Intelligent path searching system for building pipeline

Abstract

The application provides a kind of building pipeline intelligent pathfinding system, comprising: three-dimensional scene modeling module, for grid map modeling, three-dimensional pipeline modeling and obstacle modeling;Pipeline and structure area pre-division module, for each pipeline edge expansion and area division;Intelligent pathfinding algorithm decision module, for judging the input form of project, if input is the starting point, end point and intermediate point of pipeline, then call the first improved A* intelligent pathfinding algorithm to carry out pipeline pathfinding, if input is the starting point and end point of pipeline, then call the second improved A* intelligent pathfinding algorithm to carry out pipeline pathfinding;Pipeline micro-adjustment module, for judging whether each pipeline is over-mapping or conservative mapping, if over-mapping, then carry out space tightening, if conservative mapping, then carry out space expansion.The beneficial effects of the application are that different algorithms are configured for pathfinding with different inputs, to improve the efficiency of pathfinding.

Description

Technical Field

[0001] This invention relates to the field of building pipeline design technology, and in particular to an intelligent pipeline routing system. Background Technology

[0002] Currently, the independent work of various departments within the construction industry, with separate designs for the building structure and piping, leads to situations where pipe routes intersect and collide, deviating from practical engineering requirements. Existing intelligent pipe layout systems are limited, and the application of building intelligence in actual engineering projects is mostly limited to management and real-time monitoring systems, rather than intelligent pipeline design systems. Existing intelligent pipe layout systems have few constraints, resulting in routes that do not adequately meet the needs of actual projects. They also lack user design flexibility, offering few settings for selection and adjustment, and sometimes even failing to meet basic construction industry standards. Furthermore, existing intelligent pathfinding systems lack the application of pathfinding algorithms in construction. Summary of the Invention

[0003] The technical problem to be solved by this invention is to provide an intelligent pipeline routing system for buildings, which aims to avoid the problem of pipelines colliding with obstacles in buildings.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: an intelligent pipeline routing system for buildings, comprising:

[0005] The 3D scene modeling module is used for raster map modeling, 3D pipeline modeling, and obstacle modeling.

[0006] The pipeline and structural area pre-division module is used to expand the edges of each pipeline segment and divide the area.

[0007] The intelligent pathfinding algorithm decision module is used to determine the input form of the project. If the input is the start point, end point and intermediate point of the pipeline, the preset first improved A* intelligent pathfinding algorithm is called to find the pipeline path. If the input is the start point and end point of the pipeline, the preset second improved A* intelligent pathfinding algorithm is called to find the pipeline path.

[0008] The pipeline fine-tuning module is used to determine whether each pipeline segment is over-mapped or conservatively mapped. If it is over-mapped, the space is tightened; if it is conservatively mapped, the space is expanded.

[0009] Furthermore, the 3D scene modeling module is specifically used for,

[0010] Raster map modeling: This requires converting the pipe data into a 3D pipe data structure of the corresponding size, and taking the least common divisor DN of all pipe diameters. min , as the basic unit for constructing raster maps;

[0011] 3D pipe modeling: Constructing corresponding 3D spatial structures for pipes of different sizes;

[0012] Obstacle modeling: Based on the least common divisor DN of the pipe diameter min For the basic unit of raster map construction, a boundary coefficient α is defined to determine the extent to which the boundary of an obstacle expands within the raster map. The value of α ranges from 0 to 1. This is determined by the decision coefficient S of the current raster. α The value of α determines whether the current grid is 0 or 1, where 0 represents a feasible area for the pipe and 1 represents an obstacle.

[0013] Furthermore, the pre-division module for pipe and structural areas is specifically used for,

[0014] Based on each pipe group, the three-dimensional space containing each pipe group is expanded at the edge;

[0015] The obstacles within the expanded area are included in the obstacle list, and each pipe group and its corresponding obstacle list constitute a sub-region, thus completing the pre-division of the region.

