A rapid and intelligent design method for construction access roads in complex terrains

Abstract

The present invention provides a rapid and intelligent design method for construction access roads in complex terrains, comprising the following steps: The first step is to log in to the construction access road design platform; after logging in to the construction access road design platform in the second step, input the coordinate information of the starting point and the target point and the DEM data into the optimal path system of the construction access road to obtain the optimal path; the third step is to display the optimal path. The present invention adopts an evaluation method of a three-dimensional cost function, and realizes fine regulation by introducing the slope rate factor and in the form of an exponential function and its amplification coefficient k, which not only makes the path planning more in line with the requirements of the actual terrain conditions, but also provides a new solution for how to balance the operation efficiency and result accuracy of the algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of road construction, and particularly to a method for quickly and intelligently designing a construction access road in complex terrain. Background Art

[0002] A road is a three-dimensional physical entity, which is a linear structure composed of subgrade, pavement, bridges, culverts, tunnels and roadside facilities, and should be coordinated with terrain, ground features, environment and landscape; the path planning of a construction road is quite complex because it involves many restrictive conditions and many factors need to be considered comprehensively. For example, a road in a valley should avoid steep slopes, a road should avoid swamps, a road should reasonably cross rivers, pass through small villages, connect residential areas, and the shortest route to a big city also needs to be calculated. The reasonable planning of a construction road is a prerequisite for carrying out various infrastructure projects. How to design the path of a construction road and find an optimal path that is both fast and safe in complex terrain, which can reach the destination efficiently and avoid steep or difficult-to-walk terrain as much as possible, there is no solution found in the existing technology currently. Summary of the Invention

[0003] The present invention aims to at least solve the technical problems existing in the prior art, and particularly innovatively provides a method for quickly and intelligently designing a construction access road in complex terrain.

[0004] To achieve the above object of the present invention, the present invention provides a method for quickly and intelligently designing a construction access road in complex terrain, including the following steps:

[0005] The first step is to log in to the construction access road design platform;

[0006] The second step is to input the coordinate information of the starting point and the target point and the DEM data into the optimal path system of the construction access road after logging in to the construction access road design platform to obtain the optimal path;

[0007] The third step is to display the optimal path.

[0008] In a preferred embodiment of the present invention, the second step includes the following steps:

[0009] S1, preprocess the DEM data to obtain the input coordinate information of the starting point and the target point;

[0010] S2, put the starting point into the open list, traverse the nodes adjacent to the starting point, and also put the nodes adjacent to the starting point into the open list;

[0011] S3, calculate the cost value required for the adjacent nodes among the nodes adjacent to the starting point, and select the optimal node;

[0012] S4. Determine whether the optimal node is the target node. If so, output the optimal path; otherwise, proceed to the next step.

[0013] S5. Expand the child nodes of the current optimal node and determine whether the child nodes of the current optimal node exist in the open list or the closed list. If the child node does not exist in the open list and the closed list, put the child node into the open list, and then select the next optimal node. If the child node already exists in the closed list, skip the node. If the child node already exists in the open list, it means that the node has been calculated as a neighboring node of other nodes. Recalculate the cost value of the child node. Compare the new and old cost values of the node. If the new cost value is smaller, use the optimal node as the parent node of the child node. If the old cost value is smaller, keep the old parent node.

[0014] S6. Loop the above operations until reaching the end point or the open list is empty.

[0015] In a preferred embodiment of the present invention, in step S3, an evaluation function is used to select the optimal node from the neighboring nodes, and the evaluation function adopts the form of assigning scores or a three-dimensional cost function.

[0016] In a preferred embodiment of the present invention, in step S3, an evaluation function is constructed in the form of assigning scores. Considering distance determination, slope rate determination, and invalid value determination, assign values to the points to be determined according to the rules, and select the node with the largest total assigned value as the optimal node.

