JP2002288250A - Automatic routing method for piping - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an automatic routing method for piping, where a route which is highly improved in ability to be constructed, can be selected with quantitative considerations and a multiple number of route patterns that are practical, when constructing the piping can be produced. SOLUTION: In the automatic routing method for piping, where a piping route can be selected quantitatively, taking into account the workability of installation of the piping by accumulating special information of cells constituting each route, by imparting the spacial information (distance to the floor, distance to a steel beam and occupancy rate of peripheral space by instruments, etc.), as attribute data to each cell constituting the piping route and by selecting one with the highest points (high in ability of being conducted) among the multiple number of route patterns. Furthermore, by wetting a priority routing for each rank and varying the setting of routing priority for each rank, a multiple number of route pattern where the number of curves of the piping can be kept down can be produced. The combinations of routing priority for each rank can be deemed as being genetic codes, and the quasi-optimal combination of routing priority (route pattern) can b searched by applying hereditary algorithm to it.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an automatic piping method for a piping CAD system for an industrial plant.

[0002]

2. Description of the Related Art Piping line design work in industrial plants involves a wide variety of functions such as function assurance, arrangement adjustment, strength assurance, and maintainability, and requires a great deal of design time and skill. CAD manufacturers and plant manufacturers are developing automatic piping routing systems with the aim of automating piping design. At present, as a method of automatically routing piping, a method in which design constraints are added based on a route search algorithm is often applied. One of the possible algorithms for the route optimization problem is LSI
(Large scale integrated circuit), a maze method developed for wiring processing on printed circuit boards and the like. The maze method is a route search method that connects the start point and the end point with the shortest distance while avoiding various obstacles (e.g., equipment and steel frames).

FIG. 14 (a), FIG. 14 (b), FIG.
(C) shows a route search algorithm based on the maze method.
First, the routing area is divided into meshes, and obstacle determination is performed for each mesh. If an obstacle exists at any mesh position, it is painted black, and S (start) and T (terminal) are assigned to positions corresponding to the start point and end point coordinates (hereinafter, each divided mesh is referred to as a cell). ).

Next, "1" is written into a blank cell in the vertical and horizontal directions adjacent to S. The cell into which "1" has been written writes "2" into the upper, lower, left and right blank cells adjacent to the own position. This procedure is repeated until T is reached. In the example of FIG. 14C, it can be seen that the write value “24” has reached T. The route is T
Trace the path back from S to S. “2” assigned to the T cell from the adjacent cells when viewed from the T cell position.
A cell in which a value (“23”) smaller than “4” (“23”) is written is selected.

Further, the selected cell selects a cell in which a value smaller than "23"("22") is written among adjacent cells. This procedure is repeated until S is reached.
A route connecting the selected cells from T to S is a route. The maze method has an algorithm for selecting a route that minimizes the number of cells connecting the start point and the end point, and can be said to be a shortest path search algorithm. In wiring of an electronic circuit, it is important to connect as many wirings as possible and as short as possible, avoiding electronic components, and the maze method as a route search method is effective and has been applied in many cases. FIG. 14 (a), FIG.
4 (b) and FIG. 14 (c) show two dimensions,
The same can be done in three dimensions.

FIG. 15 shows an example in which the maze method is applied in a three-dimensional space. The space is sliced into a plurality of layers in the height direction, each layer is divided into meshes, and adjacent cells are selected to construct a route. When three-dimensionally considered, adjacent cells in the upper and lower layers are added to adjacent cells in the same layer. Steel frame,
Obstacles such as devices are given to cells in advance as obstacle information, and when searching for a route, cells without obstacles are selected to form a route.

An example in which the maze method as a route search technique is applied to piping routing will be described below. As shown in FIG. 6, first, a region to be routed is defined by a rectangular parallelepiped, sliced in the height direction, and the sliced plane is divided into meshes. The slicing height corresponds to the routing level of the pipe. The position of the cell is represented by a three-dimensional array Cell (i,
j, k), and when it is determined that an obstacle (equipment, steel frame, etc.) is included in the cell range, the obstacle information is set as the obstacle information of each cell. Here, Cell (i, j, k)
If there is an obstacle at Cell (i, j, k). Obstacle =
1, Cell (i, j, k) if none. It is assumed that failure = 0.

