CN113901558B - Automatic pipeline generation method and system based on AI decision tree and electronic equipment - Google Patents
Automatic pipeline generation method and system based on AI decision tree and electronic equipment Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- G06F2113/00—Details relating to the application field
- G06F2113/14—Pipes
Abstract
The invention provides an automatic pipeline generation method, a system and electronic equipment based on an AI decision tree, which comprises the steps of arranging exhaust pipelines of all spaces on the other side, opposite to a first pipeline, in a pipe well based on the position of the first pipeline in the pipe well in the space; judging the layout positions of all the spaces based on the position relation between the air outlet of each space and the pipe well; generating a first node at the position of the exhaust outlet corresponding to the height value based on the height value of the exhaust pipeline, and generating a second node under the same height value of the first node; and connecting the air outlet, the first node, the second node and one end of the air exhaust pipeline in sequence to form a pipeline path, and generating a corresponding pipe model based on the path. The algorithm can be linked in real time according to the position of the vertical exhaust pipe in the pipe well and the change of the position of the exhaust outlet of the kitchen in the toilet, so that the corresponding exhaust pipeline is automatically generated, and the position conflict between the two pipelines is avoided.
Description
Technical Field
The invention relates to the technical field of automatic pipeline generation, in particular to an automatic pipeline generation method and system based on an AI decision tree and electronic equipment.
Background
With the advancement of science and technology, the work in the field of architectural design has tended to be automated and intelligent, such as automatically generating the shape of a house, the layout of an interior, and the like.
In the building design, the indoor air exhaust pipeline and the indoor drainage pipeline are designed based on the difference of house types, and the design is pertinently carried out, so that good air exhaust and drainage effects are required to be achieved, and the purpose of saving materials is also required to be achieved.
Usually indoor exhaust duct, drainage pipe need the designer to get the size, arrange through the artifical mode of looking for the dot and draw line, but this kind of mode need consume higher human cost, and the pipeline design efficiency of arranging is lower.
Disclosure of Invention
In order to solve the problems that the existing pipeline design needs to consume higher labor cost and the pipeline arrangement design efficiency is lower, the invention provides an automatic pipeline generation method and system based on an AI decision tree and electronic equipment.
In order to solve the technical problems, the invention provides a technical scheme as follows: based on the position of a first pipeline in a pipe well, arranging exhaust pipelines of all spaces on the other side, opposite to the first pipeline, in the pipe well; judging the layout positions of all the spaces based on the position relation between the air outlet of each space and the pipe well; generating a first node at the position of the air outlet corresponding to the height value based on the height value of the air exhaust pipeline, generating a second node under the same height value of the first node, and forming a right-angle fold line by connecting lines of the first node, the second node and the air exhaust pipeline; and connecting the air outlet, the first node, the second node and one end of the air exhaust pipeline in sequence to form a pipeline path, and generating a corresponding pipeline model based on the path.
Preferably, the layout of the space includes: selecting a space adjacent to the tube well, and acquiring the depth size of the space and the tube well, wherein in the plane with the same height, the direction of the depth size is the Y direction, and the direction vertical to the Y direction is the X direction; judging whether the distance from the air outlet to the pipe well is larger than the depth size; when the distance from the air outlet to the pipe well is larger than the depth dimension, the X coordinate value of each air exhaust pipeline is differed with the X coordinate value of the air outlet, the absolute value is taken, and the air exhaust pipeline corresponding to the current air outlet is taken as the small absolute value; and when the distance from no air outlet to the pipe well is greater than the depth dimension, selecting the air exhaust pipeline with the air outlet closest to the air outlet for pairing.
Preferably, the generating of the first node and the second node includes: selecting the height direction of the exhaust pipeline as the Z direction, and generating a first node at the position of the exhaust outlet corresponding to the Z coordinate value of the exhaust pipeline; and generating a second node under the same Z coordinate value, wherein the X coordinate value of the second node is the same as that of the exhaust pipeline, and the Y coordinate value of the second node is the same as that of the first node.
Preferably, the generating of the first node and the second node further includes: judging whether the difference value between the X coordinate value of the first node and the X coordinate value of the exhaust pipeline exceeds a threshold value or not; when the difference value between the X coordinate value of the first node and the X coordinate value of the exhaust pipeline exceeds a threshold value, generating a second node; and when the difference value between the X coordinate value of the first node and the X coordinate value of the exhaust pipeline does not exceed the threshold value, removing the second node, and adjusting the X coordinate value of the exhaust pipeline to be the same as the X coordinate value of the corresponding exhaust outlet.