[0017] In a preferred embodiment of the present invention, in step S3, an evaluation function is constructed in the form of a three-dimensional cost function, where the slope rate function expression is e k*slope , calculate the evaluation values of different points to be determined, and select the node with the smallest evaluation value as the optimal node.

[0018] In a preferred embodiment of the present invention, in step S3, the cost value is the fundamental criterion for determining whether a node is an optimal node. A three-dimensional distance calculation formula is adopted, and the calculation result of this formula reflects the actual distance between two pixel points. Its expression is:

[0019]

[0020] In the formula:

[0021] X i and X e respectively represent the X-axis coordinates of node i and node e;

[0022] Y i and Y e respectively represent the Y-axis coordinates of node i and node e

[0023] Z iand Z e represent the elevation data of the i-node and the e-node respectively;

[0024] Size represents the pixel size of the DEM model.

[0025] In a preferred embodiment of the present invention, based on the distance calculation formula, an evaluation function of assigning scores and a three-dimensional cost function are adopted. By assigning scores to the child nodes of the optimal node or directly calculating the three-dimensional cost, the child node with the maximum assigned score or the minimum three-dimensional cost is used as the new optimal node.

[0026] In a preferred embodiment of the present invention, the application of the A* algorithm with assigned scores in path planning is adopted. The optimal path is determined by comparing the specific attributes between the parent node and different child nodes. The algorithm assigns scores to each possible adjacent child node of the parent node, and the score is based on the comprehensive evaluation of multiple key factors, including:

[0027] Compare the distance from the starting point to the child node (node_g) with the distance from the starting point to the parent node (cur_g);

[0028] if node_g < cur_g; score += x (2)

[0029] Compare the distance from the child node to the end point (node_d) with the distance from the parent node to the end point (cur_d);

[0030] if node_d < cur_d; score += y (3)

[0031] Determine the slope rate (slope) and invalid point between the parent node and the child node;

[0032]

[0033] x, y, z, and k in the above formula should at least satisfy the following constraint conditions;

[0034] k > x + y + z (5)

[0035] Where:

[0036] x, y, z, and k are the first assignment, the second assignment, the third assignment, and the fourth assignment respectively;

[0037] node_g, cur_g, node_d, and cur_d are calculated using formula (1);

[0038] The slope rate is calculated using formula (4);

[0039] node_h and cur_h refer to the elevations of the child node and the parent node respectively;

[0040] α represents the set slope threshold;

[0041] The elevation values of invalid points are all -9999;

[0042] score += x is equivalent to score = score + x, which means assigning the value obtained by score + x to score;

[0043] score += y is equivalent to score = score + y, which means assigning the value obtained by score + y to score;

[0044] score += z is equivalent to score = score + z, which means assigning the value obtained by score + z to score;

[0045] score -= k is equivalent to score = score - k, which means assigning the value obtained by score - k to score;

[0046] Compare the parent node with the surrounding eight child nodes as above, and select the node with the largest score as the optimal node.

[0047] In a preferred embodiment of the present invention, the algorithm preferentially selects those path points that have a longer distance from the starting point to the current point, a shorter distance from the current point to the end point, and a smaller slope.

[0048] In a preferred embodiment of the present invention, a three-dimensional cost function evaluation function is adopted, which considers the three-dimensional spatial distance of the path and incorporates the slope of the terrain into the calculation, enabling the path planning to better meet the requirements of the actual terrain conditions. The introduction of the slope is achieved through an exponential function e k*slope where slope is the slope between the parent node and the child node, and k is the amplification factor. The role of the amplification factor is to adjust the influence degree of the slope factor in the entire evaluation function. As the value of k increases, the influence of the slope on the evaluation function also increases. The algorithm pays more attention to the flatness of the path when evaluating different paths, avoids terrains with large slopes, and ensures the safety and feasibility of the path. The expression of the three-dimensional cost function is:

[0049]

[0050] Where:

