Power transmission line path planning method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a power transmission line path planning method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining geographic information data of a power transmission line path planning region, carrying out the preprocessing of the geographic information data through a geographic information system, and constructing a grid cost surface considering influence factors; constructing an optimization model based on the grid cost surface according to the requirement of power transmission line path planning; solving the optimization model by using a heuristic search algorithm to obtain a preliminary path; further optimization processing is carried out based on the preliminary path, unnecessary corners and redundant paths are eliminated, and a target planning path is obtained; according to the method, a mathematical optimization model and a corresponding solution algorithm can be introduced, the optimal line path can be automatically generated on the basis of comprehensively considering various factors, the dependence on artificial experience is reduced, and the objectivity and scientificity of planning are improved, so that the efficiency and accuracy of high-voltage transmission line planning are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power survey and design, and in particular to a method, device, electronic equipment and storage medium for power transmission line path planning. Background Art

[0002] As a carrier of electricity, high-voltage transmission lines have the characteristics of coordinated development with the national economy and are an important part of adjusting the regional energy structure. With the continuous development of social economy, the demand for electricity in countries around the world, including China, is continuing to grow. Therefore, it is necessary to build a high-voltage transmission network that matches the growing demand for electricity. The construction of traditional high-voltage overhead transmission lines mainly relies on manual operations, which not only increases the safety risks of construction workers, but also limits construction efficiency. In order to reduce these risks, reduce labor intensity and improve the working environment, power grid construction is gradually transforming to mechanization, striving to establish a modern construction model with mechanical operations as the main and manpower as the auxiliary.

[0003] However, compared with traditional line construction methods, mechanized construction has more stringent requirements on temporary road construction, environmental protection measures, construction machinery working surface and other conditions. The determination of traditional overhead transmission line schemes usually requires first delineating the line on the map, and then conducting on-site investigation and planning, which requires a lot of time, manpower and material resources. With the development of computer technology, planners began to use computer software to assist in planning lines based on remote sensing images and elevation data, which significantly improved the efficiency and accuracy of line planning, but still required planners to perform a lot of calculations and evaluations on different schemes, including comprehensive considerations of terrain information, terrain conditions, environmental impacts and other aspects, and the workload is relatively large. At the same time, the line selection results are highly correlated with the experience and judgment ability of planners, making the line selection results highly subjective.

[0004] In view of this, developing an automated transmission line path planning method to reduce dependence on personal experience and improve the scientificity, efficiency and accuracy of planning has important practical value and broad application prospects. Summary of the invention

[0005] The main purpose of the present invention is to provide a transmission line path planning method, device, electronic device and storage medium to improve the efficiency and accuracy of high-voltage transmission line planning.

[0006] To achieve the above object, the present application provides a transmission line path planning method, the method comprising:

[0007] Obtaining geographic information data of the transmission line path planning area, preprocessing the geographic information data using a geographic information system, and constructing a grid cost surface that takes into account influencing factors;

[0008] According to the requirements of transmission line path planning, an optimization model is constructed based on the grid cost surface;

[0009] Using a heuristic search algorithm to solve the optimization model and obtain a preliminary path;

[0010] Further optimization processing is performed based on the preliminary path to eliminate unnecessary corners and redundant paths and obtain the target planned path.

[0011] Optionally, the geographic information data is preprocessed using a geographic information system, including but not limited to paper map vectorization, coordinate conversion, and data clipping;

[0012] The construction of the grid cost surface taking into account the influencing factors includes:

[0013] The path planning area after the preprocessing is rasterized, and each influencing factor is graded and assigned a cost value according to the severity of the impact of the influencing factor on the route planning;

[0014] The weights are assigned to different influencing factors through the hierarchical analysis method, and then the cost values ​​of the different influencing factors are multiplied by the corresponding weights and superimposed to obtain the comprehensive cost surface by adopting the weighted linear combination method.