Preferably, the step of generating the pipeline model includes: connecting the air outlet, the first node, the second node and one end of the air exhaust pipeline in sequence to form a pipeline broken line path; generating a rectangle of the section of the pipeline at the position of the air outlet based on a preset pipeline caliber; and traversing the pipeline broken line path based on the rectangle to obtain a pipeline model.
The invention also provides an AI-decision-tree-based automatic pipeline generation system, which comprises: the first pipeline positioning unit is used for arranging the exhaust pipelines of all the spaces on the other side, far away from the first pipeline, in the pipe well based on the position of the first pipeline in the pipe well, in one space; the spatial layout judging unit is used for judging the relative positions of all the spaces based on the position relation between the air outlet of each space and the pipe well; the node generating unit is used for generating a first node at the position of the exhaust outlet corresponding to the height value based on the height value of the exhaust pipeline, generating a second node under the same height value of the first node, and forming a right-angle fold line by connecting lines of the first node, the second node and the exhaust pipeline; and the pipeline generating unit is used for sequentially connecting the air outlet, the first node, the second node and one end of the air exhaust pipeline to form a pipeline path and generating a corresponding pipeline model based on the path.
Preferably, the spatial layout determination unit further includes: the depth calculation unit is used for selecting a space adjacent to the tube well and acquiring the depth size of the space and the position of the tube well, wherein in a plane at the same height, the direction of the depth size is the Y direction, and the direction vertical to the Y direction is the X direction; the depth judging unit is used for judging whether the distance from the air outlet to the pipe well is greater than the depth size; the difference unit is used for making a difference between the X coordinate value of each exhaust pipeline and the X coordinate value of an exhaust port when the distance from the exhaust port to the pipe well is greater than the depth dimension, taking an absolute value, and taking the smaller absolute value as the exhaust pipeline corresponding to the current exhaust port; and the matching unit is used for selecting the exhaust pipeline with the exhaust outlet closest to the pipe well for matching when the distance from the exhaust outlet to the pipe well is smaller than the depth size.
Preferably, the node generation unit further includes: the first node generating unit is used for selecting the height direction of the exhaust pipeline as the Z direction and generating a first node at the position of the exhaust outlet corresponding to the Z coordinate value of the height of the exhaust pipeline; and the second node generating unit is used for generating a second node under the same Z coordinate value, wherein the X coordinate value of the second node is the same as that of the exhaust pipeline, and the Y coordinate value of the second node is the same as that of the first node.
Preferably, the node generation unit further includes: the threshold value judging unit is used for judging whether the difference value between the X coordinate value of the first node and the X coordinate value of the exhaust pipeline exceeds a threshold value or not; the fixed point unit is used for generating the second node when the difference value between the X coordinate value of the first node and the X coordinate value of the exhaust pipeline exceeds a threshold value; and the pipeline adjusting unit is used for adjusting the X coordinate value of the exhaust pipeline to be the same as the X coordinate value of the corresponding exhaust outlet when the difference value between the X coordinate value of the first node and the X coordinate value of the exhaust pipeline does not exceed the threshold value.
The invention also provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is configured to execute the automatic pipeline generation method based on the AI decision tree in any one of the above-mentioned methods when running; the processor is arranged to perform the AI decision tree based pipe auto-generation method of any of the above by the computer program.
Compared with the prior art, the automatic pipeline generation method, the automatic pipeline generation system and the electronic equipment based on the AI decision tree have the following advantages:
1. the position of the exhaust pipeline is set through the position of the first pipeline in the space in the pipe well, the position of the exhaust pipeline can be automatically set according to the change real-time linkage of the exhaust outlet and the position of the first pipeline, then a pipeline broken line path is generated at first through a point positioning mode, and a pipeline path model is formed at last, so that the pipeline can be automatically generated, the reduced labor cost is realized, and the pipeline arrangement design efficiency is improved. Meanwhile, the exhaust pipeline and the first pipeline share one pipe well, so that the space is saved. The method can be used for generating a single exhaust duct and can also be used for generating a plurality of exhaust ducts, and the universality is wide.
2. By judging the difference value between the X coordinate value of the first node and the X coordinate value of the exhaust pipeline, when the distance between the second node and the first node is smaller than the threshold value, the length of the pipeline can be shortened by deleting the second node, and the pipeline is prevented from being bent and being too short in pipeline segmentation.
3. Through confirming the position of exhaust duct with first pipeline, and the position setting of exhaust duct is in the opposite one side of first pipeline in the piping shaft for when the piping shaft width is narrower, guarantee that water pipe and tuber pipe position do not have the conflict, ensure to have sufficient space between the two.
