Optimization method for temporary construction road network layout of power transmission line

Abstract

The invention relates to the field of aided design of construction roads, and discloses a power transmission line temporary construction road network layout optimization method, which comprises the following steps: obtaining a temporary road line shape communicating all towers and an existing road, and a total excavation amount and a total length corresponding to the temporary road line shape; all towers and existing roads are used as nodes, and the sum of the excavation cost corresponding to the total excavation amount between the nodes and the cost related to the total length is used as an edge of the weighted directed graph; an optimization model is constructed, and the optimization model comprises an objective function which minimizes the sum of road network construction cost, recovery cost and tower material transportation cost, and the connectivity of a tower and an existing road and the connectivity of a road network are used as constraint conditions; and solving the optimization model through a genetic algorithm so as to obtain an optimal temporary road network planning scheme. According to the invention, a scientific and efficient cost calculation mode is designed for guiding subsequent engineering construction.

Description

Technical Field

[0001] The invention relates to the field of auxiliary design of construction roads, and in particular to a method for optimizing the network layout of temporary construction roads for power transmission lines. Background Art

[0002] For a long time, the traditional method of overhead power transmission line construction has been "manpower-based, machinery-assisted". This method mainly relies on manpower to transport construction materials, so there are certain limitations on transportation volume and speed. This traditional high-voltage transmission line construction method has problems such as long construction period, high cost and low technical content. It is particularly important to maximize the use of existing roads according to the characteristics of the project and local conditions, and reduce the damage to the terrain and environment caused by temporary road construction while meeting the geological, topographical, transportation vehicle and large machinery driving conditions of the line construction.

[0003] The different planning methods of temporary road networks between multiple towers and existing roads will result in differences in the specific paths and total lengths of the road network lines. The construction cost, restoration cost and material transportation cost of the entire road network will also be different, as follows Figure 2 As shown, the first option (a) where there may be a temporary road network is more economical, while the second option (b) is more conducive to the transportation of materials required for the tower.

[0004] Different people may have different opinions on which of these two options to choose. Therefore, it is urgent to design an optimization method for road network layout to screen the road network layout from an objective perspective. 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, it is impossible to consider the construction cost, restoration cost and transportation cost of required materials from the overall perspective of road network planning, lacks a scientific and efficient cost calculation method, and cannot be used to guide subsequent engineering construction, the present invention provides an optimization method for the temporary construction road network layout of power transmission lines. According to a weighted directed graph containing information such as temporary road linear design, excavation volume, line length, etc., temporary road linear designs with a reasonable range of cost and efficiency are screened, and a temporary road network with the minimum cost that meets the constraints is found from all temporary road linear shapes.

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

[0007] A method for optimizing the layout of a temporary construction road network for a power transmission line, comprising:

[0008] Acquire a temporary road alignment connecting all pole towers and existing roads, as well as a total excavation volume and a total length corresponding to the temporary road alignment, to form a weighted directed graph with all pole towers and existing roads as nodes and the sum of the excavation cost corresponding to the total excavation volume between the nodes and the cost related to the total length as an edge;

[0009] Constructing an optimization model, wherein the optimization model includes an objective function of minimizing the sum of road network construction cost, restoration cost and tower material transportation cost, and taking the connectivity of the tower with existing roads and the connectivity of the road network as constraints;

[0010] The optimization model is solved by a genetic algorithm to obtain the optimal temporary road network planning solution.

[0011] As an optimization, the road network construction cost includes the excavation cost and pavement reinforcement cost of the temporary road alignment, the restoration cost is proportional to the total length of the temporary road alignment, and the transportation cost includes the loading and unloading cost at the nodes and the transportation cost between nodes.

[0012] As an optimization, the objective function is specifically:

[0013]

[0014] Among them, x ij E ij U E represents the road network construction cost, x ij L ij U R represents the recovery cost, z jk m jk is the loading and unloading cost, y jk m jk D 0j is the transportation cost between nodes; E ij is the earthwork volume between node i and node j, L ij is the length between node i and node j, U E , U R is the unit cost of excavation and road restoration; K is the number of materials required for the tower, z jk ,y jk are the unit loading and unloading cost and transportation cost of material k required by node j, m jk is the mass of material k required for node j; x ij represents the connectivity between node i and node j, x ij =1 indicates the connectivity between node i and node j, x ij =0 indicates that node i and node j are not connected, n is the number of nodes, i=0, j=0 indicates that node i and node j are existing roads, and n is the total number of nodes.

