A power transmission line intelligent route selection method and system considering forest fire space risk and construction cost

Abstract

The application is suitable for the technical field of power grid planning, and provides a power transmission line intelligent route selection method and system considering mountain fire space risk and construction cost, which comprises the following steps: performing space gridding modeling by obtaining a digital elevation model corrected by a high-precision positioning reference station cluster, fusing a static construction equivalent amount with a dynamic disaster resistance equivalent amount to construct a comprehensive resistance cost surface, and performing improved ant colony algorithm iteration search on the basis, introducing a direction guide factor and corner optimization processing in the search process to eliminate redundant inflection points, and finally outputting an optimal path node sequence containing three-dimensional coordinates, calculating a three-dimensional space length based on the corrected digital elevation model, and determining a tower configuration scheme in combination with the number of reserved corner nodes. The application realizes the consideration of path disaster prevention reliability and tower configuration rationality in a complex forest environment, and provides a scientific, efficient and intelligent auxiliary decision-making means for power transmission line planning.

Description

Technical Field

[0001] This invention relates to the field of power grid planning technology, and in particular to an intelligent transmission line selection method and system that takes into account the spatial risk of wildfires and construction costs. Background Technology

[0002] Transmission line route selection is the primary step in power grid engineering planning and construction. The quality of the route directly affects the initial construction cost, the convenience of later operation and maintenance, and the safety and stability of the power grid in complex environments. As power grid construction extends to high-altitude, forested areas, and remote mountainous regions with complex geological conditions, the risk of transmission lines suffering from wildfires and geological disasters during operation has significantly increased. To address these complex route selection environments, current transmission line route selection technologies mainly encompass the following methods: Planners use a geographic information system platform to import publicly available digital elevation models and satellite imagery of conventional accuracy. Through visual interpretation and spatial analysis, they identify static construction elements such as terrain slope, deforestation areas, demolition zones, and crossing points. Based on this, they initially delineate the route corridor and then use multi-attribute decision models such as the analytic hierarchy process (AHP) or fuzzy comprehensive evaluation to compare and select the appropriate route. To further improve efficiency, heuristic search mechanisms such as ant colony optimization, Dijkstra's algorithm, or genetic algorithms are used for automatic path optimization. This involves discretizing the route selection space into a two-dimensional grid matrix, assigning a static resistance value to each grid based on land use type and terrain undulation, and using the minimum overall path resistance or shortest spatial distance as the objective function. Multiple rounds of iterative search are then conducted to find a sequence of broken lines connecting the start and end points. For environmental risks such as forest fires and geological disasters in forest areas, existing technologies typically perform buffer analysis based on historical data, designating historically high-risk areas as absolute no-selection zones and setting the resistance value to infinity in the optimization model, supplementing the search results with manual adjustments.

[0003] However, due to the limited accuracy of the underlying digital elevation model and the lack of effective direction guidance and node optimization mechanisms in the optimization algorithm, traditional heuristic algorithms often generate paths containing a large number of meaningless redundant inflection points when searching in complex grid spaces. In the actual construction of transmission line projects, each turn in the path corresponds to the need to add expensive tension angle towers and their supporting foundation engineering. The existence of redundant inflection points directly leads to a significant increase in the total project cost, making it difficult for automated route selection results to meet the actual needs of power grid planning for construction economy. Summary of the Invention

[0004] This invention provides a method and system for intelligent transmission line selection that takes into account both wildfire spatial risk and construction costs, in order to solve the problem that automated line selection results cannot meet the actual needs of power grid planning for construction economy.

[0005] The first aspect of this invention provides an intelligent transmission line selection method that takes into account both wildfire spatial risk and construction costs, comprising: Obtain the digital elevation model of the planning area after correction by a cluster of high-precision positioning reference stations and multi-source remote sensing data, and perform spatial raster modeling; For each grid cell, the static construction cost equivalent and dynamic disaster resistance equivalent between adjacent grid cells are calculated. The static construction cost equivalent and dynamic disaster resistance equivalent are then fused to construct a comprehensive resistance cost surface. An improved ant colony algorithm is executed iteratively based on the surface of the comprehensive resistance cost to drive path optimization with the goal of minimizing the comprehensive resistance cost; after each search, the obtained path is optimized for corners. The pheromone is updated based on the global fitness cost of the optimized path. The iteration terminates when the convergence condition is met, and the optimal path node sequence is output, which includes the three-dimensional coordinates of each node. Based on the modified digital elevation model, the elevation values ​​of each node in the optimal path node sequence are extracted, and the three-dimensional spatial length of the transmission line is calculated. Based on the three-dimensional spatial length and the number of corner nodes retained in the optimal path, the tower configuration scheme is determined.