4. The layout of the spaces is determined by the depth dimension, so that the layout of the spaces can be determined based on the depth dimension, for example, in this embodiment, there are two general relative positional relationships between the toilet and the kitchen: the toilet and the kitchen are arranged side by side (distributed along an X axis) and the kitchen is arranged above the toilet (distributed along a Y axis), so that the relative position relation between the toilet and the kitchen can be judged according to the position coordinates of the air openings for the two layouts. After the position relation is determined, the pipeline routing direction can be determined, and the generation efficiency of the pipeline broken line path is improved.
Drawings
Fig. 1 is an overall flowchart of an AI decision tree-based method for automatically generating a pipeline according to a first embodiment of the present invention.
Fig. 2 is a schematic diagram of a spatial layout.
Fig. 3 is a detailed flowchart of step S2 in the AI decision tree based pipeline automatic generation method according to the first embodiment of the present invention.
Fig. 4a is a schematic diagram of the depth dimension in the XY axis direction in the spatial layout of fig. 2.
Fig. 4b is a schematic diagram of two spatial transversal layouts in the spatial layout.
Fig. 5 is a schematic structural diagram of the first node and the second node generated in the XY-axis direction in the spatial layout of fig. 2.
Fig. 6 is a flowchart illustrating details of step S3 of the AI decision tree based method for automatically generating a pipeline according to the first embodiment of the present invention.
Fig. 7 is a flowchart illustrating details of step S4 of the AI decision tree based method for automatically generating a pipeline according to the first embodiment of the present invention.
Fig. 8 is a schematic diagram of a structure for generating a pipeline broken line path.
Fig. 9 is a block diagram of an AI decision tree based pipeline automatic generation system according to a second embodiment of the present invention.
Fig. 10 is a block diagram of a spatial layout determination unit in an AI decision tree based pipeline automatic generation system according to a second embodiment of the present invention.
Fig. 11 is a block diagram of a node generation unit in an AI decision tree based pipeline automatic generation system according to a second embodiment of the present invention.
Fig. 12 is a block diagram of an electronic device according to a third embodiment of the invention.
Description of reference numerals:
1. a first pipe positioning unit; 2. a spatial layout determination unit; 3. a node generation unit; 4. a pipeline generating unit;
21. a depth calculation unit; 22. a depth judgment unit; 23. a difference unit; 24. a pairing unit;
31. a first node generation unit; 32. a second node generation unit; 33. a threshold value judging unit; 34. a fixed point unit; 35. a pipe adjusting unit;
10. a memory; 20. a processor;
100. a toilet bowl; 101. a first conduit; 102. an exhaust duct; 103. a first node; 104. a second node; 105. an air outlet.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, a first embodiment of the present invention provides a method for automatically generating a pipeline based on an AI decision tree, including the following steps:
step S1: based on the position of the first pipeline in the pipe well, the exhaust pipelines of all the spaces are arranged on the other side, far away from the first pipeline, in the pipe well.
Step S2: and judging the relative positions of all the spaces based on the position relation between the air outlet of each space and the pipe well.
Step S3: based on the height value of the exhaust pipeline, a first node is generated at the position of the exhaust outlet corresponding to the height value, a second node is generated under the same height value of the first node, and a right-angle fold line is formed by connecting lines of the first node, the second node and the exhaust pipeline.
Step S4: and connecting the air outlet, the first node, the second node and one end of the air exhaust pipeline in sequence to form a pipeline path, and generating a corresponding pipeline model based on the path.
It is understood that, in step S1, the first duct may be a drainage duct or an exhaust duct, the space may be an indoor bathroom, kitchen, bedroom, etc., in this embodiment, the number of the spaces is 2, and one of the spaces is the bathroom, the other space is the kitchen, the first duct is a drainage duct in the bathroom, and the exhaust duct is an exhaust duct for exhausting air.
Optionally, in some other embodiments, the number of the spaces may also be set to be 1, 3, or 4, which is not described herein again.
It can be understood that in the design of the indoor pipeline, a pipe well is usually arranged at a wall surface position and used for accommodating the exhaust pipeline and the drainage pipeline. In step S1, the position of the exhaust duct in the pipe well is further located by the first duct determined in the toilet.
For example, as shown in fig. 2, an indoor plane is used as a spatial coordinate system, a Z coordinate is a height direction of a space, an X coordinate is a lateral direction, a Y direction is a longitudinal direction, a position of the first pipe 101 in the pipe well is determined based on a coordinate point of the toilet bowl 100 in the toilet in the space, a position of a side of the pipe well opposite to the first pipe is positioned as a position of the exhaust pipe 102, the first pipe 101 and the exhaust pipe 102 are vertical pipes in the pipe well, and both have an intersection point in a top surface (Z-axis direction) area of the space, that is, the first pipe 101 and the exhaust pipe 102 are displayed as one point when viewed from the Z-axis direction.