[0016] Furthermore, the execution steps of the first improved A* intelligent pathfinding algorithm are configured as follows:

[0017] S31. Data initialization: Initialize two tables, openlist and closelist. The openlist table is used to store unvisited rasters, and the closelist table is used to store detected rasters.

[0018] S32. Obtain the coordinates Mi of the intermediate point of the input pipe, create a new dictionary dict to store the coordinates Mi of the intermediate point of the pipe, and create a new pathlist table to record the set of n+1 paths generated by n intermediate points Mi, the starting point S and the ending point E.

[0019] S33. Add the starting point S of the pipeline to the openlist table;

[0020] S34. Check the adjacent squares Ti, i = 1, 2... 6, of the current square Tc. In 3D space, there are six adjacent squares. Add the walkable or reachable squares to the openlist. Determine the next square Tn of the adjacent square Ti according to the following core formula, and calculate its evaluation function value F(T) according to the formula. i ):

[0021] Fn=Gn+Hn

[0022] Where Fn is the valuation function value, and whether the path needs to be updated depends on F(T). iIs it smaller? Gn is the lost cost function value, the cost of moving from the starting point S along the generated path to Ti; Ln is the path length cost, B(n) is the turning cost, and a and b are adjustable weights.

[0023] Gn=a·Ln+b·B(n)

[0024] Hn is the value of the movement cost function, the estimated movement cost from Ti to the target node Mi. Hn is estimated using Manhattan distance, that is, Hn is the Manhattan distance between Ti and the coordinates of the destination E.

[0025] Hn=Mi-T i

[0026] S35, Search T i Storage records;

[0027] S36, If T i If the destination is determined to be E, then return the pathlist collection successfully; otherwise, repeat step S34.

[0028] S37. After the path is generated, redundancy removal is performed.

[0029] S38. Maintain an array path to store the final deduplication path;

[0030] S39. Query the dictionary dict that records intermediate points Mi, and merge the paths before and after each intermediate point;

[0031] S310. Determine if the dict has been traversed. If not, repeat step S32 and update path; otherwise, return path, indicating that the redundancy removal operation is complete, and exit the algorithm.

[0032] Furthermore, in step S35, T is retrieved. i The specific storage records are as follows:

[0033] S351, if T i If the item cannot be found in the openlist table, then it will be stored in the openlist table.

[0034] S352, if T i If the value already exists in the openlist table, replace the old value with the smaller value F(T) and update the openlist table;

[0035] If any of the following three situations occur, repeat step S34;

[0036] The square is not walkable; the square is not reachable; T i It already exists in the closelist table.

[0037] Furthermore, the execution steps of the second improved A* intelligent pathfinding algorithm are configured as follows:

[0038] S41. Calculate the Manhattan distance D from each starting point S to the ending point E. i And select the path with the longest distance from Manhattan as the first path P. base ;

[0039] S42, on the first path P base Based on this, the turning point is selected as the next starting point S. Steps S31-S36 are then performed on the remaining endpoints E, and the evaluation function P is used to determine the starting point. b n selects the optimal path, P b n = a·N b +b·D p ;

[0040] Where a and b are weighting factors, N b D represents the number of turns. p The Manhattan length of the path;

[0041] S43. Create a new table Memory to record each intermediate point M. i To the remaining destination E i All path coordinates;

[0042] S44. Calculate each intermediate point M. i Evaluation function P to the current endpoint b i, after calculating all P b After evaluating the values ​​of i, select the largest P. b i, will P b The path with the largest value of i is taken as the local optimal path. The evaluation function from each intermediate point to the next endpoint is calculated until n local optimal paths are obtained, where n is the number of all endpoints.

[0043] S45. Repeat steps S31-S36 to obtain all paths and integrate them into a new path path.

[0044] S46. Perform redundancy removal on the path.

[0045] Furthermore, the redundancy removal process in step S37 includes,

[0046] Remove the original path that leads back in the path, and remove the path that leads back to adjacent cells.

[0047] Furthermore, the redundancy removal process in step S46 includes,

[0048] Set a threshold range and determine whether the minimum adjustment range between the new path and the base path after iteration is within the threshold range. If so, perform path redundancy removal.