When searching for a route, adjacent cells between the coordinates of the starting point and the ending point of the pipe are continuously selected while avoiding obstacles to form a route. Note that adjacent cells include not only a plane but also a vertical direction. The route needs to avoid obstacles. However, as shown in FIG. 16, there may be obstacles between layers. For example, the cells at the coordinate points A and B are vertically adjacent to each other, and the cells cannot be connected as a route just because there is no obstacle as attribute data included in both cells. Because it collides with obstacles between the levels. In order to avoid this state, each cell is dealt with by having a distance to an obstacle located upward and downward from the corresponding cell (the shortest distance when there are a plurality of obstacles).

FIGS. 17 and 18 show a subroutine for judging whether or not connection to an adjacent cell is possible. Here, the current position of the route search cell is defined as Cell (i, j, k). The distance from the cell (i, j, k) to the obstacle in the upward direction is “Cell (i, j, k). Upper obstacle”, and the distance to the obstacle in the downward direction is “Cell (i, j, k)”. k). Lower disorder ". The processing flow of FIG. 17 and FIG.
It is determined whether connection is possible in six adjacent directions centering on ll (i, j, k).

First, it is determined whether or not it is possible to move in the Cell (i + 1, j, k) direction. Cell (i + 1, j, k). If the obstacle = 0, there is no obstacle in the Cell (i + 1, j, k) direction, so that the process proceeds to the determination of the bore size (the value obtained by converting the pipe radius into the number of cells = α). Cell (i + 1, j, k)
The direction represents the core direction of the pipe, and Cell (i +
1, j, k-α). Cell (i + 1, j, k)
+ Α). If all the faulty cells are 0, C
It can be determined that connection is possible in the ell (i + 1, j, k) direction. A similar discrimination method is applied to a horizontally adjacent cell (Cel).
l (i-1, j, k), Cell (i, j, k + 1),
Cell (i, j, k-1) direction).

The method of determining the vertical direction is slightly different. First,
In the Cell (i, j + 1, k) direction, Cell
(I, j + 1, k). It is determined that there is no obstacle in the cell adjacent to Cell (i, j + 1, k) in consideration of the obstacle = 0 and the pipe diameter, thereby determining that there is no obstacle in the cell adjacent in the upward direction. Next, it is determined that there is no obstacle between the upper layers. Since each layer level is a known value, the distance between adjacent upper layers (L = layer (j + 1) level-layer (j) level) can be calculated.

Cell (i, j, k). If upper failure> L, it means that there is no obstacle between the upper layers of Cell (i, j, k), and it can be determined that it is possible to connect to the cell adjacent in the upward direction. For the Cell (i, j-1, k) direction, it can be determined by the same processing. As described above, by executing the processing flow shown in FIGS.
From (i, j, k), it is possible to determine the connection possibility in six adjacent directions. When searching for a route, a connectable adjacent cell is selected to form a route.

FIGS. 19A and 19B show an example of a route search between coordinate points A and B. FIG. FIG. 17 and FIG.
As can be seen from the flow of the process of determining whether the cell can be moved to the adjacent cell of No. 8, when viewed from an individual cell, there are a plurality of directions of adjacent cells that can be moved, and thus various routes are formed by the selected cell. FIG. 19 shows a route search based on the maze method, in which three route patterns (route 1, route 2,
Route 3) was output as an example. Due to the nature of the piping, there is no route that bends for each cell, so it is necessary to select the same direction continuously to some extent to ensure linearity, but the route pattern that can be connected is the route shown in FIG. There are many others. Each pattern has the same route length, and if other design constraints are satisfied, one of the route patterns is selected.