It is understood that, in step S2, by determining the layout positions of the plurality of spaces, the automatic generation of the point locations of the pipelines in the subsequent step is facilitated.
It is understood that, in step S3, the first node corresponding to the exhaust outlet is generated based on the height value of the exhaust duct, i.e. the height of the same Z coordinate. The second node corresponds to the same horizontal coordinate as the exhaust duct, namely has the same X coordinate value, so as to connect each point location in a broken line form.
It is understood that, in step S3, the exhaust vent is a displacement area reserved for each space during design for connecting with a pipeline, the position of the exhaust vent in each space is different, and the depth of each exhaust vent is also different, so that a first node is generated at the top of each exhaust vent.
It will be appreciated that in step S4, the plurality of points formed are connected in sequence to obtain the desired duct polyline path.
It can be understood that, in the embodiment of the present invention, the above steps may be implemented in Rhino/Grasshopper, and then may also be implemented on a cloud platform by javascript.
Referring to fig. 3, step S2: and judging the layout positions of all the spaces based on the position relation between the air outlet of each space and the pipe well. Step S2 specifically includes steps S21 to S24:
step S21: and selecting a space adjacent to the tube well, and acquiring the depth dimension of the space and the tube well, wherein in the plane with the same height, the depth dimension is in the Y direction, and the depth dimension is perpendicular to the Y direction and is in the X direction.
Step S22: and judging whether the distance from the air outlet to the tube well is larger than the depth size, if so, entering the step S23, and if not, entering the step S24.
Step S23: and (4) making a difference between the X coordinate value of each exhaust duct and the X coordinate value of an exhaust port, taking an absolute value, and taking the absolute value as the exhaust duct corresponding to the current exhaust port.
Step S24: selecting the exhaust pipeline with the exhaust outlet closest to the exhaust outlet for pairing.
It is to be understood that, in step S21, as shown in fig. 4a, fig. 4a is a top view along the Z-axis direction in fig. 2, where the a area and the B area are room areas, and the C area is a tube well area, and since fig. 4a is a top view along the Z-axis direction, the air outlet coincides with the first node 103, that is, the angle in fig. 4a, the position of the first node 103 is the position of the air outlet point, and the depth dimension is the Y-axis direction. Since there are two spaces in fig. 4a, two exhaust ducts 102 are provided.
It is to be understood that, in step S22, as shown in fig. 4a, the depth dimension of the a space corresponds to depth 1 in fig. 4a, the depth dimension of the B space corresponds to depth 2 in fig. 4a, and in fig. 4a, the space adjacent to the C space (tube well) is the B space, and then the depth 2 dimension is selected as the determined depth dimension.
If the distance from the air outlet to the tube well in a space is larger than the depth dimension, the two spaces are judged to be arranged along the Y-axis direction, and if the distances from the air outlet to the tube well in the two spaces are smaller than the depth dimension, the two spaces are judged to be arranged along the X-axis direction, as shown in fig. 4a, the Y coordinate of the first node 103 in the A space is larger than the depth 2 dimension, and the A space and the B space are judged to be arranged along the Y-axis direction.
As shown in fig. 4B, the depth 2 dimension is the depth dimension of the B space, wherein the Y coordinates of the first node 103 in the a space and the first node 103 in the B space are both smaller than the depth dimension, so that it is determined that the two spaces are laterally distributed along the X-axis direction. Specifically, in this embodiment, in the field of box-assembled spatial layout, the space a and the space B are both low-area ceiling type spaces, so when the space a and the space B are laterally distributed, the depth of the space a is not greater than the depth of the space B, that is, the depth of the space a is consistent with the depth of the space B, and the position of the air outlet of the space a is necessarily smaller than the depth 2. For example, the space type of the low zone ceiling may be a toilet and a kitchen, and the lateral distribution of the a space and the B space may be determined based on this characteristic.
It is understood that, in steps S23 and S24, two points having a close distance are paired by the X-coordinate value of the exhaust duct and the distance of each exhaust outlet in the X-axis direction.
Referring to fig. 5 and 6, step S3: and based on the height value of the exhaust pipeline, generating a first node at the position of the exhaust outlet corresponding to the height value, and generating a second node at the same horizontal position as the position corresponding to the exhaust pipeline under the same height value of the first node. Step S3 specifically includes steps S31 to S32:
step S31: and selecting the height direction of the exhaust pipeline as the Z direction, and generating a first node at the position of the exhaust outlet corresponding to the Z coordinate value of the height of the exhaust pipeline.