[0006] Furthermore, the calculation of the static construction cost equivalent between adjacent grid cells is performed using the following formula: in: Indicates the current grid cell. Indicates and Adjacent neighbor grids; Indicates adjacent grid cells to The static construction cost equivalent, with the dimension of length; Indicates adjacent grid cells to The three-dimensional Euclidean distance between them; Indicates the terrain slope angle between two adjacent grid cells; Represents the target grid The geological and expropriation compensation equivalent distance, which is based on the target raster. The unit area compensation standard corresponding to the identified land feature types is obtained through conversion; This indicates the equivalent distance of the crossing penalty. When the line connecting two adjacent grids crosses a preset crossing facility, the corresponding equivalent distance value is calculated based on the level of the crossing facility and the crossing angle. , , These are the terrain penalty weight coefficient, geological compensation weight coefficient, and intersection / crossing weight coefficient, respectively, and satisfy the following conditions: .

[0007] Furthermore, the calculation of the dynamic disaster resistance equivalent between adjacent grids is performed using the following formula: in: Represents the target grid Dynamic disaster resistance equivalent; Represents the target grid The probability measure of wildfire occurrence is obtained by extracting target grid cells. The vegetation index and micrometeorological data were input into a pre-built wildfire risk assessment model to obtain the data. Represents the target grid The geological disaster risk index is determined based on surface micro-deformation data calculated from high-precision positioning reference station cluster monitoring data; This indicates the parameter for adjusting disaster risk sensitivity; Represented by natural constant An exponential function with base 0.

[0008] Furthermore, the integrated static construction cost equivalent and dynamic disaster resistance equivalent are combined to construct a comprehensive resistance cost surface, using the following formula: in: Indicates adjacent grid cells to The comprehensive resistance cost value constitutes the attribute value of the corresponding grid cell in the comprehensive resistance cost surface; As a construction cost preference weighting factor, The two factors are the disaster prevention and safety preference weights, and both satisfy the normalization condition. .

[0009] Furthermore, the iterative search performed using the improved ant colony algorithm based on the comprehensive resistance cost surface includes: Initialize the ant colony algorithm parameters, including ant colony size, maximum number of iterations, pheromone heuristic factor, expected heuristic factor, and direction guidance factor weight; Each ant is placed at the starting point, and a path search is performed on the surface of the comprehensive resistance cost based on the state transition probability. The state transition probability integrates the path pheromone concentration, a heuristic function defined by the inverse of the comprehensive resistance cost, and a direction guidance factor. After each complete path search, corner optimization is performed, and the path pheromone is updated based on the global fitness cost. Repeat the search and update process described above until the convergence condition is met.

[0010] Furthermore, the state transition probability is calculated using the following formula: in: Indicates the first In the next iteration, the node is located ants Select node The transition probability; Represents a node to Pheromone concentration along the path; For heuristic functions, ; This is the direction guiding factor, and its value is equal to that of the current node. To candidate nodes The connection vector and the current node The cosine of the angle between the vectors connecting the endpoints; This represents the index of any candidate node in the candidate node set; Ants The next step is to select the set of candidate nodes that are allowed to access the network and have not exceeded the boundaries. Represents a node To candidate nodes Pheromone concentration along the path; Represents a node To candidate nodes The heuristic function value; Represents a node To candidate nodes Directional guiding factor; , , These are the weights of the pheromone heuristic factor, the expectation heuristic factor, and the direction guidance factor, respectively.

[0011] Furthermore, the path optimization driven by minimizing the overall drag cost includes: After each iteration of the search, the complete paths obtained by each ant constitute a candidate path set; For each candidate path in the candidate path set, the comprehensive resistance cost between each adjacent grid in the candidate path is extracted and accumulated to obtain the comprehensive resistance cost of the candidate path. Based on the comprehensive resistance cost of the candidate path and in conjunction with the path corner penalty term, calculate the global fitness cost of the candidate path. From the set of candidate paths, the candidate path with the minimum global fitness cost is selected as the optimal path for this iteration.

[0012] Furthermore, the step of performing corner optimization processing on the obtained path after each search includes: Extract three consecutive raster nodes along the candidate path, namely the previous node, the current node, and the next node; Calculate the cosine of the turning angles of the path segments before and after the current node. The cosine of the turning angles is obtained by the vector from the previous node to the current node and the vector from the current node to the next node. When the cosine value of the turning angle is less than the preset smoothing angle threshold, and the line segment directly connecting the preceding node and the following node does not cross the extremely high disaster resistance zone, the current node is marked as a redundant node and the current node is removed from the path node sequence. The above judgment and elimination operations are performed sequentially on all nodes in the path until there are no redundant nodes in the path that meet the elimination conditions, thus obtaining the optimized path.

[0013] Furthermore, the tower configuration scheme is determined based on the three-dimensional spatial length and the number of corner nodes retained in the optimal path, using the following formula: in: This indicates the estimated total number of towers in the ranking; Representing adjacent nodes in the optimal path and The straight-line distance between them; This indicates the average economic range at the current voltage level; This represents the floor function; This represents the total number of path segments in the optimal path. This indicates the number of corner nodes retained in the optimal path after corner optimization. Corner nodes are the locations where tension corner towers need to be installed.