[0051] X i and X e respectively represent the X-axis coordinates of the i node and the e node;

[0052] Y i and Y e respectively represent the Y-axis coordinates of the i node and the e node;

[0053] Z i and Z e respectively represent the elevation data of the i-node and the e-node;

[0054] Size represents the pixel size of the DEM model;

[0055] slope is the slope rate between the parent node and the child node;

[0056] k is the amplification factor;

[0057] e k*slope The base e in it represents the natural base;

[0058] The new evaluation function is constructed as follows:

[0059]

[0060] Among them, F(n) represents the new evaluation function;

[0061] node represents the child node;

[0062] cur represents the parent node;

[0063] end represents the end point;

[0064] S(,) represents the three-dimensional cost function;

[0065] When the value of k increases, the algorithm needs to analyze the slope rate of each path more carefully, and the algorithm needs longer time to process and evaluate the path; the path results obtained by increasing the value of k meet the actual route selection requirements, and these paths take more account of the actual conditions of the terrain.

[0066] In a preferred embodiment of the present invention, the input file of the algorithm is an ASC file converted from a digital elevation model. The scale of the digital elevation model is an important factor affecting the running time of the algorithm. Under the same k-value setting, for a large digital elevation model, the algorithm needs to process more data and requires longer time to complete the evaluation and selection of the path. In order to balance the running efficiency of the algorithm and the accuracy of path planning, different k-values are designed and adjusted according to different sizes of digital elevation models. For sections with large terrain undulations, a smaller k-value is adopted; for sections with smaller terrain undulations, a larger k-value is adopted, which can not only ensure the efficient operation of the algorithm, but also guarantee the practicability and reliability of the path planning results.

[0067] In a preferred embodiment of the present invention, in the first step, the mobile phone number of the logged-in user is used to log in to the construction access road design platform. The operation steps of the present invention are simple. It only needs to input the user's mobile phone number and the six-digit SMS code received by the mobile phone number on the login interface of the computer to log in to the construction access road design platform and realize the display of the optimal path.

[0068] In a preferred embodiment of the present invention, first, the user's mobile phone number needs to be entered in the mobile phone number input box on the login interface. After the mobile phone number is entered, click to obtain the SMS code, and the system will send the six-digit SMS code to the user's mobile phone through the entered mobile phone number; after the user receives the SMS code, the user enters the SMS code into the SMS code input box, and finally clicks to log in, then the steps of logging in to the construction access road design platform can be realized.

[0069] In the above steps, the program will encrypt the mobile phone number entered by the user to ensure the security of the entered mobile phone number. The method of encrypting the mobile phone number is as follows:

[0070] First, obtain the serial number of the computer processor, and generate a table with 1 row and U columns according to the serial number and the number of digits of the mobile phone number. The size of U is equal to the sum of the number of digits of the serial number and the number of digits of the mobile phone number;

[0071] Randomly color the squares in the table. The total number of colored squares is 11, because the number of digits of the mobile phone number is 11, and the number of consecutive colored squares is 2 to 4. This can reduce the continuity of the mobile phone number and prevent cracking. After such an operation, the colored 1-row and U-column table is obtained;

[0072] After the above operations are done, fill in the mobile phone number into the colored squares in the order from left to right, fill in the serial number into the uncolored squares in the order from left to right. After completion, then take out the characters from the squares in the order from left to right, and the encrypted mobile phone number is obtained.

[0073] In summary, due to the adoption of the above technical solutions, the method for rapid and intelligent design of construction access roads in complex terrains of the present invention adopts an evaluation method of a three-dimensional cost function, and realizes fine regulation by introducing the slope rate factor and in the form of an exponential function and its amplification coefficient k. It not only makes the path planning more in line with the requirements of the actual terrain conditions, but also proposes a new solution for how to balance the operation efficiency and result accuracy of the algorithm.

