A temporary road planning method for transmission tower construction and related products

Abstract

The invention relates to the field of auxiliary design of construction roads, and in particular to a temporary road planning method for transmission tower construction and related products. The method comprises: based on geographical location information of tower positions, using a clustering algorithm to group tower positions to obtain multiple tower position clusters, and determining Steiner points in each tower position cluster; using a path planning algorithm to plan optimal paths between tower positions, between tower positions and Steiner points, and between Steiner points; combining a road network optimization algorithm to search for an optimal road network solution containing or not containing Steiner points, and connecting the optimal road network solution with existing roads to obtain an optimal temporary road plan; the invention can effectively solve the road planning complexity problem caused by uneven distribution of towers by introducing a constrained clustering algorithm, and shorten the total path length by using a Steiner point optimization technology; the path planning algorithm comprehensively considers multiple factors such as terrain slope, turning radius and construction cost.

Description

Technical Field

[0001] The present invention relates to the field of auxiliary design of construction roads, and in particular to a temporary road planning method for transmission tower construction and related products. Background Art

[0002] Traditional overhead transmission line construction is mainly manpower-intensive, with a long construction period and high labor costs. In the face of complex geological and topographical conditions, the problem of inadequacy of traditional construction technology has become increasingly prominent. How to make use of existing roads according to local conditions and reduce the damage to the environment and terrain caused by temporary road construction has become an issue that needs to be solved urgently.

[0003] With the advancement of mechanized construction throughout the entire process, it has become crucial to ensure that construction machinery such as foundation excavation, tower assembly, and wire laying can reach the tower site smoothly. However, in areas such as the southwest where mountainous areas account for a large proportion, the lines often cross high mountains and ridges, and the terrain conditions at the tower site are complex, which brings many challenges to the transportation of engineering materials and construction machinery in and out of the site. Therefore, building construction roads has become a necessary condition to ensure the smooth entry and exit of construction machinery. In this process, the exploration and selection of construction road path plans have become the core tasks.

[0004] At present, the selection of transmission line construction roads mainly relies on manual on-site surveys and site selection. However, even with the assistance of transmission line design drawings, the rationality of road selection is still highly dependent on the experience and judgment of technical workers. This leads to problems such as site selection while repairing the road and repeated construction during the construction process. This route selection method lacks overall planning and is relatively arbitrary, resulting in too many human factors, making it difficult to form a unified construction road planning standard specification, and thus unable to effectively guide subsequent engineering construction. Therefore, a more scientific and standardized method is urgently needed to optimize the selection and planning process of transmission line construction roads. Summary of the invention

[0005] In order to solve the problem that whether the temporary road network layout design of traditional technology is reasonable depends entirely on the experience and judgment of technical workers, in the construction project of tower group, it is impossible to fully utilize the existing information for temporary road design, lack of scientific and efficient cost calculation method, and unable to guide the subsequent engineering construction, a temporary road planning method and related products for transmission tower construction are provided. According to the geographical information of the construction area, the location of the tower and the location information of the existing road, reasonable road nodes are designed to complete the linear design of the road. According to different road node introduction schemes, the schemes with the lowest total cost are searched in all road linear designs, and then the optimal road network layout is selected. Finally, the tower road network is connected with the existing road to complete the overall temporary road design of the transmission project.

[0006] The present invention is achieved through the following technical solutions:

[0007] A temporary road planning method for transmission tower construction, comprising:

[0008] Based on the geographical location information of the towers, the towers are grouped using a clustering algorithm to obtain multiple tower clusters, and a Steiner point is determined in each tower cluster;

[0009] Use path planning algorithms to plan optimal paths between tower locations, between tower locations and Steiner points, and between Steiner points.

[0010] Combined with the road network optimization algorithm, the optimal road network solution including or excluding Steiner points is searched, and the optimal road network solution is connected with the existing roads to obtain the optimal temporary road planning.

[0011] Specifically, the method of grouping tower positions using a clustering algorithm includes:

[0012] Obtain the geographical location information of the tower and build a constrained K-means clustering algorithm model;

[0013] The objective function is to minimize the sum of squared Euclidean distances from each tower to its nearest cluster center, and the constraints are determined: each cluster contains at least the minimum number of towers; each cluster contains at most the maximum number of towers; each tower can only be assigned to one cluster;

[0014] The transformation optimization problem is a minimum cost flow problem, in which tower locations are defined as supply nodes and cluster centers as demand nodes. Artificial nodes are introduced to construct the flow and cost models of nodes and directed arcs.

[0015] Using the minimum cost flow problem solver, we can obtain the tower allocation results and complete the clustering.

[0016] Calculate the center of each cluster according to the allocation results and update the cluster center position;

[0017] The above tower position allocation process and cluster center update process are iterated repeatedly until the cluster center position is stable and the tower position clustering is completed.