[0014] A second aspect of the present invention provides an intelligent transmission line selection system that takes into account both wildfire spatial risk and construction costs, comprising: The spatial rasterization modeling module is used to acquire the digital elevation model of the planning area after correction by a cluster of high-precision positioning reference stations and multi-source remote sensing data, and to perform spatial rasterization modeling. The integrated resistance cost surface construction module is used to calculate the static construction cost equivalent and dynamic disaster resistance equivalent between adjacent grid cells for each grid cell, and to fuse the static construction cost equivalent and dynamic disaster resistance equivalent to construct an integrated resistance cost surface; The path optimization module is used to perform iterative search using an improved ant colony algorithm based on the comprehensive resistance cost surface, with the goal of minimizing the comprehensive resistance cost; after each search, the obtained path is optimized for corners. The optimal path node sequence output module is used to update the pheromone based on the global fitness cost of the optimized path. When the convergence condition is met, the iteration is terminated and the optimal path node sequence is output, which includes the three-dimensional coordinates of each node. The three-dimensional spatial length calculation module is used to extract the elevation values ​​of each node in the optimal path node sequence based on the modified digital elevation model, and to calculate the three-dimensional spatial length of the transmission line. The tower configuration scheme determination module is used to determine the tower configuration scheme based on the length of the three-dimensional space and the number of corner nodes retained in the optimal path.

[0015] As can be seen from the above technical solutions, the present invention has the following advantages: 1. This invention utilizes a cluster of high-precision positioning reference stations to adjust and correct conventional digital elevation data, enabling route planning to be established on a high-precision three-dimensional spatial reference. This improves the accuracy of the three-dimensional spatial length calculation of the route and avoids the problem of tower foundation misalignment caused by terrain errors. 2. This invention introduces a directional guidance factor in the state transition probability calculation, giving the ant search process a clear direction toward the endpoint. Combined with corner optimization processing, it automatically identifies and removes redundant turning points in the path that do not cross extremely high disaster risk areas. From an algorithmic perspective, this reduces the number of tension corner towers required, making the generated path straighter and more reasonable while meeting disaster avoidance safety requirements.

[0016] 3. This invention transforms the probability of wildfire occurrence and the geological disaster risk index into a dynamic disaster resistance equivalent through a nonlinear mapping function, causing the resistance value of high-risk areas to increase exponentially. This drives the optimization algorithm to automatically avoid extremely high-risk areas, realizing the transformation from static passive demarcation to dynamic active avoidance. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of an embodiment of an intelligent transmission line selection method that takes into account the spatial risk of wildfires and construction costs in this invention; Figure 2 This is a flowchart of the improved ant colony algorithm iterative search and path optimization process in this invention. Detailed Implementation

[0018] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “corresponding to,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] Example 1 The implementation method in this embodiment can be implemented in a system, on a server, or on a terminal; no specific limitation is made. The method in this application will be described below from the perspective of system implementation. Please refer to... Figure 1 The method provided in this application includes the following steps: S1. Obtain the digital elevation model and multi-source remote sensing data of the planning area after correction by a cluster of high-precision positioning reference stations, and perform spatial raster modeling; In this embodiment, high-resolution multispectral images and synthetic aperture radar images of the planning area are acquired through a satellite remote sensing platform; stereo image pairs of the planning area are acquired through aerial photogrammetry; and point cloud data of the planning area are acquired through lidar scanning. After preprocessing such as radiometric correction, atmospheric correction, and geometric correction, the aforementioned multi-source remote sensing data are used for subsequent land cover type identification and vegetation index extraction.

[0020] The digital elevation model of the planning area, corrected by a cluster of high-precision positioning reference stations, is determined as follows: Conventional digital elevation model data for the planning area is obtained; using a cluster of high-precision positioning reference stations deployed in and around the planning area, high-precision three-dimensional coordinates of each reference station are obtained through carrier phase differential positioning technology, along with measured elevation values ​​of several control points within the planning area; the measured elevation values ​​of the control points are compared with the corresponding elevation values ​​in the conventional digital elevation model to calculate the elevation residual values ​​at each control point; using the elevation residual values ​​of each control point as known quantities, Kriging interpolation or inverse distance weighted interpolation is used to spatially interpolate the elevation residual field of the entire planning area, generating an elevation residual correction surface; the conventional digital elevation model and the elevation residual correction surface are superimposed to obtain the corrected digital elevation model. In the corrected digital elevation model, the three-dimensional coordinates of any point are represented as follows: In the formula: Represents the plane coordinates of this point; This represents the elevation value after residual correction. ,in This is the elevation value of that point in a conventional digital elevation model. This is the elevation residual correction value for this point obtained through spatial interpolation.

