CN111832447B - Building drawing component identification method, electronic equipment and related product - Google Patents
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Abstract
The embodiment of the application discloses a method for identifying a building drawing component, electronic equipment and a related product, which are applied to the electronic equipment, wherein the method comprises the following steps: acquiring image information in a first CAD drawing, wherein the image information comprises any one of the following items: points and line segments, characters, space coordinates; splitting the image information to obtain at least one rectangular frame; obtaining coordinates of each vertex of the at least one rectangular frame; identifying a component corresponding to each rectangular frame according to the coordinates of each vertex of the at least one rectangular frame; and marking the at least one component in a display interface of the first CAD drawing. By adopting the method and the device, the image information in the CAD drawing can be identified, the component in the drawing can be obtained, and the intelligence and the accuracy of identification of the building drawing component can be improved.
Description
Technical Field
The application relates to the technical field of BIM, in particular to a building drawing component identification method, electronic equipment and related products.
Background
Currently, target recognition is a branch of vision technology, which is to identify objects in a field of view, such as vehicles, by first detecting, then identifying and then classifying the objects after detection. The classifiers with which the target comparison is prevalent are: an SVM support vector machine, an AdaBoost algorithm and the like. However, in the drawing component identification, the position information is an important part because in the drawing, the hand washing sink and the elevator in the kitchen are similar, and the component classification is easy to generate errors at this time, but in the case of manual component identification, some errors are easy to occur, and the component identification accuracy cannot be fully ensured.
Disclosure of Invention
The embodiment of the application provides a method for identifying a building drawing component, electronic equipment and related products, and can improve the intelligence and accuracy of identification of the building drawing component.
In a first aspect, an embodiment of the present application provides a method for identifying a building drawing component, which is applied to an electronic device, and the method includes:
acquiring image information in a first CAD drawing, wherein the image information comprises any one of the following items: point and line segment, text, space coordinates;
splitting the image information to obtain at least one rectangular frame;
obtaining coordinates of each vertex of the at least one rectangular frame;
identifying a component corresponding to each rectangular frame according to the coordinates of each vertex of the at least one rectangular frame;
and marking the at least one component in a display interface of the first CAD drawing.
In one possible example, the image information includes points and segments; splitting according to the image information to obtain at least one rectangular frame, including:
acquiring a plurality of points and a plurality of line segments;
obtaining a plurality of connecting nodes according to the plurality of points and the plurality of line segments;
and carrying out closed-loop division on the connecting nodes to obtain at least one rectangular frame.
In one possible example, the identifying the corresponding component of each rectangular box according to the coordinates of each vertex of the at least one rectangular box includes:
and inputting the coordinates of the rectangular frame of the at least one rectangular frame into a preset graph convolution neural network model to obtain at least one member in the first CAD drawing.
In one possible example, before inputting the rectangular box coordinates of the at least one rectangular box into a preset graph convolution neural network, resulting in at least one component in the first CAD drawing, the method further includes:
determining whether there are repeated rectangular box coordinates;
if so, selecting at least two rectangular frames corresponding to the repeated rectangular frame coordinates;
and performing spatial analysis on the at least two rectangular frames to determine that the at least two rectangular frames are two members each other.
In a second aspect, an embodiment of the present application provides an identification apparatus for a construction drawing component, the apparatus including: an acquisition unit, a splitting unit, an identification unit and a labeling unit, wherein,
the acquiring unit is used for acquiring image information in a first CAD drawing, wherein the image information comprises any one of the following items: point and line segment, text, space coordinates;
the splitting unit is used for splitting the image information to obtain at least one rectangular frame;
the obtaining unit is further configured to obtain coordinates of each vertex of the at least one rectangular frame;
the identification unit is used for identifying a component corresponding to each rectangular frame according to the coordinates of each vertex of the at least one rectangular frame;
the marking unit is used for marking the at least one component in a display interface of the first CAD drawing.
