Layout design automation system and layout design method

Abstract

Reduce the workload of designers. 【Solution means】The layout design automation system according to the embodiment is a layout design automation system that generates a route connecting a starting point and an ending point. The layout design automation system has a point cloud data processing unit and a route generation unit. The point cloud data processing unit identifies which range of the point cloud data is the range of each structure from the information indicating the building structure and indoor structures composed of the point cloud data, identifies the type of the structure, and generates 3D CAD information of the identified structure. The route generation unit generates a route based on the 3D CAD information generated by the point cloud data processing unit.

Description

Technical Field

[0001] The present invention relates to an automatic layout design system and a layout design method.

Background Art

[0002] There is a technology for automating the design work of piping routes in plants and the like. For example, Patent Document 1 describes a technology for automatically generating a piping route that satisfies a number of constraint conditions for connecting between the start point and the end point of the piping in the layout space information. In addition, technologies such as the generation of supports for supporting the piping route, the generation of valves for controlling the internal fluid, and the aggregation of the generated structures are described.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the layout design of existing plants and the like, it is necessary to proceed with the layout design reasonably while considering ensuring separation from structures in the existing plant and commonalization of supports. In the system described in Patent Document 1, a piping route can be generated based on the 3D CAD information of a building. This 3D CAD information also includes information composed of point cloud data obtained using a 3D laser scanner.

[0005] In performing layout design, it is necessary to identify the types of building structures such as ceilings, floors, and walls, and plan routes at appropriate positions according to the types of piping to be designed. In addition, it is necessary to distinguish the types of indoor structures such as existing piping and existing equipment. Then, it is necessary to consider arranging routes along the building structures and the separation distance from indoor structures.

[0006] However, the point cloud data is merely data in which points distributed in three dimensions are gathered. Therefore, it is necessary to accurately identify which range of the point cloud data corresponds to the building structure such as the ceiling, floor, and walls, and which range corresponds to the indoor structures such as pipes, instrumentation pipes, electrical conduits, cable racks, air conditioning ducts, pumps, and other equipment. If the identification of the type of building structure or indoor structure is incorrect, an appropriate route may not be generated. Conventionally, designers have been performing the difficult task of identifying building structures and indoor structures from point cloud data, and the workload has been large.

[0007] The present invention has been made under the above circumstances, and an object thereof is to reduce the workload of designers.

Means for Solving the Problems

[0008] An arrangement design automation system according to an embodiment for solving the above problems is an arrangement design automation system that generates a route connecting a starting point and an end point. The arrangement design automation system includes a point cloud data processing unit and a route generation unit. The point cloud data processing unit Including the ceiling, floor, and walls identifies which range of the point cloud data corresponds to the building structure of the building and Disposed within the body indoor structures, The entire structure included identifies the type of the structure, and generates 3D CAD information of the identified structure. Composed of point cloud data Based on the information indicating Based on teacher data that also includes relationship information between a group of point cloud data and attribute information indicating the type of the building body or indoor structure and point cloud data indicating surrounding structures, using AI, which range of the point cloud data is the range of each structure, The type of the body and indoor The route generation unit generates a route based on the 3D CAD information generated by the point cloud data processing unit. Overall

Brief Description of the Drawings

[0009]

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[0010] Hereinafter, an arrangement design automation system according to an embodiment will be described with reference to the drawings. The arrangement design automation system is a device that identifies a structure based on point cloud data and automatically generates a route by connecting between the start point and the end point of the route. The routes generated by the arrangement design automation system include routes for piping such as in plants and buildings, instrument piping, wire pipes, cable racks, and air conditioning ducts. Here, the case of applying it to the arrangement design of piping for passing fluids (gases, liquids, etc.) in the plant field will be described.

[0011] Here, instrument piping refers to piping for accommodating cables for transmitting information on measurement results such as temperature, pressure, and flow rate, and cables for transmitting signals for controlling the system, or piping for guiding a fluid (gas, liquid, etc.) to be measured to an instrument. A wire pipe refers to a pipe for accommodating a cable for transmitting electric power. In the following description, structures of a building such as the ceiling, floor, and wall are referred to as the building structure, and structures other than the building such as piping and devices are referred to as indoor structures. The building structure and indoor structures together are sometimes referred to as structures.