[0015] Optionally, constructing an optimization model based on the grid cost surface according to the requirements of transmission line path planning includes:

[0016] According to the requirements of the transmission line path planning, a mathematical optimization model is established which comprehensively considers linear cost and corner cost, wherein the linear cost reflects geographical environment factors, and the corner cost reflects the impact of path corners on line costs.

[0017] Optionally, constructing an optimization model based on the grid cost surface according to the requirements of transmission line path planning includes:

[0018] Step 1: Set optimization goals:

[0019]

[0020] Among them, the linear cost function c:E→R + Map an edge in the graph G to a linear cost, corresponding to the comprehensive cost surface, the quadratic cost function q:E×E→R + A pair of elements in the graph G is mapped to a quadratic cost, which evaluates the impact of the angle between two adjacent edges on the line cost; for two adjacent edges ((i, j), (j, k))∈E×E, the quadratic cost can be described by the following formula:

[0021]

[0022] Among them, θ (i,j),(j,k) is the angle between the two adjacent sides, function is a piecewise function that maps angles to cost values;

[0023] Step 2: Set constraints:

[0024]

[0025] Among them, δ - (i) = {j∈V|(j,i)∈E}, δ + (i)={j∈V|(i,j)∈E} are the predecessor node and successor node of vertex i respectively.

[0026] Optionally, the using of a heuristic search algorithm to solve the optimization model and obtain a preliminary path includes:

[0027] Set the starting point s and the end point t, initialize the heuristic value and the cumulative cost;

[0028] Consider only the linear cost in the described problem, calculate the cumulative cost from other vertices in the graph to the end point, and store these cost values ​​as heuristic information for the A* algorithm forward search in the next step;

[0029] The A* algorithm is used to perform a forward search in the graph from the starting point to the end point. In each iteration, a new edge is selected based on the minimum value of the sum of the cumulative total cost from the preselected vertex to the starting point and the cumulative cost from the preselected vertex to the end point obtained in the previous step, thereby obtaining the path with the minimum cost to reach the end point.

[0030] Optionally, the further optimization process based on the preliminary path includes:

[0031] Considering the jagged distribution of paths caused by the limitation of neighborhood expansion mode, straight-line reachability detection is performed, and the line-of-sight algorithm is introduced to detect whether there is a prohibited area for construction of transmission lines between two nodes;

[0032] Based on the detection results, determine whether non-adjacent nodes in the path can be directly connected; if they can be directly connected and the total path cost of this connection method is smaller, then this connection method is adopted.

[0033] Optionally, the geographic information data of the transmission line path planning area includes but is not limited to terrain elevation, land use type, environmental protection area, transportation network, insulator contamination condition, icing condition, and earthquake grade condition of the transmission line path planning area;

[0034] The further optimization process includes straight line reachability detection and path smoothing.

[0035] On the other hand, the present application also provides a transmission line path planning device, comprising:

[0036] A data acquisition module is used to acquire geographic information data of the transmission line path planning area, including but not limited to digital elevation model (DEM), land use type, geological conditions and meteorological data. The data acquisition module is specifically used to pre-process the geographic information data using a geographic information system to construct a grid cost surface that comprehensively considers influencing factors;

[0037] A model building module, used to build an optimization model based on the grid cost surface according to the requirements of the transmission line path planning, and the model building module comprehensively considers the line length, project cost, and environmental impact factors to adjust the optimization target and weight;

[0038] A path search module, used to solve the optimization model using a heuristic search algorithm to obtain a preliminary path;

[0039] The path optimization module is used to perform further optimization processing based on the preliminary path, eliminate unnecessary corners and redundant paths, and obtain the final target planning path.

[0040] On the other hand, the present application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method as described in any one of the first aspects above.

[0041] On the other hand, the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the method as described in any one of the first aspects above.