[0049] Furthermore, it determines whether each pipeline segment is over-mapped or conservatively mapped. If it is over-mapped, then space tightening is performed.

[0050] Determine if the distance between the pipe and the obstacle is greater than the preset constraint distance of the installation space. If it is greater, it is an over-mapping. Translate the pipe so that the distance between the pipe and the obstacle is equal to the preset constraint distance of the installation space.

[0051] Furthermore, it determines whether each pipeline segment is over-mapped or conservatively mapped. If it is conservatively mapped, then spatial expansion is performed, including...

[0052] Determine whether the distance between the pipe and the obstacle is less than the preset constraint distance of the installation space. If it is less, it is a conservative mapping, and the pipe is translated so that the distance between the pipe and the obstacle is equal to the preset constraint distance of the installation space.

[0053] The beneficial effects of this invention are as follows: the intelligent pipeline path finding system models the pipeline using a 3D scene module; it expands and divides the pipeline edge using a pipeline and structural area pre-division module; it performs pipeline path finding using an intelligent path finding algorithm decision module, and configures different algorithms to improve path finding efficiency by considering whether there are fixed intermediate point constraints in the pipeline input; and it adjusts the position of the pipeline using a pipeline micro-adjustment module, so that the pipeline is placed at a preset distance from obstacles. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0055] Figure 1 This is a flowchart of the intelligent pipeline routing system according to an embodiment of the present invention;

[0056] Figure 2 A schematic diagram of the raster mapping result with a boundary coefficient α of 0.5 in an embodiment of the present invention;

[0057] Figure 3 A schematic diagram of the raster mapping result when the boundary coefficient α is 0 according to an embodiment of the present invention;

[0058] Figure 4 A schematic diagram of non-overlapping sub-regions according to an embodiment of the present invention;

[0059] Figure 5 A schematic diagram of overlapping sub-regions according to an embodiment of the present invention;

[0060] Figure 6 A schematic diagram of the basic path for the first input form in an embodiment of the present invention;

[0061] Figure 7 A schematic diagram of the basic path for the second input form in this embodiment of the invention;

[0062] Figure 8 A schematic diagram of the redundancy removal operation for input form one according to an embodiment of the present invention;

[0063] Figure 9 A schematic diagram of the basic path formation in an embodiment of the present invention;

[0064] Figure 10 A schematic diagram illustrating the formation of the remaining locally optimal path in an embodiment of the present invention;

[0065] Figure 11 A schematic diagram of the formation of a new path after iteration in an embodiment of the present invention;

[0066] Figure 12 A schematic diagram of the redundancy removal operation for input form two in this embodiment of the invention;

[0067] Figure 13 Flowchart of the intelligent pathfinding algorithm decision-making process according to an embodiment of the present invention;

[0068] Figure 14 A schematic diagram of pipe space tightening adjustment according to an embodiment of the present invention;

[0069] Figure 15 A schematic diagram of pipe space expansion and adjustment according to an embodiment of the present invention. Detailed Implementation

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0071] It should be noted that the descriptions involving "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Furthermore, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0072] Please see Figure 1 An embodiment of the present invention is: an intelligent pipeline routing system for buildings, comprising:

[0073] The 3D scene modeling module is used for raster map modeling, 3D pipeline modeling, and obstacle modeling.

[0074] In this module, the grid mapping method mainly uses a boundary factor α to determine whether obstacles are conservatively mapped or over-mapped. α can be set according to the actual engineering scenario requirements.

[0075] The pipeline and structural area pre-division module is used to expand the edges of each pipeline segment and divide the area.

[0076] In this module, since the pipe data extracted from Revit modeling software is treated as a single pipe segment as an object instance, while actual engineering projects treat a whole group of pipes as a whole, it is necessary to divide, classify, and re-integrate the pipes.