[0015] As an optimization, the constraints are specifically:

[0016]

[0017] m jk ≥0;

[0018] D 0j ≥0.

[0019] As an optimization, the specific process of solving the optimization model by genetic algorithm is as follows:

[0020] A1. Binary encoding is performed on the connected relationships between nodes to obtain a coding matrix of the individual to be solved, wherein the individual to be solved is a connection matrix formed by the connected relationships between all nodes;

[0021] A2. Based on the road network construction cost, a minimum spanning tree algorithm is used under the constraint conditions to generate an initial population formed by a number of the individuals to be solved, and the initial population is set as the parent population, and the individuals to be solved in the parent population are set as the parent individuals. At the same time, the number of iterations of the genetic algorithm and the number of individuals to be solved in each generation of the population are set;

[0022] A3. Calculate the total cost according to the code value of each parent individual of the parent population, where the total cost is the sum of the road network construction cost, restoration cost and tower material transportation cost, thereby obtaining a calculation result;

[0023] A4. Calculate a fitness function according to the calculation result, and calculate and sort the fitness value of each parent individual in the parent population according to the fitness function;

[0024] A5, save the first M parent individuals with the largest fitness values ​​in the parent population, select parent individuals from all parent individuals except the first M parent individuals with the largest fitness values ​​through roulette wheel to perform crossover mutation operations to obtain child individuals, calculate the fitness values ​​of the child individuals after crossover mutation and sort them, reinsert the child individuals into the parent population according to the fitness values, select a set number of individuals to be solved to form a new parent population, and then return to A3;

[0025] A6. Repeat A3-A5 until the number of iterations is reached or the objective function value is within the specified threshold range. The parent individual with the largest fitness value in the parent population is the optimal temporary road network planning solution.

[0026] As an optimization, if two temporary roads appear between adjacent nodes in the optimal temporary road network planning scheme, one of the temporary roads is removed under the constraint condition.

[0027] The present invention also discloses an optimization system for the layout of a temporary construction road network for a power transmission line, comprising:

[0028] A weighted graph construction module is used to obtain the temporary road alignment connecting all the pole towers and the existing roads, as well as the total excavation volume and total length corresponding to the temporary road alignment, so as to form a weighted directed graph with all the pole towers and the existing roads as nodes and the excavation cost corresponding to the total excavation volume between the nodes and the total length as the sum of the costs related to the edges;

[0029] A modeling module, for constructing an optimization model, wherein the optimization model includes an objective function of minimizing the sum of road network construction cost, restoration cost and tower material transportation cost, and takes the connectivity of the tower with existing roads and the connectivity of the road network as constraints;

[0030] The solution module is used to solve the optimization model through a genetic algorithm to obtain the optimal temporary road network planning solution.

[0031] As an optimization, the specific solution process of the solution module is:

[0032] A1. Binary encoding is performed on the connected relationships between nodes to obtain a coding matrix of the individual to be solved, wherein the individual to be solved is a connection matrix formed by the connected relationships between all nodes;

[0033] A2. Based on the road network construction cost, a minimum spanning tree algorithm is used under the constraint conditions to generate an initial population formed by a number of the individuals to be solved, and the initial population is set as the parent population, and the individuals to be solved in the parent population are set as the parent individuals. At the same time, the number of iterations of the genetic algorithm and the number of individuals to be solved in each generation of the population are set;

[0034] A3. Calculate the total cost according to the code value of each parent individual of the parent population, where the total cost is the sum of the road network construction cost, restoration cost and tower material transportation cost, thereby obtaining a calculation result;

[0035] A4. Calculate a fitness function according to the calculation result, and calculate and sort the fitness value of each parent individual in the parent population according to the fitness function;

[0036] A5, save the first M parent individuals with the largest fitness values ​​in the parent population, select parent individuals from all parent individuals except the first M parent individuals with the largest fitness values ​​through roulette wheel to perform crossover mutation operations to obtain child individuals, calculate the fitness values ​​of the child individuals after crossover mutation and sort them, reinsert the child individuals into the parent population according to the fitness values, select a set number of individuals to be solved to form a new parent population, and then return to A3;

[0037] A6. Repeat A3-A5 until the number of iterations is reached or the objective function value is within the specified threshold range. The parent individual with the largest fitness value in the parent population is the optimal temporary road network planning solution.