Step S32: and generating a second node under the same Z coordinate value, wherein the X coordinate value of the second node is the same as that of the exhaust pipeline, and the Y coordinate value of the second node is the same as that of the first node.
It is to be understood that fig. 5 is a top view along the Z-axis direction in fig. 2, as shown in fig. 5, wherein the a and B areas are room areas and the C area is a tube well area, i.e., the same as in fig. 4 a.
It is to be understood that, in step S31, as shown in fig. 5, the position where the top of the air outlet is equal to the height of the air outlet duct 102 is set as the first node 103, and since fig. 5 is a plan view of the XY axis, the air outlet coincides with the position of the first node 103, that is, the position of the air outlet is the same as the X coordinate and the Y coordinate of the first node 103, and the height of the first node 103 is higher than the height of the air outlet.
It is to be understood that, in step S32, as shown in fig. 5, the second node 104 is an extended arrangement of the first node 103, which is the same as the X-coordinate value of the exhaust duct. In fig. 5, the dashed lines are the respective independent pipeline connecting lines for space a and space B.
Optionally, as an embodiment, continuing to refer to fig. 6, after step S32, the method further includes:
step S33: and judging whether the difference value between the X coordinate value of the first node and the X coordinate value of the exhaust pipeline exceeds a threshold value, if so, entering step S34, otherwise, entering step S35.
Step S34: and generating the second node.
Step S35: and removing the second node, and adjusting the X coordinate value of the exhaust pipeline to be the same as the X coordinate value of the corresponding exhaust outlet.
It is understood that, in step S33, the threshold may be set to 200mm, that is, whether the distance between the first node and the exhaust duct in the X-axis direction exceeds the threshold, and when the distance does not exceed the threshold, in order to avoid the occurrence of a too short duct segment, the second node may be deleted in step S35, and the X coordinate of the exhaust duct may be adjusted so that the two nodes may be directly connected.
Referring to fig. 7, step S4: and connecting the air outlet, the first node, the second node and one end of the air exhaust pipeline in sequence to form a pipeline path, and generating a corresponding pipe model based on the path. Step S4 specifically includes steps S41 to S43:
step S41: and the air outlet, the first node, the second node and one end of the air exhaust pipeline are sequentially connected to form a pipeline broken line path.
Step S42: and generating a rectangle of the section of the pipeline at the position of the air outlet based on the preset pipeline caliber.
Step S43: and traversing the pipeline broken line path based on the rectangle to obtain a pipeline model.
It is understood that, in step S41, the generated points are sequentially connected to form a pipeline broken line path, and in particular, if the second node 104 is deleted after step S33, the first node 103 and the exhaust pipeline 102 may be directly connected to reduce the number of line segments. As shown in fig. 8, the pipe broken line path obtained after the connection is a path of two types, one is a type in which the second node 104 is retained, and the other is a type in which the first node 103 is directly connected to the exhaust pipe 102 after the second node 104 is deleted. Specifically, the two types of polyline paths are independent from each other, and as can be seen in fig. 8, the connection sequence of one of the polyline paths is: the exhaust port 105 is connected to the first node 103 upward (along the Z-axis), then connected to the second node 104 along the Y-axis, and further connected to the exhaust duct 102 at the same height along the X-axis.
It can be understood that, in step S42, based on the caliber size of the pipeline, a circumscribed rectangle frame of the pipeline interface is made, and based on the rectangle frame, the pipeline polyline path is traversed, so that the pipeline model can be automatically generated.
Referring to fig. 9, a system for automatically generating a pipeline based on an AI decision tree is further provided according to a second embodiment of the present invention. For executing the AI decision tree based pipeline automatic generation method in the first embodiment, the AI decision tree based pipeline automatic generation system may include:
a first pipeline positioning unit 1 for executing the step S1 for arranging the exhaust pipelines of all the spaces in the pipe well at the other side far away from the first pipeline based on the position of the first pipeline in the pipe well.
The spatial layout determination unit 2 is configured to execute the step S2 described above, and is configured to determine the relative positions of all the spaces based on the positional relationship between the air outlets and the pipe wells of each space.
The node generating unit 3 is configured to execute the step S3, and is configured to generate a first node at a position of the exhaust outlet corresponding to the height value based on the height value of the exhaust duct, and generate a second node at the same height value of the first node, where connection lines of the first node, the second node, and the exhaust duct form a right-angle folding line.
The duct generating unit 4 is configured to execute the step S4, and is configured to sequentially connect the exhaust outlet, the first node, the second node, and one end of the exhaust duct to form a duct path, and generate a corresponding duct model based on the path.