In one possible example, the image information includes points and segments; in the aspect of splitting according to the image information to obtain at least one rectangular frame, the splitting unit is specifically configured to:
acquiring a plurality of points and a plurality of line segments;
obtaining a plurality of connecting nodes according to the plurality of points and the plurality of line segments;
and carrying out closed-loop division on the connecting nodes to obtain at least one rectangular frame.
In one possible example, in terms of the identifying the corresponding component of each rectangular frame according to the coordinates of each vertex of the at least one rectangular frame, the identifying unit is specifically configured to:
and inputting the coordinates of the rectangular frame of the at least one rectangular frame into a preset graph convolution neural network model to obtain at least one member in the first CAD drawing.
In a possible example, before inputting the coordinates of the at least one rectangular box into a preset graph convolution neural network to obtain the at least one component in the first CAD drawing, the identifying unit is further specifically configured to:
determining whether there are repeated rectangular box coordinates;
if so, selecting at least two rectangular frames corresponding to the repeated rectangular frame coordinates;
and carrying out spatial analysis on the at least two rectangular frames to determine that the at least two rectangular frames are two members each other.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing the steps in the first aspect of the embodiment of the present application.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program enables a computer to perform some or all of the steps described in the first aspect of the embodiment of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to cause a computer to perform some or all of the steps as described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.
The embodiment of the application has the following beneficial effects:
it can be seen that, in the embodiment of the present application, the electronic device first acquires image information in the first CAD drawing, where the image information includes any one of the following: point and line segment, text, space coordinates; splitting the image information to obtain at least one rectangular frame, then obtaining coordinates of each vertex of the at least one rectangular frame, then identifying a component corresponding to each rectangular frame according to the coordinates of each vertex of the at least one rectangular frame, and finally marking the at least one component in a display interface of the first CAD drawing. Therefore, in the example, the electronic device processes the image information in the CAD drawing to further obtain a plurality of members of different types in the drawing, and the intelligence and the accuracy of identification of the construction drawing members are improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a server provided in an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for identifying components in a construction drawing provided in an embodiment of the present application;
FIG. 3 is a schematic flow chart of another identification method for components in construction drawing provided in the embodiments of the present application;
fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present application;
fig. 5 is a functional unit composition block diagram of a method for identifying a component of a construction drawing provided in an embodiment of the present application.
Detailed Description
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The electronic device described in the embodiment of the present application may include a smart Phone (e.g., an Android Phone, an iOS Phone, a Windows Phone, etc.), a tablet computer, a palm computer, a notebook computer, a video matrix, a monitoring platform, a Mobile Internet device (MID, mobile Internet Devices), or a wearable device, which are merely examples, but not exhaustive, and include but are not limited to the foregoing Devices.
The hardware architecture of the embodiment of the present application may be any one of:
(1) Client and server architectures, namely CS architecture modes, such as AI examination APP installed on a mobile phone or a computer;
(2) Browser and server architecture mode, BS architecture mode, for example, AI examination page version accessed by mobile phone or computer through browser;
(3) Combining CS and BS architectures, for example, an AI image examination applet loaded in the WeChat applet, an AI image examination application of the fast application center, and the like;
(4) The local device can run a lightweight graphic engine, and is specifically realized by an AI processing chip architecture, wherein the AI processing chip architecture can include a CPU and at least one neural network processor NPU, the CPU is connected with the at least one NPU, and the at least one NPU can process part or all of data processing logic in the AI examination map.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a server according to an embodiment of the present disclosure, where the server includes a processor, a Memory, a signal processor, a communication interface, a touch display screen, a WiFi module, a speaker, a microphone, a Random Access Memory (RAM), a camera, and the like.
The device comprises a memory, a signal processor, a WiFi module, a touch screen, a loudspeaker, a microphone, an RAM and a camera, wherein the memory, the signal processor, the WiFi module, the touch screen, the loudspeaker, the microphone, the RAM and the camera are connected with the processor, and a communication interface is connected with the signal processor.
