Mountain wind power road design optimization method and system based on ant colony algorithm

Abstract

The invention relates to the technical field of wind power road design, in particular to a mountain wind power road design optimization method and system based on an ant colony algorithm. A mountain wind power road design optimization method based on an ant colony algorithm comprises the following steps: obtaining mountain wind power road data including path points, global point cloud data and related design parameters; extracting point cloud data by using the extended rectangle, and generating an effective data set for mapping; constructing a topological graph based on the point cloud data, and screening nodes meeting topographic conditions and gradient requirements; an improved ant colony algorithm is adopted for path search, and the path cost is optimized and the optimal path is output while constraint conditions such as the turning radius, the gradient adaptability and the region legality are met. Through the method and system, the design efficiency, accuracy and intelligence can be effectively improved, the technical problem that standardization and economy are difficult to consider in mountain wind power road design is effectively solved, and the method and system are suitable for road design optimization in complex mountain wind power plants and similar engineering fields.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power road design, and in particular to a mountain wind power road design optimization method and system based on ant colony algorithm. Background Art

[0002] Due to factors such as complex terrain and variability in climate, road design in mountainous wind farms has become a crucial part of wind farm construction. Efficient and reasonable road design not only affects the safety of equipment transportation and construction progress, but is also directly related to the project's construction cost and economic benefits in later operations. However, the current traditional mountainous wind farm road design methods still have the following shortcomings.

[0003] (1) The design is highly subjective. Mountainous terrain is mostly steep slopes, valleys, and hilly areas with significant elevation differences. These terrain features impose strict requirements on road line selection, slope control, and turning radius. Traditional design methods usually rely on experience accumulation and manual line selection. The design results are often highly subjective and lack precision. It is difficult to fully consider terrain restrictions, geological risks, and construction feasibility.

[0004] (2) The cost is too high. Traditional road design requires repeated on-site surveys and scheme adjustments, which takes a long time and is difficult to meet the urgent project progress requirements. Multiple adjustments are required during the design process to meet regulatory requirements and economic needs. The traditional manual route selection and adjustment process is inefficient and costly. At the same time, there is a lack of efficient calculation models for complex terrain, making it difficult to achieve scientific treatment of special areas such as steep slopes and sharp bends. Unreasonable road route selection may lead to excessive earthwork volume, which in turn causes waste of resources and damage to the ecological environment.

[0005] Currently, artificial intelligence algorithms are booming in many fields, such as autonomous driving, industrial optimization, and medical diagnosis. However, there are few cases of applying artificial intelligence algorithms to mountain wind farm road design, and related research is still in its infancy. General algorithms cannot combine the unique complex terrain characteristics of mountain wind farms and the regulatory requirements for mountain wind farm roads. Especially when faced with multiple constraints and dynamic constraints, they cannot be adjusted adaptively, making the design scheme insufficiently flexible and unable to meet multiple constraints or dynamic design requirements at the same time.

[0006] Therefore, it is urgent to propose a technical solution that combines multi-source data with intelligent optimization algorithms to achieve a comprehensive improvement in design efficiency and accuracy while taking into account economy and standardization. Summary of the invention

[0007] In order to solve the above technical problems, the present invention provides a mountain wind power road design optimization method based on ant colony algorithm, which is characterized by comprising the following steps: S1. Obtain mountain wind power road data and CAD topographic map data, and convert the CAD topographic map data into point cloud data; S2. Classify the point cloud data according to the mountain wind power road data to obtain the mountain wind power road point cloud data and the non-mountain wind power road point cloud data; S3. Based on the obtained mountain wind power road point cloud data, calculate the spatial relationship data and slope data of adjacent nodes, obtain the point cloud structure, and construct a point cloud node topology model; S4. Based on the ant colony algorithm, the mountain wind power road is calculated according to the point cloud node topology model, and the road radius detection and the passing area detection are respectively performed on the mountain wind power calculated road. When the road radius detection and the passing area detection are both passed, the mountain wind power road design result is obtained and step S5 is executed. When the road radius detection fails and / or the passing area detection fails, step S4 is repeated; S5. Output the design results of the mountain wind power road that has passed the inspection to CAD to generate a visualized mountain wind power road; The mountain wind power road data includes the road starting point, the road must-pass point, the road end point, the number of roads, the road extension distance, the road minimum curve radius, the road minimum arc length, the road curve minimum interval distance, the road area slope threshold and the range of the inaccessible area.

[0008] Further, step S2 includes the following steps: S201. Obtain the coordinates of the road starting point, the road end point and the road extension distance; S202. Determine the line vector and the line normal vector according to the road starting point coordinates and the road end point coordinates; S203. Establish a mountain wind power road extension rectangle based on the line vector, line normal vector and extension distance; S204. Based on the obtained mountain wind power road extension rectangle, extract the point cloud data in the mountain wind power road extension rectangle from the point cloud data to obtain the mountain wind power road point cloud data, and the data outside the mountain wind power road extension rectangle is the non-mountain wind power road point cloud data.