[0018] Specifically, the methods for determining the Steiner point include:

[0019] Obtain the location information of all towers in the tower cluster and use it as input data for calculating the Steiner point;

[0020] Get the number of towers in the tower cluster; if the number of towers is less than 5, directly calculate the position of the Steiner point by geometric method;

[0021] If the number is not less than 5, construct a connection network between the towers and calculate the lengths of all possible Steiner trees; introduce virtual auxiliary points between the towers as candidate Steiner point locations; select the auxiliary points that can minimize the length of the connection path through an optimization algorithm and determine them as Steiner points.

[0022] Specifically, the method of planning the optimal path using the path planning algorithm includes:

[0023] Obtain geographic information model, tower location and Steiner point location, and store the information using raster method;

[0024] Each grid is defined as a node, each node contains coordinates, elevation and allowed turning angle range; edges are defined between tower sites and Steiner points, between tower sites and tower sites, and between Steiner points and Steiner points, and the edges contain distance, slope, turning angle and comprehensive weight;

[0025] Establish a total cost function consisting of fixed cost and estimated cost; the fixed cost includes basic distance cost, slope cost and turning cost; the estimated cost is the estimated cost from the current node to the target node, including slope and turning angle;

[0026] Define the starting and ending points of the path and set boundary conditions; build an open list and a closed list; the open list is used to store the nodes to be evaluated, initially only containing the starting point; the closed list records the evaluated nodes;

[0027] Select the node with the lowest cost from the open list, search its adjacent nodes, select nodes that meet the turning angle and slope restrictions, and update the cost value of the node; if the adjacent node has not been evaluated, add it to the open list; when the target node is updated, the algorithm terminates; trace back the parent node pointer of the target node to the starting node, and use Bezier curve or B-spline algorithm to smooth the path;

[0028] Output the minimum cost path linear design between two points as the optimal path.

[0029] Specifically, the method of searching for the optimal road network solution including or excluding Steiner points in combination with the road network optimization algorithm includes:

[0030] Put the tower points into the necessarily connected set P, and put the Steiner points into the connectable set Q, where Q contains n elements;

[0031] Each time a candidate network is searched, a node set C is constructed according to the following rules: all points in the must-connect set P are put into C, and then i points are randomly selected from the connectable set Q and added to C to obtain a node set C, where i = 1, 2, ..., n, and there are 2 n Combination of

[0032] Based on the generated node set C, all possible temporary roads are considered as edges connecting the tower positions to construct a candidate road network. The edge weights include material transportation cost, road construction cost, road restoration cost, and loading and unloading cost.

[0033] Using the road network optimization algorithm, calculate the total cost of multiple candidate road network solutions with or without Steiner points;

[0034] The road network plan with the lowest total cost is taken as the optimal road network plan.

[0035] Specifically, the road network optimization algorithm used is the A* algorithm, and the method includes:

[0036] Construct a weighted connected graph of possible road networks based on Steiner points and a weighted connected graph of possible road networks without Steiner points; the weights of the edges include material transportation cost, road construction cost, road restoration cost, and loading and unloading cost;

[0037] Select a tower node in the weighted connected graph as the starting point, and initialize the vertex set and edge set of the minimized spanning tree;

[0038] Select the edge with the smallest current weight in the priority queue and add the two nodes connected to it to the minimum spanning tree; update the edge weights of the unconnected nodes related to the newly connected node and add them to the priority queue;

[0039] Repeat the process of selecting the minimum edge and updating the nodes until all tower nodes are connected and a complete minimum spanning tree is generated;

[0040] The candidate road network solutions corresponding to the multiple weighted connected graphs are obtained cyclically, and the total cost of each road network solution is calculated.

[0041] Specifically, the method of connecting the optimal road network solution with the existing roads includes:

[0042] According to the grid division method, the optimal road network solution is discretized into the starting point set A, and the existing roads are discretized into the end point set B;

[0043] Take all the points in the starting point set A as the starting points and all the points in the ending point set B as the ending points, and generate all possible combinations of the starting points and the ending points;

[0044] Use the A* algorithm to calculate the minimum cost path between any pair of start points and end points in the start point set A and the end point set B; generate a path set R;

[0045] The path with the lowest total cost is selected from the path set R as the temporary road connecting the optimal road network solution and the existing roads to obtain the optimal temporary road plan.

[0046] A temporary road planning terminal for power transmission tower construction comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a temporary road planning method for power transmission tower construction as described above is implemented.

[0047] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for planning a temporary road for transmission tower construction as described above is implemented.

[0048] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the above-mentioned method for planning a temporary road for transmission tower construction.

[0049] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0050] The present invention can effectively solve the road planning complexity problem caused by uneven distribution of poles and towers by introducing a constrained clustering algorithm, and further shorten the total path length by Steiner point optimization technology; the path planning algorithm comprehensively considers multiple factors such as terrain slope, turning radius and construction cost, and significantly improves the rationality and practicality of road design; the road network optimization algorithm ensures that the selected solution has the lowest total cost by comparing road network solutions containing and excluding Steiner points; at the same time, the optimal road network solution is integrated with existing roads to maximize road utilization, reduce the mileage of new roads, and reduce construction costs and environmental restoration costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings illustrate exemplary embodiments of the present invention and, together with the description thereof, are used to explain the principles of the present invention. These drawings are included to provide a further understanding of the present invention, and the accompanying drawings are included in and constitute a part of this specification and do not constitute a limitation of the embodiments of the present invention.