Wherein, the memory can store the data of Building Information Modeling (BIM); the processor can call the BIM data in the memory, and then process by combining the BIM data and the received information.
The server may include various handheld devices with wireless communication functions, vehicle-mounted devices, wearable devices (such as smartwatches, smartbands, pedometers, etc.), computing devices or other processing devices connected to wireless modems, as well as various forms of User Equipment (UE), mobile Stations (MS), terminal Equipment (terminal device), and so on. For convenience of description, the above-mentioned devices are collectively referred to as a server.
The following describes embodiments of the present application in detail.
Referring to fig. 2, fig. 2 is a schematic flow chart of a method for identifying a building drawing component according to an embodiment of the present application, where as shown in the figure, the method for identifying a building drawing component includes:
201. acquiring image information in a first CAD drawing, wherein the image information comprises any one of the following items: points and line segments, text, spatial coordinates.
In the embodiment of the application, the electronic device may import the CAD construction drawing into Building Information Modeling (BIM) model software. In the embodiment of the present application, the CAD drawing may be at least one of the following: airports, train stations, bus stations, office buildings, residential buildings, hospitals, museums, tourist attractions, churches, schools, parks, and the like, without limitation thereto.
The spatial coordinates may be coordinates of a vertex existing on the drawing by taking the drawing center of the CAD drawing as an origin of coordinates and taking the origin of coordinates as a center as a spatial rectangular coordinate system.
202. And splitting the image information to obtain at least one rectangular frame.
In one possible example, in step 202 above, the image information includes points and line segments; the splitting according to the image information to obtain at least one rectangular frame includes: acquiring a plurality of points and a plurality of line segments; obtaining a plurality of connecting nodes according to the plurality of points and the plurality of line segments; and carrying out closed-loop division on the connecting nodes to obtain at least one rectangular frame.
The connection between the points and the line segments can form a closed-loop space, and the closed-loop space is a rectangular frame of the component.
Optionally, after the connection node is divided into closed loops, whether a repeated line segment or a repeated point exists needs to be detected; if the repeated line segments and the repeated points exist, checking whether the repeated connection nodes exist, if so, extracting at least two rectangular frames corresponding to the repeated connection nodes for space analysis, determining whether the at least two rectangular frames are rectangular frames which accord with the space and have rationality, if so, keeping, otherwise, marking the at least two rectangular frames, and pushing on a display interface.
Therefore, in the example, the electronic equipment splits the CAD drawing through the splitting point and the line segment, so that the rectangular frames of a plurality of components in the drawing are accurately obtained, and the intelligence and the accuracy of identification of the components of the building drawing are improved.
203. And acquiring the coordinates of each vertex of the at least one rectangular frame.
The coordinates of each vertex can be obtained from the above-mentioned spatial rectangular coordinate system.
204. And identifying the component corresponding to each rectangular frame according to the coordinates of each vertex of the at least one rectangular frame.
In one possible example, the identifying 204, according to the coordinates of each vertex of the at least one rectangular frame, a corresponding component of each rectangular frame includes: and inputting the coordinates of the rectangular frame of the at least one rectangular frame into a preset graph convolution neural network model to obtain at least one member in the first CAD drawing.
The preset Graph convolution neural network GCN model researches the representation (Graph Embedding) of Graph nodes, graph edge structure prediction tasks and the classification problem of graphs. The GCN is a natural popularization of a convolutional neural network on graph domains, can simultaneously carry out end-to-end learning on node characteristic information and structural information, and is the best choice for a graph data learning task at present. The graph convolution has extremely wide applicability and is suitable for nodes and graphs with any topological structures. On tasks such as node classification and edge prediction, the effect on the public data set is far better than that of other methods. And determining a convolution network structure through local first-order approximation of spectrogram convolution, and training a convolution neural network structure model through part of labeled node data in the graph structure data to further classify the rest unlabeled data by the network model.
