinct optimal paths.

[0046] The following is based on Figure 4 The content shown describes the training process of the path search model in detail. Figure 4 The diagram shows the evolutionary iteration flowchart of the path search model provided in an embodiment of the present invention.

[0047] Taking into account factors such as path length, project cost, environmental impact, and construction difficulty, a fitness function is designed, defined as follows: in C 1 represents the basic high-voltage transmission line route cost, which is based on six influencing factors: water system, forest area, pollution area, ice cover, topography and space, and wind zone. C 2 represents the cost of clearing the strip of road through which the route passes. C 3. Costs for crossing existing lines; C 4 represents the cornering cost, which is the engineering cost when a corner occurs between two line segments.

[0048] in C The calculation process for 1 is as follows: In the above formula ( a i , b i , c i , d i , e i , f i These represent the cost weights for six influencing factors within the raster: water system, forest area, pollution area, icing, topography, and wind zone. β This indicates the base cost per kilometer, specifically the cost per kilometer of route based on flat ground, 0mm icing, 25m / s wind speed, no forest crossings, altitude below 1000m, no crossings, access clearing fees, a 5km vehicle transport distance, and an unmanned transport distance. l This represents the length of each grid cell, i.e., the spatial resolution corresponding to the grid cell.

[0049] In the design of genetic operators, the crossover operator adopts a combination of single-point crossover and multi-point crossover methods, such as... Figure 3 As shown, when two paths intersect, one or more segments of the two paths are swapped. For example, in a single-point intersection, a random intersection is selected from the parent paths, and the two parent paths are split at this point and parts of the paths are swapped; in a multi-point intersection, multiple intersections are selected for more complex path swaps. These methods increase the diversity of paths and the ability to explore new solutions.

[0050] The mutation operator employs a method that connects fragment self-searching mutation with a linear connection between fragment classes, such as... Figure 3 As shown, in the fragment self-search mutation, a segment of the path is randomly selected and local search optimization is performed to find a better path segment; in the fragment class straight line connection, an attempt is made to connect two points in the path with a straight line to reduce the total length of the path. These mutation operations enhance the path optimization capability and the breadth of the search.

[0051] The selection operators employ the optimal individual method and the roulette wheel method. The optimal individual method ensures that the current optimal path is not eliminated, while the roulette wheel method selects based on the fitness value, retaining individuals with high fitness. In this way, both excellent solutions are preserved and the diversity of the population is guaranteed.

[0052] To avoid the populations converging to the same solution and affecting the diversity of paths, this embodiment adopts a multi-population independent operation method for evolutionary operations. Different populations do not interfere with each other during the evolution process, which can maintain the diversity of global search.

[0053] To ensure significant differences between different populations and prevent premature convergence to local optima, a periodic population differentiation operation is performed during population evolution. Specifically, this involves periodically comparing the solutions from different populations. For example, in the... T In the iterative process, since the starting and ending points of different routes are the same, the area enclosed by the optimal route for any two populations can be calculated. S ,use S limit Let represent the threshold for distinguishing between populations. Then, the optimal routes between different populations must satisfy the following relationship: When the area enclosed by the optimal paths of two populations does not satisfy the above formula, a population is randomly selected with a 50% probability, and its optimal path is deleted. The area of ​​the second-best path and the optimal path of the other population is calculated, and it is determined whether it satisfies the above formula. If it does not, the second-best path is deleted, and the area of ​​the third-best path and the optimal path of the other population is calculated, and it is determined whether it satisfies the above formula. This process is repeated. If the k-th best path satisfies the above formula, the k-th best path is set as the optimal path of that population to avoid the optimal paths of different populations converging to the same path.

[0054] Step S14: Based on the previous N optimal target paths, determine the optimal path planning result for the current transmission line.

[0055] After multiple generations of genetic operations, the optimal path for each population is obtained, which are N optimal paths under different tendency criteria. These paths have different advantages and disadvantages in terms of different influencing factors, and can provide multiple choices for practical engineering.

[0056] Preferably, a detailed comprehensive evaluation of the generated N optimal paths can be performed, calculating the performance of each path in terms of path length, engineering cost, environmental impact, and construction difficulty, so as to determine a path with high adaptability that takes into account all aspects as the transmission line path.