[0009] Further, step S3 includes the following steps: S301. Clean the mountain wind power road point cloud data, and establish an initial graph structure for the mountain wind power road data point cloud through a connection table or a connection matrix. The initial graph structure is represented as: Among them, G is the initial graph structure, V is the node set of the initial graph structure, and v n represents the nth node, n represents the total number of nodes in the set, E represents the set of connections between nodes, e ijis the connection relationship between the i-th node and the j-th node; S302. Based on the road area slope threshold, the slope values ​​of each node and the adjacent point are calculated respectively to obtain the node whose slope value meets the road area slope threshold, wherein the adjacent point determination formula is expressed as: Among them, N i is the set of neighboring points, v j is the jth neighboring point, v i is the current computing node, r is the neighboring range, , , is the neighboring point v j The point cloud coordinates, , , is the point cloud coordinate of the current calculation node; The slope value calculation formula is expressed as: Among them, S ij is the slope value, S max Road area slope threshold, is the set of neighboring points that meet the road area slope threshold, S303. Based on the neighboring points that meet the road area slope threshold, the initial graph structure is updated to obtain a point cloud graph structure, which is represented by: Among them, G ’ is the point cloud structure, E' is the set of node connection relationships that meet the road area slope threshold; S304. Traverse the point cloud graph structure through breadth-first search. If the number of traversed nodes is equal to |V|, all points in the point cloud graph structure are connected, and execute step S305. Otherwise, execute steps S301-S303. S305. Generate a point cloud node topology model, expressed as: Among them, AdjMatrix(i, j) is the adjacency matrix.

[0010] Further, step S4 includes the following steps: S401. Obtaining the point cloud structure, initializing the ant colony algorithm, and setting the ant colony algorithm parameters, the ant colony algorithm parameters including the number of ants, pheromone level, pheromone volatility coefficient, pheromone importance factor and heuristic information importance factor; S402. According to the point cloud structure, the heuristic value for each ant to select the next node in the ant colony algorithm is calculated, and the calculation formula is: Among them, η ij is the heuristic value when the ant selects node j at node i, d ij is the Euclidean distance between node i and node j. When the minimum radius of the road is met, η ij The value of , when the minimum road radius is not met, the heuristic value is reduced to half of the current heuristic value, η ij The value of , and iterate to get the selection probability of each node, the calculation formula is: Among them, P ij The probability of selecting the next node j for node i, is the pheromone level of the edge between node i and node j, is the heuristic value of node i and node k, is the pheromone level of the edge between node i and node k, is the heuristic value between node i and node k, k is all paths in the current decision range, τ is the pheromone level, α is the pheromone importance factor, and β is the heuristic pheromone importance factor; S403. According to the calculated P ij , based on the maximum probability value, the next node of the ant is obtained and recorded as the point to be selected; S404. Perform road radius detection and passing area detection on the selected point respectively. When both the road radius detection and the passing area detection are passed, the selected point is added to the mountain wind power road and the road node sequence is updated. When the road radius detection fails and / or the passing area detection fails, repeat steps S402-S403; S405. Compare the road node sequence with the road path points. When the road node sequence contains all the road path points, the mountain wind power road is obtained and step S406 is executed. When the road node sequence does not contain any road path point, steps S402-S403 are repeated. S406. Calculate the path cost of the mountain wind power road based on the obtained mountain wind power road, and the calculation formula is: Among them, C path is the path cost of mountain wind power roads, K ’ is the mountain wind power road, w 1 、w 2 、w 3is the path cost weight coefficient, D ij is the path length of the mountain wind power road, S ij is the slope of the mountain wind power road segment, is the turning radius of the path section of the mountain wind power road; S407. Update the pheromone intensity of the mountain wind power road according to the path cost value of the mountain wind power road, and the formula is: in, ρ is the pheromone volatility coefficient, is the pheromone change, It is expressed as: Where Q is a constant. When the edge where node i and node j are located is in the path, The value of , when the edge between node i and node j is not in the path, The value of is 0; S408. Iterate the updated pheromone intensity of the mountain wind power road. When the maximum number of iterations is reached or the path result no longer changes, the optimal mountain wind power road is obtained. Otherwise, repeat steps S402-S407.

[0011] Furthermore, the road radius detection includes the following steps: S404-A-1. Obtain all current candidate points and construct a candidate point sequence including the road start point and the road end point; S404-A-2. Calculate the number of nodes in the current sequence of candidate points. When the number of nodes is less than 3, it means that there is no turning radius on the current mountain wind power road. The detection is compliant and the road radius detection is terminated. Otherwise, proceed to the next step. S404-A-3. Record the candidate point with two adjacent nodes as the middle node in the candidate point sequence, and calculate the road radius data of the middle node and the two adjacent nodes respectively, wherein the road radius data includes the edge length data, the edge angle data and the arc length data; S404-A-4. Traverse and calculate to obtain the road radius data of all intermediate nodes and two adjacent nodes in the sequence of points to be selected, obtain the maximum road radius data in the current sequence of points to be selected, and compare it with the mountain wind power road data. When the maximum road radius data is smaller than the mountain wind power road data, the road radius detection is qualified, otherwise it is unqualified.