[0012] Point cloud data is a collection of points in three-dimensional coordinates, which can be obtained by photographing buildings, existing facilities, existing piping, etc. from multiple positions with a three-dimensional laser scanner. The point cloud data includes information on the three-dimensional shapes of indoor structures such as the frameworks of buildings such as ceilings, floors, and walls, and existing cable racks, air-conditioning ducts, support members, equipment such as tanks and pumps. However, it is unclear which range of the point cloud data is the framework and which range is which indoor structure. Also, the types of structures are unknown.

[0013] The layout design automation system 1 is physically a computer equipped with a CPU, memory, etc. The layout design automation system operates based on layout design application software stored in the memory.

[0014] FIG. 1 is a functional configuration diagram of the layout design automation system 1. The layout design automation system 1 includes an input unit 10, a storage unit 21, a route generation unit 22, a support positioning unit 23, a piping member determination unit 24, a quantity aggregation unit 25, an output unit 30, and a point cloud data processing unit 40.

[0015] The input unit 10 is composed of a touch panel, a keyboard, etc. The input unit 10 displays the three-dimensional CAD information of the building on the screen of the touch panel, and acquires the coordinate information of the start and end points of a route such as piping according to the designer's designation. Also, the input unit 10 displays a list of the type and weight of the fluid passing through the piping, the material of the piping, the thickness of the piping, etc. on the screen of the touch panel, and acquires this information according to the designer's designation. Further, when there are a plurality of pipes, the input unit 10 acquires the priority order for determining which pipe to prioritize for placement according to the designer's designation.

[0016] As shown in FIG. 2, the storage unit 21 stores layout space information 21a, dedicated layout space information 21b, existing structure information 21c, operation and maintenance information 21d, piping layout standard information 21e, detailed design information 21f, and teacher data 21g.

[0017] The arrangement space information 21a is 3D CAD information indicating the structure of the building body (such as ceiling, floor, wall, etc.). The dedicated arrangement space information 21b is 3D CAD information indicating the area for arranging piping, etc. inside the building. The existing structure information 21c is 3D CAD information indicating indoor structures such as piping, instrument piping, wire conduits, cable racks, air conditioning ducts, and equipment. In this embodiment, the arrangement space information 21a and the existing structure information 21c are information composed of point cloud data acquired using a 3D laser scanner.

[0018] The operation and maintenance information 21d is information indicating conditions of positions suitable for maintenance inspections of instruments that require visual confirmation, conditions of positions suitable for operations of components that require operations, etc. Instruments that require visual confirmation include flow meters, pressure gauges, thermometers, wattmeters, etc. Components that require operations include valves such as motor-operated valves and manual valves. The operation and maintenance information 21d includes conditions such as the height of instruments that are easy for maintenance personnel to visually confirm, the height and orientation of valves that are easy for maintenance personnel to operate, and the distance between instruments and valves, etc. and the wall. Also, the operation and maintenance information 21d includes information on prohibited arrangement areas. The prohibited arrangement area is an area where piping arrangement is prohibited, such as near passages and storage locations for dangerous goods, and an area necessary for maintenance work of valves, etc.

[0019] The piping arrangement standard information 21e is information indicating the basic design conditions in piping arrangement design. This piping arrangement standard information 21e includes conditions defining the distance between the floor, ceiling, wall and the piping, the distance between parallel pipings, the distance between the cable rack and the piping, the selection of bent pipes for the piping, the diameter of the through-hole when providing a through-hole in the piping, the position of the through-hole in an area with a high radiation dose in the case of a nuclear power plant, etc. Note that a bent pipe is a member obtained by bending a straight pipe. Also, the piping arrangement standard information 21e includes separation conditions for safety systems. The separation conditions for safety systems are, for example, a configuration in which when an accident occurs in the piping route of the first system, it is switched to the piping route of the second system, and conditions such as arranging the piping route of the first system and the piping route of the second system with a wall or the like in between. These information are set according to the type of fluid flowing through the piping, etc.

[0020] The detailed design information 21f is information indicating detailed design conditions. This detailed design information 21f includes conditions such as the length of the straight pipe (the straight part of the piping) before and after the instrument and orifice, construction errors, the gradient of the piping, the distance from other equipment, etc. Further, the detailed design information 21f includes design conditions based on the experience and knowledge of skilled designers. Furthermore, the detailed design information 21f also includes the centralized arrangement conditions when there are multiple pipes.