[0021] The specific method for spatial rasterization modeling is as follows: The envelope formed by the start and end points of the planning area is used as the selection range. This selection range is then divided into a regular two-dimensional square grid on the horizontal projection plane, with each grid cell being a raster node. Let the grid resolution be... Then the first in the planning area Line number Column grid nodes The coordinates of the center plane are: In the formula: and These are the minimum values ​​of the abscissa and ordinate of the selected line range on the horizontal projection plane, respectively. The planar coordinates of each grid node are spatially overlaid with the corrected digital elevation model, and the elevation value corresponding to the center position of each grid node is extracted. This assigns complete three-dimensional spatial coordinates to each grid node. This completes the construction of a three-dimensional grid spatial model covering the entire planning area, in which any grid node... Represented as: In the formula: , For grid nodes plane coordinates, For grid nodes Elevation values ​​extracted based on the modified digital elevation model.

[0022] S2. For each grid cell, calculate the static construction cost equivalent and dynamic disaster resistance equivalent between adjacent grid cells, and merge the static construction cost equivalent and dynamic disaster resistance equivalent to construct a comprehensive resistance cost surface; In the three-dimensional raster space model established in step S1, each raster cell is a raster node with an assigned elevation value. Adjacent grid cells refer to the cells in the same grid space that are adjacent to the current grid cell. The neighboring grates that share edges or vertices on the horizontal projection plane constitute the set of neighboring grates for the current grate. This step needs to be performed on each pair of adjacent grates. and Quantify its static construction cost equivalent and target grid respectively. The dynamic disaster resistance equivalent is used as a unit, and the two are integrated to form a comprehensive resistance cost surface. Among them, the static construction cost equivalent reflects the relative cost caused by construction factors such as topographic relief, relocation of ground features, and crossings, while the dynamic disaster resistance equivalent reflects the potential threat level of wildfires and geological disasters to the safe operation of the line.

[0023] In this embodiment, the equivalent static construction cost between adjacent grid cells is calculated using the following formula: in: Indicates the current grid cell. Indicates and Adjacent neighbor grids; Indicates adjacent grid cells to The static construction cost equivalent, with the dimension of length; Indicates adjacent grid cells to The three-dimensional Euclidean distance between them; Indicates the terrain slope angle between two adjacent grid cells; Represents the target grid The geological and expropriation compensation equivalent distance, which is based on the target raster. The unit area compensation standard corresponding to the identified land feature types is obtained through conversion; This indicates the equivalent distance of the crossing penalty. When the line connecting two adjacent grids crosses a preset crossing facility, the corresponding equivalent distance value is calculated based on the level of the crossing facility and the crossing angle. , , These are the terrain penalty weight coefficient, geological compensation weight coefficient, and intersection / crossing weight coefficient, respectively, and satisfy the following conditions: .

[0024] In this embodiment, the dynamic disaster resistance equivalent between adjacent grids is calculated using the following formula: in: Represents the target grid Dynamic disaster resistance equivalent; Represents the target grid The probability measure of wildfire occurrence is obtained by extracting target grid cells. The vegetation index and micrometeorological data were input into a pre-built wildfire risk assessment model to obtain the data. Represents the target grid The geological disaster risk index is determined based on surface micro-deformation data calculated from high-precision positioning reference station cluster monitoring data; This indicates the parameter for adjusting disaster risk sensitivity. The value range is from 0.5 to 2.0; Represented by natural constant An exponential function with base 0.

[0025] Using the aforementioned static construction cost equivalent formula and dynamic disaster resistance equivalent formula, the static cost between each pair of adjacent grids and the dynamic disaster cost targeting neighboring grids can be calculated respectively. To unify these two types of costs into a spatial cost benchmark that can be directly used by subsequent optimization algorithms, they need to be fused.

[0026] In this embodiment, the static construction cost equivalent and the dynamic disaster resistance equivalent are integrated to construct a comprehensive resistance cost surface, using the following formula: in: Indicates adjacent grid cells to The comprehensive resistance cost value constitutes the attribute value of the corresponding grid cell in the comprehensive resistance cost surface; As a construction cost preference weighting factor, The two factors are the disaster prevention and safety preference weights, and both satisfy the normalization condition. .

[0027] The above is the combined resistance value between all adjacent grid pairs. The resulting set forms a comprehensive resistance cost surface covering the entire planning area. This surface maps static construction factors such as topography, landform relocation, and intersections, along with dynamic risk factors such as wildfires and geological disasters, to a unified quantified resistance space. This provides a heuristic cost benchmark for subsequent path optimization improvements in the ant colony algorithm.