[0038] The present invention also discloses an electronic device, comprising at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for optimizing the layout of a temporary construction road network for a transmission line as described above.

[0039] The present invention also discloses a storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the aforementioned method for optimizing the layout of a temporary construction road network for a power transmission line is implemented.

[0040] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0041] The present invention considers the construction cost, restoration cost and transportation cost of required materials from the overall perspective of road network planning, and designs a scientific and efficient cost calculation method, which can be used to guide subsequent engineering construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0043] Figure 1 It is a flow chart of a method for optimizing the layout of a temporary construction road network for a power transmission line according to the present invention;

[0044] Figure 2 Schematic diagram of different transportation schemes in the background technology;

[0045] Figure 3 Design directed graphs for temporary road network planning;

[0046] Figure 4 It is a schematic diagram of the road network layout scheme and its corresponding coding matrix;

[0047] Figure 5 Schematic diagram of temporary road alignment with intersections

[0048] Figure 6 A schematic diagram of a temporary road alignment in an embodiment;

[0049] Figure 7 It is the trend diagram of the final temporary road network in the embodiment. DETAILED DESCRIPTION

[0050] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0051] This embodiment 1 provides a method for optimizing the layout of a temporary construction road network for a transmission line. Figure 1 As shown, including:

[0052] S1. Obtain the temporary road alignment connecting all pole towers and existing roads, as well as the total excavation volume and total length corresponding to the temporary road alignment, to form a weighted directed graph with all pole towers and existing roads as nodes and the sum of the excavation cost corresponding to the total excavation volume between the nodes and the cost related to the total length as an edge;

[0053] Here, the costs related to length are pavement strengthening cost, restoration cost and transportation cost.

[0054] The input contains the cut quantity and line length information for all temporary road alignments, which are stored in a weighted directed graph.

[0055] In the directed graph G = (V, E), V = {V0, V1, ..., V i ,…,V n}, is the set of all nodes, E is the set of directed edges, E={(V i , V)|i=0,1,…,n;j=0,1,…,n;i≠j}. The present invention defines V0 to represent the existing road, V1~V n The node represents the tower, (V i ,V j ) is the two end points V i and V j The directed edges of (V0, V1) indicate that there is a temporary road alignment that satisfies the constraints between the existing road and tower 1, and (V1, V2) indicate that there is a temporary road alignment that satisfies the constraints between tower 1 and tower 2. W and L are the excavation volume and line length of the temporary road alignment, respectively. For example, W 01 ,L 01 are the total excavation volume and total length of the temporary road alignment between tower 1 and the existing road. The process of road network optimization is as follows Figure 3 shown.

[0056] In some embodiments, the road network construction cost includes the excavation cost and pavement reinforcement cost of the temporary road alignment, the restoration cost is proportional to the total length of the temporary road alignment, and the transportation cost includes the loading and unloading cost at the node and the transportation cost between nodes.

[0057] Consider the cost of mechanical excavation of the road surface (earthwork cost), the cost of restoration of the temporary road network and the transportation cost of materials. Among them, the transportation cost includes the loading, unloading, transportation and return of materials required at each node, and construct the objective function. The present invention only considers automobile transportation as a mode of transportation.

[0058] The objective function includes: ① Road network construction cost: considering the excavation cost and pavement reinforcement cost of the temporary road line; ② Road network restoration cost: related to the total length of the road network; ③ Tower material transportation cost: considering the type, weight and transportation distance of the material. The transportation distance calculation method is to assume that the planned linear length between the tower and the existing road, and between the tower and the tower is the corresponding transportation distance. Specifically, S2, construct an optimization model, the optimization model includes an objective function to minimize the sum of the road network construction cost, restoration cost and tower material transportation cost, and the connectivity between the tower and the existing road and the connectivity of the road network as constraints; reasonably simplify the model according to actual needs, set constraints and objective functions, and convert the problem into a mathematical model to solve the problem.