[0021] The teacher data 21g is data used by the AI (Artificial Intelligence) that constitutes the point cloud data processing unit 40. The teacher data 21g is data that associates a group of point cloud data (hereinafter sometimes referred to as a point cloud group) with attribute information indicating the type of the structure. The point cloud group refers to a group of point cloud data in which the distribution of the point cloud gathers at a predetermined density. For example, the teacher data of "ceiling" is data obtained by grouping the point cloud data that constitutes the "ceiling" and And is associated with the attribute information of "ceiling".

[0022] Returning to FIG. 1, the point cloud data processing unit 40 identifies which range of the point cloud data is the range of each structure from the information indicating the building frame and indoor structures composed of the point cloud data, identifies the type of the structure, and generates 3D CAD information of the identified structure. Specifically, the point cloud data processing unit 40 identifies which range of the point cloud data is the range of each structure such as the ceiling, floor, and wall, and existing pipes and existing devices from the point cloud data indicating the whole of a plurality of structures including the building frame such as the ceiling, floor, and wall, and indoor structures. Then, the point cloud data processing unit 40 identifies the type of each structure (ceiling, floor, wall, existing pipe, existing device) and generates 3D CAD information of the identified structure.

[0023] Specifically, the point cloud data processing unit 40 incorporates AI, and based on the training data 21g that associates a group of point cloud data with attribute information indicating the type of structure, it uses AI to identify the type of each structure from the information of the point cloud data containing the information of all structures. Then, the point cloud data processing unit 40 generates 3D CAD information of the identified structure. In this way, the point cloud data processing unit 40 converts the information composed of point cloud data into 3D CAD information.

[0024] The route generation unit 22 generates a route from the coordinate indicating the starting point to the coordinate indicating the ending point based on at least any one of the 3D CAD information generated by the point cloud data processing unit 40, the arrangement space information 21a, the dedicated arrangement space information 21b, the existing structure information 21c, the operation and maintenance information 21d, the piping arrangement standard information 21e, and the detailed design information 21f. The routes generated by the route generation unit 22 include routes for pipes through which fluids (gases, liquids, etc.) pass, instrument pipes, wire pipes, cable racks, and air conditioning ducts. Also, when the route generation unit 22 generates a plurality of routes, it generates the plurality of routes so that the arrangement positions of the plurality of routes do not overlap (do not interfere) with each other. Further, when the route generation unit 22 generates a route that penetrates the floor, ceiling, and wall, it generates a 3D shape model indicating the position, size, etc. of the through holes formed in the floor, ceiling, and wall and a summary list of the through holes.

[0025] The support position determination unit 23 determines the support position for arranging support members for supporting pipes and the like based on the fixed pitch span method (also referred to as the standard support interval method). For example, the support position determination unit 23 determines the support position by the fixed pitch span method based on the standard span (the interval between the support position and the adjacent support position) preset for the straight pipe portion and the bent pipe portion. This support position determination unit 23 determines the support position for arranging support members for supporting pipes and the like based on the weight of the pipe, the weight of the fluid passing through the pipe, and the like. For example, the support position determination unit 23 determines the support position so as to support near the portions where the loads concentrate, such as the starting point, ending point, bending portion, and branching portion of the route.

[0026] Further, when the routes of a plurality of pipes or the like generated by the route generation unit 22 are adjacent or parallel, the support position determination unit 23 determines, as a support position, a position where two or more of the plurality of pipes can be supported at one support position.

[0027] Based on the routes of pipes or the like generated by the route generation unit 22 and the preset transportation conditions, the pipe member determination unit 24 determines the length of each of the plurality of members constituting the pipes or the like. For example, the pipe member determination unit 24 determines the length of each of the plurality of members constituting the pipe so that the length of one member is 10 m or less. Further, the pipe member determination unit 24 determines the length of each of the plurality of members constituting the pipe so that the weight of one member is 100 kg or less.

[0028] Further, the pipe member determination unit 24 determines a support member for supporting the pipe based on the weight of the pipe, the weight of the fluid passing through the pipe, etc., and the location for fixing the support member for supporting the pipe. The pipe member determination unit 24 determines the support member based on the fixing location (floor, ceiling, wall, etc.), the number of pipes to be supported, the weight of the pipe and the fluid passing through the pipe, etc.

[0029] The quantity aggregating unit 25 creates a quantity table in which the number of each member determined by the pipe member determination unit 24 is aggregated.