Referring to fig. 10, the spatial layout determining unit 2 further includes:
a depth calculating unit 21, configured to execute the step S21, configured to select a space adjacent to the tube well, and obtain a depth dimension of the space and the tube well, where in a plane at the same height, a direction of the depth dimension is a Y direction, and a direction perpendicular to the Y direction is an X direction;
a depth determination unit 22, configured to perform step S22, and determine whether a distance from the air outlet to the tube well is greater than the depth;
a difference unit 23, configured to execute step S23, where when the distance from the air outlet to the pipe well is greater than the depth dimension, the difference is made between the X-coordinate value of each air exhaust pipe and the X-coordinate value of an air outlet, and an absolute value is obtained, and the absolute value is smaller and is used as the air exhaust pipe corresponding to the current air outlet;
the matching unit 24 is configured to execute the step S24, and is configured to select the exhaust duct with the exhaust outlet closest to the pipe well for matching when the distance from the exhaust outlet to the pipe well is smaller than the depth size.
Referring to fig. 11, the node generating unit 3 further includes:
a first node generating unit 31, configured to execute the step S31, configured to select the height direction of the exhaust duct as the Z direction, and generate a first node at a Z coordinate value of the exhaust outlet corresponding to the height of the exhaust duct;
the second node generating unit 32 is configured to execute the above step S32, and is configured to generate a second node under the same Z coordinate value, where the X coordinate value of the second node is the same as the X coordinate value of the exhaust duct, and the Y coordinate value of the second node is the same as the Y coordinate value of the first node.
Optionally, as an embodiment, please continue to refer to fig. 11, the node generating unit 3 further includes:
the threshold determination unit 33 is configured to execute the step S33, and is configured to determine whether a difference between the X coordinate value of the first node and the X coordinate value of the exhaust duct exceeds a threshold.
The fixed point unit 34 is configured to execute the step S34, and is configured to generate the second node when a difference between the X-coordinate value of the first node and the X-coordinate value of the exhaust duct exceeds a threshold.
The duct adjusting unit 35 is configured to execute the step S35, and is configured to adjust the X coordinate value of the exhaust duct to be the same as the X coordinate value of the corresponding exhaust outlet when the difference between the X coordinate value of the first node and the X coordinate value of the exhaust duct does not exceed the threshold.
It can be understood that the AI decision tree based automatic pipeline generation system according to the second embodiment of the present invention is particularly suitable for an automatic indoor exhaust pipeline generation system, and after the exhaust vertical pipes are positioned in the pipe wells by the indoor exhaust pipelines, the pipe broken line paths are automatically generated in sequence by point positioning, so that the labor cost is reduced and the pipeline layout design efficiency is improved.
Referring to fig. 12, a third embodiment of the present invention provides an electronic device for implementing the above-mentioned AI decision tree-based automatic pipeline generation method, where the electronic device includes a memory 10 and a processor 20, and the memory 10 stores therein an arithmetic machine program configured to execute the steps in any of the above-mentioned AI decision tree-based automatic pipeline generation method embodiments when running. The processor 20 is configured to execute the steps of any one of the above embodiments of the AI decision tree based pipeline automatic generation method by the operator program.
Optionally, in this embodiment, the electronic device may be located in at least one network device of a plurality of network devices of an operating machine network.
Compared with the prior art, the automatic pipeline generation method, the automatic pipeline generation system and the electronic equipment based on the AI decision tree have the following advantages:
1. the position of the exhaust pipeline is set through the position of the first pipeline in the space in the pipe well, the position of the exhaust pipeline can be automatically set according to the change real-time linkage of the exhaust outlet and the position of the first pipeline, then a pipeline broken line path is generated at first through a point positioning mode, and a pipeline path model is formed at last, so that the pipeline can be automatically generated, the reduced labor cost is realized, and the pipeline arrangement design efficiency is improved. Meanwhile, the exhaust pipeline and the first pipeline share one pipe well, so that the space is saved. The method can be used for generating a single exhaust duct and can also be used for generating a plurality of exhaust ducts, and the universality is wide.
2. By judging the difference value between the X coordinate value of the first node and the X coordinate value of the exhaust pipeline, when the distance between the second node and the first node is smaller than the threshold value, the length of the pipeline can be shortened by deleting the second node, and the pipeline is prevented from being bent and being too short in pipeline segmentation.
3. Through confirming the position of exhaust duct with first pipeline, and the position setting of exhaust duct is in the opposite one side of first pipeline in the piping shaft for when the piping shaft width is narrower, guarantee that water pipe and tuber pipe position do not have the conflict, ensure to have sufficient space between the two.