[0052] Figure 1 It is a flow chart of a temporary road planning method for transmission tower construction according to the present invention.

[0053] Figure 2 Schematic diagram of the Steiner point construction of three points according to the present invention.

[0054] Figure 3 Schematic diagram of the construction of the convex quadrilateral Steiner point according to the present invention.

[0055] Figure 4 Schematic diagram of the construction of the non-convex quadrilateral Steiner point according to the present invention.

[0056] Figure 5Schematic diagram of the construction of five-point Steiner points according to the present invention.

[0057] Figure 6 Schematic diagram of the position of the Steiner point according to the seventh embodiment of the present invention.

[0058] Figure 7 Schematic diagram of an output road network according to Embodiment 7 of the present invention. DETAILED DESCRIPTION

[0059] To make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and implementation methods. It is understood that the specific implementation methods described herein are only used to explain the relevant content, rather than to limit the present invention.

[0060] It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings.

[0061] In the absence of conflict, the embodiments and features of the embodiments of the present invention may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0062] Embodiment 1

[0063] like Figure 1 As shown, a method for planning a temporary road for transmission tower construction is provided, comprising:

[0064] Based on the geographical location information of the towers, the towers are grouped using a clustering algorithm to obtain multiple tower clusters, and the Steiner points are determined in each tower cluster; the towers distributed in complex terrain are grouped using a clustering algorithm (such as a constrained K-means algorithm), and the towers with similar geographical locations are classified into the same group. The clustering algorithm achieves reasonable grouping of towers by optimizing the objective function (such as minimizing the sum of squares of the distances from the tower to the cluster center). At the same time, in each cluster, additional connection points are determined by Steiner point optimization technology. The Steiner point is an auxiliary node, usually located at the geometric center of the path, which can minimize the path length required to connect all towers in the cluster.

[0065] The path planning algorithm is used to plan the optimal path between towers, between towers and Steiner points, and between Steiner points. Path planning takes into account multiple factors, such as terrain slope, path length, turning angle, etc. The algorithm is usually based on the A* algorithm, combining fixed costs (such as path length) and estimated costs (such as the cost from the current node to the target node) to optimize the path.

[0066] Combined with the road network optimization algorithm, the optimal road network solution with or without Steiner points is searched, and the optimal road network solution is connected with the existing roads to obtain the optimal temporary road plan. Combined with the minimum spanning tree (MST) algorithm and road network optimization technology, multiple road network solutions with and without Steiner points are calculated respectively. By comparing the total cost of each solution, the solution with the lowest cost is selected as the optimal road network solution. Subsequently, the A* algorithm is used to connect the optimal road network solution with the existing roads to generate a complete temporary road plan.

[0067] Embodiment 2

[0068] In this embodiment, a constrained K-means clustering algorithm is used to cluster the towers according to their geographical locations, with the objective function of the constrained K-means algorithm being to minimize the sum of the squares of the Euclidean distances from each data point to its nearest cluster center. The specific clustering methods include:

[0069] Obtain the geographical location information of the tower and build a constrained K-means clustering algorithm model;

[0070] The objective function is to minimize the sum of squared Euclidean distances from each tower to its nearest cluster center, and the constraints are determined: each cluster contains at least the minimum number of towers; each cluster contains at most the maximum number of towers; each tower can only be assigned to one cluster;

[0071] The transformation optimization problem is a minimum cost flow problem, in which tower locations are defined as supply nodes and cluster centers as demand nodes. Artificial nodes are introduced to construct the flow and cost models of nodes and directed arcs.

[0072] Using the minimum cost flow problem solver, we can obtain the tower allocation results and complete the clustering.

[0073] Calculate the center of each cluster according to the allocation results and update the cluster center position;

[0074] The above tower position allocation process and cluster center update process are iterated repeatedly until the cluster center position is stable and the tower position clustering is completed.

[0075] The specific steps are as follows:

[0076] According to the geographical location information of the tower, select the initial cluster center C 1 , C 2 , ..., C k , where k is the preset number of clusters;

[0077] The objective is to minimize the sum of squared Euclidean distances from each tower to its nearest cluster center, and the objective function is determined as Among them, x i is the data point of the i-th tower, Ch is the hth cluster center, T i，h is the allocation indicator variable. When the i-th tower is assigned to the h-th cluster, T i，h =1, otherwise 0; m is the total number of tower positions;

[0078] Identify the constraints:

[0079] Each cluster contains at least a minimum number of tower positions: Among them, τ h is the minimum number of tower positions for each cluster;

[0080] Each cluster contains at most a maximum number of tower positions: Among them, τ h′ is the maximum number of tower positions for each cluster;

[0081] Each tower position can only be assigned to one cluster:

[0082] Assign indicator variable non-negative: T i，h ≥0(i=1,...,m; h=1,...,k);