[0059] Objective function construction. For the optimization of temporary road network layout, the present invention mainly considers the road mechanical excavation cost (earthwork cost), restoration cost and material transportation cost. Among them, the transportation cost includes the loading, unloading, transportation and return of materials required at each node. The present invention only considers automobile transportation as a mode of transportation.

[0060] In some embodiments, the objective function is specifically:

[0061]

[0062] Among them, x ij E ij U E represents the road network construction cost, x ij L ij U R represents the recovery cost, z jk m jk is the loading and unloading cost, y jk m jk D 0j is the transportation cost between nodes; E ij is the earthwork volume between node i and node j / (m 3 ), L ij is the length between node i and node j / (m), U E , U R is the unit cost of excavation and road restoration; K is the number of materials required for the tower, z jk ,y jk are the unit loading and unloading cost (t) and transportation cost (t·m -1 ), mjk is the mass of material k required for node j; x ij represents the connectivity between node i and node j, x ij =1 indicates the connectivity between node i and node j, x ij =0 indicates that node i and node j are not connected, n is the number of nodes, i=0, j=0 indicates that node i and node j are existing roads, and n is the total number of nodes.

[0063]

[0064] The constraints are specifically:

[0065]

[0066] m jk ≥0#(5);

[0067] D 0j ≥0#(6).

[0068] Formula (3) indicates that all poles or temporary roads have at least one node connected to them. Formula (4) indicates that no independent subsets are generated, that is, any subsets are connected. Formulas (5) and (6) require that the material mass required by the pole and the shortest distance from the pole to the existing road are non-negative, respectively.

[0069] S represents a subset of the directed graph, A represents the edge in the directed graph, that is, the edge from the i-th node to the j-th node in the directed graph, and D represents the directed graph.

[0070] S3. Solve the optimization model through genetic algorithm to obtain the optimal temporary road network planning scheme. When the genetic algorithm determines the road network layout, it mainly considers the temporary road network construction cost, restoration cost and material transportation cost, and does not consider whether there is an intersection between the lines of different nodes. The algorithm flow has been introduced in detail in the previous article, and only the specific implementation principle is introduced here.

[0071] The road network layout determined by genetic algorithms mainly considers the construction cost, restoration cost and material transportation cost of the temporary road network, and does not consider whether there are intersections between the lines of different nodes.

[0072] Specifically include:

[0073] Encoding and decoding method. Use binary encoding method to encode and decode chromosomes; here, Figure 4 As shown in (b), a chromosome (individual) in the genetic algorithm is a coding matrix, and a gene in the chromosome can be understood as a column or a row in the coding matrix.

[0074] Initialize the population. Use the minimum spanning tree (MST) algorithm to generate a temporary road network connection method based only on the weight of the temporary road linear construction cost;

[0075] Fitness function. The fitness function is constructed based on the total cost of the road network construction cost, restoration cost and transportation cost of the tower materials.

[0076] Selection of genetic operators. Selection operator: use tournament selection operator; Crossover operator: use sequential crossover method; Mutation operator: define edge shift mutation operator, edge addition mutation operator and edge subtraction mutation operator; Check and repair operator: ensure the connectivity of the road network.

[0077] When performing mutation, if a node (tower) is an isolated node, that is, it has no connection with any node in the road network, then the check and repair operator will connect the node to any node. For example, if T5 is an isolated node, then Figure 4 In the matrix (b), the last row and the last column are all 0s. Then, the check repair operator will change any 0 in the last row to 1. At the same time, the 0 at the corresponding position in the last column will also be changed to 1. In this way, the isolated node is connected to the corresponding node.

[0078] Elite retention strategy. Sort the population chromosomes in descending order according to their fitness, and select new populations to enter the next step;

[0079] Termination condition: The termination condition is to reach the evolutionary generation.

[0080] The specific process is:

[0081] A1. Binary encoding is performed on the connected relationships between nodes to obtain a coding matrix of the individual to be solved, wherein the individual to be solved is a connection matrix formed by the connected relationships between all nodes;

[0082] Chromosome encoding and decoding. Using the binary encoding and decoding method, here we take an existing road and five towers as an example to give a feasible connection method, such as Figure 4 (a). The specific encoding method is shown in Figure 4 (b) is a symmetric matrix. The upper and lower matrices represent the same road network connection mode. The decoding operation is the inverse process of the encoding operation, that is, the process of mapping the chromosome encoding vector to a feasible solution that satisfies all constraints. Figure 4 In (b), Road represents the existing road, T1-Tn represents towers 1-n, therefore, the 1 in the 2nd and 4th columns of the 1st row represents the edge connecting towers 1 and 4 with the existing road, the 1 in the 3rd column of the 2nd row represents the edge connecting towers 1 and 2, and so on.