Optionally, before inputting the coordinates of the rectangular box of the at least one rectangular box into a preset graph convolution neural network to obtain the at least one component in the first CAD drawing, the method further includes: determining whether there are repeated rectangular box coordinates; if so, selecting at least two rectangular frames corresponding to the repeated rectangular frame coordinates; and carrying out spatial analysis on the at least two rectangular frames to determine that the at least two rectangular frames are two members each other.
Wherein the spatial analysis is to analyze whether the relationship of the at least two rectangular frames is two members, one member, or a repeated rectangular frame generated by member confusion.
For example, in the CAD drawing, room information is retained in a text format, and the room number a and the room rectangular frame coordinates are obtained from the viewport, and the rectangular frame coordinates and the rectangular frame number b are obtained by performing repeated detection before component identification; and traversing the coordinates of the rectangular frame, and if the component rectangular frame is in the rectangular frame of the room, judging that an edge exists between the component rectangular frame and the rectangular frame of the room. Further, all rooms and all facilities in the drawing are concentrated, and according to the information, a number of rooms and b facilities are shared in the drawing, the points comprise the rooms and the facilities, and a + b facilities are shared in the drawing, and the rooms, the facilities and the facilities are arranged on the sides. Converting the graph just constructed into a degree matrix, an adjacency matrix and a Laplace matrix; assuming that the first matrix is a degree matrix D, the second matrix is an adjacent matrix A, and the third matrix is a Laplace matrix L, inputting the three matrixes D and A into a preset graph convolution neural network, and extracting a feature vector. Equivalently, through one convolution, the preset graph convolution neural network model can enable each node to have the information of the neighbor nodes. Finally, optimization is performed through a loss function. Y is label, and Z is obtained after graph convolution, at the moment, a cross entropy loss function is adopted, model training is optimized by a gradient descent method, a preset graph convolution model is optimized, at the moment, a picture obtained after target detection is input, and a classification result is obtained.
205. And marking the at least one component in a display interface of the first CAD drawing.
In one possible example, in step 205, the marking the at least one component in the display interface of the first CAD drawing includes: acquiring component information of the at least one component, wherein the component information comprises any one of the following: component coordinates, component name, component image; carrying out priority sequencing on the component information according to a space utilization rate to obtain a first priority sequence; color labeling each of the at least one member according to the first priority sequence, respectively.
The method is particularly used for improving the accuracy greatly if space information is provided, such as a hand washing sink in a kitchen and an elevator in a shared space, so that component classification is carried out.
The space utilization rate can be obtained through big data acquisition user habits or obtained according to analysis of engineers, and is not limited uniquely at this time.
The color marking refers to marking components with different space utilization rates in different colors, so that the CAD drawing is clearer in the display process.
Therefore, in the example, the electronic device performs different color labeling according to the components with different priorities, and the components with the same priority perform the same color labeling, so that the intelligence and the difference of the CAD drawing labeling are improved.
Optionally, before the at least one component is marked in the display interface of the first CAD drawing, the method further includes: acquiring the category of each member in the at least one member; determining the accuracy of each component according to the type of each component and the position coordinate of each component; and if the accuracy is lower than a preset threshold value, re-inputting the position coordinates of the first member lower than the preset threshold value into the preset graph convolution neural network again to obtain an updated first member.
The preset threshold value may be set by the manufacturer or obtained according to data acquired from big data, and is not limited herein.
In the step of determining the accuracy of each component according to the type of each component and the position coordinate of each component, a first database can be queried to obtain the position information corresponding to each component in the first database, and the first database comprises the mapping relation between the components and the position information.
Wherein the position information includes position coordinates.
Therefore, in the example, the electronic device determines whether the component is accurate or not through analysis of the component, and if the component is inaccurate, the accurate component is obtained through the preset graph convolutional neural network again, so that the identification of the wrong component is avoided, and the intelligence and the accuracy of identification of the construction drawing component are further improved.