[0083] Convert the objective function to the minimum cost flow problem: For each tower position x i Transformed into supply node, supply value Each cluster center C h Converted into demand node, demand value Define an artificial node a, whose required value is: From each tower position x i To cluster center C h Define a directed arc with cost c i，h =||x i -C h || 2 ; From each cluster center C h Define a directed arc to artificial node a with a cost of 0;

[0084] In the minimum cost flow problem, each node i has a value b associated with it i , this value can indicate that this node is a supply node (b i >0), demand node (b i <0) or a transfer node (b i =0), if ∑b of the entire minimum cost flow problem i = 0, then the problem is feasible. Let A be the set of directed arcs between nodes i and j. For each directed arc (i, j), y i,j represents the flow on this directed arc, c i,jRepresents the cost per unit flow on the directed arc. u i,j represents the maximum allowable flow rate of each directed arc, that is, the flow rate y on each directed arc i,j Cannot exceed the allowable value u i,j .

[0085] Using the minimum cost flow problem solver, calculate the allocation variable T i，h , satisfying the constraints: Among them, y i，j is the flow on arc (i, j), c i，j is the cost per unit flow on arc (i, j), u i，j is the maximum allowable flow rate of arc (i, j), b i is the supply value of node i;

[0086] When solving the above combinatorial optimization problem, we transform it into a minimum cost flow problem, where each data point x i Transformed into a supply node with a supply value of Convert the previously defined cluster center into a demand node with a demand value of Each directed arc is x i to C h , put each arc into set A, arc (x i ,C h ) is ||x i -C h || 2 In order to satisfy the constraint that the sum of supply is equal to the sum of demand, we need to define an artificial node a, whose demand is From each cluster node C h The cost to node a is 0, and there is no directed arc between the data node and node a. With the above definitions and supply and demand nodes, the previous optimization problem is transformed into an MCF equation, which allows us to calculate T i,h Find integer solutions, and T i,h The maximum value of is 1.

[0087] For each data point x i , using the minimum cost flow problem solver (MCF) to solve T i,h After that, allocate T i,h So that x i Assigned to the nearest cluster center C h , while satisfying the above constraints. According to the minimum cost flow solution, calculate the mean of the data points assigned to each cluster, and set the mean as the new cluster center. The update rule of the cluster center is, in, t is the number of iterations;

[0088] Iterate the minimum cost flow solution and cluster center update steps until the cluster center position is stable or the maximum number of iterations is reached, and the tower positions are grouped.

[0089] Embodiment 3

[0090] This embodiment provides a specific method for determining Steiner points. In the present invention, the number of tower positions in the tower position cluster is 3-5, that is, when performing a clustering operation, the minimum number of tower positions in the constraint condition is 3, and the maximum number of tower positions is 5.

[0091] Obtain the location information of all towers in the tower cluster and use it as input data for calculating the Steiner point;

[0092] Get the number of towers in the tower cluster; if the number of towers is less than 5, directly calculate the position of the Steiner point by geometric method;

[0093] If the number is not less than 5, construct a connection network between the towers and calculate the lengths of all possible Steiner trees; introduce virtual auxiliary points between the towers as candidate Steiner point locations; select the auxiliary points that can minimize the length of the connection path through an optimization algorithm and determine them as Steiner points.

[0094] After obtaining the tower clustering results, search for Steiner points in each cluster. According to the number of towers in each cluster, the Steiner points are solved in three cases.

[0095] like Figure 2 As shown, there are 3 points in the tower location cluster.

[0096] The three points are labeled A, B, and C respectively. Use the three sides of ▲ABC to make an equilateral triangle outward, and then make the circumscribed circle of the equilateral triangle. The intersection point of the circumscribed circle is the desired point.

[0097] like Figure 3 and Figure 4 As shown, there are 4 points in the tower location cluster.

[0098] Check the convex hull: First, determine the convex hull of the four points. If the four points form a convex quadrilateral, then the Steiner minimum tree is the diagonal of the quadrilateral. That is, the Steiner point is the intersection of the diagonal lines, and there is only one Steiner point. Figure 3 shown.

[0099] Determine the Stenier points: If the four points do not form a convex quadrilateral, use a geometric method: For four points, the Stenier points can be determined by the following geometric method:

[0100] If the three points are collinear, then the triangle formed by the fourth point and these three points will determine whether it is necessary to introduce Steiner points.

[0101] If there are no three points on a line, then the location of the Steiner point can be determined by constructing an equilateral triangle. For example, for any three points, three equilateral triangles can be constructed, whose vertices are the three points, and then possible Steiner points can be found according to the case of n=3, and these points can be used as candidate Steiner points. Figure 3 shown.

[0102] Calculate the distance: Once the Steiner point is determined, the length of the line segment connecting all points (including the Steiner point) can be calculated to determine the total length of the Steiner minimum tree. The Steiner point used by the minimum tree is the desired Steiner point.

[0103] like Figure 5 As shown, there are 5 points in the tower location cluster.