[0042] The present application provides a transmission line path planning method, device, electronic device and storage medium. By acquiring geographic information data of the transmission line path planning area, the geographic information data is preprocessed by using a geographic information system to construct a grid cost surface that takes into account influencing factors; according to the requirements of the transmission line path planning, an optimization model is constructed based on the grid cost surface; a heuristic search algorithm is used to solve the optimization model to obtain a preliminary path; further optimization processing is performed based on the preliminary path to eliminate unnecessary corners and redundant paths to obtain a target planning path; the method automatically generates an optimal line path based on comprehensive consideration of multiple factors, reduces dependence on manual experience, improves the objectivity and scientificity of planning, and thus significantly improves the efficiency and accuracy of high-voltage transmission line planning, wherein, by combining the detailed geographic data of the geographic information system with mathematical optimization technology, not only the scientificity and accuracy of the path selection are improved, but also the large-scale geographic spatial data are effectively processed through the heuristic search algorithm, reducing the calculation time; in addition, the present application further optimizes the smoothness of the path through a path correction mechanism, so that the path planned by the computer is more in line with reality. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] in:

[0045] Figure 1 A schematic diagram of a flow chart of a transmission line path planning method provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of a linear cost calculation method provided in an embodiment of the present application;

[0047] Figure 3 A schematic diagram of a secondary cost provided in an embodiment of the present application;

[0048] Figure 4 A schematic diagram of comparison before and after path optimization provided in an embodiment of the present application;

[0049] Figure 5 A schematic diagram of the structure of a transmission line path planning device provided in an embodiment of the present application;

[0050] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0052] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0053] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0054] The present application aims to provide a transmission line path planning method, which mainly introduces a mathematical optimization model and a corresponding solution algorithm, and can automatically generate an optimal line path based on comprehensive consideration of multiple factors, thereby reducing dependence on manual experience, improving the objectivity and scientificity of planning, and thus significantly improving the efficiency and accuracy of high-voltage transmission line planning.

[0055] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0056] See also Figure 1 , is a flow chart of a transmission line path planning method provided in an embodiment of the present application. Figure 1 As shown, the method includes:

[0057] 101. Obtain geographic information data of the transmission line path planning area, use the geographic information system to pre-process the above geographic information data, and construct a raster cost surface that takes into account influencing factors.

[0058] The transmission line path planning method in the embodiment of the present application can be executed by a transmission line path planning device, which can be an electronic device, such as a terminal device, in a specific implementation.

[0059] First, detailed geographic information of the transmission line route planning area can be collected, including terrain elevation, land use type, environmental protection area, etc. Then, the data is preprocessed using the Geographic Information System (GIS) to construct a raster cost surface that comprehensively considers various influencing factors.

[0060] Optionally, the geographic information data of the above-mentioned transmission line path planning area may include, but is not limited to, terrain elevation data, land use type, environmental protection area, transportation network, insulator contamination conditions, icing conditions, and earthquake levels.

[0061] In an optional implementation, the geographic information data is preprocessed using a geographic information system, including but not limited to paper map vectorization, coordinate conversion, and data clipping;

[0062] The above construction takes into account the grid cost surface of the influencing factors, including:

[0063] The path planning area after the above preprocessing is rasterized, and each influencing factor is graded and assigned a cost value according to the severity of the impact of the influencing factor on the route planning;

[0064] The weights are assigned to different influencing factors through the hierarchical analysis method, and then the cost values ​​of the above-mentioned different influencing factors are multiplied by the corresponding weights and superimposed to obtain the comprehensive cost surface using the weighted linear combination method.

[0065] See also Figure 2 , Figure 2 A flow chart of a linear cost calculation method provided for an embodiment of the present application shows geographic information data of some transmission line path planning areas, such as slope, elevation difference, traffic, avoidance zone and other influencing factors. It mainly uses a weighted linear combination method based on the allocation weights of different influencing factors to superimpose the cost values ​​of different influencing factors to obtain a comprehensive cost value, thereby constructing a comprehensive cost surface related to environmental factors.