[0012] Furthermore, the passing area detection includes the following steps: S404-B-1. Obtain all current candidate points and construct a candidate point sequence including the road start point and the road end point; S404-B-2. Based on the mountain wind power road data, the coordinates of the candidate points in the current candidate point sequence are calculated by the ray method to see whether they are within the inaccessible range. If so, the test fails, otherwise, the next step is executed; S404-B-3. Based on the mountain wind power road data, the vector method is used to calculate whether the line connecting any two points in the current sequence of selected points intersects with the boundary of the non-passable range. If so, the detection fails, otherwise the passing area detection passes.

[0013] Further, step S5 includes the following steps: S501. According to the mountain wind power road obtained in step S4, the coordinates of each path point in the mountain wind power road are obtained; S502. According to the path point coordinates, based on the CAD secondary development API, the path point coordinates are converted into polyline segments; S503. According to the starting point of the road, the must-pass points of the road and the end point of the road, the polyline segments are sequentially connected end to end along the direction from the starting point of the road to the end point of the road to obtain a visualized mountain wind power road.

[0014] The present invention also provides a mountain wind power road design system based on ant colony algorithm, which is implemented based on any one of the mountain wind power road design optimization methods based on ant colony algorithm described above, and includes a topographic map data conversion unit, a mapping unit, a data extraction unit, a compliance detection unit, a route planning unit and a user interaction unit; The topographic map data conversion unit is signal-connected to the data extraction unit and is used to pre-process the format of the topographic map data in the CAD; The mapping unit is respectively connected to the data extraction unit and the route planning unit for calculating the spatial position and slope relationship between the point cloud nodes, screening out the nodes within the slope threshold range, and retaining them in the topological map as neighboring points; The data extraction unit is respectively connected to the topographic map data conversion unit, the mapping unit and the user interaction unit by signal, and is used to extract mountain wind power road point cloud data from the point cloud data; The compliance detection unit is connected to the route planning unit signal, and is used to perform compliance detection on each line selection node and the final path through road radius detection and passing area detection; The route planning unit is connected to the compliance detection unit signal, and is used to calculate the proximity value, slope value, radius compliance value and passing area legal value of each node through an ant colony algorithm to obtain a road plan that complies with the mountain wind power road data; The user interaction unit is signal-connected to the data extraction unit and the route planning unit respectively, and is used to generate a visualized mountain wind power road according to the obtained road plan.

[0015] The beneficial effect of the present invention is that, by converting CAD topographic map data, the elevation point information in the planning area is extracted, and a node-based terrain data model is constructed. Combined with the improved ant colony algorithm, the path cost between nodes and adjacent nodes, slope restrictions, road radius, and non-passable areas are comprehensively considered, and the land use scope and node cost to be avoided are intelligently processed, and finally the optimal road design scheme that meets the planning requirements is generated, which significantly reduces the workload and potential errors in the manual design process, improves the design speed and accuracy under complex terrain conditions, effectively avoids the risk of violating regulations, and reduces resource consumption, which can effectively meet the needs of engineering projects for efficient and reliable design. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 , a schematic flow chart of a mountain wind power road design optimization method based on ant colony algorithm of the present invention.

[0017] Figure 2 , a structural schematic diagram of a mountain wind power road design system based on ant colony algorithm of the present invention.

[0018] Figure 3 , topographic map data conversion flow chart of an embodiment of the present invention.

[0019] Figure 4 , a flow chart of relevant data extraction according to an embodiment of the present invention.

[0020] Figure 5 , schematic diagram of an extended rectangle according to an embodiment of the present invention.

[0021] Figure 6 , a schematic diagram of the point cloud node topology model construction process according to an embodiment of the present invention.

[0022] Figure 7 , a path search flow chart of an embodiment of the present invention.

[0023] Figure 8 , a flow chart of radius qualification detection according to an embodiment of the present invention.

[0024] Fig. 9 , parameter description of the radius qualification detection process of an embodiment of the present invention.

[0025] Fig.10 , a flow chart of the qualified detection of the passing area according to an embodiment of the present invention.

[0026] Fig.11 , a flowchart of the search result display according to an embodiment of the present invention.

[0027] In the figure, 1-topographic map data conversion unit, 2-mapping unit, 3-data extraction unit, 4-compliance detection unit, 5-route planning unit, 6-user interaction unit. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the content of the present invention and make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the embodiments and drawings. The illustrative embodiments of the present invention and their description are only used to explain the present invention and are not intended to further limit the present invention.

[0029] Embodiment 1: like Figure 1 As shown, Embodiment 1 of the present invention provides a mountain wind power road design optimization method based on an ant colony algorithm, comprising the following steps: S1. Obtain mountain wind power road data and CAD topographic map data, and convert the CAD topographic map data into point cloud data; Specifically, Figure 3 As shown, the following steps are included: S101.CAD topographic map data input; S102. Set the extraction interval, that is, specify the distance at which a coordinate position is taken on the multi-segment line; S103. Select a polyline and select the target polyline to be extracted; S104. Extract coordinates, and obtain coordinate data of each polyline segment according to the extraction interval; S105. Build a three-dimensional point, call the secondary development API of CAD, and establish a three-dimensional point at each coordinate position; S106. Output the point cloud and draw the point cloud data in the CAD interface.