[0030] The output unit 30 is configured by a display or a printing device. The output unit 30 displays a 3D CAD image showing the routes of pipes or the like generated by the route generation unit 22 and the positions of the support members determined by the support position determination unit 23. Further, the output unit 30 outputs the quantity table aggregated by the quantity aggregating unit 25.

[0031] Next, a method for generating 3D CAD information of a body, indoor structures, etc. from the point cloud data will be described with reference to the flowchart shown in FIG. 3.

[0032] First, the point cloud data processing unit 40 of the layout design automation system 1 captures point cloud data from the storage unit 21 (step t01). The point cloud data is obtained by photographing the building structure and indoor structures from multiple positions with a 3D laser scanner and is stored in the storage unit 21 in advance. FIG. 4 is an image diagram of the acquired point cloud data. FIG. 4 is an image diagram of the point cloud data viewed from the side (+Y direction). FIG. 5 is an image diagram of the point cloud data viewed from the plane (+Z direction). In the figure, the point cloud data is shown as points. Although the acquired point cloud data is data in which 3D points are gathered, it is unclear which range of the point cloud data is the structure such as the ceiling, floor, and wall, and which range of the point cloud data is the indoor structure such as piping and equipment. Also, since the attribute information of the structure and indoor structure is not given, it is also unclear which range of the point cloud data is what kind of structure.

[0033] Subsequently, the point cloud data processing unit 40 identifies a room from a spatial region within a predetermined range surrounded by the point cloud (step t02). Specifically, the AI that constitutes the point cloud data processing unit 40 performs this process. In the example shown in FIGS. 4 and 5, since the spatial region surrounded by the point cloud groups T11, T12, T13, T14, T15, and T16, which are groups of point cloud data, has a predetermined width, the point cloud data processing unit 40 identifies the spatial region T10 as a room.

[0034] Subsequently, the point cloud data processing unit 40 identifies the ceiling, floor, and wall (step t03). Steps t02 and t03 are the structure identification steps. The teacher data 21g used by the AI that constitutes the point cloud data processing unit 40 includes teacher data for the ceiling, floor, and wall. For example, the teacher data for "ceiling" has the attribute information of "ceiling", and the point cloud group has a feature that it constitutes the upper surface of the space (the +Z side surface of the space) and has a predetermined area. In the example shown in FIGS. 4 and 5, since the feature of the teacher data with the attribute information "ceiling" is approximated to the feature of the point cloud group T11, the point cloud data processing unit 40 identifies the point cloud group T11 shown by the thick solid line in FIG. 6 as the "ceiling".

[0035] For example, the teacher data of "floor" has attribute information of "floor", and the point cloud group thereof has a feature quantity of constituting the lower surface of the space (the surface on the -Z side of the space) and having a predetermined area. In the examples shown in FIGS. 4 and 5, since the feature quantity of the teacher data with the attribute information "floor" is approximated to the feature quantity of the point cloud group T12, the point cloud data processing unit 40 identifies the point cloud group T12 shown by the thick solid line in FIG. 6 as "floor".

[0036] For example, the teacher data of "wall" has attribute information of "wall", and the point cloud group thereof has a feature quantity of being located on the side surface of the space constituting the room and having a predetermined area. In the examples shown in FIGS. 4 and 5, since the feature quantity of the teacher data with the attribute information "wall" is approximated to the feature quantities of the point cloud groups T13, T14, T15, and T16, the point cloud data processing unit 40 identifies the point cloud groups T13, T14, T15, and T16 shown by the thick solid line in FIGS. 6 and 7 as "wall".

[0037] Next, the point cloud data processing unit 40 identifies indoor structures located within the area (room) surrounded by the identified ceiling, floor, and walls (step t04). The point cloud data processing unit 40 also identifies indoor structures from the characteristics of the point cloud distribution connected to adjacent rooms.

[0038] Next, the point cloud data processing unit 40 identifies the type of indoor structure (step t05). Step t04 and step t05 are the indoor structure identification steps. The teacher data 21g used by the AI constituting the point cloud data processing unit 40 includes teacher data of indoor structures such as tanks, pumps, pipes, generators, boilers, and cable racks. For example, the teacher data of "tank" has attribute information of "tank", and the point cloud group thereof has a feature quantity of having a predetermined height and diameter and being cylindrical. In the examples shown in FIGS. 4 and 5, since the feature quantity of the teacher data with the attribute information "tank" is approximated to the feature quantity of the point cloud group T21, the point cloud data processing unit 40 identifies the point cloud group T21 shown by the solid line in FIGS. 8 and 9 as "tank".