4. The layout of the spaces is determined by the depth dimension, so that the layout of the spaces can be determined based on the depth dimension, for example, in this embodiment, there are two general relative positional relationships between the toilet and the kitchen: the toilet and the kitchen are arranged side by side (distributed along an X axis) and the kitchen is arranged above the toilet (distributed along a Y axis), so that the relative position relation between the toilet and the kitchen can be judged according to the position coordinates of the air openings for the two layouts. After the position relation is determined, the pipeline routing direction can be determined, and the generation efficiency of the pipeline broken line path is improved.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart.
Which when executed by a processor performs the above-described functions defined in the method of the present application. It should be noted that the computer memory described herein may be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer memory may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing.
More specific examples of computer memory may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable signal medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a first pipeline positioning unit, a spatial layout judging unit, a node generating unit, and a pipeline generating unit. Here, the names of the units do not constitute a limitation to the unit itself in some cases, and for example, the spatial layout determination unit may also be described as "a unit that determines the layout positions of all the spaces based on the positional relationship of the air outlet and the pipe well for each space".
As another aspect, the present application also provides a computer memory, which may be included in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer memory carries one or more programs that, when executed by the apparatus, cause the apparatus to: arranging the exhaust pipelines of all the spaces on the other side, far away from the first pipeline, in the pipe well based on the position of the first pipeline in the pipe well in the space; judging the relative positions of all the spaces based on the position relation between the air outlet of each space and the pipe well; generating a first node at the position of the air outlet corresponding to the height value based on the height value of the air exhaust pipeline, generating a second node under the same height value of the first node, and forming a right-angle fold line by connecting lines of the first node, the second node and the air exhaust pipeline; and connecting the air outlet, the first node, the second node and one end of the air exhaust pipeline in sequence to form a pipeline path, and generating a corresponding pipeline based on the path.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent alterations and improvements made within the spirit of the present invention should be included in the scope of the present invention.
Claims (10)
1. The automatic pipeline generation method based on the AI decision tree is characterized by comprising the following steps of:
arranging the exhaust pipelines of all the spaces on the other side, far away from the first pipeline, in the pipe well based on the position of the first pipeline in the pipe well in the space;
judging the relative positions of all the spaces based on the position relation between the air outlet of each space and the pipe well;
generating a first node at the position of the air outlet corresponding to the height value based on the height value of the air exhaust pipeline, generating a second node under the same height value of the first node, and forming a right-angle fold line by connecting lines of the first node, the second node and the air exhaust pipeline;
and connecting the air outlet, the first node, the second node and one end of the air exhaust pipeline in sequence to form a pipeline path, and generating a corresponding pipeline model based on the path.
2. The AI decision tree-based pipeline automatic generation method of claim 1, wherein the layout of the space comprises: selecting a space adjacent to the tube well, and acquiring the depth size of the space and the tube well, wherein in the plane with the same height, the direction of the depth size is the Y direction, and the direction vertical to the Y direction is the X direction;
judging whether the distance from the air outlet to the pipe well is larger than the depth size;
when the distance from the air outlet to the pipe well is larger than the depth dimension, the X coordinate value of each air exhaust pipeline is differed with the X coordinate value of the air outlet, the absolute value is taken, and the air exhaust pipeline corresponding to the current air outlet is taken as the small absolute value;
and when the distance from no air outlet to the pipe well is greater than the depth dimension, selecting the air outlet pipeline closest to the air outlet for pairing.
3. The AI decision tree based pipeline automatic generation method of claim 2, wherein the generation of the first node and the second node comprises:
selecting the height direction of the exhaust pipeline as the Z direction, and generating a first node at the position of the exhaust outlet corresponding to the Z coordinate value of the height of the exhaust pipeline;
and generating a second node under the same Z coordinate value, wherein the X coordinate value of the second node is the same as that of the exhaust pipeline, and the Y coordinate value of the second node is the same as that of the first node.
4. The AI decision tree based pipeline automatic generation method of claim 3, wherein the generation of the first node and the second node further comprises:
judging whether the difference value between the X coordinate value of the first node and the X coordinate value of the exhaust pipeline exceeds a threshold value or not;
when the difference value between the X coordinate value of the first node and the X coordinate value of the exhaust pipeline exceeds a threshold value, generating a second node;
and when the difference value between the X coordinate value of the first node and the X coordinate value of the exhaust pipeline does not exceed the threshold value, removing the second node, and adjusting the X coordinate value of the exhaust pipeline to be the same as the X coordinate value of the corresponding exhaust outlet.
5. The AI decision tree based pipeline automatic generation method of claim 1, wherein the step of generating the pipeline model comprises:
connecting the air outlet, the first node, the second node and one end of the air exhaust pipeline in sequence to form a pipeline broken line path;
generating a rectangle of the section of the pipeline at the position of the air outlet based on a preset pipeline caliber;
and traversing the pipeline broken line path based on the rectangle to obtain a pipeline model.