[0077] The intelligent pathfinding algorithm decision module determines the input format of the project. If the input consists of the pipeline's start point, end point, and intermediate points, it is input format one, and the preset first improved A* intelligent pathfinding algorithm is invoked for pipeline pathfinding. If the input consists of the pipeline's start point and end point, it is input format two, and the preset second improved A* intelligent pathfinding algorithm is invoked for pipeline pathfinding. Specifically, as follows... Figure 6 , Figure 7 As shown.

[0078] In this module, the A* intelligent routing algorithm, based on two different input formats, is used. In practical engineering design, this system defines two pipeline input methods, which are also commonly used wiring methods for building pipeline information. The traditional A* algorithm configures different loss values ​​for data information in the map, and pipelines will default to being laid in areas with lower loss values. Therefore, this algorithm increases the loss value of building structures such as walls, beams, and columns, while reducing the cost of pipelines in passable areas near walls. At the same time, it sets up checkpoints for impassable areas, where the cost is infinitely high, forcing pipelines to avoid passing through these areas. The improvement of this system adds the setting of intermediate points and redundancy removal operations, that is, splitting the route traversed by the pipeline's start and end points into multiple segments, i.e., adding a certain number of intermediate points. When all intermediate points are connected together, a new path is formed.

[0079] The pipeline fine-tuning module is used to determine whether each pipeline segment is over-mapped or conservatively mapped. If it is over-mapped, the space is tightened; if it is conservatively mapped, the space is expanded.

[0080] In this module, there may be some errors after the 3D scene is modeled. The pipeline fine-tuning module is used to make fine adjustments to the position of the pipeline to avoid wasting space or even causing false hard collisions.

[0081] The 3D scene modeling module is specifically used for,

[0082] Raster map modeling: This requires converting the pipe data into a 3D pipe data structure of the corresponding size, and taking the least common divisor DN of all pipe diameters. min , as the basic unit for constructing raster maps;

[0083] 3D pipe modeling: Constructing corresponding 3D spatial structures for pipes of different sizes;

[0084] Obstacle modeling: Based on the least common divisor DN of the pipe diameter min For the basic unit of raster map construction, a boundary coefficient α is defined to determine the extent to which the boundary of an obstacle expands within the raster map. The value of α ranges from 0 to 1. This is determined by the decision coefficient S of the current raster. α The value of α determines whether the current grid cell is 0 or 1, where 0 represents a feasible area for the pipeline and 1 represents an obstacle. Specifically, as shown... Figure 2 , Figure 3 As shown.

[0085] Specifically, the pre-division module for pipe and structural areas is used for:

[0086] Based on each pipe group, the three-dimensional space containing each pipe group is expanded at the edge;

[0087] The obstacles within the expanded area are included in the obstacle list, and each pipe group and its corresponding obstacle list constitute a sub-region, thus completing the pre-division of the region.

[0088] This intelligent pipeline routing system adapts to two different routing algorithms, and to save computing power, different regional divisions are required for the scene. The division is based on a pipe group (pipeGroup), which is then expanded in its 3D space. Obstacles within the expanded area are added to an obstacle list (blockList). The space composed of the pipeGroup and the blockList is called a sub-region, thus completing the pre-division of the region. For example... Figure 4 , Figure 5 Whether sub-regions overlap does not affect the operation of the algorithm, because this region is virtual and is only used for obstacle group classification to exclude obstacles that do not need to be considered when laying pipelines, thus saving computing power.

[0089] like Figure 13 As shown, the execution steps of the first improved A* intelligent pathfinding algorithm are configured as follows:

[0090] S31. Data initialization: Initialize two tables, openlist and closelist. The openlist table is used to store unvisited rasters, and the closelist table is used to store detected rasters.

[0091] S32. Obtain the coordinates Mi of the intermediate point of the input pipe, create a new dictionary dict to store the coordinates Mi of the intermediate point of the pipe, and create a new pathlist table to record the set of n+1 paths generated by n intermediate points Mi, the starting point S and the ending point E.