[0039] Also, for example, the teacher data of "pump" has attribute information of "pump", and the point group has features such as having a predetermined height and width and being rectangular or cylindrical. In the examples shown in FIGS. 4 and 5, since the features of the teacher data with the attribute information " Pump " are approximated to the features of the point group T22, the point group data processing unit 40 identifies the point group T22 shown by the solid line in FIGS. 8 and 9 as "pump".

[0040] Also, for example, the teacher data of "pipe" has attribute information of "pipe", and the point group has features such as being slender cylindrical, having pipe supports (support members) arranged at a predetermined interval, and passing through the wall and connecting to an adjacent room. In the examples shown in FIGS. 4 and 5, since the features of the teacher data with the attribute information "pipe" are approximated to the features of the point group T23, the point group data processing unit 40 identifies the point group T23 shown by the solid line in FIGS. 8 and 9 as "pipe".

[0041] The point group data processing unit 40 generates 3D CAD information with attribute information indicating the type of the identified structure, and stores it in the storage unit 21 (step t06). Step t06 is a 3D CAD information generation process. Specifically, the point group data processing unit 40 identifies the types of structures such as ceilings, floors, and walls, and the types of indoor structures such as existing pipes and existing devices, and stores them in the storage unit 21 as 3D CAD information corresponding to the arrangement space information 21a and the existing structure information 21c composed of point group data. By this process, the information composed of point group data is converted into 3D CAD information.

[0042] The point group data processing unit 40 creates teacher data associating the point group of the structure identified in steps t02 and t03 and the point group of the indoor structure identified in step t05 with the attribute information indicating the type of the identified structure, and adds it to the teacher data 21g in the storage unit 21 (step t07). Step t07 is a teacher data creation process.

[0043] Specifically, the point cloud data processing unit 40 adds data in which the point cloud group T11 is associated with the attribute information of "ceiling" to the training data 21g. The point cloud data processing unit 40 adds data in which the point cloud group T12 is associated with the attribute information of "floor" to the training data 21g. The point cloud data processing unit 40 adds data in which the point cloud groups T13, T14, T15, and T16 are associated with the attribute information of "wall" to the training data 21g. The point cloud data processing unit 40 adds data in which the space surrounded by the point cloud groups T11, T12, T13, T14, T15, and T16 is associated with the attribute information of "room" to the training data 21g. The point cloud data processing unit 40 adds data in which the point cloud group T21 is associated with the attribute information of "tank" to the training data 21g. The point cloud data processing unit 40 adds data in which the point cloud group T22 is associated with the attribute information of "pump" to the training data 21g. The point cloud data processing unit 40 adds data in which the point cloud group T23 is associated with the attribute information of "pipe" to the training data 21g. The point cloud data processing unit 40 also creates training data for the support members.

[0044] Based on this 3D CAD information, the route generation unit 22 generates a piping route along the ceiling T11, floor T12, and walls T13 to T16 while avoiding indoor structures such as the tank T21, pump T22, and pipe T23 (route generation step). Note that when the identified indoor structure is a temporary or removable structure, the route generation unit 22 can also generate a route that does not avoid the removable indoor structure. Since the details of the generation of the piping route by the layout design automation system 1 are the same as those in Patent Document 1, the description is omitted.

[0045] As described above, the layout design automation system 1 according to the embodiment has a point cloud data processing unit 40 that specifies which range of the point cloud data corresponds to the range of each structure, specifies the type of the structure, and generates 3D CAD information of the specified structure from the information of the building body and indoor structures represented by the point cloud data. The point cloud data processing unit 40 uses AI to specify the type of each structure from the information of the point cloud data including the information of all structures based on the teacher data in which the group of the point cloud data is associated with the attribute information indicating the type of the structure. Therefore, the designer does not need to specify which range of the point cloud data corresponds to what kind of structure and perform the process of converting the point cloud data into 3D CAD information of the corresponding structure. Thus, the layout design automation system 1 according to the embodiment can reduce the burden on the designer.

[0046] In addition, the point cloud data processing unit 40 can accurately specify the type of the structure. The route generation unit 22 generates a route based on the 3D CAD information generated by the point cloud data processing unit 40. Therefore, the layout design automation system 1 according to the embodiment can improve the design quality of the layout design automation system using the point cloud data.