[0104] Let X = {A, B, C, D, E}, first fix one of the points, such as B, the other four points can be divided into two pairs (there are at least two ways to divide them here), such as (A, E) and (C, D), and make regular triangles outwards on (A, E) and (C, D) to get vertices F and I, such as Figure 5 , there will be three situations:

[0105] On FI, draw an equilateral triangle ▲FIH relative to the position of B, and connect BH. Find a point J in BH such that ∠CJF=120°. Connect JI and JF, and find K and L on JI and JF respectively, such that ∠CKD=120°, ∠ALE=120°. Connect JK and JL, then AL+LE+LJ+JK+KD+KC+JB is the required Steiner tree, and its length is BH.

[0106] Connect BI, draw an equilateral triangle ▲BIG on BI relative to the direction of AE, connect GF, and use the method in (1) above to obtain the Steiner tree which is still AL+LE+LJ+JK+KD+KC+JB.

[0107] Connect BF, draw an equilateral triangle ▲BFM on BF relative to the direction of CD, connect IM, and use the method in (1) above to obtain the Steiner tree which is still AL+LE+LJ+JK+KD+KC+JB.

[0108] According to this method, when a given point is fixed, there are two combinations of quadrilaterals made from the remaining four points. For one of the combinations, three Steiner trees can be made, but the three Steiner trees of the same combination are all the same, so only one needs to be made. That is, when a given point is fixed, two Steiner trees can be obtained. In fact, only one of the two Steiner trees is possible. Just pay attention to: ∠FBG≤120° must be met, and BH must separate points C and D from points A and E. It is impossible to make another straight line segment through B that separates points A and C from points E and D and satisfies the condition of ∠FBG≤120". Therefore, to make the minimum Steiner tree of X=(A, B, C, D, E), you only need to isolate one of the points each time and make the corresponding Steiner tree: then select the shortest one from the five Steiner trees, and the Steiner point used by this Steiner tree is the desired Steiner point.

[0109] Embodiment 4

[0110] This embodiment uses the A* algorithm to calculate the optimal linear design between towers, between towers and Steiner points, and between Steiner points. That is, the method of using the path planning algorithm to plan the optimal path includes:

[0111] 1. Obtain the geographic information model, tower location and Steiner point location, and store the information using the raster method;

[0112] 2. Define each grid as a node, each node contains coordinates (x, y, z), elevation and the allowed turning angle range; define edges between tower sites and Steiner points, between tower sites and tower sites, and between Steiner points, and the edges contain distance, slope, turning angle and comprehensive weight; here, each grid is regarded as a node, and the tower sites and Steiner points are marked. The edge connects two adjacent nodes, including distance, slope (calculated by node elevation difference), turning angle, weight (comprehensively considering distance, slope and turning cost, as well as corresponding road repair cost and regional restoration cost).

[0113] 3. Establish a total cost function F=G+H consisting of fixed cost and estimated cost; the fixed cost G includes basic distance cost, slope cost and turning cost; the estimated cost H is the estimated cost from the current node to the target node, including slope and turning angle.

[0114] The fixed cost (G) consists of three parts: basic distance cost, slope cost and turn cost.

[0115] Basic distance cost (G distance ) is defined using the Euclidean distance: Slope cost refers to the additional cost or consumption caused by the change of terrain slope during the path search process: G slope =k·|tanδ|, where k is the slope weight coefficient and δ is the slope angle between the two points.

[0116] Turn cost G turn , set the weight for sharp turns according to the size of the turning angle: In the formula, C 1 It can be set according to the minimum turning radius requirements of the road design, for example, increasing the weight for narrower paths; C 2 Used to emphasize the importance of turning angle, usually C1+C2=1.

[0117] R is the turning radius (in meters), which reflects the smoothness of the curve. It is usually calculated from the current node and the parent node and child node positions. The calculation method is as follows: Where, d 1 ·d 2 ·d 3 is the side length between the parent node, the current node, and the child node (m), A is the area of ​​the triangle formed by the three points (m2), Δθ is the angle between the parent node pointing to the current node and the current node pointing to the child node (rad), is the direction vector from the parent node to the current node, It is the direction vector from the current node to the child node.

[0118] The fixed cost G is thus: G = G distance +G slope +G turn .

[0119] The estimated cost H is used to estimate the cost from the current node to the end point, taking into account the slope and turning angle, and is defined as: Among them, ω is the weight, c is the turning adjustment coefficient, and Δθ is the turning angle difference.

[0120] 4. Define the starting and ending points of the path and set boundary conditions; build an open list and a closed list; the open list is used to store the nodes to be evaluated, initially containing only the starting point; the closed list records the evaluated nodes to avoid redundant calculations.

[0121] 5. Initialize the cost function, select the node with the lowest cost from the open list, search its adjacent nodes, filter out nodes that meet the turning angle and slope restrictions, and update the cost value of the node; if the adjacent node has not been evaluated, add it to the open list; when the target node is updated, the algorithm terminates; backtrack the parent node pointer of the target node to the starting node, and use Bezier curves or B-spline algorithms to smooth the path to reduce sharp turns in the path; output the lowest cost path linear design between the two points as the optimal path.