[0030] S2. Classify the point cloud data according to the mountain wind power road data to obtain the mountain wind power road point cloud data and the non-mountain wind power road point cloud data; S3. Based on the obtained mountain wind power road point cloud data, calculate the spatial relationship data and slope data of adjacent nodes, obtain the point cloud structure, and construct a point cloud node topology model; S4. Based on the ant colony algorithm, the mountain wind power road is calculated according to the point cloud node topology model, and the road radius detection and the passing area detection are respectively performed on the mountain wind power calculated road. When the road radius detection and the passing area detection are both passed, the mountain wind power road design result is obtained and step S5 is executed. When the road radius detection fails and / or the passing area detection fails, step S4 is repeated; S5. Output the design results of the mountain wind power road that has passed the inspection to CAD to generate a visualized mountain wind power road; The mountain wind power road data includes the road starting point, the road must-pass point, the road end point, the number of roads, the road extension distance, the road minimum curve radius, the road minimum arc length, the road curve minimum interval distance, the road area slope threshold and the range of the inaccessible area.

[0031] Further, such as Figure 4 As shown, step S2 includes the following steps: S201. Obtaining the road starting point coordinates P 1 (x, y), road end coordinates P 2 (x ’ ,y ’ ) The extended distance d from the road; S202. Determine the line vector v=(x ’ -x,y ’ -y) and the line normal vector n=(-v y ,v x ); S203. According to the connection vector, the connection normal vector and the extension distance, establish the mountain wind power road extension rectangle, such as Figure 5 As shown; Specifically, the following steps are included: S203-1. First, the standardized line vector and normal vector are calculated, and the calculation formula is: in, is the normalized connection vector, is the normalized normal vector; S203-2. Calculate the coordinates of the four vertices of the extended rectangle according to the obtained line vector and line normal vector. The calculation formula is: Among them, P 1 , P 2 , Q 1 , Q 2 The coordinates of the four vertices of the extended rectangle; S204. Based on the obtained mountain wind power road extension rectangle, extract the point cloud data in the mountain wind power road extension rectangle from the point cloud data to obtain the mountain wind power road point cloud data, and the data outside the mountain wind power road extension rectangle is the non-mountain wind power road point cloud data.

[0032] Further, such as Figure 6 As shown, step S3 includes the following steps: S301. Clean the mountain wind power road point cloud data, and establish an initial graph structure for the mountain wind power road data point cloud through a connection table or a connection matrix. The initial graph structure is represented as: Among them, G is the initial graph structure, V is the node set of the initial graph structure, and v n represents the nth node, n represents the total number of nodes in the set, E represents the set of connections between nodes, e ij is the connection relationship between the i-th node and the j-th node; S302. Based on the road area slope threshold, the slope values ​​of each node and the adjacent nodes are calculated respectively to obtain the nodes whose slope values ​​meet the road area slope threshold, wherein the adjacent point determination formula is expressed as: Among them, N i is the set of neighboring points, v j is the jth neighboring point, v i is the current computing node, r is the neighboring range, , , is the neighboring point v j The point cloud coordinates, , , is the point cloud coordinate of the current calculation node; The slope value calculation formula is expressed as: And filter the points v that meet the following conditions j , expressed as: Among them, S ij is the slope value, S max Road area slope threshold, is the set of neighboring points that meet the road area slope threshold, S303. Based on the neighboring points that meet the road area slope threshold, the initial graph structure is updated to obtain a point cloud graph structure, which is represented by: Among them, G ’ is the point cloud structure, E' is the set of node connection relationships that meet the road area slope threshold; S304. Verify whether the updated graph structure meets the following two requirements: 1. Whether the adjacent nodes of all nodes meet the road area slope threshold; 2. Whether the graph structure is connected; Specifically, the point cloud graph structure is traversed through breadth-first search. If the number of traversed nodes is equal to |V|, all points in the point cloud graph structure are connected, and step S305 is executed. Otherwise, steps S301-S303 are executed. S305. Generate a point cloud node topology model, expressed as: Among them, AdjMatrix(i, j) is the adjacency matrix.