It can be seen that, in the embodiment of the present application, the electronic device first acquires image information in the first CAD drawing, where the image information includes any one of the following: points and line segments, characters, space coordinates; splitting the image information to obtain at least one rectangular frame, then obtaining coordinates of each vertex of the at least one rectangular frame, then identifying a component corresponding to each rectangular frame according to the coordinates of each vertex of the at least one rectangular frame, and finally marking the at least one component in a display interface of the first CAD drawing. Therefore, in the example, the electronic device processes the image information in the CAD drawing to further obtain a plurality of members of different types in the drawing, and the intelligence and the accuracy of identification of the construction drawing members are improved.
Referring to fig. 3, in accordance with the embodiment shown in fig. 1, fig. 3 is a schematic flow chart of a method for identifying a building drawing component provided in an embodiment of the present application, and as shown in the figure, the method for identifying a building drawing component is applied to an electronic device, and includes:
301. and acquiring image information in the first CAD drawing.
302. A plurality of points and a plurality of line segments are acquired.
303. And obtaining a plurality of connecting nodes according to the plurality of points and the plurality of line segments.
304. And carrying out closed-loop division on the connecting nodes to obtain at least one rectangular frame.
305. And acquiring the coordinates of each vertex of the at least one rectangular frame.
306. And inputting the coordinates of the rectangular frame of the at least one rectangular frame into a preset graph convolution neural network model to obtain at least one member in the first CAD drawing.
307. And marking the at least one component in a display interface of the first CAD drawing.
The specific description of the steps 301 to 307 may refer to the corresponding steps of the building drawing component identification described in fig. 1, and are not repeated herein.
It can be seen that, in the embodiment of the present application, the electronic device first acquires image information in the first CAD drawing, where the image information includes any one of the following: points and line segments, characters, space coordinates; splitting the image information to obtain at least one rectangular frame, then obtaining coordinates of each vertex of the at least one rectangular frame, then identifying a component corresponding to each rectangular frame according to the coordinates of each vertex of the at least one rectangular frame, and finally marking the at least one component in a display interface of the first CAD drawing. Therefore, in the example, the electronic device processes the image information in the CAD drawing to obtain a plurality of members of different types in the drawing, and the intelligence and the accuracy of identification of the construction drawing members are improved.
In accordance with the foregoing embodiments, please refer to fig. 4, where fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present application, and as shown in the drawing, the electronic device includes a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and in an embodiment of the present application, the programs include instructions for performing the following steps:
acquiring image information in a first CAD drawing, wherein the image information comprises any one of the following items: point and line segment, text, space coordinates;
splitting the image information to obtain at least one rectangular frame;
obtaining coordinates of each vertex of the at least one rectangular frame;
identifying a component corresponding to each rectangular frame according to the coordinates of each vertex of the at least one rectangular frame;
and marking the at least one component in a display interface of the first CAD drawing.
It can be seen that, in the embodiment of the present application, the electronic device first obtains image information in a first CAD drawing, where the image information includes any one of: point and line segment, text, space coordinates; splitting the image information to obtain at least one rectangular frame, then obtaining coordinates of each vertex of the at least one rectangular frame, then identifying a component corresponding to each rectangular frame according to the coordinates of each vertex of the at least one rectangular frame, and finally marking the at least one component in a display interface of the first CAD drawing. Therefore, in the example, the electronic device processes the image information in the CAD drawing to further obtain a plurality of members of different types in the drawing, and the intelligence and the accuracy of identification of the construction drawing members are improved.
In one possible example, the image information includes points and segments; in the aspect of splitting according to the image information to obtain at least one rectangular frame, the program includes instructions for performing the following steps: acquiring a plurality of points and a plurality of line segments; obtaining a plurality of connecting nodes according to the plurality of points and the plurality of line segments; and carrying out closed-loop division on the connecting nodes to obtain at least one rectangular frame.