[0074] The additional aspects and advantages of the present invention will be partially given in the following description, partially will become obvious from the following description, or will be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] The above and / or additional aspects and advantages of the present invention will become obvious and easy to understand from the description of the embodiments in conjunction with the following drawings, where:

[0076] Figure 1 is the flowchart of the A* algorithm of the present invention.

[0077] Figure 2It is the construction road planning route obtained by the A* algorithm with an evaluation function using a score assignment method as the core.

[0078] Figure 3 It is the construction road planning route obtained by the A* algorithm with an evaluation function using a three-dimensional cost function method as the core, and its k value is taken as 6.

[0079] Figure 4 It is the construction road planning route obtained by the A* algorithm with an evaluation function using a three-dimensional cost function method as the core, and its k value is taken as 7.

[0080] Figure 5 It is the construction road planning route obtained by the A* algorithm with an evaluation function using a three-dimensional cost function method as the core, and its k value is taken as 8.

[0081] Figure 6 It is the step flow chart of the present invention. Detailed implementation manner

[0082] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described by referring to the drawings below are exemplary and are only used to explain the present invention and should not be construed as a limitation to the present invention.

[0083] The present invention discloses a method for quickly and intelligently designing construction access roads in complex terrains, including the following steps:

[0084] The first step is to log in to the construction access road design platform; in the first step, the mobile phone number of the logged-in user is used to log in to the construction access road design platform. The operation steps of the present invention are simple. It only needs to input the user's mobile phone number and the six-digit SMS code received by the mobile phone number on the computer's login interface to log in to the construction access road design platform and display the optimal path.

[0085] In a preferred implementation manner of the present invention, first, the user's mobile phone number needs to be input in the mobile phone number input box on the login interface. After the mobile phone number is input, click to obtain the SMS code, and the system will send the six-digit SMS code to the user's mobile phone through the filled-in mobile phone number; after the user receives the SMS code, the user inputs the SMS code into the SMS code input box, and finally clicks to log in to complete the step of logging in to the construction access road design platform.

[0086] In the above steps, the program will encrypt the mobile phone number input by the user to ensure the security of the mobile phone number input by the user. The method for encrypting the mobile phone number is:

[0087] First, obtain the serial number of the computer processor. Generate a table with 1 row and U columns based on the serial number and the number of digits of the mobile phone number. The size of U is equal to the sum of the number of digits of the serial number and the number of digits of the mobile phone number;

[0088] Randomly color the squares in the table. The total number of colored squares is 11 because the number of digits of the mobile phone number is 11, and the number of consecutive colored squares is 2 to 4. This can reduce the continuity of the mobile phone number and prevent cracking. After such an operation, a 1-row U-column table after coloring is obtained;

[0089] After performing the above operations, fill in the colored squares with the mobile phone number in order from left to right, and fill in the uncolored squares with the serial number in order from left to right. After completion, take out the characters from the squares in order from left to right, and the encrypted mobile phone number is obtained;

[0090] Send the encrypted mobile phone number to the construction access road design platform. The construction access road design platform parses the encrypted mobile phone number:

[0091] Fill in the encrypted mobile phone number into the 1-row U-column table after coloring in order from left to right. After completion, take out the characters from the uncolored squares in order from left to right to obtain the serial number, and take out the characters from the colored squares in order from left to right to obtain the mobile phone number;

[0092] After the construction access road design platform obtains the mobile phone number and the serial number, it judges whether the obtained mobile phone number exists on the platform:

[0093] If the obtained mobile phone number exists on the platform, randomly select six characters from the serial number as the SMS code and send it to the mobile phone number; and associate the mobile phone number with the SMS code for judgment during subsequent verification;

[0094] If the obtained mobile phone number does not exist on the platform, the mobile phone number has not been set on the platform and needs to be set by the administrator.