[0057] like Figure 5 As shown, Figure 5 The diagram shown is a verification example provided in an embodiment of the present invention. By comparing the cost with existing paths, the application effect of the algorithm of the present invention in high-voltage transmission line path planning is verified. Specific steps include comparing and analyzing the path generated by this scheme with the path generated by traditional methods in terms of engineering cost, environmental impact, etc., demonstrating the advantages of the present invention in practical engineering. Figure 5 This is a comparison between the two paths (i.e., N = 2) output by the path search model of this invention and the existing paths. Figure 5 Each grid corresponds to a spatial scale of 30m. The cyan grid path is the original path, the red grid path is the optimal path selected by population one, and the black grid path is the optimal path selected by population two. Each grid considers the impact of six types of multi-source heterogeneous data (ice zone, terrain, wind zone, elevation, forest zone, and pollution zone) on the route selection cost. Population one converged to 319 million yuan, population two converged to 322 million yuan, and the cost of the existing original path was 372 million yuan. After determining the two optimal routes, statistical indicators of the six types of multi-source heterogeneous data (ice zone, terrain, wind zone, elevation, forest zone, and pollution zone) traversed by the two routes can be derived. These statistical indicators can provide data support for decision-making. The cost of the two selected routes is lower than that of the existing routes, demonstrating the superior performance of this algorithm.

[0058] Through the above implementation steps, this invention demonstrates significant advantages in high-voltage transmission line path planning. First, the introduction of multiple population genetic algorithms greatly improves the global search capability of path optimization and the efficiency in solving complex problems. Second, the multi-path generation and optimization method provides multiple options for practical engineering, increasing the flexibility of path planning. Each group of paths evolves independently, and the addition of a differentiation step between multiple populations prevents premature convergence to local optima during evolution, thus ensuring the diversity of path search algorithm results and global search capability. Finally, through detailed multi-index comparison and verification, this invention significantly reduces engineering costs, improves the accuracy and efficiency of path planning, and provides an efficient and reliable solution for path planning in power systems.

[0059] This invention also provides a transmission line path determination apparatus for performing the transmission line path determination method described above. Figure 6 This is a structural block diagram of a transmission line path determination device according to an embodiment of the present invention. The device includes: Data processing module 21 is used to optimize the data of several types of influencing factors affecting the planning of transmission lines based on the raster table method, to obtain a comprehensive influence value matrix corresponding to the grid of the transmission line area, and to determine the attribute values ​​in the grid of the transmission line area using the comprehensive influence value matrix.

[0060] The path generation module 22 is used to generate multiple sets of paths to be processed in the transmission line area grid according to different path tendency criteria.

[0061] The genetic iteration module 23 is used to input the paths to be processed in each group into the path search model constructed by the improved multi-peak optimization genetic algorithm, and to screen and determine the optimal target path corresponding to each group of paths. In the process of processing, the path search model is designed to analyze the area enclosed by the two paths to redetermine the optimal target path corresponding to each group.

[0062] The path determination module 24 is used to determine the optimal path planning result of the current transmission line based on each of the optimal target paths.

[0063] The technical features and effects of the device proposed in the embodiments of the present invention are the same as those of the method proposed in the embodiments of the present invention, and will not be repeated here. Each module in the above-described device can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0064] This invention also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the transmission line path determination method as described above.

[0065] This invention also provides a computer device. Figure 7 This is a structural block diagram of a preferred embodiment of a computer device provided by the present invention. The computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the transmission line path determination method as described above.

[0066] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2, ...), and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0067] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can be any conventional processor. The processor is the control center of the computer device, connecting various parts of the computer device through various interfaces and lines.

[0068] The memory mainly includes a program storage area and a data storage area. The program storage area can store the operating system, applications required for at least one function, etc., while the data storage area can store related data, etc. Furthermore, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard drive, a SmartMedia Card (SMC), a Secure Digital (SD) card, and a Flash Card, or other volatile solid-state storage devices.

[0069] It should be noted that the aforementioned computer equipment may include, but is not limited to, processors and memory, as will be understood by those skilled in the art. Figure 7 The block diagram is merely an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than shown, or combine certain components, or different components.

[0070] In summary, the transmission line path determination method, apparatus, device, and storage medium provided by the embodiments of the present invention have advantages over the prior art in at least one of the following aspects: (1) Integrate multi-source heterogeneous data, quantify various influencing factors, and comprehensively consider the impact of geographical, economic and environmental factors in route selection to improve the rationality of transmission line route selection; (2) Each group of paths is independently evolved by the population, and multiple steps are added to distinguish between the populations to prevent the population from converging to a local optimum too early during the evolution process, thus ensuring the diversity of the path search algorithm results and the global search capability.