In one possible example, in the aspect of identifying the corresponding component of each rectangular box according to the coordinates of each vertex of the at least one rectangular box, the program includes instructions for:
and inputting the coordinates of the rectangular frame of the at least one rectangular frame into a preset graph convolution neural network model to obtain at least one member in the first CAD drawing.
In one possible example, before inputting the rectangular box coordinates of the at least one rectangular box into a preset graph convolution neural network, resulting in at least one component in the first CAD drawing, the program includes instructions for further performing the steps of: determining whether there are repeated rectangular box coordinates; if so, selecting at least two rectangular frames corresponding to the repeated rectangular frame coordinates; and carrying out spatial analysis on the at least two rectangular frames to determine that the at least two rectangular frames are two members each other.
In one possible example, in the aspect of marking the at least one component in the display interface of the first CAD drawing, the program includes instructions for: acquiring component information of the at least one component, wherein the component information comprises any one of the following: component coordinates, component name, component image; carrying out priority ordering on the component information according to the space utilization rate to obtain a first priority sequence; color labeling each of the at least one member according to the first priority sequence, respectively.
In one possible example, before the marking the at least one component in the display interface of the first CAD drawing, the program includes instructions for further performing the steps of: acquiring the category of each member in the at least one member; determining the accuracy of each component according to the type of each component and the position coordinate of each component; and if the accuracy is lower than a preset threshold value, re-inputting the position coordinates of the first member lower than the preset threshold value into the preset graph convolution neural network again to obtain an updated first member.
The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It is understood that the electronic device comprises corresponding hardware structures and/or software modules for performing the respective functions in order to realize the above-mentioned functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments provided herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiment of the present application, the electronic device may be divided into the functional units according to the method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
Fig. 5 is a block diagram of functional units of a construction drawing component recognition apparatus 500 according to an embodiment of the present application. The construction drawing component recognition device 500 is applied to electronic equipment, and the device 500 comprises: an acquisition unit 501, a splitting unit 502, an identification unit 503 and a labeling unit 504, wherein,
the obtaining unit 501 is configured to obtain image information in a first CAD drawing, where the image information includes any one of: point and line segment, text, space coordinates;
the splitting unit 502 is configured to split the image information to obtain at least one rectangular frame;
the obtaining unit 501 is further configured to obtain coordinates of each vertex of the at least one rectangular frame;
the identifying unit 503 is configured to identify a component corresponding to each rectangular frame according to the coordinates of each vertex of the at least one rectangular frame;
the marking unit 504 is configured to mark the at least one component in the display interface of the first CAD drawing.
It can be seen that, in the embodiment of the present application, the electronic device first obtains image information in a first CAD drawing, where the image information includes any one of: points and line segments, characters, space coordinates; splitting the image information to obtain at least one rectangular frame, then obtaining coordinates of each vertex of the at least one rectangular frame, then identifying a component corresponding to each rectangular frame according to the coordinates of each vertex of the at least one rectangular frame, and finally marking the at least one component in a display interface of the first CAD drawing. Therefore, in the example, the electronic device processes the image information in the CAD drawing to further obtain a plurality of members of different types in the drawing, and the intelligence and the accuracy of identification of the construction drawing members are improved.
In one possible example, the image information includes points and segments; in the aspect of splitting according to the image information to obtain at least one rectangular frame, the splitting unit 502 is specifically configured to:
acquiring a plurality of points and a plurality of line segments;
obtaining a plurality of connecting nodes according to the plurality of points and the plurality of line segments;
and carrying out closed-loop division on the connecting nodes to obtain at least one rectangular frame.
In one possible example, in terms of identifying the corresponding component of each rectangular frame according to the coordinates of each vertex of the at least one rectangular frame, the identifying unit 503 is specifically configured to:
and inputting the coordinates of the rectangular frame of the at least one rectangular frame into a preset graph convolution neural network model to obtain at least one member in the first CAD drawing.