[0033] Further, such as Figure 7 As shown, step S4 includes the following steps: S401. Obtain the point cloud structure, initialize the ant colony algorithm, and set the ant colony algorithm parameters, which include the number of ants m, the pheromone level τ, and the pheromone volatility coefficient. ρ , pheromone importance factor α and heuristic information importance factor β, the pheromone level τ of each node is usually initialized to a small constant, and the default values ​​of each parameter are shown in Table 1: Table 1 Default values ​​of ant colony algorithm parameters S402. According to the point cloud structure, the heuristic value for each ant to select the next node in the ant colony algorithm is calculated, and the calculation formula is: Among them, η ij is the heuristic value when the ant selects node j at node i, d ij is the Euclidean distance between node i and node j. When the minimum radius of the road is met, η ij The value of , when the minimum road radius is not met, the heuristic value is reduced to half of the current heuristic value, η ij The value of , and iterate to get the selection probability of each node, the calculation formula is: Among them, P ij The probability of selecting the next node j for node i, is the pheromone level of the edge between node i and node j, is the heuristic value of node i and node k, is the pheromone level of the edge between node i and node k, is the heuristic value between node i and node k, k is all paths in the current decision range, τ is the pheromone level, α is the pheromone importance factor, and β is the heuristic pheromone importance factor; S403. According to the calculated P ij, based on the maximum probability value, the next node of the ant is obtained and recorded as the point to be selected; S404. Perform road radius detection and passing area detection on the selected point respectively. When both the road radius detection and the passing area detection are passed, the selected point is added to the mountain wind power road and the road node sequence is updated. When the road radius detection fails and / or the passing area detection fails, repeat steps S402-S403; S405. Compare the road node sequence with the road path points. When the road node sequence contains all the road path points, the mountain wind power road is obtained and step S406 is executed. When the road node sequence does not contain any road path point, steps S402-S403 are repeated. S406. Calculate the path cost of the mountain wind power road based on the obtained mountain wind power road, and the calculation formula is: Among them, C path is the path cost of mountain wind power roads, K ’ is the mountain wind power road, w 1 、w 2 、w 3 is the path cost weight coefficient, D ij is the path length of the mountain wind power road, S ij is the slope of the mountain wind power road segment, is the turning radius of the path section of the mountain wind power road; S407. Update the pheromone intensity of the mountain wind power road according to the path cost value of the mountain wind power road, and the formula is: in, ρ is the pheromone volatility coefficient, is the pheromone change, It is expressed as: Where Q is a constant. When the edge where node i and node j are located is in the path, The value of , when the edge between node i and node j is not in the path, The value of is 0; S408. Iterate the updated pheromone intensity of the mountain wind power road. When the maximum number of iterations is reached or the path result no longer changes, the optimal mountain wind power road is obtained. Otherwise, repeat steps S402-S407.

[0034] Further, such as Figure 8 As shown, the road radius detection includes the following steps: S404-A-1. Obtain all current candidate points and construct a candidate point sequence including the road start point and the road end point; S404-A-2. Calculate the number of nodes in the current sequence of candidate points. When the number of nodes is less than 3, that is, the current sequence of candidate points only includes the road starting point, the road end point, or only a single edge, it means that there is no turning radius on the current mountain wind power road. The detection is compliant and the road radius detection is terminated. Otherwise, execute the next step; S404-A-3. Fig. 9 As shown, the candidate point with two adjacent nodes is recorded as the intermediate node P in the candidate point sequence. i , and mark the two adjacent nodes of the intermediate node as P i-1 With P i+1 As a benchmark for detection, it should be noted that Fig. 9 The route node P recorded in i The intermediate node P i For the same node; S404-A-4. Calculate the road radius data of the middle node and two adjacent nodes respectively, wherein the road radius data includes edge length data, edge angle data and arc length data; Specifically, the side length data calculation formula is: Among them, L 1 The intermediate node P i With adjacent nodes P i-1 The side length data, L 2 The intermediate node P i With adjacent nodes P i+1 The edge length data of Specifically, the side angle data calculation formula is: Where θ is the side angle, θ=arcos(cosθ); Specifically, the arc length data calculation needs to first calculate the middle node P i The calculation formula of the tangent point on both sides is: Among them, P it1 is the first tangent point, P it2 is the second tangent point, R min is the minimum curve radius of the road. According to the calculated coordinates of the two tangent points, the arc length data is calculated. The calculation formula is: Among them, L C is the arc length data; S404-A-5. Traverse and calculate the road radius data of all intermediate nodes and two adjacent nodes in the sequence of points to be selected, obtain the maximum road radius data in the current sequence of points to be selected, and compare it with the mountain wind power road data. When the maximum road radius data is smaller than the mountain wind power road data, the road radius detection is qualified, otherwise it is unqualified; Specifically, extract the minimum side length data L of the road in the mountain wind power road data min The minimum arc length data L of the road Cmin , if there is a previous detection node P i-1 The right tangent point P it0 , then enter its coordinates for the current tangent point P it1 The distance is calculated, and for each intermediate node P i The following conditional judgments are performed. When any conditional judgment is not satisfied, it is judged that the road radius detection is unqualified. The conditional judgment can be expressed as: According to the above conditions, all intermediate nodes P in the current sequence of candidate points are traversed at once. i ,If any intermediate node fails to pass the detection, it is judged that the road radius detection fails, otherwise it passes.