[0066] Specifically, geographic information processing software such as ArcGIS can be used to pre-process the collected data, including vectorizing paper maps, unifying coordinate systems, and clipping data, and converting data from different sources and formats into a standard format suitable for geographic information system (GIS) analysis;

[0067] Geographic Information System (GIS) software, such as ArcGIS, can be used to rasterize the preprocessed path planning area. Rasterization is the process of converting continuous geographic spatial data into discrete grid cells for subsequent analysis and calculation.

[0068] Then, for each grid cell, the potential impact on route planning is evaluated based on factors such as terrain elevation, land use type, environmental protection area, and transportation network, and these factors are divided into different levels according to the severity of the impact. For each level, a corresponding cost value is assigned, which reflects the contribution of different factors to the route planning cost. This step can be achieved through the "reclassification" function of ArcGIS;

[0069] Then, weights can be assigned to different influencing factors through the analytic hierarchy process (AHP), which is a multi-criteria decision-making method that determines the relative importance of each factor through pairwise comparison and assigns weights accordingly;

[0070] Finally, through the “overlay analysis” function of ArcGIS software, the cost values ​​of different influencing factors can be superimposed to obtain a comprehensive cost value using a weighted linear combination method, thereby constructing a comprehensive cost surface related to environmental factors.

[0071] 102. According to the requirements of transmission line path planning, an optimization model is constructed based on the above grid cost surface.

[0072] In the embodiment of the present application, an optimization model can be constructed based on mathematical optimization technology. According to the requirements of transmission line path planning, a mathematical optimization model that comprehensively considers linear cost and corner cost is established, wherein the linear cost reflects geographical environmental factors, and the corner cost reflects the impact of path corners on line costs.

[0073] The construction of the above optimization model will be introduced in detail later.

[0074] 103. Use the heuristic search algorithm to solve the above optimization model and obtain the preliminary path.

[0075] After the above step 102, in the embodiment of the present application, a heuristic search algorithm based on the improved A* algorithm can be used to solve the optimization model established in the above step 102 to obtain a preliminary overhead transmission line path.

[0076] The A* algorithm mentioned in the embodiment of the present application is the A* search algorithm, which is a kind of heuristic search algorithm. It is an algorithm for finding the lowest passing cost for a path with multiple nodes on a graph plane.

[0077] The above improved A* algorithm will be introduced in detail later.

[0078] 104. Further optimization is performed based on the above preliminary path to eliminate unnecessary corners and redundant paths and obtain the target planning path.

[0079] Further optimization processing is performed based on the preliminary path obtained in step 103, which may include straight line accessibility detection and path smoothing processing to eliminate unnecessary corners and redundant paths.

[0080] Based on the foregoing method description, the specific steps of constructing an optimization model, solving an optimization model, and path optimization mentioned in the embodiments of the present application are further introduced below.

[0081] After collecting and processing geographic information and environmental data, the next step is to build an optimization model to plan the transmission line path.

[0082] See also Figure 3 , Figure 3 A schematic diagram of a quadratic cost provided in an embodiment of the present application shows vertices i, j, k, l and their connectivity (edges), where the quadratic cost corresponding to vertex i to k (both sides of j) is expressed as The quadratic cost corresponding to vertex j to l (both sides of k) is expressed as The details will be explained later.

[0083] Furthermore, the above step 102 may specifically include:

[0084] Step S21: The transmission line path planning problem is essentially to find the shortest path between two given points (such as substations) in a given graph with the goal of minimizing the cost. (i,j) ∈{0,1} is a decision variable. When edge (i,j) is selected, x (i,j) =1, otherwise x (i,j) =0, thus modeling the problem as an integer quadratic programming problem.