[0092] S33. Add the starting point S of the pipeline to the openlist table;

[0093] S34. Check the adjacent squares Ti, i = 1, 2... 6, of the current square Tc. In 3D space, there are six adjacent squares. Add the walkable or reachable squares to the openlist. Determine the next square Tn of the adjacent square Ti according to the following core formula, and calculate its evaluation function value F(T) according to the formula. i ):

[0094] Fn=Gn+Hn

[0095] Where Fn is the valuation function value, and whether the path needs to be updated depends on F(T). i Is it smaller? Gn is the lost cost function value, the cost of moving from the starting point S along the generated path to Ti; Ln is the path length cost, B(n) is the turning cost, and a and b are adjustable weights.

[0096] Gn=a·Ln+b·B(n)

[0097] Hn is the value of the movement cost function, the estimated movement cost from Ti to the target node Mi. Hn is estimated using Manhattan distance, that is, Hn is the Manhattan distance between Ti and the coordinates of the destination E.

[0098] Hn=Mi-T i

[0099] S35, Search T i Storage records;

[0100] S36, If T i If the destination is determined to be E, then return the pathlist collection successfully; otherwise, repeat step S34.

[0101] S37. After the path is generated, redundancy removal is performed.

[0102] S38. Maintain an array path to store the final deduplication path;

[0103] S39. Query the dictionary dict that records intermediate points Mi, and merge the paths before and after each intermediate point;

[0104] S310. Determine if the dict has been traversed. If not, repeat step S32 and update path; otherwise, return path, indicating that the redundancy removal operation is complete, and exit the algorithm.

[0105] Furthermore, in step S35, T is retrieved. i The specific storage records are as follows:

[0106] S351, if T i If the item cannot be found in the openlist table, then it will be stored in the openlist table.

[0107] S352, if T i If the value already exists in the openlist table, replace the old value with the smaller value F(T) and update the openlist table;

[0108] If any of the following three situations occur, repeat step S34;

[0109] The square is not walkable; the square is not reachable; T i It already exists in the closelist table.

[0110] The execution steps of the second improved A* intelligent pathfinding algorithm are configured as follows:

[0111] S41. Calculate the Manhattan distance D from each starting point S to the ending point E. i And select the path with the longest distance from Manhattan as the first path P. base ;like Figure 9 As shown.

[0112] S42, on the first path P base Based on this, the turning point is selected as the next starting point S. Steps S31-S36 are then performed on the remaining endpoints E, and the evaluation function P is used to determine the starting point. b n selects the optimal path, P b n = a·N b +b·D p ;

[0113] Where a and b are weighting factors, N b D represents the number of turns. p The Manhattan length of the path;

[0114] S43. Create a new table Memory to record each intermediate point M. i To the remaining destination E i All path coordinates;

[0115] S44. Calculate each intermediate point M. i Evaluation function P to the current endpoint b i, after calculating all P b After evaluating the values ​​of i, select the largest P. b i, will P b The path with the largest value for i is taken as the local optimum. The evaluation function from each intermediate point to the next endpoint is then calculated until n local optima are obtained, where n is the total number of endpoints. For example... Figure 10 As shown.

[0116] S45. Repeat steps S31-S36 to obtain all paths and integrate them into a new path; for example... Figure 11 As shown.

[0117] S46. Perform redundancy removal on the path.

[0118] Among them, such as Figure 8 As shown, the redundancy removal process in step S37 includes,

[0119] Remove the original path that leads back in the path, and remove the path that leads back to adjacent cells.

[0120] Among them, such as Figure 12 As shown, the redundancy removal process in step S46 includes,

[0121] Set a threshold range and determine whether the minimum adjustment range between the new path and the base path after iteration is within the threshold range. If so, perform path redundancy removal.

[0122] This includes determining whether each pipe segment is over-mapped or conservatively mapped; if over-mapped, space tightening is performed.

[0123] Determine if the distance between the pipe and the obstacle is greater than the preset installation space constraint distance. If it is, it is an over-mapping, and the pipe is translated so that the distance between the pipe and the obstacle equals the preset installation space constraint distance. Figure 14 As shown.