[0071] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for determining the path of a power transmission line, characterized in that, include: Based on the raster table method, the data of several types of influencing factors affecting the planning of transmission lines are optimized to obtain a comprehensive influence value matrix corresponding to the grid of the transmission line area. The comprehensive influence value matrix is ​​used to determine the attribute values ​​in the grid of the transmission line area. Based on the preset path preference criteria and the number of path searches N, N sets of paths to be processed are generated in the transmission line area grid. Each group of paths to be processed is input into a path search model constructed by an improved multi-peak optimization genetic algorithm. The optimal target path corresponding to each group of paths is selected and determined to obtain the first N optimal paths. In the process of processing, the path search model is designed to analyze the area enclosed by two paths to redetermine the optimal target path corresponding to each group. Based on the previous N optimal target paths, determine the optimal path planning result for the current transmission line; The construction of the path search model includes: Input a preset path start and end point, and randomly generate several sets of initial paths, each set of initial paths being represented by a population; Symbolic coding is used to encode the paths of each transmission line in various groups; Design fitness functions and genetic operators to perform genetic iterations on the encoded populations in order to complete the construction of the path search model; The genetic iteration of the encoded populations includes: Each group of paths represents a population that evolves independently and iteratively according to the fitness function and the genetic operator. During the iterative evolution process, the populations are periodically differentiated, and the differentiation specifically includes: Select a population as the target population, calculate the area enclosed between the optimal target path in each of the other populations and the optimal target path in the target population. If the area is less than a preset population differentiation area threshold, delete the optimal target path of the corresponding population, use the second optimal target path as the new optimal target path for that population, and recalculate the area between the optimal target path of that population and the optimal target path of the target population until the area is not less than the preset population differentiation area threshold.

2. The method for determining the transmission line path as described in claim 1, characterized in that, The raster table method is used to optimize the data of several types of influencing factors affecting transmission line planning, resulting in a comprehensive influence value matrix corresponding to the transmission line area grid, including: The data on several types of influencing factors affecting transmission line planning are classified into levels, and weights are assigned to each of the influencing factors based on the classification results. The planned transmission line area is divided into grids using the grid table method. The weights of each grid are calculated based on the type and weight of the influencing factor data corresponding to each grid area to obtain the comprehensive influence value matrix corresponding to the transmission line area.

3. The method for determining the transmission line path as described in claim 2, characterized in that, The influencing factors data include at least land spatial planning data, power resource distribution data, and geographic information system data.

4. The method for determining the transmission line path as described in claim 1, characterized in that, The design fitness function and genetic operators include: The fitness function is designed based on actual planning requirements, which include path cost, corridor clearing, route intersections, and route turns. Genetic operators are designed for the path search model, including: a crossover operator that combines single-point crossover and multi-point crossover; a mutation operator that combines self-searching mutation with segment-type linear connections; and a selection operator that combines the optimal individual method with the roulette wheel method.

5. The method for determining the transmission line path as described in claim 1, characterized in that, The optimal path planning result for the current transmission line, derived from the previous N optimal target paths, includes: Based on the optimal target path in each group of paths, a comprehensive evaluation is conducted to determine the transmission line path; the comprehensive evaluation indicators include at least path length, engineering cost, environmental impact, and construction difficulty.

6. A transmission line path determination device, characterized in that, include: The data processing module is used to optimize the data of several types of influencing factors affecting the planning of transmission lines based on the raster table method, and obtain a comprehensive influence value matrix corresponding to the grid of the transmission line area. The comprehensive influence value matrix is ​​used to determine the attribute values ​​in the grid of the transmission line area. The path generation module is used to generate N sets of paths to be processed in the transmission line area grid according to the preset path preference criteria and the number of path searches N. The genetic iteration module is used to input the paths to be processed in each group into the path search model constructed by the improved multi-peak optimization genetic algorithm, screen and determine the optimal target path corresponding to each group of paths, and obtain the first N optimal paths. In the process of processing, the path search model is designed to analyze the area enclosed by two paths to redetermine the optimal target path corresponding to each group. The path determination module is used to determine the optimal path planning result for the current transmission line based on the previous N optimal target paths. The construction of the path search model includes: Input a preset path start and end point, and randomly generate several sets of initial paths, each set of initial paths being represented by a population; Symbolic coding is used to encode the paths of each transmission line in various groups; Design fitness functions and genetic operators to perform genetic iterations on the encoded populations in order to complete the construction of the path search model; The genetic iteration of the encoded populations includes: Each group of paths represents a population that evolves independently and iteratively according to the fitness function and the genetic operator. During the iterative evolution process, the populations are periodically differentiated, and the differentiation specifically includes: Select a population as the target population, calculate the area enclosed between the optimal target path in each of the other populations and the optimal target path in the target population. If the area is less than a preset population differentiation area threshold, delete the optimal target path of the corresponding population, use the second optimal target path as the new optimal target path for that population, and recalculate the area between the optimal target path of that population and the optimal target path of the target population until the area is not less than the preset population differentiation area threshold.

7. A computer device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the transmission line path determination method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the transmission line path determination method as described in any one of claims 1 to 5.

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