[0083] A2. Based on the road network construction cost, a minimum spanning tree algorithm is used under the constraint conditions to generate an initial population formed by a number of the individuals to be solved, and the initial population is set as the parent population, and the individuals to be solved in the parent population are set as the parent individuals. At the same time, the number of iterations of the genetic algorithm and the number of individuals to be solved in each generation of the population are set;

[0084] Through the minimum spanning tree (MST) algorithm, a temporary road network connection method is generated according to the weight of the temporary road linear construction cost without considering the temporary road network restoration cost and material transportation cost.

[0085] The minimum spanning tree (MST) algorithm is an important problem in graph theory. It is used to find a tree in a weighted connected graph that contains all nodes and has the smallest sum of edge weights. MST is often used to solve problems such as network design, power transmission, and road planning.

[0086] A3. Calculate the total cost according to the code value of each parent individual of the parent population, where the total cost is the sum of the road network construction cost, restoration cost and tower material transportation cost, thereby obtaining a calculation result;

[0087] A4. Calculate a fitness function according to the calculation result, and calculate and sort the fitness value of each parent individual in the parent population according to the fitness function;

[0088] Fitness function setting. Consider the excavation cost and pavement reinforcement cost of the temporary road line shape. To facilitate construction calculation, the unit excavation price, unit length pavement reinforcement cost and unit length pavement restoration cost can be determined according to the actual situation of the planned area. The fitness function can be

[0089] Materials for building towers include concrete, arm support, cement, and sand and gravel. Different tower positions play different roles in supporting transmission lines, so the weight of materials required for each tower is different. According to the "Electric Power Construction Project Budget Quota", the present invention determines the base price for loading and unloading and transportation of materials of different types and weights, and further calculates the transportation cost of materials required for each tower.

[0090] A5, save the first M parent individuals with the largest fitness values ​​in the parent population, select parent individuals from all parent individuals except the first M parent individuals with the largest fitness values ​​through roulette wheel to perform crossover mutation operations to obtain child individuals, calculate the fitness values ​​of the child individuals after crossover mutation and sort them, reinsert the child individuals into the parent population according to the fitness values, select a set number of individuals to be solved to form a new parent population, and then return to A3;

[0091] Selection of genetic operation operators: including selection operator, crossover operator, mutation operator and check-and-repair operator.

[0092] Elite retention strategy. After the crossover and mutation operations, in order to keep the population size consistent with the initial population, the population chromosomes are sorted in descending order according to their fitness, and then new populations with the same size as the initial population are selected one by one.

[0093] A6. Repeat A3-A5 until the number of iterations is reached or the objective function value is within the specified threshold range. The parent individual with the largest fitness value in the parent population is the optimal temporary road network planning solution.

[0094] Termination condition: The present invention pre-sets the evolutionary number to effectively control the running time and solution accuracy of the algorithm, and adopts the maximum number of iterations determined in advance as the termination rule, that is, to determine whether the number of evolutionary generations is the required number of generations. If so, the evolution is stopped, and the road network connection layout corresponding to the chromosome with the best performance is selected as the optimal solution output of the problem. Otherwise, the evolutionary operation continues.

[0095] If two temporary roads appear between adjacent nodes in the optimal temporary road network planning solution, one of the temporary roads is removed under the constraint condition. The specific temporary road to be removed can be selected according to the actual situation, and the temporary road with a higher total cost is usually selected.

[0096] For the possible intersecting loop paths that may appear when there are a large number of towers and the distance between adjacent towers is close, one of the loop edges can be deleted. On the basis of ensuring connectivity, the total excavation volume and total length of the entire temporary road network can be reduced, such as Figure 5 shown.