[0028] S3. Based on the comprehensive resistance cost surface, perform an improved ant colony algorithm for iterative search, with the goal of minimizing the comprehensive resistance cost to drive path optimization; after each search, perform corner optimization on the obtained path; In this embodiment, the improvements of the improved ant colony algorithm compared to the traditional ant colony algorithm are reflected in the following three aspects: First, a direction guidance factor is introduced in the calculation of state transition probability, using the consistency of the spatial direction from the current node to the candidate node and from the current node to the destination as heuristic information, giving the ant search a clear goal orientation and avoiding blind detours caused by a lack of prior knowledge in the early stages of the search; Second, after each search, corner optimization processing is performed on the path, automatically eliminating redundant inflection points based on the cosine value of the corner and the constraint of the disaster area, reducing unnecessary turns in the path from the algorithm level; Third, the pheromone update adopts an adaptive mechanism based on global fitness cost, combined with the extreme value constraint of the max-min ant colony strategy, balancing the ability to explore unknown areas in the early stages of the search with the convergence speed of the optimal solution in the later stages, avoiding the algorithm from getting trapped in local optima. Please refer to [link to relevant documentation]. Figure 2 The following is a detailed explanation of each step of the algorithm: In this embodiment, an improved ant colony algorithm is used for iterative search based on the comprehensive resistance cost surface, including the following: 1. Initialize ant colony algorithm parameters, including ant colony size. Maximum number of iterations Pheromones as heuristic factors Expected heuristic factors and the weight of the directional guiding factor ; 2. Place each ant at the starting point and perform path search on the surface of the comprehensive resistance cost based on the state transition probability. The state transition probability integrates the path pheromone concentration, the heuristic function defined by the inverse of the comprehensive resistance cost, and the direction guidance factor. The state transition probability is calculated using the following formula: in: Indicates the first In the next iteration, the node is located ants Select node The transition probability; Represents a node to Pheromone concentration along the path; For heuristic functions, ; This is the direction guiding factor, and its value is equal to that of the current node. To candidate nodes The connection vector and the current node The cosine of the angle between the vectors connecting the endpoints; This represents the index of any candidate node in the candidate node set; Ants The next step is to select the set of candidate nodes that are allowed to access the network and have not exceeded the boundaries. Represents a node To candidate nodes Pheromone concentration along the path; Represents a node To candidate nodes The heuristic function value; Represents a node To candidate nodes Directional guiding factor; , , These are the weights of the pheromone heuristic factor, the expectation heuristic factor, and the direction guidance factor, respectively.

[0029] 3. After each complete path search, corner optimization is performed, and the path pheromone is updated based on the global fitness cost; the pheromone update adopts an adaptive evaporation mechanism, with an evaporation coefficient... The decay is non-linear with the number of iterations, and the expression is: in: and These are the preset upper and lower limits for the pheromone evaporation coefficient, respectively. This represents the current iteration number. This mechanism allows pheromones to evaporate quickly in the early stages of the search to encourage exploration of unknown regions, and then evaporate more slowly in the later stages to accelerate convergence to the optimal solution.

[0030] The pheromone update uses an incremental allocation model based on the globally optimal ant, and the update formula is as follows: Among them: the upper limit of pheromones is The lower limit of pheromones is , This represents the total number of grid nodes. The pheromone concentration on all paths is forcibly limited to a certain value. Within the interval, to avoid blindly locking a suboptimal path due to pheromone positive feedback overload, the algorithm's escape capability in complex disaster resistance space is improved.

[0031] 4. Repeat the above search and update process until the convergence condition is met.

[0032] In this embodiment, the convergence condition is determined using a dual discrimination mechanism based on variance stability: an iteration counter is set. When the number of iterations Reaching the preset maximum number of iterations Terminate at time; or extract continuous The cost of the global optimal fitness in the next iteration Calculate its convergence variance: In the formula: For near The average cost of the global optimal fitness in each iteration. Less than the preset convergence threshold approaching zero When the algorithm has reached global convergence, the iteration is terminated.

[0033] In this embodiment, the path optimization is driven by minimizing the overall drag cost, including the following: 1. After each iteration of the search, the complete paths obtained by each ant constitute a candidate path set; 2. For each candidate path in the candidate path set, extract the comprehensive resistance cost between each adjacent grid in the candidate path and sum them up to obtain the comprehensive resistance cost of the candidate path; 3. Based on the comprehensive resistance cost of the candidate path and in conjunction with the path turning penalty term, calculate the global fitness cost of the candidate path; the global fitness cost of the candidate path is calculated using the following formula: in: This represents the global fitness cost of the candidate path; This represents the sequence of raster nodes traversed by the candidate path; This represents the set of corner nodes retained after corner optimization of the candidate path; Represents a corner node The angle between the preceding and following path segments, The cosine value of the included angle is the value obtained through the corner node. It is obtained by calculating two vectors consisting of the preceding and following nodes in the path; This represents the equivalent foundation cost of adding a single-base tension angle tower. In this formula, the first term accumulates the comprehensive resistance cost along the path, reflecting the combined impact of terrain, features, and disaster factors; the second term imposes a penalty cost on unavoidable angle nodes that is positively correlated with the angle; the sharper the angle, the higher the cost. The larger the value, the heavier the penalty, forcing the algorithm to select a straighter path in subsequent iterations.

[0034] 4. From the candidate path set, select the candidate path with the minimum global fitness cost as the optimal path for this iteration.

[0035] In this embodiment, after each search is completed, the obtained path undergoes corner optimization processing, including the following: 1. Extract three consecutive raster nodes along the candidate path, which are the preceding nodes. Current node and subsequent nodes ; 2. Calculate the cosine of the turning angles of the path segments before and after the current node. The cosine of the turning angles is obtained by using the vector from the previous node to the current node and the vector from the current node to the next node. In the formula: This represents the three-dimensional Euclidean distance between the previous node and the current node. This represents the three-dimensional Euclidean distance between the current node and its successor node.