In a possible example, before inputting the coordinates of the at least one rectangular box into a preset graph convolution neural network to obtain the at least one component in the first CAD drawing, the identifying unit 503 is specifically configured to:
determining whether there are repeated rectangular box coordinates;
if yes, selecting at least two rectangular frames corresponding to the repeated rectangular frame coordinates;
and carrying out spatial analysis on the at least two rectangular frames to determine that the at least two rectangular frames are two members each other.
In a possible example, in the marking the at least one component in the display interface of the first CAD drawing, the marking unit 504 is specifically configured to:
acquiring component information of the at least one component, wherein the component information comprises any one of the following: component coordinates, component name, component image;
carrying out priority ordering on the component information according to the space utilization rate to obtain a first priority sequence;
color labeling each of the at least one member according to the first priority sequence, respectively.
In a possible example, before the marking the at least one component in the display interface of the first CAD drawing, the marking unit 504 is further specifically configured to:
acquiring the category of each member in the at least one member;
determining the accuracy of each component according to the type of each component and the position coordinate of each component;
and if the accuracy is lower than a preset threshold value, re-inputting the position coordinates of the first member lower than the preset threshold value into the preset graph convolution neural network again to obtain an updated first member.
It can be understood that the functions of the program modules of the construction drawing component identification apparatus in this embodiment may be specifically implemented according to the method in the foregoing method embodiment, and the specific implementation process may refer to the related description of the foregoing method embodiment, which is not described herein again.
Embodiments of the present application also provide a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, the computer program enabling a computer to execute part or all of the steps of any one of the methods described in the above method embodiments, and the computer includes an electronic device.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any one of the methods as set out in the above method embodiments. The computer program product may be a software installation package, the computer comprising an electronic device.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the above-described units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above-mentioned method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, read-Only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, the specific implementation manner and the application scope may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (8)
1. A method for identifying a construction drawing component is applied to electronic equipment, and comprises the following steps:
importing a first CAD drawing into a Building Information Model (BIM) to obtain image information of the first CAD drawing, wherein the image information comprises any one of the following items: point and line segment, text, space coordinates;
splitting the image information to obtain at least one rectangular frame;
obtaining coordinates of each vertex of the at least one rectangular frame;
identifying a component corresponding to each rectangular frame according to the coordinates of each vertex of the at least one rectangular frame; the method comprises the following steps: inputting the rectangular frame coordinates of the at least one rectangular frame into a preset graph convolution neural network (GCN) model to obtain at least one component in the first CAD drawing;
acquiring the category of each member in the at least one member; determining the accuracy of each component according to the type of each component and the position coordinate of each component; the method comprises the following steps: inquiring a first database to obtain position information corresponding to each component in the first database, wherein the first database comprises a mapping relation between each component and the position information;
if the accuracy is lower than a preset threshold value, re-inputting the position coordinates of the first component lower than the preset threshold value into the GCN model again to obtain an updated first component;
and marking the at least one component in a display interface of the first CAD drawing.
2. The method of claim 1, wherein the image information comprises points and segments; splitting according to the image information to obtain at least one rectangular frame, including:
acquiring a plurality of points and a plurality of line segments;
obtaining a plurality of connecting nodes according to the plurality of points and the plurality of line segments;
and carrying out closed-loop division on the connecting nodes to obtain at least one rectangular frame.
3. The method of claim 1, wherein before inputting the rectangular box coordinates of the at least one rectangular box into a preset graph convolution neural network, resulting in at least one component in the first CAD drawing, the method further comprises:
determining whether there are repeated rectangular box coordinates;
if so, selecting at least two rectangular frames corresponding to the repeated rectangular frame coordinates;
and carrying out spatial analysis on the at least two rectangular frames to determine that the at least two rectangular frames are two members each other.
4. The method of claim 1, wherein the marking the at least one component in the display interface of the first CAD drawing includes:
acquiring component information of the at least one component, the component information including any one of: component coordinates, component name, component image;
carrying out priority ordering on the component information according to the space utilization rate to obtain a first priority sequence;
color labeling each of the at least one member according to the first priority sequence, respectively.