[0097] That is, the route loop is eliminated: in order to overcome the elevation difference, the temporary road line shape adopts a zigzag shape to climb the slope, which may cause intersections between different line shapes. Figure 5 As shown in the figure, the temporary road alignment between tower 2 and tower 3 and the temporary road alignment between tower 3 and tower 4 have an intersection point A near tower 3. This intersection point forms a ring with tower 3, and the two sides of the ring are represented by l1 and l2 respectively. This is not allowed in the temporary road network design, and the alignment l1 or alignment l2 needs to be removed.

[0098] In summary, the present invention considers automobile transportation, combines the above constraints to perform mathematical modeling to obtain an optimization model, and uses a genetic algorithm to solve the optimization model. The genetic algorithm uses a binary method for encoding and decoding, uses the MST algorithm to generate a temporary road network connection method as the initial population, constructs a fitness function based on the total cost, designs the required genetic operators, adopts an elite retention strategy and sets termination conditions for the solution process, and uses the route loop elimination operation to further optimize the road network. Finally, a temporary road network with the minimum total cost that meets the constraints is formed.

[0099] Next, the method of the present invention is described using specific data.

[0100] Data input.

[0101] Input the excavation volume and length of the temporary road alignment between the towers and the towers, and between the towers and the existing roads in the area, as shown in Table 2. The comprehensive geographic information in the study area includes the location information of the 11 towers, existing roads, water areas, forestry and other special areas. Figure 7 Among them, the pink border indicates economic areas such as forestry, where the economic value of crops in the two areas is different, and the dark red border indicates impassable areas such as water areas. Enter the temporary road alignments between towers (T1-T11) and towers, and between towers and existing roads, a total of 66 temporary road alignments, such as Figure 6 shown.

[0102] 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 genetic algorithm, the minimum cost temporary road network that meets the tower connectivity is selected from 66 temporary road lines. The materials required for each tower are shown in Table 3. The final temporary road network is as follows Figure 6 Shown in red is the temporary road network.

[0103] Table 2 Excavation volume and length of temporary road alignment

[0104]

[0105] Table 3 Types and weights of materials required for each tower

[0106]

[0107] At the same time, without considering special areas, the genetic algorithm is used to solve the temporary road network between the tower and the existing road in the same construction area. The final result is as follows: Figure 7 The specific connection mode of the temporary road network with and without considering special areas is shown in Table 4 (0 in the table represents the existing road, and 1-11 represents the tower 1-11).

[0108] Table 4 Road network layout connections with and without considering special areas

[0109]

[0110] The main difference between the road network layout connection in the two cases lies in the connection method of the pole towers connecting the existing road and the pole tower 5. Compared with the blue temporary road network, due to the existence of water areas, the temporary road connecting the pole tower 3 and the existing road needs to avoid the water area, resulting in an increase in line length and excavation volume, so the total cost is greatly increased. At the same time, the temporary road alignment of the pole tower 5 and the pole tower 10 passes through the economic area, which requires compensation, and its construction and restoration costs are also increased accordingly. Therefore, the red road network considering special areas no longer contains the temporary road alignment of the pole tower 3 and the existing road, the pole tower 5 and the pole tower 10, and in order to ensure the connectivity of the pole towers, it becomes the pole tower 2 connected to the existing road, and the pole tower 5 is connected to the pole tower 6. In the final road network, the red line network avoids the water area, and the linear length in the forestry area is shorter.

[0111] Embodiment 2 also discloses a system for optimizing the layout of a temporary construction road network for a power transmission line, comprising:

[0112] A weighted graph construction module is used to obtain the temporary road alignment connecting all the poles and existing roads, as well as the total excavation volume and total length corresponding to the temporary road alignment, so as to form a weighted directed graph with all the poles and existing roads as nodes and the sum of the excavation cost corresponding to the total excavation volume between the nodes and the cost related to the total length as an edge;

[0113] A modeling module, for constructing an optimization model, wherein the optimization model includes an objective function of minimizing the sum of road network construction cost, restoration cost and tower material transportation cost, and takes the connectivity of the tower with existing roads and the connectivity of the road network as constraints;

[0114] The solution module is used to solve the optimization model through a genetic algorithm to obtain the optimal temporary road network planning solution.