[0085] Given a graph G = (V(G), E(G)), V(G) represents the set of vertices in the graph G, and E(G) represents the set of edges. Assuming a vertex s∈V as the source vertex and t∈V as the target vertex, the optimization objective is established as follows:

[0086]

[0087] In the formula, x@{x (i,j)} (i,j)∈E ,c:E→R + is a linear cost function that maps an edge in the graph G to a linear cost, i.e., the cost related to the geographical environment obtained in step 101; q: E×E→R +is a quadratic cost function, which maps a pair of elements in E(G) to a quadratic cost. It evaluates the impact of the angle between two adjacent edges on the line cost. Figure 3 As shown, for two adjacent edges ((i,j), (j,k))∈E×E, their quadratic cost can be expressed as follows:

[0088]

[0089] In the formula, θ (i,j),(j,k) is the angle between two adjacent sides, function It is a piecewise function that maps angles to a certain cost value. When there is no angle between two adjacent edges, the quadratic cost function q (i,j),(j,k) = 0, when there is an angle between two adjacent sides, q (i,j),(j,k) is a value strictly greater than 0, and the value of q is related to the angle θ between the two adjacent sides. (i,j),(j,k) There is a positive correlation.

[0090] Step S22: To ensure the connectivity of the path, set the constraints as follows:

[0091]

[0092] In the formula, δ - (i) = {j∈V|(j,i)∈E}, δ + (i)={j∈V|(i,j)∈E} are the predecessor node and successor node of vertex i respectively.

[0093] Based on the above description, after completing the construction of the optimization model, the next step is to determine the optimal path through a solving algorithm.

[0094] Furthermore, the above step 103 is specifically described as follows:

[0095] The above initial path solution in the embodiment of the present application can be implemented based on the python3.10 programming language. Step 103 of the present application proposes a new solution method based on the traditional A* algorithm, which optimizes the calculation method of the heuristic information, thereby effectively solving the overhead transmission line path optimization model in a large-scale geospatial data environment. The detailed steps of the proposed algorithm are as follows:

[0096] Step S31: set the starting point s and the end point t, and initialize the heuristic value and the cumulative cost;

[0097] Step S32: Consider only the linear cost in the described problem, calculate the cumulative cost from other vertices in the graph to the end point, and store these cost values ​​as heuristic information for the forward search of the A* algorithm in the next step.

[0098] Specifically, we pre-calculate the heuristic value from each node to the target node, only consider the linear cost in the described problem, calculate the minimum cumulative cost from other vertices i∈V\{t} in the graph to the end point t∈V, and after completing the visit of all vertices in the graph, the minimum cumulative cost {B j} j∈V will be stored;

[0099] Step S33: Use the A* algorithm to perform a forward search in the graph G from the starting point to the end point. In each iteration, a new edge is selected based on the minimum value of the cumulative total cost from the pre-selected vertex to the starting point and the cumulative cost from the pre-selected vertex to the end point obtained in the previous step, so as to obtain the minimum cost path to the end point.

[0100] Specifically, the minimum cumulative cost obtained in the previous step S32 can be used as heuristic information, and the A* algorithm is used to perform a forward search in the graph G from the starting point s∈V to the end point t∈V; in each iteration of the search process, the cumulative total cost dist(s, j) from the pre-selected vertex j∈V to the source point dist(s, j) is compared with the minimum estimated cost {B j} j∈V The minimum value of the sum of the edges (i, j)∈E is selected to obtain the path with the minimum cost to reach the end point t∈V.

[0101] After obtaining the preliminary path solution, further optimization may be performed, which may include straight line accessibility detection and path smoothing to eliminate unnecessary corners and redundant paths. Further, the above step 104 may specifically include:

[0102] Step S41: Considering the jagged distribution of the path caused by the limitation of the neighborhood expansion method, a straight-line reachability test is performed, and a line-of-sight algorithm is introduced to detect whether there is an area between two nodes where the construction of power transmission lines is prohibited (such as residential areas, environmental protection areas, etc.), that is, to detect whether there is an area between two nodes where the construction cost is set to infinity;

[0103] Step S42: Based on the result obtained in step S41, determine whether non-adjacent nodes in the path can be directly connected; if they can be directly connected and the total path cost of this connection method is smaller, then this connection method is adopted to improve the path quality and obtain the optimal transmission line path.