[0095] Correspondingly, after the user's mobile phone receives the SMS code, the user enters the SMS code into the SMS code input box, and finally clicks to log in. The above steps will still be executed, but only the serial number is replaced with the SMS code, that is:

[0096] The program will encrypt the mobile phone number entered by the user to ensure the security of the mobile phone number entered by the user. The method of encrypting the mobile phone number is:

[0097] First, obtain the input SMS code. Generate a table with 1 row and V columns based on the SMS code and the number of digits of the mobile phone number. The size of V is equal to the sum of the number of digits of the SMS code and the number of digits of the mobile phone number;

[0098] Randomly color the squares in the table. The total number of colored squares is 11, because the mobile phone number has 11 digits, and the number of continuously colored squares is 2 - 4. This can reduce the continuity of the mobile phone number and prevent cracking. After such an operation, a 1-row V-column table after coloring is obtained;

[0099] After performing the above operations, fill in the colored squares with the mobile phone number in the order from left to right, and fill in the uncolored squares with the SMS code in the order from left to right. After completion, take out the characters from the squares in the order from left to right, and the encrypted mobile phone number is obtained;

[0100] Send the encrypted mobile phone number to the construction access road design platform, and the construction access road design platform analyzes the encrypted mobile phone number:

[0101] Fill in the encrypted mobile phone number in the 1-row V-column table after coloring in the order from left to right. After completion, take out the characters from the uncolored squares in the order from left to right to obtain the SMS code, and take out the characters from the colored squares in the order from left to right to obtain the mobile phone number;

[0102] After the construction access road design platform obtains the mobile phone number and the SMS code, it judges whether the obtained mobile phone number and SMS code are consistent with the associated mobile phone number and SMS code:

[0103] If the obtained mobile phone number and SMS code are consistent with the associated mobile phone number and SMS code, then the construction platform can be safely logged in using the mobile phone number; otherwise, it fails.

[0104] In the second step, after logging in to the construction access road design platform, input the starting point and target point coordinate information and DEM data into the optimal path system for the construction access road to obtain the optimal path; Refer to Figure 6 As shown, to obtain the optimal path, the following steps are included:

[0105] S1, preprocess the DEM data to obtain the input starting point and target point coordinate information;

[0106] S2, put the starting point into the open list, traverse the nodes adjacent to the starting point, and also put the nodes adjacent to the starting point into the open list;

[0107] S3, among the nodes adjacent to the starting point, select the optimal node by calculating the cost value required for each node;

[0108] S4, judge whether the optimal node is the target node. If so, output the optimal path; otherwise, execute the next step;

[0109] S5. Expand the children nodes of the current optimal node and check whether the children nodes of the current optimal node exist in the open list or the closed list. If the child node does not exist in the open list and the closed list, add the child node to the open list, and then select the next optimal node. If the child node already exists in the closed list, skip this node. If the child node already exists in the open list, it means that this node has been calculated as a neighboring node of other nodes. Recalculate the cost value as a child node, compare the new and old cost values of this node. If the new cost value is smaller, set the optimal node as the parent node of the child node. If the old cost value is smaller, keep the old parent node.

[0110] S6. Loop the above operations until reaching the end point or the open list is empty.

[0111] Specifically, the process of the A* algorithm is as shown in Figure 1 and includes the following steps:

[0112] S1. Preprocess the DEM data to obtain the coordinate information of the starting point and the target point.

[0113] S2. First, add the starting point to the open list, and traverse the nodes adjacent to the starting point, and add these nodes to the open list as well.

[0114] S3. Use an evaluation function that adopts a scoring value or a three-dimensional cost function to select the optimal node (bestnode) from the neighboring nodes.

[0115] S4. Check whether the bestnode is the target node. If so, output the optimal path; otherwise, proceed to the next step.