[0122] Embodiment 5

[0123] Use the MST method to retrieve the roads obtained by A*, and plan the road network to connect all the towers according to the total cost. Consider both the case where all Steiner points are added to the road network planning and the case where all Steiner points are not added. For example, if there are 3 Steiner points, there are 2^3=8 road network solutions, and the solution with the lowest total cost is selected. Then connect the road network with the existing roads to obtain the final complete temporary road network.

[0124] The specific implementation of MST retrieval is as follows:

[0125] The Prim algorithm is used to solve the MST problem with the minimum total connectivity cost as the optimization goal, and a connected subgraph connecting all tower locations and existing roads is generated, and the connected subgraph is used as a construction road planning scheme.

[0126] Input: Weighted connected graph: Each node in the graph represents a pole tower, and each edge represents a temporary road between two pole towers. The weight of the edge is composed of the following four parts: (The weight data is provided by the output of the A* algorithm)

[0127] Construction cost: The construction cost of each road, taking into account factors such as terrain, construction difficulty, and construction period.

[0128] Restoration cost: The cost of restoring the road to its original state after construction is completed, calculated based on the type of road and restoration requirements.

[0129] Material transportation cost: The cost of transporting materials from one tower to another is affected by factors such as transportation distance and material weight.

[0130] Loading and unloading costs: The loading and unloading costs involved in the transportation of materials are calculated based on the weight of the materials and the frequency of loading and unloading.

[0131] Input data for nodes and edges: Contains information such as the location of all towers, the weight of required materials, loading and unloading requirements, the length of the road, and the difficulty of construction.

[0132] Output: Minimum cost temporary road network: The minimum cost road network that satisfies the connectivity of all towers, including detailed information of each path (towers on the path, path length, construction cost, material transportation cost, recovery cost, loading and unloading cost, etc.).

[0133] The specific implementation steps are as follows:

[0134] 1. Put the tower points into the must-connect set P, and put the Steiner points into the connectable set Q, where Q contains n elements;

[0135] Each time a candidate network is searched, a node set C is constructed according to the following rules: all points in the must-connect set P are put into C, and then i points are randomly selected from the connectable set Q and added to C to obtain a node set C, where i = 1, 2, ..., n, and there are 2 n combinations; assuming that B contains two Steiner points, the combinations of node set C are: {tower site 1, tower site 2}, {tower site 1, tower site 2, Steiner point 1}, {tower site 1, tower site 2, Steiner point 2}, {tower site 1, tower site 2, Steiner point 1, Steiner point 2}, a total of 2 2 =4 kinds. "

[0136] Based on the generated node set C, all possible temporary roads are considered as edges connecting the tower positions to construct a candidate road network. The edge weights include material transportation cost, road construction cost, road restoration cost, and loading and unloading cost.

[0137] Construct a weighted connected graph of possible road networks based on Steiner points and a weighted connected graph based on possible road networks excluding Steiner points; the weights of edges include material transportation cost, road construction cost, road restoration cost and loading and unloading cost; the total weight of edges = material transportation cost + construction cost + restoration cost + loading and unloading cost.

[0138] 2. Initialize the Prim algorithm and use the road network optimization algorithm to calculate the total cost of multiple road network solutions including possible road networks with Steiner points and possible road networks without Steiner points; the road network solution with the lowest total cost is taken as the optimal road network solution.

[0139] The steps of Prim's algorithm to construct MST are as follows:

[0140] 1. Select a tower node in the weighted connected graph as the starting point, and initialize the vertex set and edge set of the minimized spanning tree; suppose the weighted connected graph G = (V, E), Prim's algorithm starts from an arbitrary vertex v0, initializes the vertex set U = {v0} of the MST, the edge set A = {}, B = {}.

[0141] 2. Select the edge with the smallest current weight in the priority queue and add the two nodes connected to it to the minimum spanning tree; Select the minimum edge: find the edge with the smallest weight connecting the vertices in set U and the vertices in set VU. This edge is the edge with the smallest weight between the vertices in set V that have not been added to the MST and the vertices that already exist in the MST.

[0142] 3. Update the edge weights of the unconnected nodes related to the newly connected node and add them to the priority queue; if adding the weighted edge does not cause the UA graph to form a loop, add the minimum weighted edge found to the edge set A, and add the vertices in VU connected by this edge to U. If adding the weighted edge causes the UA graph to form a loop, add this edge to the edge set B = {} and return to step 2.

[0143] 4. Repeat steps 2 and 3, that is, repeat the process of selecting the minimum edge and updating the node, until U contains all vertices, that is, U = V, all tower nodes are connected, and a complete minimum spanning tree is generated; obtain candidate road network solutions corresponding to multiple weighted connected graphs.

[0144] 5. Loop through the calculations to calculate the total cost of each road network solution.