[0014]

Here, the first problem of the route pattern generated by the above-mentioned conventional technique will be described. 20 (a) and 20 (b) show an image of a pipe installation work situation. The worker who installs the pipe basically works on the floor below the pipe. Whether there is no floor,
Alternatively, if the position to the floor is far away, it is necessary to install a scaffold, which leads to an increase in piping installation man-hours. In addition, since the pipe is suspended from a steel frame or a floor above the pipe route, it is desirable that a suspending member be provided above the pipe route. Also,
The work tends to be easier when there is no equipment near the piping route. As described above, in the case of piping, the working conditions vary greatly depending on the piping route. Therefore, it is required to improve the workability (reduce the number of installation steps) in order to improve the workability. There is a problem that the route is not selected in consideration of the situation.

Next, a second problem will be described. In order to solve the first problem, it is conceivable to digitize spatial information of a plurality of route patterns and select a route having spatial information with the best workability. In this case, if the mesh size is small, the number of route patterns connecting two points becomes enormous. Executing route spatial information calculation for all route patterns requires a great deal of calculation time. Among these route patterns, there are route patterns that cannot be used as piping, such as a pattern in which the direction of the center line of piping changes for each cell, and these must be removed. A method for generating a plurality of practical route patterns is required.

A first object of the present invention is to provide a piping automatic routing method capable of quantitatively selecting a route having high pipe workability. A second object of the present invention is to provide a piping automatic routing method capable of generating a plurality of practical route patterns at the time of piping construction.

[0017]

SUMMARY OF THE INVENTION The first object of the present invention is to provide spatial information (distance to floor, distance to steel frame, equipment occupancy rate of peripheral space, etc.) as attribute data in each cell constituting a piping route. ), The spatial information of the cells that make up each route is integrated, and the best scoring (with good workability) is selected from a plurality of route patterns to improve pipe installation workability. The problem can be solved by a piping automatic routing method that quantitatively selects the piping route considered.

The second problem is to generate a plurality of route patterns in which the number of pipe bending points is suppressed by setting the routing priority for each layer and changing the setting of the routing priority for each layer. it can. By considering a combination of routing priorities for each hierarchy as a genetic code and applying a genetic algorithm, the problem can be solved by a piping automatic routing method that searches for a sub-optimal combination of routing priorities (route pattern).

[0019]

(Function to solve the first problem) The piping route is one in which adjacent cells are continuously selected. Each cell has spatial information as attribute data, and by integrating the spatial information of each cell, it is possible to digitize spatial information representing the workability of the entire route.

In the above prior art, Cell (i,
j, k) have attribute data “Cell (i, j, k). Obstacle”, and the distance to the obstacle in the upward direction “Cell (i, j, k). Obstacle ", distance to the obstacle in the downward direction" Cell (i, j,
k). There is a lower obstacle. In addition to these attribute data, the distance to the downward floor, the distance to the upward floor, and the degree of congestion of surrounding obstacles are added as spatial information data. Each of these spatial information data is referred to as “Cell (i, j,
k). Lower floor surface "," Cell (i, j, k). Upper floor surface ", and" Cell (i, j, k). Congestion degree ".

Note that "Cell (i, j, k). Congestion degree" is obtained by the following procedure. Corresponding cell Cell
FIG. 7 shows a method of calculating the congestion degree in the work area viewed from (i, j, k). Here, four-point coordinates “Cell (ia, j, ka), Ce, centering on Cell (i, j, k)”
11 (ia, j, k + a), Cell (i + a, j,
(ka), Cell (i + a, j, k + a) ".

[0022] The work area 2m square, if one cell size was 20cm angle, that Do and a = 5. The obstacle occupancy in all 121 ((2 × a + 1) ² ) cells in the coordinates surrounded by four points is 0.231 (28
/ 121). In this way, the obstacle occupancy is calculated centering on each cell, and the cell (i, j, k). Store it in the degree of congestion.

The workability evaluation value (DF) of the piping route using the distance to the downward floor as an index is calculated by equation (1).
The value calculated here is the average distance to the downward floor surface of the entire piping route. A plurality of route patterns are evaluated using equation (1), and the route with the smallest DF value is selected as the route with the shortest distance to the floor and good workability.