[0035] Further, such as Fig.10 As shown, the passing area detection includes the following steps: S404-B-1. Obtain all current candidate points and construct a candidate point sequence including the road start point and the road end point; S404-B-2. Based on the mountain wind power road data, the coordinates of the candidate points in the current candidate point sequence are calculated by the ray method to see whether they are within the inaccessible range. If so, the test fails, otherwise, the next step is executed; Specifically, add the candidate point P that needs to be added to the path in the current calculation new As the basic point of this round of detection, and through the ray method to determine P new Whether it is located in a non-passable area, the ray method includes the following steps: (1) Let the inaccessible area be a polygon R, and obtain the boundary vertex V of polygon R, V=(V 1 ,V 2 ,…,V n ); (2) P newAs the starting point, emit a ray in any direction, and count the number of intersections N between the ray and the border of the polygon R. When N is an odd number, P new If it is inside the polygon, it will fail the regional detection, otherwise it will go to the next step; S404-B-3. Based on the mountain wind power road data, use the vector method to calculate whether the line connecting any two points in the current sequence of selected points intersects with the boundary of the inaccessible range. If so, the test fails, otherwise the passing area test passes; Specifically, obtain the generated qualified mountain wind power roads, extract road nodes, and construct a path point sequence P, P = (P 1 ,P 2 ,…,P n );The candidate point P that has passed the regional detection is new Add the path point sequence and form a new path according to the node order. The new path can be expressed as P 1 →P 2 →…→P n →P new ; Extract the newly added edge P in the new path n →P new , and determine whether it is connected to any edge V of polygon R n →V n+1 The calculation formula is: If an intersection is detected, the detection fails, otherwise the area detection passes.

[0036] Further, such as Fig.11 As shown, step S5 includes the following steps: S501. According to the mountain wind power road obtained in step S4, the coordinates of each path point in the mountain wind power road are obtained; S502. According to the path point coordinates, based on the CAD secondary development API, the path point coordinates are converted into polyline segments; S503. According to the starting point of the road, the must-pass point of the road and the end point of the road, the polyline segments are sequentially connected from the beginning to the end of the road to obtain a visualized mountain wind power road; S504. Output visualized mountain wind power roads.

[0037] Example 2 like Figure 2As shown, on the basis of Example 1, Example 2 of the present invention proposes a mountain wind power road design system based on an ant colony algorithm based on a mountain wind power road design optimization method, including a topographic map data conversion unit 1, a mapping unit 2, a data extraction unit 3, a compliance detection unit 4, a route planning unit 5 and a user interaction unit 6; The topographic map data conversion unit 1 is signal-connected to the data extraction unit 3 and is used to unify the topographic map data format in the CAD; The mapping unit 2 is connected to the data extraction unit 3 and the route planning unit 5 respectively, and is used to calculate the spatial position, slope relationship, etc. between the point cloud nodes, and filter out the nodes within the slope threshold range, and retain them as neighboring points in the topological map; The data extraction unit 3 is respectively connected to the topographic map data conversion unit 1, the mapping unit 2 and the user interaction unit 6 by signals, and is used to extract mountain wind power road point cloud data from the point cloud data; The compliance detection unit 4 is connected to the route planning unit 5 by signal, and is used to verify in real time whether each line selection node and the final path conform to the mountain wind power road data; The route planning unit 5 is connected to the compliance detection unit 4 by signal, and is used to analyze the proximity, slope adaptability, radius compliance and legality of the passing area of ​​each node through the ant colony algorithm, and obtain the optimal road plan that meets the mountain wind power road data; The user interaction unit 6 is signal-connected to the data extraction unit 3 and the route planning unit 5 respectively, and is used to generate a visualized mountain wind power road according to the obtained optimal road plan.

[0038] Specifically, the specific workflow is as follows: A1. Topographic map data conversion: The topographic map data conversion unit 1 converts the polyline selected by the user in the CAD software into three-dimensional points according to the specified distance interval to form point cloud data; A2. User data input: Through the mapping unit 2, the system prompts the user to select path points (starting point, must-pass point, end point), and prompts the user to enter the number of expected paths to be generated, the number of iterations, the extension distance, the minimum curve radius, the minimum arc length, the minimum interval distance between curves, the slope threshold and other parameters. Finally, the user selects the inaccessible area and passes all data to the subsequent process; A3. Extraction of relevant data: The data extraction unit 3 processes the path points and global point cloud data selected by the user, and extracts the point cloud within a certain range as the data basis for subsequent mapping and path planning; A4. Mapping: The spatial relationship and slope conditions of adjacent nodes in the point cloud data are calculated through mapping unit 2, and nodes that meet the design specifications are screened out to form a topological map. The topological map provides a feasibility basis for subsequent path planning.

[0039] A5. Path search: The improved ant colony algorithm is used to complete the path planning task. By comprehensively analyzing the proximity, slope adaptability, radius compliance, and legality of each node, the algorithm can continuously optimize the path selection during the search process and ultimately generate an optimal road plan that meets all regulatory requirements. A6. Radius compliance detection: Each time a new point is added, the generated path plan is tested for minimum turning radius and other relevant specifications through the compliance detection unit 4, so as to ensure that the road design meets the actual engineering requirements; A7. Passing area compliance detection: The compliance detection unit 4 is used to detect whether the path plan occupies the selected red line range and crosses the non-passing area, so as to meet the compliance requirements; A8. Output path results: The optimized and verified path results are output in a visual form through the user interaction unit 6, and the path polyline is drawn in the CAD interface. If a compliant path to the end point is not found within the maximum number of iterations, an empty array is output, indicating that there is no result.

[0040] The present invention is explained from the viewpoints of purpose of use, efficiency, progress and novelty, and the practical progress it has is in line with the functional enhancement and use requirements emphasized by the Patent Law. The above description and drawings of this application are only preferred embodiments of this application, and are not intended to limit this application. Therefore, all structures, devices, features, etc. that are similar or identical to those of this application, that is, all equivalent replacements or modifications made in accordance with the scope of the patent application of this application, should fall within the scope of protection of the patent application of this application.