6. The automatic pipeline generation system based on the AI decision tree is characterized by comprising the following steps:
the first pipeline positioning unit is used for arranging the exhaust pipelines of all the spaces on the other side, far away from the first pipeline, in the pipe well based on the position of the first pipeline in the pipe well, in one space;
the spatial layout judging unit is used for judging the relative positions of all the spaces based on the position relation between the air outlet of each space and the pipe well;
the node generating unit is used for generating a first node at the position of the exhaust outlet corresponding to the height value based on the height value of the exhaust pipeline, generating a second node under the same height value of the first node, and forming a right-angle fold line by connecting lines of the first node, the second node and the exhaust pipeline;
and the pipeline generating unit is used for sequentially connecting the air outlet, the first node, the second node and one end of the air exhaust pipeline to form a pipeline path and generating a corresponding pipeline model based on the path.
7. The AI decision tree based pipeline automatic generation system of claim 6, wherein the spatial layout decision unit further comprises:
the depth calculation unit is used for selecting a space adjacent to the tube well and acquiring the depth size of the space and the position of the tube well, wherein in the plane with the same height, the direction of the depth size is the Y direction, and the direction vertical to the Y direction is the X direction;
the depth judging unit is used for judging whether the distance from the air outlet to the pipe well is greater than the depth size;
the difference unit is used for making a difference between the X coordinate value of each exhaust pipeline and the X coordinate value of an exhaust port when the distance from the exhaust port to the pipe well is greater than the depth dimension, taking an absolute value, and taking the smaller absolute value as the exhaust pipeline corresponding to the current exhaust port;
and the matching unit is used for selecting the exhaust pipeline with the exhaust outlet closest to the pipe well for matching when the distance from the exhaust outlet to the pipe well is smaller than the depth size.
8. The AI decision tree based pipeline automatic generation system of claim 6, wherein the node generation unit further comprises:
the first node generating unit is used for selecting the height direction of the exhaust pipeline as the Z direction and generating a first node at the position of the exhaust outlet corresponding to the Z coordinate value of the height of the exhaust pipeline;
and the second node generating unit is used for generating a second node under the same Z coordinate value, wherein the X coordinate value of the second node is the same as that of the exhaust pipeline, and the Y coordinate value of the second node is the same as that of the first node.
9. The AI decision tree based pipeline automatic generation system of claim 7, wherein the node generation unit further comprises:
the threshold value judging unit is used for judging whether the difference value between the X coordinate value of the first node and the X coordinate value of the exhaust pipeline exceeds a threshold value or not;
the fixed point unit is used for generating the second node when the difference value between the X coordinate value of the first node and the X coordinate value of the exhaust pipeline exceeds a threshold value;
and the pipeline adjusting unit is used for adjusting the X coordinate value of the exhaust pipeline to be the same as the X coordinate value of the corresponding exhaust outlet when the difference value between the X coordinate value of the first node and the X coordinate value of the exhaust pipeline does not exceed the threshold value.
10. An electronic device comprising a memory and a processor, characterized in that: the memory having stored therein a computer program arranged in operation to execute the AI decision tree based pipe auto-generation method of any one of claims 1 to 5;
the processor is arranged to execute the AI decision tree based pipe auto-generation method of any of claims 1 to 5 by means of the computer program.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112241563A (en) * | 2020-10-09 | 2021-01-19 | 同济大学 | Optimization algorithm for pipe well |
| CN113158395A (en) * | 2020-01-23 | 2021-07-23 | 久瓴(上海)智能科技有限公司 | Method and device for generating drainage pipeline model, computer equipment and storage medium |
| CN113486479A (en) * | 2021-06-15 | 2021-10-08 | 德成建设集团有限公司 | BIM model-based rapid arrangement method for indoor water supply and drainage pipelines |
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|---|---|---|---|---|
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-
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113158395A (en) * | 2020-01-23 | 2021-07-23 | 久瓴(上海)智能科技有限公司 | Method and device for generating drainage pipeline model, computer equipment and storage medium |
| CN112241563A (en) * | 2020-10-09 | 2021-01-19 | 同济大学 | Optimization algorithm for pipe well |
| CN113486479A (en) * | 2021-06-15 | 2021-10-08 | 德成建设集团有限公司 | BIM model-based rapid arrangement method for indoor water supply and drainage pipelines |
Non-Patent Citations (1)
| Title |
|---|
| Revit MEP软件在地下车库管线综合设计中的应用;高攀祥等;《现代建筑电气》;20140730;第29-32页 * |
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