[0024]

(Equation 1) DF: Piping route workability evaluation value using the distance to the downward floor as an evaluation parameter m: Number of cells constituting the piping route

The workability evaluation value (U) of the piping route using the distance to the upward floor as an index similarly to the distance to the downward floor
F) is calculated by equation (2). The value calculated here is the average distance to the upper floor of the entire piping route. Evaluate a plurality of route patterns using equation (2), and
A route with a small value is selected as a route with good conditions for hanging pipes from the floor.

[0026]

(Equation 2) UF: Piping route workability evaluation value using the distance to the upward floor as an evaluation parameter m: Number of cells constituting the piping route

The workability evaluation value (CR) of the piping route using the degree of congestion as an index is calculated by equation (3). The value calculated by Expression (3) is the congestion degree of the entire piping route. A plurality of route patterns are evaluated using Expression (3), and a route with the smallest CR value is selected as a route with a low congestion degree in the work area and good workability.

[0028]

[Equation 3] CR: Piping route workability evaluation value using the degree of equipment congestion around the pipe as an evaluation parameter m: Number of cells constituting the piping route When comparing with individual workability evaluation indexes, use the above evaluation formula to calculate the magnitude of the calculated value. The route with the best workability can be quantitatively selected from the relationship.

Next, a procedure for comprehensively evaluating a plurality of conditions (three conditions) will be described below. Since the evaluation values calculated by the above equations (1), (2) and (3) have different units, the data is normalized so that they can be easily compared with each other. Among the evaluation results obtained by calculating a plurality of route patterns using the respective evaluation expressions (expressions (1), (2), and (3)), the respective maximum values (poor workability).
Is converted to 0 and the minimum value (the workability is good) to 10. (The output result of each evaluation formula is expressed by a maximum of 10 points.) Equation (4),
(5) and (6) show conversion equations.

[0030]

(Equation 4) DFstd: Standardization of the pipe route workability evaluation value using the downward floor distance as the evaluation index DFmin: Minimum value of the pipe route workability evaluation value using the downward floor distance as the evaluation index DFmax: The lower floor distance Maximum value of pipe route workability evaluation value as evaluation index

[0031]

(Equation 5) UFstd: Standardization of the pipe route workability evaluation value using the upward floor distance as the evaluation index UFmin: Minimum value of the pipe route workability evaluation value using the upward floor distance as the evaluation index UFmax: The upper floor distance Maximum value of pipe route workability evaluation value as evaluation index

[0032]

(Equation 6) CRstd: Standardization of evaluation value of pipe route workability using congestion degree as evaluation index CRmin: Minimum value of pipe route workability evaluation value using congestion degree as evaluation index CRmax: Evaluation of pipe route workability using congestion degree as evaluation index Maximum value

FIG. 13 shows an example of a radar chart output for comprehensively evaluating the calculation results by the above three equations (Equations (4), (5) and (6)). The number of piping route patterns to be compared is assumed to be four (routes 1, 2, 3, 4), and the data system having the largest area (route 1 in the example of FIG. 13) is considered to be the route that takes the workability into consideration as a whole. to decide.

FIG. 4 shows a method for generating a plurality of route patterns. First, a route condition (pipe direction priority) is set for each routing hierarchy. For example, when the route condition “1” is set in the hierarchy (n), the piping route in the hierarchy (n) gives priority to the x-axis direction, and then lowers the priority in the y-axis direction and the z-axis direction. By setting the priority of the piping route, it is easy to select a route in a specific direction on the same hierarchy, and it is possible to avoid generation of a route pattern that is impossible as piping, such as a pattern in which the direction of the piping changes for each cell.

In the case of routing between two points, different route patterns can be generated by changing the route condition set value of the hierarchy. The relationship between the route condition setting value and the route priority direction is shown in the right frame of FIG.

FIGS. 8 and 9 show examples of generating a plurality of route patterns. Consider a case where routing is performed between coordinate points A and B in three layers. The lower table in FIG. 9 shows the route conditions for each hierarchy of each route. FIG. 9 shows a route pattern generated based on this condition. By changing the setting of the route priority direction for each layer as shown in FIG. 9, a plurality of route patterns can be generated.