[0115] In some embodiments, the specific solution process of the solution module is:

[0116] A1. Binary encoding is performed on the connected relationships between nodes to obtain a coding matrix of the individual to be solved, wherein the individual to be solved is a connection matrix formed by the connected relationships between all nodes;

[0117] A2. Based on the road network construction cost, a minimum spanning tree algorithm is used under the constraint conditions to generate an initial population formed by a number of the individuals to be solved, and the initial population is set as the parent population, and the individuals to be solved in the parent population are set as the parent individuals. At the same time, the number of iterations of the genetic algorithm and the number of individuals to be solved in each generation of the population are set;

[0118] A3. Calculate the total cost according to the code value of each parent individual of the parent population, where the total cost is the sum of the road network construction cost, restoration cost and tower material transportation cost, thereby obtaining a calculation result;

[0119] A4. Calculate a fitness function according to the calculation result, and calculate and sort the fitness value of each parent individual in the parent population according to the fitness function;

[0120] A5, save the first M parent individuals with the largest fitness values ​​in the parent population, select parent individuals from all parent individuals except the first M parent individuals with the largest fitness values ​​through roulette wheel to perform crossover mutation operations to obtain child individuals, calculate the fitness values ​​of the child individuals after crossover mutation and sort them, reinsert the child individuals into the parent population according to the fitness values, select a set number of individuals to be solved to form a new parent population, and then return to A3;

[0121] A6. Repeat A3-A5 until the number of iterations is reached or the objective function value is within the specified threshold range. The parent individual with the largest fitness value in the parent population is the optimal temporary road network planning solution.

[0122] Example 3 also discloses an electronic device, comprising at least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for optimizing the layout of a temporary construction road network for a transmission line as described in Example 1.

[0123] Embodiment 4 further discloses a storage medium storing a computer program, which, when executed by a processor, implements a method for optimizing the layout of a temporary construction road network for a transmission line as described in Embodiment 1.

[0124] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing the layout of a temporary construction road network for a power transmission line, characterized in that: include: Acquire a temporary road alignment connecting all pole towers and existing roads, as well as a total excavation volume and a total length corresponding to the temporary road alignment, to form a weighted directed graph with all pole towers and existing roads as nodes and the sum of the excavation cost corresponding to the total excavation volume between the nodes and the cost related to the total length as an edge; Constructing an optimization model, wherein the optimization model includes an objective function of minimizing the sum of road network construction cost, restoration cost and tower material transportation cost, and taking the connectivity of the tower with existing roads and the connectivity of the road network as constraints; The optimization model is solved by a genetic algorithm to obtain the optimal temporary road network planning solution.

2. The method for optimizing the layout of a temporary construction road network for a power transmission line according to claim 1, characterized in that: The road network construction cost includes the excavation cost and pavement reinforcement cost of the temporary road alignment, the restoration cost is proportional to the total length of the temporary road alignment, and the transportation cost includes the loading and unloading cost at the nodes and the transportation cost between nodes.

3. The method for optimizing the layout of a temporary construction road network for a power transmission line according to claim 1, characterized in that: The objective function is specifically: Among them, x ij E ij U E represents the road network construction cost, x ij L ij U R represents the recovery cost, z jk m jk is the loading and unloading cost, y jk m jk D 0j is the transportation cost between nodes; E ij is the earthwork volume between node i and node j, L ij is the length between node i and node j, U E , U R is the unit cost of excavation and road restoration; K is the number of materials required for the tower, z jk ,y jk are the unit loading and unloading cost and transportation cost of material k required by node j, m jk is the mass of material k required for node j; x ij represents the connectivity between node i and node j, x ij =1 indicates the connectivity between node i and node j, x ij =0 means that node i and node j are not connected, n is the number of nodes, i=0, j=0 means that node i and node j are existing roads; D 0j represents the total distance between the existing road and the jth node, and n is the total number of nodes.

