CN114329711A - Prefabricated part data processing method and system based on graph computation platform - Google Patents
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Abstract
The embodiment of the application discloses a prefabricated part data processing method and a system based on a graph computation amount platform, belonging to the technical field of graph computation amount, wherein the method comprises the following steps: establishing a steel bar database for storing steel bar patterns and accessory information of various preset steel bars; obtaining a CAD drawing of the assembly type component, wherein the CAD drawing of the assembly type component comprises a detailed reinforcing steel bar list; acquiring steel bar data of a plurality of steel bars to be identified of the prefabricated part from the steel bar list, wherein the steel bar data comprises the data information of the steel bars and steel bar patterns; for each type of steel bar to be identified, acquiring a target preset steel bar corresponding to the steel bar to be identified from the plurality of preset steel bars based on the steel bar graph of the steel bar to be identified and the steel bar graphs of the plurality of preset steel bars, and acquiring auxiliary information of the steel bar to be identified based on the auxiliary information of the target preset steel bar; the data information and the auxiliary information are filled in the member attribute of the graph computation software, and the method has the advantage of automatically acquiring the related information of the steel bar of the assembly type member.
Description
Technical Field
The invention relates to a prefabricated part data processing method and system based on a graph computation amount platform, and belongs to the field of graph computation amount.
Background
The modeling of the assembly type component (for example, a prefabricated wall) by the graph computation software is generally to import a CAD drawing for rollover, and automatically generate a three-dimensional model of the assembly type component, and the CAD drawing also generally has a steel bar detail table for recording relevant information of the steel bars of the assembly type component, such as the number, the diameter, the size, the number, the graph and the like.
Therefore, it is desirable to provide a data processing method and system for prefabricated parts based on a graphical computation platform, which is used for automatically acquiring the relevant information of the steel bars of the assembled parts.
Disclosure of Invention
The invention aims to provide an earth and stone processing method and system based on a graph computation platform, which are used for automatically acquiring relevant information of a steel bar of an assembled component.
In order to achieve the purpose, the invention provides the following technical scheme:
a prefabricated part data processing method based on a graphic computation amount platform comprises the following steps: establishing a steel bar database, wherein the steel bar database stores steel bar graphs and accessory information of various preset steel bars; obtaining a CAD drawing of an assembly type component, wherein the CAD drawing of the assembly type component comprises a steel bar list; identifying the reinforcing steel bar detail table, and acquiring reinforcing steel bar data of a plurality of reinforcing steel bars to be identified of the prefabricated part, wherein the reinforcing steel bar data comprises data information of the reinforcing steel bars and reinforcing steel bar patterns; for each type of the steel bar to be identified, acquiring a target preset steel bar corresponding to the steel bar to be identified from the plurality of types of preset steel bars based on the steel bar graph of the steel bar to be identified and the steel bar graphs of the plurality of types of preset steel bars, and acquiring auxiliary information of the steel bar to be identified based on the auxiliary information of the target preset steel bar; and filling the data information and the accessory information of the various steel bars to be identified into the component attributes of the graph computation software.
Further, the obtaining of the target preset steel bar corresponding to the steel bar to be identified from the multiple preset steel bars based on the steel bar pattern of the steel bar to be identified and the steel bar patterns of the multiple preset steel bars includes: extracting the steel bar characteristics of the steel bar graph of the steel bar to be identified; acquiring at least one candidate preset steel bar from the multiple preset steel bars based on the steel bar characteristics of the steel bar graph of the steel bar to be identified; and acquiring a target preset steel bar corresponding to the steel bar to be identified from the at least one first candidate preset steel bar based on the steel bar graph of the steel bar to be identified and the steel bar graph of the at least one first candidate preset steel bar.
Further, the steel bar characteristics include at least one of the number of bends, the number of hooks, the shape of stirrups, the number of each type of stirrups, the shape of the steel bars of the special shape, and the number of the steel bars of each special shape.