[0116] S5. Expand the children nodes of the current bestnode, and check whether the children nodes of the current bestnode exist in the open list or the closed list. If the child node does not exist in the open list and the closed list, add the child node to the open list, and then select bestnode. If the child node already exists in the closed list, skip this node. If the child node already exists in the open list, recalculate the F value or the score value, compare the new and old values of F(n) or score(n), take the smaller F(n) value, and select the child node with F(n) = min{F(n)} or score(n) = max{score(n)} as the next bestnode.

[0117] S6. Loop the above operations until reaching the end point or the open list is empty.

[0118] Embodiment 1

[0119] Preprocessing of DEM data: Parse the DEM model of the plot corresponding to the road route selection operation, and convert it into the ASC file required by this algorithm. The ASC file contains file format description, number of rows and columns, map origin coordinates, pixel size, missing value identifier, and raster elevation data. The algorithm completes the route selection by reading these data. Similarly, this algorithm will also generate an ASC file. By opening this data format file in software such as ArcGis, the operation of generating path visualization can be realized.

[0120] This algorithm also needs to obtain the starting and ending points of the road to be selected in advance. The starting and ending points of the route selection cases used in the present invention are both specified by experts, and then the digital coordinates are obtained through ArcGis software. These digital coordinates can directly become the input quantities of the algorithm.

[0121] The implementation of the search algorithm is as follows:

[0122] Initialize the open list and closed list, put the starting point A into the open list, and traverse the nodes adjacent to the starting point, and put these nodes into the open list as well. Then set point A as the parent node and the adjacent nodes as the child nodes. Finally, put the starting point A into the closed list.

[0123] After that, the selection of the optimal node is carried out, and two methods are adopted respectively:

[0124] Adopt an evaluation function with score assignment

[0125] As shown in Table 1 are the elevation data of a group of parent nodes and child nodes and the elevation data of the starting and ending points;

[0126] Table 1 Node elevation data

[0127] Starting elevation Ending elevation Parent node elevation Node1 Node2 Node3 Node4 Node5 Node6 Node7 Node8 120 140 101.27 101.26 101.1 101.75 100.77 101.74 100.7 101.86 100.8

[0128] As shown in Table 2 are the two-dimensional coordinate data of a group of parent nodes and child nodes and the two-dimensional coordinate data of the starting and ending points;

[0129] Table 2 Node coordinate data

[0130]

[0131]

[0132] Based on the above data, we can calculate values such as cur_d, cur_g, node_d, node_g, and slope. We choose to set the slope ratio and invalid value determination as decisive conditions, assign k the value of 100, assign z the value of 20, set the distance determination as an influential condition, assign x and y the value of 10, and set the slope ratio threshold α to 0.09. After calculation and comparison, the scores of each sub-node are shown in Table 3.

[0133] Table 3 Score values of each sub-node

[0134] Node1 Node2 Node3 Node4 Node5 Node6 Node7 Node8 Score 30 10 0 10 20 10 10 0

[0135] Therefore, Node1 is selected as the optimal node.

[0136] Adopt an evaluation function of a three-dimensional cost function

[0137] Using the same above data, since the selected road section is relatively flat, a larger value of k should be assigned. In this example, k takes 6, 7, and 8 respectively, and the calculated F(n) values are shown in Table 4;

[0138] Table 4 F(n) values of each sub-node

[0139]

[0140] Continue to iterate until reaching the end point or the open list is empty. As Figure 2 shown, it shows the route selection result of the construction road obtained by using the scoring evaluation function considering the slope condition; as Figure 3 、 Figure 4 and Figure 5 shown, it shows the route selection results of the construction road obtained by using the evaluation function of the three-dimensional cost function when k is 6, 7, and 8 respectively considering the slope condition. It has been proven by practice that the two route selection results considering the slope are consistent with the manual route selection results.

[0141] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and purposes of the present invention. The scope of the present invention is defined by the claims and their equivalents.