[0145] Embodiment 6

[0146] Methods for connecting the optimal road network solution with existing roads include:

[0147] According to the grid partitioning method (see A* input data processing), the optimal road network solution is discretized into the starting point set A, and the existing roads are discretized into the end point set B;

[0148] Take all the points in the starting point set A as the starting points and all the points in the ending point set B as the ending points, and generate all possible combinations of the starting points and the ending points;

[0149] Use the A* algorithm to calculate the minimum cost path between any pair of start points and end points in the start point set A and the end point set B; generate a path set R;

[0150] The path with the lowest total cost is selected from the path set R as the temporary road connecting the optimal road network solution and the existing roads to obtain the optimal temporary road plan.

[0151] Embodiment 7

[0152] This embodiment provides a specific example.

[0153] Input the regional geographic information and the location information of the towers and existing roads. Use the A* algorithm to calculate the excavation volume and length of the temporary road alignment between towers, Steiner points and towers, Steiner points and existing roads, and towers and existing roads. See Table 2 for details. The comprehensive geographic information in the study area includes the location information of 11 towers, existing roads, waters, forestry and other special areas. Figure 6 Among them, the pink border represents economic areas such as forestry, where the economic value of crops in the two areas is different, and the dark red border represents impassable areas such as water areas.

[0154] Combined with the type and weight of materials required for each tower, the construction cost, restoration cost and material transportation cost of the temporary road network are calculated. With the help of Steiner points, the A* algorithm and MST method are used to retrieve the minimum cost temporary road network that meets the connectivity of the towers. The materials required for each tower are shown in Table 3. The final temporary road network is as follows Figure 7 The temporary road network is shown in red. (ST1-3 in the table indicates Steiner points 1-3)

[0155] Table 2 Excavation volume and length of temporary road alignment

[0156]

[0157] Table 3 Types and weights of materials required for each tower

[0158]

[0159] Considering special areas, the specific connection method of the temporary road network is shown in Table 4 (in the table, 0 represents the existing road, 1-11 represents the tower 1-11, and ST1-3 represents the Steiner point 1-3).

[0160] Table 4 Road network layout connections considering special areas

[0161]

[0162] Embodiment 8

[0163] A temporary road planning terminal for power transmission tower construction comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a temporary road planning method for power transmission tower construction as described above is implemented.

[0164] The memory can be used to store software programs and modules. The processor executes various functional applications and data processing of the terminal by running the software programs and modules stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an execution program required for at least one function, etc.

[0165] The data storage area can store data created according to the use of the terminal, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0166] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for planning a temporary road for transmission tower construction as described above is implemented.

[0167] Without loss of generality, computer readable media may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer readable instruction data structures, program modules or other data. Computer storage media include RAM, ROM, EPROM, EEPROM, flash memory or other solid-state storage technology, CD-ROM, DVD or other optical storage, cassettes, magnetic tapes, disk storage or other magnetic storage devices. Of course, those skilled in the art will appreciate that computer storage media are not limited to the above. The above-mentioned system memory and mass storage devices can be collectively referred to as memory.

[0168] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the above-mentioned method for planning a temporary road for transmission tower construction.

[0169] A computer program product includes a computer program or set of instructions for performing specific tasks or implementing specific functions. These programs or instructions are designed to be executed by a processor to implement a series of predefined steps or operations. The program product may be stored in various forms of computer storage media, such as memory, hard disk, solid-state drive, optical disk or other forms of digital storage devices. It may exist in the form of compiled binary code or in the form of scripts or bytecodes that can be executed by an interpreter. The program product uses carefully designed algorithms and logical instructions to enable the processor to process data in a specific order and manner to complete various functions such as data analysis, user interaction, device control, etc.

[0170] In the description of this specification, the description with reference to the terms "one embodiment / method", "some embodiments / methods", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment / method or example are included in at least one embodiment / method or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment / method or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments / methods or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments / methods or examples described in this specification and the features of the different embodiments / methods or examples, unless they are contradictory.

[0171] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0172] It should be understood by those skilled in the art that the above embodiments are only for the purpose of clearly illustrating the present invention, and are not intended to limit the scope of the present invention. For those skilled in the art, other changes or modifications may be made based on the above invention, and these changes or modifications are still within the scope of the present invention.

Claims

1. A method for planning temporary roads for transmission tower construction, characterized in that: include: Based on the geographical location information of the towers, the towers are grouped using a clustering algorithm to obtain multiple tower clusters, and a Steiner point is determined in each tower cluster; Use path planning algorithms to plan optimal paths between tower locations, between tower locations and Steiner points, and between Steiner points. Combined with the road network optimization algorithm, the optimal road network solution including or excluding Steiner points is searched, and the optimal road network solution is connected with the existing roads to obtain the optimal temporary road planning.

2. A method for planning temporary roads for transmission tower construction according to claim 1, characterized in that: Methods for grouping tower locations using clustering algorithms include: Obtain the geographical location information of the tower and build a constrained K-means clustering algorithm model; The objective function is to minimize the sum of squared Euclidean distances from each tower to its nearest cluster center, and the constraints are determined: each cluster contains at least the minimum number of towers; each cluster contains at most the maximum number of towers; each tower can only be assigned to one cluster; The transformation optimization problem is a minimum cost flow problem, in which tower locations are defined as supply nodes and cluster centers as demand nodes. Artificial nodes are introduced to construct the flow and cost models of nodes and directed arcs. Using the minimum cost flow problem solver, we can obtain the tower allocation results and complete the clustering. Calculate the center of each cluster according to the allocation results and update the cluster center position; The above tower position allocation process and cluster center update process are iterated repeatedly until the cluster center position is stable and the tower position clustering is completed.