[0036] 3. When the cosine value of the turning angle is less than the preset smoothing angle threshold, and the line segment directly connecting the preceding node and the following node does not cross the extremely high disaster resistance zone, mark the current node as a redundant node and remove the current node from the path node sequence; The preset smoothing angle threshold is If the turning angle of the path at this node is too large, it is further determined that it is directly connected to the preceding node. With subsequent nodes Does the line segment cross an extremely high disaster resistance zone? An extremely high disaster resistance zone refers to the grid area in step S2 where the dynamic disaster resistance equivalent exceeds a preset warning threshold. If the line segment does not cross an extremely high disaster resistance zone, then the current node is determined to be... For redundant nodes, the current node is removed from the path node sequence to force the path to straighten at that point; if the line segment crosses an area with extremely high disaster resistance, the current node is retained to ensure the disaster avoidance safety of the path.

[0037] 4. Perform the above judgment and elimination operations sequentially on all nodes in the path until there are no redundant nodes in the path that meet the elimination conditions, thus obtaining the optimized path. This corner optimization mechanism automatically identifies and eliminates redundant turning points in the path, reducing the number of tension corner towers required in actual projects while ensuring that the line does not intrude into extremely high disaster areas, thereby reducing the overall construction cost of the project from the source.

[0038] S4. Update the pheromone based on the global fitness cost of the optimized path. Terminate the iteration when the convergence condition is met, and output the optimal path node sequence, which contains the three-dimensional coordinates of each node. In each iteration of step S3, the specific implementation of pheromone updates follows the adaptive evaporation mechanism and extreme value truncation constraint from S3. When the convergence condition is met, the algorithm terminates the iteration and outputs the optimal path node sequence that minimizes the global fitness cost. ,in As the starting point for planning, To plan the endpoint, each node contains complete three-dimensional spatial coordinates.

[0039] S5. Based on the corrected digital elevation model, extract the elevation values ​​of each node in the optimal path node sequence and calculate the three-dimensional spatial length of the transmission line; In the optimal path node sequence output by step S4, each node already contains planar coordinates based on the rasterization modeling assigned in step S1. This step utilizes the corrected digital elevation model obtained in step S1, and performs spatial location matching of each node's planar coordinates within the corrected digital elevation model to extract the corresponding elevation value for each node. This allows each node in the optimal path node sequence to be assigned accurate elevation information that truly reflects the micro-topographic undulations.

[0040] Based on the complete three-dimensional coordinates of the nodes described above, the three-dimensional spatial length of the transmission line is calculated. The following formula is used: in: This represents the total number of path segments in the optimal path. For the next adjacent node The elevation value. This formula obtains the true spatial length of the line by accumulating the three-dimensional Euclidean distance between each pair of adjacent nodes on the optimal path, taking into account both the planar distance and the terrain elevation undulation. Compared with the traditional method of calculating the line length based only on the planar projection distance, it can more accurately reflect the contribution of micro-topography to the actual length of the transmission line.

[0041] S6. Determine the tower configuration scheme based on the three-dimensional spatial length and the number of corner nodes retained in the optimal path.

[0042] Obtaining the three-dimensional spatial length of the transmission line Based on this, the tower configuration scheme is determined here by considering the number of corner nodes retained after corner optimization in step S3 of the optimal path. The tower configuration scheme is determined by the expected total number of towers in the ranking. The total number is characterized by two parts: the number of straight-line towers and the number of tension corner towers. The number of straight-line towers is obtained by dividing the straight-line distance between adjacent nodes by the average economic span at the current voltage level, rounding up, and then summing the results. The number of tension corner towers is directly determined by the number of corner nodes retained after corner optimization in the optimal path, with each corner node corresponding to one tension corner tower. The tower configuration scheme is determined using the following formula: in: This indicates the estimated total number of towers in the ranking; Representing adjacent nodes in the optimal path and The straight-line distance between them is calculated based on the three-dimensional coordinates of each node in step S5; This represents the average economic span at the current voltage level. This value is a preset reference value based on the voltage level of the transmission line and engineering experience. This represents the floor function; This represents the total number of path segments in the optimal path. This indicates the number of corner nodes retained in the optimal path after corner optimization. Corner nodes are the locations where tension corner towers need to be installed.

[0043] This tower configuration scheme directly maps the optimization effect of the path optimization algorithm to specific engineering configuration parameters: corner optimization reduces the number of redundant corner points in the path by eliminating redundant corner points. The improved ant colony algorithm reduces the number of tension-resistant angle towers required by optimizing the numerical values. The improved algorithm searches for straight paths on the surface of comprehensive resistance cost, resulting in a more reasonable distribution of the spatial straight-line distances between adjacent nodes, thus avoiding an additional increase in the number of straight-line towers due to path detours.