5. An identification device for construction drawing components, which is applied to electronic equipment, the device comprising: an acquisition unit, a splitting unit, an identification unit and a labeling unit, wherein,
the acquiring unit is used for importing a first CAD drawing into a building information model BIM, and acquiring image information of the first CAD drawing, wherein the image information includes any one of the following items: point and line segment, text, space coordinates;
the splitting unit is used for splitting the image information to obtain at least one rectangular frame;
the obtaining unit is further configured to obtain coordinates of each vertex of the at least one rectangular frame;
the identification unit is used for identifying a component corresponding to each rectangular frame according to the coordinates of each vertex of the at least one rectangular frame; the method comprises the following steps: inputting the rectangular frame coordinates of the at least one rectangular frame into a preset graph convolution neural network (GCN) model to obtain at least one component in the first CAD drawing;
the identification unit is further used for acquiring the category of each component in the at least one component; determining the accuracy of each component according to the type of each component and the position coordinate of each component; the method comprises the following steps: inquiring a first database to obtain position information corresponding to each component in the first database, wherein the first database comprises a mapping relation between each component and the position information;
if the accuracy is lower than a preset threshold value, re-inputting the position coordinates of the first component lower than the preset threshold value into the GCN model again to obtain an updated first component;
the marking unit is used for marking the at least one component in a display interface of the first CAD drawing.
6. The apparatus of claim 5, wherein the image information comprises points and line segments; in the aspect of splitting according to the image information to obtain at least one rectangular frame, the splitting unit is specifically configured to:
acquiring a plurality of points and a plurality of line segments;
obtaining a plurality of connecting nodes according to the plurality of points and the plurality of line segments;
and carrying out closed-loop division on the connecting nodes to obtain at least one rectangular frame.
7. An electronic device comprising a processor, a memory for storing one or more programs and configured for execution by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-4.
8. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any one of claims 1-4.
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| CN113128457A (en) * | 2021-04-30 | 2021-07-16 | 杭州品茗安控信息技术股份有限公司 | Building model identification method, system and related device |
| CN112990143B (en) * | 2021-04-30 | 2021-08-31 | 杭州品茗安控信息技术股份有限公司 | Model matching method and system of building drawing and related device |
| CN113392453B (en) * | 2021-06-10 | 2022-10-11 | 万翼科技有限公司 | Space extraction method and device in engineering drawing, electronic equipment and storage medium |
| CN113435289A (en) * | 2021-06-22 | 2021-09-24 | 万翼科技有限公司 | Space division method, device, equipment and storage medium |
| CN113673413A (en) * | 2021-08-16 | 2021-11-19 | 金地（集团）股份有限公司 | Drawing approval method, device, computer readable medium and electronic equipment for architectural drawings |
| CN113901550B (en) * | 2021-09-30 | 2024-09-06 | 深圳市万翼数字技术有限公司 | Method and related equipment for generating BIM (building information modeling) model of assembled building |
| CN114357545B (en) * | 2021-12-15 | 2024-09-06 | 北京构力科技有限公司 | Method and product for arranging construction icon injection positions |
| CN114239124A (en) * | 2022-02-28 | 2022-03-25 | 江西少科智能建造科技有限公司 | Building drawing component identification method, system, storage medium and equipment |
| CN114741754A (en) * | 2022-03-30 | 2022-07-12 | 广东博智林机器人有限公司 | A method and system for room area identification based on architectural drawings |
| CN114973299B (en) * | 2022-08-01 | 2023-01-10 | 万翼科技有限公司 | Building drawing component identification method and device, electronic equipment and storage medium |
| CN116108520A (en) * | 2022-12-09 | 2023-05-12 | 深圳市金地数字科技有限公司 | Method, device and equipment for functional space recognition of whole house drawings |
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