[0104] Figure 4A schematic diagram of a comparison of paths before and after optimization provided in the embodiment of the present application, with the left side showing the path before optimization and the right side showing the path after optimization, where the shorter the distance, the smoother the path. It can be seen that the transmission line path planning method in the embodiment of the present application not only improves the scientificity and accuracy of path selection, but also eliminates unnecessary corners and redundant paths through straight line accessibility detection and path smoothing, further optimizing the smoothness of the path, making the path planned by the computer more realistic.

[0105] The transmission line path planning method in the embodiment of the present application combines the detailed geographic data of the geographic information system (GIS) with mathematical optimization technology. The proposed transmission line path planning method not only improves the scientificity and accuracy of path selection, but also effectively processes large-scale geographic spatial data through the improved A* algorithm, reducing the calculation time. In addition, the method in the embodiment of the present application further optimizes the smoothness of the path through the path correction mechanism, making the path planned by the computer more realistic.

[0106] Based on the description of the foregoing method embodiment, an embodiment of the present application also provides a transmission line path planning device.

[0107] Figure 5 This is a schematic diagram of the structure of a transmission line path planning device provided in an embodiment of the present application. Figure 5 As shown, the device 500 includes:

[0108] The data acquisition module 510 is used to acquire geographic information data of the transmission line path planning area, including high-precision digital elevation model (DEM), land use type, geological conditions, meteorological data, etc., and pre-process the geographic information data using a geographic information system. Through data fusion technology, a grid cost surface considering multiple influencing factors is constructed;

[0109] A model building module 520 is used to build an optimization model based on the grid cost surface according to the specific requirements of the transmission line path planning; this module supports dynamic adjustment of optimization objectives and weights, such as line length, project cost, environmental protection and other factors, to meet the planning requirements of different projects;

[0110] A path search module 530 is used to solve the optimization model using a heuristic search algorithm to obtain a preliminary path;

[0111] The path optimization module 540 is used to perform further optimization processing based on the preliminary path, eliminate unnecessary corners and redundant paths, and obtain a target planned path.

[0112] It can be understood that the relevant contents of each module in the above device have been described in detail in the aforementioned method embodiment, and the details can be referred to the contents in the method embodiment; that is, a transmission line path planning device provided in the present application can be executed as follows: Figure 1 Any steps in the illustrated embodiment will not be described in detail here.

[0113] In one embodiment of the present application, an electronic device is also provided. Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the electronic device 600 may include a processor 601 and a memory 602. The memory 602 stores a computer program. When the computer program is executed by the processor 601, the following operations will be performed: Figure 1 Any step in the method embodiment shown. The electronic device 600 may also include input / output devices and the like.

[0114] In one embodiment, a computer-readable storage medium is further provided, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor executes any step in the method embodiment.

[0115] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0116] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0117] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A transmission line path planning method, It is characterized in that The method comprises: Obtaining geographic information data of the transmission line path planning area, preprocessing the geographic information data using a geographic information system, and constructing a grid cost surface that takes into account influencing factors; According to the requirements of transmission line path planning, an optimization model is constructed based on the grid cost surface; Using a heuristic search algorithm to solve the optimization model and obtain a preliminary path; Further optimization processing is performed based on the preliminary path to eliminate unnecessary corners and redundant paths and obtain the target planned path.

2. The transmission line path planning method according to claim 1, It is characterized in that The use of a geographic information system to pre-process the geographic information data includes but is not limited to paper map vectorization, coordinate conversion, and data clipping; The construction of the grid cost surface taking into account the influencing factors includes: The path planning area after the preprocessing is rasterized, and each influencing factor is graded and assigned a cost value according to the severity of the impact of the influencing factor on the route planning; The weights are assigned to different influencing factors through the hierarchical analysis method, and then the cost values ​​of the different influencing factors are multiplied by the corresponding weights and superimposed to obtain the comprehensive cost surface by adopting the weighted linear combination method.