[0044] Example 2 An embodiment of the intelligent transmission line selection system of the present invention, which takes into account the spatial risk of wildfires and construction costs, includes the following: The spatial rasterization modeling module is used to acquire the digital elevation model of the planning area after correction by a cluster of high-precision positioning reference stations and multi-source remote sensing data, and to perform spatial rasterization modeling. The integrated resistance cost surface construction module is used to calculate the static construction cost equivalent and dynamic disaster resistance equivalent between adjacent grid cells for each grid cell, and to merge the static construction cost equivalent and dynamic disaster resistance equivalent to construct the integrated resistance cost surface; The path optimization module is used to perform iterative search using an improved ant colony algorithm based on the comprehensive resistance cost surface, with the goal of minimizing the comprehensive resistance cost; after each search, the obtained path is optimized for corners. The optimal path node sequence output module is used to update the pheromone based on the global fitness cost of the optimized path. When the convergence condition is met, the iteration terminates and the optimal path node sequence is output, which contains the three-dimensional coordinates of each node. The three-dimensional spatial length calculation module is used to extract the elevation values ​​of each node in the optimal path node sequence based on the corrected digital elevation model, and to calculate the three-dimensional spatial length of the transmission line. The tower configuration scheme determination module is used to determine the tower configuration scheme based on the three-dimensional spatial length and the number of corner nodes retained in the optimal path.

[0045] For specific limitations regarding the system, please refer to the method limitations described above, which will not be repeated here. Each module in the above system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0046] It is understood that those skilled in the art can combine various implementation methods in the above embodiments under the guidance of the above examples to obtain technical solutions with multiple implementation methods.

[0047] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent transmission line selection that considers both wildfire spatial risk and construction cost, characterized in that, include: Obtain the digital elevation model of the planning area after correction by a cluster of high-precision positioning reference stations and multi-source remote sensing data, and perform spatial raster modeling; For each grid cell, the static construction cost equivalent and dynamic disaster resistance equivalent between adjacent grid cells are calculated. The static construction cost equivalent and dynamic disaster resistance equivalent are then fused to construct a comprehensive resistance cost surface. Based on the comprehensive resistance cost surface, an improved ant colony algorithm is executed for iterative search, with the goal of minimizing the comprehensive resistance cost to drive path optimization; After each search, the resulting path is optimized for corners; The pheromone is updated based on the global fitness cost of the optimized path. The iteration terminates when the convergence condition is met, and the optimal path node sequence is output, which includes the three-dimensional coordinates of each node. Based on the modified digital elevation model, the elevation values ​​of each node in the optimal path node sequence are extracted, and the three-dimensional spatial length of the transmission line is calculated. Based on the three-dimensional spatial length and the number of corner nodes retained in the optimal path, the tower configuration scheme is determined.

2. The intelligent transmission line selection method considering both wildfire spatial risk and construction cost as described in claim 1, characterized in that, The calculation of the equivalent static construction cost between adjacent grid cells is performed using the following formula: in: Indicates the current grid cell. Indicates and Adjacent neighbor grids; Indicates adjacent grid cells to The static construction cost equivalent, with the dimension of length; Indicates adjacent grid cells to The three-dimensional Euclidean distance between them; Indicates the terrain slope angle between two adjacent grid cells; Represents the target grid The geological and expropriation compensation equivalent distance, which is based on the target raster. The unit area compensation standard corresponding to the identified land feature types is obtained through conversion; This indicates the equivalent distance of the crossing penalty. When the line connecting two adjacent grids crosses a preset crossing facility, the corresponding equivalent distance value is calculated based on the level of the crossing facility and the crossing angle. , , These are the terrain penalty weight coefficient, geological compensation weight coefficient, and intersection / crossing weight coefficient, respectively, and satisfy the following conditions: .

3. The intelligent transmission line selection method considering both wildfire spatial risk and construction cost according to claim 2, characterized in that, The dynamic disaster resistance equivalent between adjacent grids is calculated using the following formula: in: Represents the target grid Dynamic disaster resistance equivalent; Represents the target grid The probability measure of wildfire occurrence is obtained by extracting target grid cells. The vegetation index and micrometeorological data were input into a pre-built wildfire risk assessment model to obtain the data. Represents the target grid The geological disaster risk index is determined based on surface micro-deformation data calculated from high-precision positioning reference station cluster monitoring data; This indicates the parameter for adjusting disaster risk sensitivity; Represented by natural constant An exponential function with base 0.

4. The intelligent transmission line selection method considering wildfire spatial risk and construction cost according to any one of claims 1 to 3, characterized in that, The comprehensive resistance cost surface is constructed by integrating the static construction cost equivalent and the dynamic disaster resistance equivalent, using the following formula: in: Indicates adjacent grid cells to The comprehensive resistance cost value constitutes the attribute value of the corresponding grid cell in the comprehensive resistance cost surface; As a construction cost preference weighting factor, The two factors are the disaster prevention and safety preference weights, and both satisfy the normalization condition. .