As shown in the lower table of FIG. 9, the number of combinations of the root priority of each layer is represented by 6 raised to the power of n (the number of layers), and the number of combinations increases explosively as the number of layers (n) increases. . Although all combinations can be confirmed with a small number of layers, an enormous amount of calculation time is required to confirm all combinations with a large number of layers. A genetic algorithm is applied as a method for efficiently searching for a suboptimal, if not optimal, combination from a large number of combinations.

Here, how to look at FIG. 9 will be described. The route conditions are shown in FIG. 10. According to the table shown in FIG. 9, route 1 has a route condition of hierarchy (1) of 6, route condition of hierarchy (2) of 1, and route condition of hierarchy (3) of 2 Indicates that it was given. Also, the route 2 is given the route condition of the layer (1) as 2, the route condition of the layer (2) as 4, and the route condition of the layer (3) as 6, and the route 3 is the route condition of the layer (1) as 3 The lower table in FIG. 9 shows that the root condition of the hierarchy (2) is given as 3 and the root condition of the hierarchy (3) is given as 5.

FIG. 4 shows the set values of the route conditions and the priority directions. A route search connecting the coordinate point A to the coordinate point B is considered together with a route condition. For example, if the route is 1, a route connecting the coordinate points A to B can be considered in all directions of x, y, and z. Hierarchy (1) has a priority condition of 6 (z>y>
Since x) is set, routing is performed linearly in the z direction until an obstacle is hit. Since there is an obstacle, the directions in which movement is possible are the x and y directions, and routing is performed in the y direction based on the priority of the hierarchy (1). The route descends to the second level, and the directions in which movement is possible at this point are the x and y directions. Since 1 (x>y> z) is assigned to the route condition of the hierarchy (2), the route is extended in the x direction. The next movable direction may be the y and z directions.
From the priority condition in the y direction. The movable direction of the layer (3) is the z direction, and the layer (3) is connected to the coordinate point B. Routes 2 and 3 are routed in a similar procedure. The point is that different route patterns can be created by changing the route conditions.

FIG. 10 shows an example in which a root condition assigned to each hierarchy is a gene code sequence. FIG. 11 shows a flow of a route search process to which a genetic algorithm is added. First, a plurality of gene codes as shown in FIG. 10 are generated using a random number generator (setting of an initial generation gene code). Next, a route pattern is generated based on the gene code, and an evaluation value of each route is calculated (evaluation of the route pattern). Based on the evaluation of the route pattern, a gene code having a low evaluation value is selected and replaced with a gene code having a high evaluation value (reproduction).
Next, a route pattern is generated based on the gene codes obtained through partial rearrangement (crossover) and mutation of the gene codes, and the evaluation value of each route is calculated. The processing flow of the reproduction, crossover, mutation, and evaluation is set to one generation, and the generations are repeated until the termination condition is satisfied, thereby obtaining a sub-optimal gene code.

[0041]

Embodiments of the present invention will be described with reference to the drawings. The 3D-CAD 1-1 which inputs the steel frame, floor, equipment, stairs, and the like of the plant and displays the input result in three dimensions is provided by the automatic piping routing system according to the embodiment shown in FIG.
And a piping system DB (database) 1-2 having piping connection information (coordinates, diameter, material, wall thickness, etc.) and an automatic piping router 1-3. The shape of the equipment is extracted from the 3D-CAD 1-1 system and used as equipment obstacle information when piping and routing are performed. A system configuration that extracts information such as pipe coordinates and diameter from the piping system DB1-2, combines it with obstacle information, searches for a route with the automatic piping router 1-3, and displays the route search result on the 3D-CAD1-1. It is. The automatic piping router corresponds to the main part of this patent.

FIG. 2 shows a processing flow of the automatic piping router of the automatic piping routing system of FIG. First,
Define the piping routing area and set the routing hierarchy level and the number of mesh divisions for each hierarchy. Spatial information is allocated to cells that are divided into meshes based on the shape and coordinate information of an obstacle (steel frame, floor, equipment, etc.). Each cell has 1) presence or absence of obstacles, 2) distance to the downward obstacle, 3)
Distance to the upper obstacle, 4) distance to the lower floor,
5) The distance to the floor above, and 6) the degree of obstacle congestion around the cell is stored as attribute data.