[0047] Also, as described in the process of step t07, the point cloud data processing unit 40 of the layout design automation system 1 according to the embodiment accumulates teacher data 21g in which the attribute information indicating the type of the structure specified for the group of the point cloud data is associated with the group of the point cloud data. Therefore, the more the layout design automation system 1 is used, the more the teacher data 21g increases, and the design quality by the layout design automation system 1 is further improved.

[0048] In the above description, the case of arranging piping inside the building has been described. However, the routes generated by the route generation unit 22 of the layout design automation system 1 include routes of piping, instrument piping, wire conduits, cable racks, and air conditioning ducts. Further, the layout design automation system 1 can also be applied to the generation of piping routes outside the building. In this case, as the layout space information 21a, 3D CAD information indicating the structure within the site is used.

[0049] Also, in the above, the method of generating 3D CAD information of a body and indoor structures, etc. from point cloud data was described using the flowchart shown in FIG. 3. However, this flowchart is just an example and is not limited thereto. For example, the processing of step t02 and step t03 may be reversed. Since the teacher data of the ceiling, floor, and wall includes surrounding information, the point cloud data processing unit 40 can identify each of the ceiling, floor, and wall from the point cloud data of the entire structure position. The point cloud data processing unit 40 can identify the space surrounded by the identified ceiling, floor, and wall as a room.

[0050] As described above, the embodiments of the present invention have been described. However, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalent scope thereof.

Explanation of Reference Numerals

[0051] 1... Automatic layout design system 10... Input unit 21... Storage unit 21a... Layout space information 21b... Dedicated layout space information 21c... Existing structure information 21d... Operation and maintenance information 21e... Pipe layout standard information 21f... Detailed design information 22... Route generation unit 23... Support positioning unit 24... Pipe member determination unit 25... Quantity aggregation unit 30... Output unit 40... Point cloud data processing unit

Claims

1. An arrangement design automation system that generates a route connecting a starting point and an ending point, from information composed of point cloud data showing the entire structure including the building structure including the ceiling, floor, and walls and the indoor structures arranged within the structure, based on teacher data in which a group of point cloud data and attribute information indicating the type of the building structure or the indoor structure are associated including relationship information with the point cloud data showing the surrounding structures, using AI to identify which range of the point cloud data is the range of each of the structures, identify the type of the structure and the type of the indoor structure, and generate 3D CAD information of the entire identified structure; a point cloud data processing unit, An arrangement design automation system having a route generation unit that generates the route based on the 3D CAD information generated by the point cloud data processing unit.

2. The teacher data has, as relationship information with the point cloud data showing the surrounding structures for piping, which is one of the indoor structures, support information of the piping arranged at a predetermined interval, and information that the piping passes through a wall and connects to an adjacent room. The arrangement design automation system according to Claim 1.

3. The point cloud data processing unit creates teacher data in which attribute information indicating the type of the identified structure and a group of point cloud data indicating the identified structure are associated and stores it in a storage unit. The arrangement design automation system according to Claim 1.

4. The route generated by the route generation unit includes at least one of the routes of piping, process piping, electrical conduit, cable rack, and air conditioning duct. The arrangement design automation system according to Claim 1.

5. The point cloud data processing unit uses AI based on teacher data in which a group of point cloud data and attribute information indicating the type of the building structure or the indoor structure are associated including relationship information with the point cloud data showing the surrounding structures, from information composed of point cloud data showing the entire structure including the building structure including the ceiling, floor, and walls and the indoor structures arranged within the structure, to identify which range of the point cloud data is the range of each of the structures, and a structure identification step of identifying the structure including the ceiling, floor, and walls; An indoor structure identification step in which the point cloud data processing unit identifies the type of the indoor structure located in the area surrounded by the identified ceiling, floor, and walls. A three-dimensional CAD information generation step in which the point group data processing unit generates three-dimensional CAD information of the identified body and the indoor structure; A layout design method including a route generation step in which a route generation unit generates at least one route of piping, instrument piping, electrical conduit, cable rack, or air conditioning duct based on the generated three-dimensional CAD information. The layout design method according to claim 5, further comprising a teacher data creation step in which the point group data processing unit creates teacher data associating a group of point group data indicating the identified structure with attribute information indicating the type of the identified structure. The layout design method according to claim 5.

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