5. The intelligent transmission line selection method considering both wildfire spatial risk and construction cost according to claim 1, characterized in that, The iterative search based on the improved ant colony algorithm on the comprehensive resistance cost surface includes: Initialize the ant colony algorithm parameters, including ant colony size, maximum number of iterations, pheromone heuristic factor, expected heuristic factor, and direction guidance factor weight; Each ant is placed at the starting point, and a path search is performed on the surface of the comprehensive resistance cost based on the state transition probability. The state transition probability integrates the path pheromone concentration, a heuristic function defined by the inverse of the comprehensive resistance cost, and a direction guidance factor. After each complete path search, corner optimization is performed, and the path pheromone is updated based on the global fitness cost. Repeat the search and update process described above until the convergence condition is met.

6. The intelligent transmission line selection method considering wildfire spatial risk and construction cost according to claim 5, characterized in that, The state transition probability is calculated using the following formula: in: Indicates the first In the next iteration, the node is located ants Select node The transition probability; Represents a node to Pheromone concentration along the path; For heuristic functions, ; This is the direction guiding factor, and its value is equal to that of the current node. To candidate nodes The connection vector and the current node The cosine of the angle between the vectors connecting the endpoints; This represents the index of any candidate node in the candidate node set; Ants The next step is to select the set of candidate nodes that are allowed to access the network and have not exceeded the boundaries. Represents a node To candidate nodes Pheromone concentration along the path; Represents a node To candidate nodes The heuristic function value; Represents a node To candidate nodes Directional guiding factor; , , These are the weights of the pheromone heuristic factor, the expectation heuristic factor, and the direction guidance factor, respectively.

7. The intelligent transmission line selection method considering both wildfire spatial risk and construction cost according to claim 5, characterized in that, The path optimization driven by minimizing the overall drag cost includes: After each iteration of the search, the complete paths obtained by each ant constitute a candidate path set; For each candidate path in the candidate path set, the comprehensive resistance cost between each adjacent grid in the candidate path is extracted and accumulated to obtain the comprehensive resistance cost of the candidate path. Based on the comprehensive resistance cost of the candidate path and in conjunction with the path corner penalty term, calculate the global fitness cost of the candidate path. From the set of candidate paths, the candidate path with the minimum global fitness cost is selected as the optimal path for this iteration.

8. The intelligent transmission line selection method considering wildfire spatial risk and construction cost according to claim 7, characterized in that, The step of optimizing the corners of the obtained path after each search includes: Extract three consecutive raster nodes along the candidate path, namely the previous node, the current node, and the next node; Calculate the cosine of the turning angles of the path segments before and after the current node. The cosine of the turning angles is obtained by the vector from the previous node to the current node and the vector from the current node to the next node. When the cosine value of the turning angle is less than the preset smoothing angle threshold, and the line segment directly connecting the preceding node and the following node does not cross the extremely high disaster resistance zone, the current node is marked as a redundant node and the current node is removed from the path node sequence. The above judgment and elimination operations are performed sequentially on all nodes in the path until there are no redundant nodes in the path that meet the elimination conditions, thus obtaining the optimized path.

9. The intelligent transmission line selection method considering wildfire spatial risk and construction cost according to claim 1, characterized in that, The tower configuration scheme is determined based on the three-dimensional spatial length and the number of corner nodes retained in the optimal path, using the following formula: in: This indicates the estimated total number of towers in the ranking; Representing adjacent nodes in the optimal path and The straight-line distance between them; This indicates the average economic range at the current voltage level; This represents the floor function; This represents the total number of path segments in the optimal path. This indicates the number of corner nodes retained in the optimal path after corner optimization. Corner nodes are the locations where tension corner towers need to be installed.

10. A smart transmission line selection system that considers both wildfire spatial risk and construction cost, characterized in that, The intelligent transmission line selection method based on any one of claims 1 to 9, which considers both wildfire spatial risk and construction costs, includes: The spatial rasterization modeling module is used to acquire the digital elevation model of the planning area after correction by a cluster of high-precision positioning reference stations and multi-source remote sensing data, and to perform spatial rasterization modeling. The integrated resistance cost surface construction module is used to calculate the static construction cost equivalent and dynamic disaster resistance equivalent between adjacent grid cells for each grid cell, and to fuse the static construction cost equivalent and dynamic disaster resistance equivalent to construct an integrated resistance cost surface; The path optimization module is used to perform iterative search using an improved ant colony algorithm based on the comprehensive resistance cost surface, with the goal of minimizing the comprehensive resistance cost; after each search, the obtained path is optimized for corners. The optimal path node sequence output module is used to update the pheromone based on the global fitness cost of the optimized path. When the convergence condition is met, the iteration is terminated and the optimal path node sequence is output, which includes the three-dimensional coordinates of each node. The three-dimensional spatial length calculation module is used to extract the elevation values ​​of each node in the optimal path node sequence based on the modified digital elevation model, and to calculate the three-dimensional spatial length of the transmission line. The tower configuration scheme determination module is used to determine the tower configuration scheme based on the length of the three-dimensional space and the number of corner nodes retained in the optimal path.

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