[0041] 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 mountain wind power road design optimization method based on ant colony algorithm, characterized in that: The following steps are involved: S1. Obtain mountain wind power road data and CAD topographic map data, and convert the CAD topographic map data into point cloud data; S2. Classify the point cloud data according to the mountain wind power road data to obtain the mountain wind power road point cloud data and the non-mountain wind power road point cloud data; S3. Based on the obtained mountain wind power road point cloud data, calculate the spatial relationship data and slope data of adjacent nodes, obtain the point cloud structure, and construct a point cloud node topology model; S4. Based on the ant colony algorithm, the mountain wind power road is calculated according to the point cloud node topology model, and the road radius detection and the passing area detection are respectively performed on the mountain wind power calculated road. When the road radius detection and the passing area detection are both passed, the mountain wind power road design result is obtained and step S5 is executed. When the road radius detection fails and / or the passing area detection fails, step S4 is repeated; S5. Output the design results of the mountain wind power road that has passed the inspection to CAD to generate a visualized mountain wind power road; The mountain wind power road data includes the road starting point, the road must-pass point, the road end point, the number of roads, the road extension distance, the road minimum curve radius, the road minimum arc length, the road curve minimum interval distance, the road area slope threshold and the range of the inaccessible area.

2. The mountain wind power road design optimization method based on ant colony algorithm according to claim 1 is characterized in that: Step S2 includes the following steps: S201. Obtain the coordinates of the road starting point, the road end point and the road extension distance; S202. Determine the line vector and the line normal vector according to the road starting point coordinates and the road end point coordinates; S203. Establish a mountain wind power road extension rectangle based on the line vector, line normal vector and extension distance; S204. Based on the obtained mountain wind power road extension rectangle, extract the point cloud data in the mountain wind power road extension rectangle from the point cloud data to obtain the mountain wind power road point cloud data, and the data outside the mountain wind power road extension rectangle is the non-mountain wind power road point cloud data.

3. The mountain wind power road design optimization method based on ant colony algorithm according to claim 1 is characterized in that: Step S3 includes the following steps: S301. Clean the mountain wind power road point cloud data, and establish an initial graph structure for the mountain wind power road data point cloud through a connection table or a connection matrix. The initial graph structure is represented as: Among them, G is the initial graph structure, V is the node set of the initial graph structure, and v n represents the nth node, n represents the total number of nodes in the set, E represents the set of connections between nodes, e ij is the connection relationship between the i-th node and the j-th node; S302. Based on the road area slope threshold, the slope values ​​of each node and the adjacent point are calculated respectively to obtain the node whose slope value meets the road area slope threshold, wherein the adjacent point determination formula is expressed as: Among them, N i is the set of neighboring points, v j is the jth neighboring point, v i is the current computing node, r is the neighboring range, , , is the neighboring point v j The point cloud coordinates, , , is the point cloud coordinate of the current calculation node; The slope value calculation formula is expressed as: Among them, S ij is the slope value, S max Road area slope threshold, is the set of neighboring points that meet the road area slope threshold, S303. Based on the neighboring points that meet the road area slope threshold, the initial graph structure is updated to obtain a point cloud graph structure, which is represented by: Among them, G ’ is the point cloud structure, E' is the set of node connection relationships that meet the road area slope threshold; S304. Traverse the point cloud graph structure through breadth-first search. If the number of traversed nodes is equal to |V|, all points in the point cloud graph structure are connected, and execute step S305. Otherwise, execute steps S301-S303. S305. Generate a point cloud node topology model, expressed as: Among them, AdjMatrix(i, j) is the adjacency matrix.

4. The mountain wind power road design optimization method based on ant colony algorithm according to claim 1 is characterized in that: Step S4 includes the following steps: S401. Obtaining the point cloud structure, initializing the ant colony algorithm, and setting the ant colony algorithm parameters, the ant colony algorithm parameters including the number of ants, pheromone level, pheromone volatility coefficient, pheromone importance factor and heuristic information importance factor; S402. According to the point cloud structure, the heuristic value for each ant to select the next node in the ant colony algorithm is calculated, and the calculation formula is: Among them, η ij is the heuristic value when the ant selects node j at node i, d ij is the Euclidean distance between node i and node j. When the minimum radius of the road is met, η ij The value of , when the minimum road radius is not met, the heuristic value is reduced to half of the current heuristic value, η ij The value of , and iterate to get the selection probability of each node, the calculation formula is: Among them, P ij The probability of selecting the next node j for node i, is the pheromone level of the edge between node i and node j, is the heuristic value of node i and node k, is the pheromone level of the edge between node i and node k, is the heuristic value between node i and node k, k is all paths in the current decision range, τ is the pheromone level, α is the pheromone importance factor, and β is the heuristic pheromone importance factor; S403. According to the calculated P ij , based on the maximum probability value, the next node of the ant is obtained and recorded as the point to be selected; S404. Perform road radius detection and passing area detection on the selected point respectively. When both the road radius detection and the passing area detection are passed, the selected point is added to the mountain wind power road and the road node sequence is updated. When the road radius detection fails and / or the passing area detection fails, repeat steps S402-S403; S405. Compare the road node sequence with the road path points. When the road node sequence contains all the road path points, the mountain wind power road is obtained and step S406 is executed. When the road node sequence does not contain any road path point, steps S402-S403 are repeated. S406. Calculate the path cost of the mountain wind power road based on the obtained mountain wind power road, and the calculation formula is: Among them, C path is the path cost of mountain wind power roads, K ’ is the obtained mountain wind power road, w1, w2, w3 are the path cost weight coefficients, D ij is the path length of the mountain wind power road, S ij is the slope of the mountain wind power road segment, is the turning radius of the path section of the mountain wind power road; S407. Update the pheromone intensity of the mountain wind power road according to the path cost value of the mountain wind power road, and the formula is: in, ρ is the pheromone volatility coefficient, is the pheromone change, It is expressed as: Where Q is a constant. When the edge where node i and node j are located is in the path, The value of , when the edge between node i and node j is not in the path, The value of is 0; S408. Iterate the updated pheromone intensity of the mountain wind power road. When the maximum number of iterations is reached or the path result no longer changes, the optimal mountain wind power road is obtained. Otherwise, repeat steps S402-S407.