The piping information (start point, end point coordinates, etc.) is extracted from those having the highest routing priority in the piping system DB1-2. The routing priority is determined from various elements of the piping. For example, pipes routed later have more bends to avoid obstacles, so pipes with larger diameters have higher priority.In the case of pipes with the same diameter, higher quality materials and thicker pipes have higher priority. Higher.

Next, the coordinates of the starting point and the ending point of the selected pipe are assigned to the cells, and the routing conditions of each layer are set.
A route search between two points based on the maze method is performed to obtain a route on the cell. Until the desired number of patterns is generated, the set values of the routing conditions of each layer are changed, and different piping route patterns are generated. When the generation of all the route patterns is completed, the route pattern having the best workability evaluation value is selected as the piping route, and the route on the cell is obtained.

When the optimum route pattern is selected, the cell coordinates are converted to real coordinates, and the routing result is stored. The selected piping route is recognized as an obstacle for the subsequent routing. This procedure is repeated until the route search for all pipes is completed. FIG. 2 shows an outline of the processing flow of the present invention, and specific processing contents of the present invention will be described below.

Next, a specific processing procedure of the routing hierarchy and cell creation will be described. FIG. 3 shows a configuration example of an input setting screen for a routing hierarchy and mesh division conditions. The input setting unit is roughly divided into 1) a routing area, 2) a mesh division number, and 3) a hierarchy definition. In the routing area, the area is defined as a rectangular parallelepiped (diagonal coordinates of the rectangular parallelepiped are input). In the number of mesh divisions, the number of mesh divisions in the x, y, and z-axis directions in the routing area is set. In the layer definition, the level of each layer in the y-axis direction (vertical direction) and the initial value of the routing condition are set. As described above, the screen settings shown in FIG.
The mesh is divided as shown in FIG. From the size and position of each cell and the positional relationship with obstacles (steel frame, equipment, etc.), spatial information (existence of obstacles,
The distance to the upward obstacle, the distance to the downward obstacle, the distance to the upward floor, the distance to the downward floor, and the degree of congestion in the work space are stored. As shown in FIG. 7, the congestion degree of the work space is determined by a corresponding cell (Cell (i,
j, k)) is calculated, and the ratio of the cell defined as the obstacle to the number of cells within the preset work range is calculated.

FIGS. 4 and 5 show a routing condition setting method in the flow shown in FIG. First, a route condition (pipe direction priority) is set for each routing hierarchy. It is assumed that any one of 1 to 6 is output from the random number generator as the routing condition. Here, “1” indicates that the pipe priority direction is x>y> z, and “2” indicates that the pipe priority direction is x.
>Z> y, “3” means that the pipe priority direction is y>x> z, “4”
Indicates that the pipe priority direction is y>z> x, "5" indicates that the pipe priority direction is z>x> y, and "6" indicates that the pipe priority direction is z>y> x. After assigning route conditions to all layers by a random number generator, a route search based on a maze method is executed, thereby generating a plurality of piping route patterns in which the coordinates between two points are reduced and the number of pipe bending points is suppressed. be able to.

After generating a plurality of route patterns, the workability of each route pattern is quantitatively evaluated. As evaluation indexes, 1) workability of the entire piping route on the floor surface, 2) suspendability of the entire piping route from the upper floor, 3) working space of the entire piping route, 4) 1) to 3) There is a comprehensive evaluation of.

1) Formula (1) for evaluating the workability of the piping route on the floor is shown below. This calculates the distance to the floor in the downward direction in the attribute data of each cell constituting the route,
The value is divided by the number of cells, and represents the average distance from the entire piping route to the floor in the downward direction. Since the smaller the value, the better the workability, a plurality of route patterns are evaluated by Expression (1), and the minimum value is selected as the route with the good workability.