3. The transmission line path planning method according to claim 2, It is characterized in that According to the requirements of transmission line path planning, the optimization model is constructed based on the grid cost surface, including: According to the requirements of the transmission line path planning, a mathematical optimization model is established which comprehensively considers linear cost and corner cost, wherein the linear cost reflects geographical environment factors, and the corner cost reflects the impact of path corners on line costs.

4. The transmission line path planning method according to claim 2, It is characterized in that According to the requirements of transmission line path planning, the optimization model is constructed based on the grid cost surface, including: Step 1: Set optimization goals: Among them, the linear cost function c:E→R + Map an edge in the graph G to a linear cost, corresponding to the comprehensive cost surface, the quadratic cost function q:E×E→R + A pair of elements in the graph G is mapped to a quadratic cost, which evaluates the impact of the angle between two adjacent edges on the line cost; for two adjacent edges ((i, j), (j, k))∈E×E, the quadratic cost can be described by the following formula: Among them, θ (i,j),(j,k) is the angle between the two adjacent sides, function is a piecewise function that maps angles to cost values; Step 2: Set constraints: Among them, δ - (i) = {j∈V|(j,i)∈E}, δ + (i)={j∈V|(i,j)∈E} are the predecessor node and successor node of vertex i respectively.

5. The transmission line path planning method according to claim 4, It is characterized in that The method of using a heuristic search algorithm to solve the optimization model and obtain a preliminary path includes: Set the starting point s and the end point t, initialize the heuristic value and the cumulative cost; Consider only the linear cost in the described problem, calculate the cumulative cost from other vertices in the graph to the end point, and store these cost values ​​as heuristic information for the A* algorithm forward search in the next step; The A* algorithm is used to perform a forward search in the graph from the starting point to the end point. In each iteration, a new edge is selected based on the minimum value of the sum of the cumulative total cost from the preselected vertex to the starting point and the cumulative cost from the preselected vertex to the end point obtained in the previous step, thereby obtaining the path with the minimum cost to reach the end point.

6. The transmission line path planning method according to claim 5, It is characterized in that The further optimization process based on the preliminary path includes: Considering the jagged distribution of paths caused by the limitation of neighborhood expansion mode, straight-line reachability detection is performed, and the line-of-sight algorithm is introduced to detect whether there is a prohibited area for construction of transmission lines between two nodes; According to the detection results, it is determined whether the non-adjacent nodes in the path can be directly connected; if they can be directly connected and the total path cost of this connection method is smaller, then this connection method is adopted.

7. The transmission line path planning method according to claim 1, It is characterized in that The geographic information data of the transmission line path planning area includes but is not limited to the terrain elevation, land use type, environmental protection area, transportation network, insulator contamination, ice coverage, and earthquake level of the transmission line path planning area; The further optimization process includes straight line reachability detection and path smoothing.

8. A transmission line path planning device, It is characterized in that include: A data acquisition module is used to acquire geographic information data of the transmission line path planning area, including but not limited to digital elevation model (DEM), land use type, geological conditions and meteorological data. The data acquisition module is specifically used to pre-process the geographic information data using a geographic information system to construct a grid cost surface that comprehensively considers influencing factors; A model building module, used to build an optimization model based on the grid cost surface according to the requirements of the transmission line path planning, and the model building module comprehensively considers the line length, project cost, and environmental impact factors to adjust the optimization target and weight; A path search module, used to solve the optimization model using a heuristic search algorithm to obtain a preliminary path; The path optimization module is used to perform further optimization processing based on the preliminary path, eliminate unnecessary corners and redundant paths, and obtain the final target planning path.

9. An electronic device, It is characterized in that The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, It is characterized in that When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

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