Further, the rebar database divides the preset rebars into a plurality of categories based on rebar characteristics of rebar patterns of the preset rebars, wherein each category comprises at least one preset rebar; the acquiring of at least one first candidate preset steel bar from the multiple preset steel bars based on the steel bar features of the steel bar graph of the steel bar to be identified comprises: and determining a target large class corresponding to the steel bars to be identified from the large classes based on the steel bar characteristics of the steel bar patterns of the steel bars to be identified, and taking the steel bar patterns of at least one preset steel bar included in the target large class as the at least one first candidate preset steel bar.
Further, the obtaining, from the at least one first candidate preset rebar, a target preset rebar corresponding to the to-be-identified rebar based on the rebar pattern of the to-be-identified rebar and the rebar pattern of the at least one first candidate preset rebar includes: extracting the steel bar characteristics of the steel bar graph of the first candidate preset steel bar; acquiring at least one second candidate preset steel bar from the at least one first candidate preset steel bar based on the steel bar characteristics of the steel bar pattern of the steel bar to be identified and the steel bar characteristics of the steel bar pattern of the first candidate preset steel bar; and acquiring a target preset steel bar corresponding to the steel bar to be identified from the at least one second candidate preset steel bar through a planar geometric path feature matching algorithm based on the steel bar graph of the steel bar to be identified and the steel bar graph of the at least one second candidate preset steel bar.
A prefabricated component data processing system based on a graphical computation volume platform, comprising: the system comprises a database module, a database module and a database module, wherein the database module is used for establishing a steel bar database, and the steel bar database stores steel bar graphs and accessory information of various preset steel bars; the data acquisition module is used for acquiring an assembly type member CAD drawing, and the assembly type member CAD drawing comprises a steel bar detail list; the data identification module is used for identifying the reinforcing steel bar list and acquiring reinforcing steel bar data of various reinforcing steel bars to be identified of the prefabricated part, wherein the reinforcing steel bar data comprises data information of the reinforcing steel bars and reinforcing steel bar patterns; the steel bar identification module is used for acquiring a target preset steel bar corresponding to the steel bar to be identified from the various preset steel bars based on the steel bar graph of the steel bar to be identified and the steel bar graphs of the various preset steel bars for each type of the steel bar to be identified, and acquiring the auxiliary information of the steel bar to be identified based on the auxiliary information of the target preset steel bar; and the data filling module is used for filling the data information and the auxiliary information of the various steel bars to be identified into the component attributes of the graph computation software.
Further, the rebar identification module is further configured to: extracting the steel bar characteristics of the steel bar graph of the steel bar to be identified; acquiring at least one candidate preset steel bar from the multiple preset steel bars based on the steel bar characteristics of the steel bar graph of the steel bar to be identified; and acquiring a target preset steel bar corresponding to the steel bar to be identified from the at least one first candidate preset steel bar based on the steel bar graph of the steel bar to be identified and the steel bar graph of the at least one first candidate preset steel bar.
Further, the steel bar characteristics include at least one of the number of bends, the number of hooks, the shape of stirrups, the number of each type of stirrups, the shape of the steel bars of the special shape, and the number of the steel bars of each special shape.
Further, the rebar database divides the preset rebars into a plurality of categories based on rebar characteristics of rebar patterns of the preset rebars, wherein each category comprises at least one preset rebar; the rebar identification module is further configured to: and determining a target large class corresponding to the steel bars to be identified from the large classes based on the steel bar characteristics of the steel bar patterns of the steel bars to be identified, and taking the steel bar patterns of at least one preset steel bar included in the target large class as the at least one first candidate preset steel bar.