[0124] This includes determining whether each pipeline segment is over-mapped or conservatively mapped; if conservatively mapped, then performing space expansion.

[0125] Determine if the distance between the pipe and the obstacle is less than the preset constraint distance of the installation space. If it is less, a conservative mapping is used, and the pipe is translated so that the distance between the pipe and the obstacle equals the preset constraint distance of the installation space. Figure 15 As shown.

[0126] The advancements of this building's intelligent pipeline routing system lie in:

[0127] 1. Better aligned with engineering design. The pipeline input method in engineering design, due to the presence of intermediate points, better matches the A* path planning form of this system. Furthermore, by concretely abstracting engineering constraints, these abstract constraints become traceable, numerically referable, and adjustable, reasonably catering to user needs and providing users with significant design flexibility when performing CAD input.

[0128] 2. Saves more computing resources. Since the A* algorithm requires a certain amount of computing power, in actual engineering, for some pipelines that are already well-maintained, only a path adjustment algorithm based on analytical geometry is needed, and the A* algorithm is not required for error correction.

[0129] 3. Better adapted to complex scenarios. Because this system is designed with better pipeline planning decisions, it will perform pre-analysis based on parameters such as pipeline input and the number of obstacles inside the scene after pre-division, and then select a more targeted pipeline layout strategy to improve the overall algorithm performance.

[0130] 4. Easier algorithm reuse. Because the pipeline input and classification methods designed in this system have certain standardization and scalability, they are applicable to most pipeline layout engineering scenarios. Furthermore, the system has different algorithm branches to deal with different scenarios. For some more complex scenarios, specific algorithms can be appropriately ported or extended for targeted processing.

[0131] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A building pipeline intelligent path finding system, characterized in that, include: The 3D scene modeling module is used for raster map modeling, 3D pipeline modeling, and obstacle modeling. The pipeline and structural area pre-division module is used to expand the edges of each pipeline segment and divide the area. The intelligent pathfinding algorithm decision module is used to determine the input form of the project. If the input is the start point, end point and intermediate point of the pipeline, the preset first improved A* intelligent pathfinding algorithm is called to find the pipeline path. If the input is the start point and end point of the pipeline, the preset second improved A* intelligent pathfinding algorithm is called to find the pipeline path. The execution steps of the first improved A* intelligent pathfinding algorithm are configured as follows: S31. Data initialization: Initialize two tables, open list and close list. The open list is used to store unvisited rasters, and the close list is used to store detected rasters. S32. Obtain the coordinates Mi of the intermediate point of the input pipe, create a new dictionary dict to store the coordinates Mi of the intermediate point of the pipe, and create a new path list table to record the set of n+1 paths generated by n intermediate points Mi, the starting point S and the ending point E. S33. Add the starting point S of the pipeline to the open list; S34. Check the adjacent squares Ti, i=1,2...6, of the current square Tc. In 3D space, there are six adjacent squares. Add the walkable or reachable squares to the open list. Determine the next square Tn of the adjacent square Ti according to the following core formula, and calculate its evaluation function value according to the formula. : ； in, Whether the path needs to be updated depends on the valuation function value. Is it smaller? The lost cost function value is the movement cost from the starting point S along the generated path to Ti. For path length cost, The cost of turning; a and b are adjustable weights. ； Let Ti be the estimated movement cost from the target node Mi, representing the movement cost function value. The estimation method uses the Manhattan distance, i.e. It is the Manhattan distance between Ti and the coordinates of the endpoint E: ； S35, Search Storage records; S36, if If the destination is determined to be E, then return the path list collection successfully; otherwise, repeat step S34. S37. After the path is generated, redundancy removal is performed. S38. Maintain an array path to store the final deduplication path; S39. Query the dictionary dict that records intermediate points Mi, and merge the paths before and after each intermediate point; S310. Determine if the dict has been traversed. If not, repeat step S32 and update path; otherwise, return path, indicating that the redundancy removal operation is complete and exit the algorithm. The execution steps of the second improved A* intelligent pathfinding algorithm are configured as follows: S41. Calculate the Manhattan distance from each starting point S to the ending point E. Then, the path with the longest distance from Manhattan is selected as the first pathfinding path. ; S42, on the first pathfinding path Based on this, the turning point is selected as the next starting point S. Steps S31-S36 are then performed on the remaining endpoints E, and the evaluation function is used to determine the starting point. Select the optimal path. ; Where a and b are weighting factors. For the number of turns, The Manhattan length of the path; S43. Create a new table named Memory to record the intermediate points. To the remaining destination All path coordinates; S44. Calculate each intermediate point. Evaluation function to the current endpoint After calculating all After evaluating the values, select the largest one. ,Will The path with the longest corresponding value is taken as the local optimal path. The evaluation function from each intermediate point to the next endpoint is calculated until n local optimal paths are obtained, where n is the number of all endpoints. S45. Repeat steps S31-S36 to obtain all paths and integrate them into a new path path. S46. Perform redundancy removal on the path; The pipeline fine-tuning module is used to determine whether each pipeline segment is over-mapped or conservatively mapped. If it is over-mapped, the space is tightened; if it is conservatively mapped, the space is expanded.