Claims

1. A fast and intelligent design method for construction access roads in complex terrain, characterized in that: The following steps are involved: The first step is to log in to the construction access road design platform; In the second step, after logging into the construction access road design platform, the coordinate information of the starting point and the target point and the DEM data are input into the construction access road optimal path system to obtain the optimal path; the method for obtaining the optimal path in the second step includes the following steps: S1, preprocessing the DEM data to obtain the input starting point and target point coordinate information; S2, put the starting point into the open list, traverse the nodes adjacent to the starting point, and put the nodes adjacent to the starting point into the open list; S3, among the nodes adjacent to the starting point, the optimal node is selected by calculating the cost value required for each node; the optimal node is selected from the adjacent nodes using an evaluation function, which takes the form of a three-dimensional cost function. The expression of the three-dimensional cost function is: in: X i and X e Represent the X-axis coordinates of the i-node and the e-node respectively; Y i and Y e Represent the Y-axis coordinates of the i-node and the e-node respectively; Z i and Z e Represents the elevation data of i-node and e-node respectively; Size indicates the pixel size of the DEM model; Slope is the slope between the parent node and the child node; k is the magnification factor; The base of Represents the natural base; S4, determine whether the optimal node is the target node, if so, output the optimal path, otherwise proceed to the next step; S5, expand the child nodes of the current optimal node, and determine whether the child nodes of the current optimal node exist in the open list and the closed list; if the child node does not exist in the open list and the closed list, put the child node in the open list, and then select the next optimal node; if the child node already exists in the closed list, skip the child node; if the child node already exists in the open list, it means that the child node has been calculated as a neighboring node of other nodes, and recalculate the cost value as the child node, compare the new and old cost values ​​of the child node, if the new cost value is smaller, take the optimal node as the parent node of the child node; if the old cost value is smaller, keep the old parent node; S6, repeat the above operations until reaching the end point; The third step is to display the optimal path.

2. The method for rapid intelligent design of construction access roads in complex terrain according to claim 1 is characterized in that: In step S3, the evaluation function is constructed in the form of a three-dimensional cost function, where the slope function expression is e k*slope , where slope is the slope between the parent node and the child node, k is the magnification coefficient, calculate the cost of different points to be determined, and select the node with the smallest cost as the optimal node.

3. The method for rapid intelligent design of construction access roads in complex terrain according to claim 1 is characterized in that: In step S3, the cost value is the fundamental criterion for determining whether a node is the optimal node. The three-dimensional distance calculation formula is used. The calculation result of the formula reflects the actual distance between two pixels. The expression is: Where: X i and X e Corresponding to the X-axis coordinates of the i-node and the e-node; Y i and Y e Corresponding to the Y-axis coordinates of the i-node and the e-node; Z i and Z e Elevation data corresponding to i-node and e-node; Size corresponds to the pixel size of the DEM model.

4. The method for rapid intelligent design of construction access roads in complex terrain according to claim 3 is characterized in that: On the basis of the distance calculation formula, the evaluation function of the three-dimensional cost function is adopted. The cost value is directly calculated for the child nodes of the optimal node, and the child node with the smallest cost value is taken as the new optimal node.

5. The method for rapid intelligent design of construction access roads in complex terrain according to claim 4 is characterized in that: The evaluation function of the three-dimensional cost function takes into account the three-dimensional spatial distance of the path and takes the slope of the terrain into account, so that the path planning can be closer to the actual terrain conditions. The introduction of the slope is through an exponential function e k*slope to achieve this, where slope is the slope between the parent node and the child node, and k is the magnification factor. The function of the magnification factor is to adjust the influence of the slope factor in the entire evaluation function. As the k value increases, the influence of the slope on the evaluation function also increases. When evaluating different paths, the algorithm pays more attention to the flatness of the path, avoids terrains with large slopes, and ensures the safety and feasibility of the path.

6. The method for rapid intelligent design of construction access roads in complex terrain according to claim 1 is characterized in that: In the first step, the mobile phone number of the logged-in user is used to log in to the construction access road design platform.

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