3. A method for planning temporary roads for transmission tower construction according to claim 2, characterized in that: Methods for determining the Steiner point include: Obtain the location information of all towers in the tower cluster and use it as input data for calculating the Steiner point; Get the number of towers in the tower cluster; if the number of towers is less than 5, directly calculate the position of the Steiner point by geometric method; If the number is not less than 5, construct a connection network between the towers and calculate the lengths of all possible Steiner trees; introduce virtual auxiliary points between the towers as candidate Steiner point locations; select the auxiliary points that can minimize the length of the connection path through an optimization algorithm and determine them as Steiner points.

4. A method for planning temporary roads for transmission tower construction according to claim 1, characterized in that: Methods for planning the optimal path using path planning algorithms include: Obtain geographic information model, tower location and Steiner point location, and store the information using raster method; Each grid is defined as a node, each node contains coordinates, elevation and allowed turning angle range; edges are defined between tower sites and Steiner points, between tower sites and tower sites, and between Steiner points and Steiner points, and the edges contain distance, slope, turning angle and comprehensive weight; Establish a total cost function consisting of fixed cost and estimated cost; the fixed cost includes basic distance cost, slope cost and turning cost; the estimated cost is the estimated cost from the current node to the target node, including slope and turning angle; Define the starting and ending points of the path and set boundary conditions; build an open list and a closed list; the open list is used to store the nodes to be evaluated, initially only containing the starting point; the closed list records the evaluated nodes; Select the node with the lowest cost from the open list, search its adjacent nodes, select nodes that meet the turning angle and slope restrictions, and update the cost value of the node; if the adjacent node has not been evaluated, add it to the open list; when the target node is updated, the algorithm terminates; trace back the parent node pointer of the target node to the starting node, and use Bezier curve or B-spline algorithm to smooth the path; Output the minimum cost path linear design between two points as the optimal path.

5. A method for planning temporary roads for transmission tower construction according to claim 1, characterized in that: Methods for searching for optimal road network solutions with or without Steiner points in combination with road network optimization algorithms include: Put the tower points into the necessarily connected set P, and put the Steiner points into the connectable set Q, where Q contains n elements; Each time a candidate network is searched, a node set C is constructed according to the following rules: all points in the must-connect set P are put into C, and then i points are randomly selected from the connectable set Q and added to C to obtain a node set C, where i = 1, 2, ..., n, and there are 2 n Combination of Based on the generated node set C, all possible temporary roads are considered as edges connecting the tower positions to construct a candidate road network. The edge weights include material transportation cost, road construction cost, road restoration cost, and loading and unloading cost. Using the road network optimization algorithm, calculate the total cost of multiple candidate road network solutions with or without Steiner points; The road network plan with the lowest total cost is taken as the optimal road network plan.

6. A method for planning temporary roads for transmission tower construction according to claim 5, characterized in that: The road network optimization algorithm used is the A* algorithm, and the methods include: Construct a weighted connected graph of possible road networks based on Steiner points and a weighted connected graph of possible road networks without Steiner points; the weights of the edges include material transportation cost, road construction cost, road restoration cost, and loading and unloading cost; Select a tower node in the weighted connected graph as the starting point, and initialize the vertex set and edge set of the minimized spanning tree; Select the edge with the smallest current weight in the priority queue and add the two nodes connected to it to the minimum spanning tree; update the edge weights of the unconnected nodes related to the newly connected node and add them to the priority queue; Repeat the process of selecting the minimum edge and updating the nodes until all tower nodes are connected and a complete minimum spanning tree is generated; The candidate road network solutions corresponding to the multiple weighted connected graphs are obtained cyclically, and the total cost of each road network solution is calculated.

7. A method for planning temporary roads for transmission tower construction according to claim 6, characterized in that: Methods for connecting the optimal road network solution with existing roads include: According to the grid division method, the optimal road network solution is discretized into the starting point set A, and the existing roads are discretized into the end point set B; Take all the points in the starting point set A as the starting points and all the points in the ending point set B as the ending points, and generate all possible combinations of the starting points and the ending points; Use the A* algorithm to calculate the minimum cost path between any pair of start points and end points in the start point set A and the end point set B; generate a path set R; The path with the lowest total cost is selected from the path set R as the temporary road connecting the optimal road network solution and the existing roads to obtain the optimal temporary road plan.

8. A temporary road planning terminal for transmission tower construction, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, a temporary road planning method for transmission tower construction is implemented as described in any one of claims 1-7.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a temporary road planning method for transmission tower construction is implemented as described in any one of claims 1-7.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, a temporary road planning method for transmission tower construction as described in any one of claims 1-7 is implemented.

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