2) The expression for evaluating the workability of suspending the piping route from the floor is shown in Expression (2). This is the value obtained by integrating the distance to the floor in the upward direction in the attribute data of each cell constituting the route and dividing by the number of cells. It represents the distance to the surface. Since the smaller the value, the better the workability, a plurality of route patterns are evaluated by Expression (2), and the minimum value is selected as the route with the good workability.

3) The workability evaluation formula based on the degree of congestion of the piping route is shown in formula (3). This is a value obtained by multiplying the congestion degree in each cell in the attribute data of each cell constituting the route and dividing by the number of cells, and represents the average congestion degree around the route as viewed from the entire piping route. ing. Since the smaller the value, the better the workability, a plurality of route patterns are evaluated by Expression (3), and the minimum value is selected as the route with the good workability.

4) A case of comprehensive evaluation using the above three indices 1) to 3) will be described. Since the three types of evaluation formulas (1) to (3) have different units, the output ranges are adjusted so that they can be easily compared with each other. Each evaluation formula (1)-
Among the results of the multiple route patterns calculated in (3),
The maximum value (bad workability) is 0, and the minimum value (good workability) is 1.
Convert to 0 (each evaluation formula is represented by a maximum of 10 points). The conversion expressions of the evaluation expressions (1) to (3) are expressed by Expressions (4), (5), and (6).
Shown in The conversion results of the plurality of evaluation formulas (4) to (6) are represented by a radar chart (FIG. 13), and the route pattern having the largest included area can be determined to be the route having the best workability overall.

Note that the workability evaluation indexes 1) to 4) can be selected by a designer according to the application. In addition, by adding a genetic algorithm to the route search method according to the above method, a combination that provides a sub-optimal solution with less calculation time from among the route conditions (pipe direction priority) of each routing hierarchy having a large number of combinations. Can be searched.

A search method to which a genetic algorithm is added will be described below. FIG. 10 shows an example in which a route condition assigned to each hierarchy is a gene code sequence. FIG. 11 shows a flow of a route search process to which a genetic algorithm is added. First, a plurality of gene codes as shown in FIG. 10 are generated using a random number generator (setting of an initial generation gene code). A route pattern is generated based on the genetic code, and an evaluation value of each route is calculated (evaluation of the route pattern).
Based on the result of the evaluation of the route pattern, a gene code having a low evaluation value is selected and replaced with a gene code having a high evaluation value (reproduction). Next, a route pattern is generated based on the gene codes obtained through partial rearrangement (crossover) and mutation of the gene codes, and the evaluation value of each route is calculated. The processing flow of the reproduction, crossover, mutation, and evaluation is set to one generation, and the generations are repeated until the termination condition is satisfied, thereby obtaining a sub-optimal gene code.

FIG. 12 shows a specific process of evolution of a genetic code. In the initial generation, a gene code is set based on a plurality of different route conditions. The initial value of the genetic code is given by a random number generator. In the example of FIG. 12, 10 gene codes and 10 layers of route search conditions are assumed. The gene code represents a root priority condition of each hierarchy, and a root pattern obtained by a rule based on each code is used to calculate an evaluation value using any of the evaluation formulas (1) to (6). Perform the calculation. The calculated evaluation value is described in parentheses below the gene code. Gene codes with low evaluation values are selected. In the reproduction process, the gene code with the lowest evaluation value is replaced with the gene code with the highest evaluation value. Next, in the process of crossover, the value of the gene code is crossed at any position between any gene codes. Further, in the course of the mutation, the priority condition at an arbitrary hierarchical position of an arbitrary genetic code is converted into an arbitrary value. Here, “arbitrary” refers to a value generated by a random number generator.

Through the regeneration and crossover processes, the gene code with a high evaluation value remains as it is, and several gene codes similar to the gene code are created, so that the gene code with a high evaluation value is generated. It becomes possible to search for a nearby code. In addition, it is considered not to fall into a local solution by causing a mutation. By repeating this procedure for a plurality of generations, a gene code with a higher evaluation value (a root pattern with a higher evaluation value) can be efficiently searched.