5. The mountain wind power road design optimization method based on ant colony algorithm according to claim 4 is characterized in that: The road radius detection comprises the following steps: S404-A-1. Obtain all current candidate points and construct a candidate point sequence including the road start point and the road end point; S404-A-2. Calculate the number of nodes in the current sequence of candidate points. When the number of nodes is less than 3, it means that there is no turning radius on the current mountain wind power road. The detection is compliant and the road radius detection is terminated. Otherwise, proceed to the next step. S404-A-3. Record the candidate point with two adjacent nodes as the middle node in the candidate point sequence, and calculate the road radius data of the middle node and the two adjacent nodes respectively, wherein the road radius data includes the edge length data, the edge angle data and the arc length data; S404-A-4. Traverse and calculate to obtain the road radius data of all intermediate nodes and two adjacent nodes in the sequence of points to be selected, obtain the maximum road radius data in the current sequence of points to be selected, and compare it with the mountain wind power road data. When the maximum road radius data is smaller than the mountain wind power road data, the road radius detection is qualified, otherwise it is unqualified.

6. The mountain wind power road design optimization method based on ant colony algorithm according to claim 4 is characterized in that: The passing area detection comprises the following steps: S404-B-1. Obtain all current candidate points and construct a candidate point sequence including the road start point and the road end point; S404-B-2. Based on the mountain wind power road data, the coordinates of the candidate points in the current candidate point sequence are calculated by the ray method to see whether they are within the inaccessible range. If so, the test fails, otherwise, the next step is executed; S404-B-3. Based on the mountain wind power road data, the vector method is used to calculate whether the line connecting any two points in the current sequence of selected points intersects with the boundary of the non-passable range. If so, the detection fails, otherwise the passing area detection passes.

7. The mountain wind power road design optimization method based on ant colony algorithm according to claim 1 is characterized in that: Step S5 includes the following steps: S501. According to the mountain wind power road obtained in step S4, the coordinates of each path point in the mountain wind power road are obtained; S502. According to the path point coordinates, based on the CAD secondary development API, the path point coordinates are converted into polyline segments; S503. According to the starting point of the road, the must-pass points of the road and the end point of the road, the polyline segments are sequentially connected end to end along the direction from the starting point of the road to the end point of the road to obtain a visualized mountain wind power road.

8. A mountain wind power road design system based on ant colony algorithm, the system is implemented based on a mountain wind power road design optimization method based on ant colony algorithm as claimed in any one of claims 1 to 7, characterized in that: It includes a topographic map data conversion unit (1), a mapping unit (2), a data extraction unit (3), a compliance detection unit (4), a route planning unit (5) and a user interaction unit (6); The topographic map data conversion unit (1) is signal-connected to the data extraction unit (3) and is used to pre-process the format of the topographic map data in the CAD; The mapping unit (2) is respectively connected to the data extraction unit (3) and the route planning unit (5) by signals, and is used to calculate the spatial position and slope relationship between the point cloud nodes, and to select the nodes within the slope threshold range and retain them as neighboring points in the topological map; The data extraction unit (3) is respectively connected to the topographic map data conversion unit (1), the mapping unit (2) and the user interaction unit (6) by signals, and is used to extract mountain wind power road point cloud data from the point cloud data; The compliance detection unit (4) is connected to the route planning unit (5) by signal, and is used to perform compliance detection on each line selection node and the final path through road radius detection and passing area detection; The route planning unit (5) is connected to the compliance detection unit (4) by signal, and is used to calculate the proximity value, slope value, radius compliance value and passing area legal value of each node through an ant colony algorithm, so as to obtain a road plan that complies with the mountain wind power road data; The user interaction unit (6) is respectively connected to the data extraction unit (3) and the route planning unit (5) by signals, and is used to generate a visualized mountain wind power road according to the obtained road plan.

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