4. The method for optimizing the layout of a temporary construction road network for a power transmission line according to claim 3, characterized in that: The constraints are specifically: m jk ≥0； D 0j ≥0。 5. The method for optimizing the layout of a temporary construction road network for a power transmission line according to claim 4, characterized in that: The specific process of solving the optimization model by adaptive genetic algorithm is as follows: A1. Binary encoding is performed on the connected relationships between nodes to obtain a coding matrix of the individual to be solved, wherein the individual to be solved is a connection matrix formed by the connected relationships between all nodes; A2. Based on the road network construction cost, a minimum spanning tree algorithm is used under the constraint conditions to generate an individual to be solved with the minimum road network construction cost, and the individual to be solved is mutated under the constraint conditions to obtain an initial population formed by a number of the individuals to be solved, and the initial population is set as the parent population, and the individuals to be solved in the parent population are set as the parent individuals. At the same time, the number of iterations of the genetic algorithm and the number of individuals to be solved in each generation of the population are set; A3. Calculate the total cost according to the code value of each parent individual of the parent population, where the total cost is the sum of the road network construction cost, restoration cost and tower material transportation cost, thereby obtaining a calculation result; A4. Calculate a fitness function according to the calculation result, and calculate and sort the fitness value of each parent individual in the parent population according to the fitness function; A5, save the first M parent individuals with the largest fitness values ​​in the parent population, select parent individuals from all parent individuals except the first M parent individuals with the largest fitness values ​​through roulette wheel to perform crossover mutation operations to obtain child individuals, calculate the fitness values ​​of the child individuals after crossover mutation and sort them, reinsert the child individuals into the parent population according to the fitness values, select a set number of individuals to be solved to form a new parent population, and then return to A3; A6. Repeat A3-A5 until the number of iterations is reached or the objective function value is within the specified threshold range. The parent individual with the largest fitness value in the parent population is the optimal temporary road network planning solution.

6. The optimization system for the temporary construction road network layout of a power transmission line according to claim 5, characterized in that: If two temporary roads appear between adjacent nodes in the optimal temporary road network planning scheme, one of the temporary roads is removed under constraint conditions.

7. An optimization system for the layout of temporary construction roads for power transmission lines, characterized in that: include: A weighted graph construction module is used to obtain the temporary road alignment connecting all the poles and existing roads, as well as the total excavation volume and total length corresponding to the temporary road alignment, so as to form a weighted directed graph with all the poles and existing roads as nodes and the sum of the excavation cost corresponding to the total excavation volume between the nodes and the cost related to the total length as an edge; A modeling module, for constructing an optimization model, wherein the optimization model includes an objective function of minimizing the sum of road network construction cost, restoration cost and tower material transportation cost, and takes the connectivity of the tower with existing roads and the connectivity of the road network as constraints; The solution module is used to solve the optimization model through a genetic algorithm to obtain the optimal temporary road network planning solution.

8. The method for optimizing the layout of temporary construction roads for power transmission lines according to claim 7, characterized in that: The specific solution process of the solution module is as follows: A1. Binary encoding is performed on the connected relationships between nodes to obtain a coding matrix of the individual to be solved, wherein the individual to be solved is a connection matrix formed by the connected relationships between all nodes; A2. Based on the road network construction cost, a minimum spanning tree algorithm is used under the constraint conditions to generate an initial population formed by a number of the individuals to be solved, and the initial population is set as the parent population, and the individuals to be solved in the parent population are set as the parent individuals. At the same time, the number of iterations of the genetic algorithm and the number of individuals to be solved in each generation of the population are set; A3. Calculate the total cost according to the code value of each parent individual of the parent population, where the total cost is the sum of the road network construction cost, restoration cost and tower material transportation cost, thereby obtaining a calculation result; A4. Calculate a fitness function according to the calculation result, and calculate and sort the fitness value of each parent individual in the parent population according to the fitness function; A5, save the first M parent individuals with the largest fitness values ​​in the parent population, select parent individuals from all parent individuals except the first M parent individuals with the largest fitness values ​​through roulette wheel to perform crossover mutation operations to obtain child individuals, calculate the fitness values ​​of the child individuals after crossover mutation and sort them, reinsert the child individuals into the parent population according to the fitness values, select a set number of individuals to be solved to form a new parent population, and then return to A3; A6. Repeat A3-A5 until the number of iterations is reached or the objective function value is within the specified threshold range. The parent individual with the largest fitness value in the parent population is the optimal temporary road network planning solution.

9. An electronic device, characterized in that: It includes at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for optimizing the layout of a temporary construction road network for a transmission line as described in any one of claims 1 to 6.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for optimizing the layout of a temporary construction road network for a power transmission line as described in any one of claims 1 to 6 is implemented.

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