[0057]

According to the present invention, the workability evaluation value can be calculated on the basis of the spatial information of the route, so that there is an effect that the search for the piping route that quantitatively evaluates the workability can be realized.

[Brief description of the drawings]

FIG. 1 is a configuration diagram of a piping automatic routing system according to an embodiment of the present invention.

FIG. 2 is a diagram showing a processing flow of an automatic piping router according to an embodiment of the present invention.

FIG. 3 is an input screen of a routing hierarchy and a mesh division condition according to the embodiment of the present invention.

FIG. 4 is a diagram illustrating a method of generating a plurality of route patterns according to an embodiment of the present invention.

FIG. 5 is a diagram showing a flow of setting routing conditions to each layer according to the embodiment of the present invention.

FIG. 6 is a mesh division diagram according to the embodiment of the present invention.

FIG. 7 is a diagram illustrating a method for calculating a congestion degree around a cell according to an embodiment of the present invention.

FIG. 8 is a diagram showing an example of generating a plurality of route patterns according to the embodiment of the present invention.

FIG. 9 is a diagram showing an example of generating a plurality of route patterns according to the embodiment of the present invention.

FIG. 10 is a diagram showing a route search processing flow using a genetic algorithm according to the embodiment of the present invention.

FIG. 11 is a diagram showing a route search processing flow using a genetic algorithm according to the embodiment of the present invention.

FIG. 12 is a diagram illustrating an example of a route search process using a genetic algorithm according to an embodiment of the present invention.

FIG. 13 is a diagram illustrating a radar chart based on a plurality of evaluation indices according to the embodiment of the present invention.

FIG. 14 is a diagram for explaining a maze algorithm.

FIG. 15 is a diagram illustrating an example in which the maze method is applied in a three-dimensional space.

FIG. 16 is a diagram illustrating a problem of an inter-layer obstacle.

FIG. 17 is a flowchart of a connectable adjacent cell determination process.

FIG. 18 is a flowchart of a connectable adjacent cell determination process.

FIG. 19 is a diagram for explaining a plurality of route pattern generation situations.

FIG. 20 is a diagram for explaining the status of a pipe installation work at a construction site (basic form of the pipe installation work (FIG. 20 (a)) and a scaffold installation method for the pipe installation work (FIG. 20 (b)).

[Explanation of symbols]

 1-1 3D-CAD 1-2 Piping system DB (database) 1-3 Automatic piping router

Claims (4)

[Claims] 1. A 3D-CAD for inputting and displaying plant equipment, steel frame, floor surface, etc., a piping system database having piping information such as a starting point, an ending point, and a diameter of a piping route, and a 3D-CAD. In an automatic piping routing system configured with an automatic piping router that searches for a piping route based on obstacle information to be extracted, a mesh of a piping routing area is divided into meshes, and distances to upper and lower floors and congestion around each mesh. The degree is stored as attribute information, and among a plurality of pipe route candidates from the pipe start point to the end point, a calculation value calculated by an evaluation formula for workability using the attribute information included in each mesh is obtained. A piping automatic routing method characterized in that the workability is quantitatively evaluated by selecting a route having the best workability among the calculated values. 2. The method according to claim 1, wherein the setting of the priority direction of the piping direction is changed for each of the mesh-divided hierarchies, and the combination of the piping route priority directions of the hierarchies is different (a plurality of route candidates are generated). Piping automatic routing method. 3. The automatic pipe routing according to claim 2, wherein a plurality of pipe route candidates are generated using a random number generator so that combinations of the pipe route priority directions of the mesh-divided layers are different from each other. Method. 4. A combination of a hierarchy priority direction in a hierarchy is regarded as a gene code, and a code having a high evaluation score is stored at a fixed rate from a calculated evaluation expression of a route corresponding to a plurality of generated codes, and a code having a low evaluation score is stored. It is characterized by searching for a genetic code with a high evaluation score, which is a suboptimal solution, by removing the code and repeating the genetic algorithm processing for converting the code by crossover and mutation for multiple generations. The automatic piping method according to claim 1.