2. The intelligent pipeline routing system as described in claim 1, characterized in that, The 3D scene modeling module is specifically used for, Raster map modeling: Converting pipe data into a 3D pipe data structure of the corresponding size, and taking the least common divisor DN of all pipe diameters. min , as the basic unit for constructing raster maps; 3D pipe modeling: Constructing corresponding 3D spatial structures for pipes of different sizes; Obstacle modeling: Based on the least common divisor DN of the pipe diameter min For the basic unit of raster map construction, a boundary coefficient α is defined to determine the extent to which the boundary of an obstacle expands within the raster map. The value of α ranges from 0 to 1. This is determined by the decision coefficient S of the current raster. α The value of α determines whether the current grid is 0 or 1, where 0 represents a feasible area for the pipe and 1 represents an obstacle.

3. The intelligent pipeline routing system as described in claim 2, characterized in that, The pipeline and structural area pre-division module is specifically used for, Based on each pipe group, the three-dimensional space containing each pipe group is expanded at the edge; The obstacles within the expanded area are included in the obstacle list, and each pipe group and its corresponding obstacle list constitute a sub-region, thus completing the pre-division of the region.

4. The intelligent pipeline routing system as described in claim 1, characterized in that, In step S35, the retrieval... The specific storage records are as follows: S351, if If the item cannot be found in the open list table, then it will be stored in the open list table. S352, if If it already exists in the open list, use the smaller value. Replace the old values ​​and update the open list table; If any of the following three situations occur, repeat step S34; The squares are not walkable; the squares are not reachable. It already exists in the close list.

5. The intelligent pipeline routing system as described in claim 1, characterized in that, The redundancy removal process in step S37 includes, Remove the original path that leads back in the path, and remove the path that leads back to adjacent cells.

6. The intelligent pipeline routing system as described in claim 1, characterized in that, The redundancy removal process in step S46 includes, Set a threshold range and determine whether the minimum adjustment range between the new path and the base path after iteration is within the threshold range. If so, perform path redundancy removal.

7. The intelligent pipeline routing system as described in claim 1, characterized in that, Determine whether each pipe segment is over-mapped or conservatively mapped. If it is over-mapped, perform space tightening. Determine if the distance between the pipe and the obstacle is greater than the preset constraint distance of the installation space. If it is greater, it is an over-mapping. Translate the pipe so that the distance between the pipe and the obstacle is equal to the preset constraint distance of the installation space.

8. The intelligent pipeline routing system as described in claim 7, characterized in that, Determine whether each pipe segment is over-mapped or conservatively mapped. If it is conservatively mapped, then perform space expansion, including... Determine whether the distance between the pipe and the obstacle is less than the preset constraint distance of the installation space. If it is less, it is a conservative mapping, and the pipe is translated so that the distance between the pipe and the obstacle is equal to the preset constraint distance of the installation space.

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