Further, the rebar identification module is further configured to: extracting the steel bar characteristics of the steel bar graph of the first candidate preset steel bar; acquiring at least one second candidate preset steel bar from the at least one first candidate preset steel bar based on the steel bar characteristics of the steel bar pattern of the steel bar to be identified and the steel bar characteristics of the steel bar pattern of the first candidate preset steel bar; and acquiring a target preset steel bar corresponding to the steel bar to be identified from the at least one second candidate preset steel bar through a planar geometric path feature matching algorithm based on the steel bar graph of the steel bar to be identified and the steel bar graph of the at least one second candidate preset steel bar.
The invention has the beneficial effects that:
and automatically identifying a prefabricated member steel bar form on the drawing, automatically matching steel bars in a steel bar library of the graph computation platform, and realizing automatic filling configuration of the prefabricated steel bar attributes of the assembly type member.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.
Drawings
The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:
FIG. 1 is a schematic illustration of an application scenario of a prefabricated component data processing system based on a graphical computation load platform according to some embodiments of the present application;
FIG. 2 is an exemplary block diagram of a prefabricated component data processing system based on a graphical computation volume platform according to some embodiments of the present application;
FIG. 3 is an exemplary flow diagram of a method for processing prefabricated component data based on a graphical computation load platform according to some embodiments of the present application.
In the figure, 100, application scenarios; 110. a processing device; 120. a network; 130. a user terminal; 140. a storage device; 200. a prefabricated component data processing system based on a graphical computation platform.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. It is understood that these exemplary embodiments are given solely to enable those skilled in the relevant art to better understand and implement the present invention, and are not intended to limit the scope of the invention in any way. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Although various references are made herein to certain modules or units in a system according to embodiments of the present application, any number of different modules or units may be used and run on a client and/or server. The modules are merely illustrative and different aspects of the systems and methods may use different modules.
Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
FIG. 1 is a schematic diagram of an application scenario 100 of a prefabricated component data processing system 200 based on a graphical computation volume platform according to some embodiments of the present application.
As shown in fig. 1, the application scenario 100 may include a processing device 110, a network 120, a user terminal 130, and a storage device 140.
In some embodiments, the graphic computation volume platform based prefabricated component data processing system 200 may process data for prefabricated component rebar of a fabricated component. In some embodiments, in the application scenario 100, the prefabricated part data processing system 200 based on the graphical computation amount platform may automatically obtain the relevant information of the rebar of the fabricated part and enter the prefabricated part rebar attributes of the graphical computation amount software after the user imports the CAD drawing of the fabricated part into the graphical computation amount software for modeling. It should be noted that the prefabricated component data processing system 200 based on the graphics computation amount platform can also be applied to other devices, scenarios and applications that need to perform information query, and is not limited herein, and any device, scenario and/or application that can use the prefabricated component data processing method based on the graphics computation amount platform included in the present application is within the scope of protection of the present application.
In some embodiments, processing device 110 may be used to process information and/or data related to filling of prefabricated component rebar properties in a graphical computation volume platform. For example, the processing device 110 may obtain a CAD drawing of the fabricated component, obtain data information and accompanying information of a plurality of rebars to be identified, and fill the data information and accompanying information in the component attributes of the graphical computation software. In some embodiments, the processing device 110 may be regional or remote. For example, processing device 110 may access information and/or profiles stored in user terminal 130 and storage device 140 via network 120. In some embodiments, processing device 110 may be directly connected to user terminal 130 and storage device 140 to access information and/or material stored therein. In some embodiments, the processing device 110 may execute on a cloud platform. In some embodiments, the processing device 110 may include a processor 210, and the processor 210 may include one or more sub-processors (e.g., a single core processing device or a multi-core processing device). Merely by way of example, a processor may comprise a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Processor (ASIP), a Graphics Processor (GPU), the like, or any combination thereof.
The network 120 may facilitate the exchange of data and/or information in the application scenario 100. In some embodiments, one or more components (e.g., processing device 110, user terminal 130, and storage device 140) in the application scenario 100 may send data and/or information to other components in the application scenario 100 via the network 120. For example, the fabricated component CAD drawings stored by the storage device 140 may be transmitted to the processing device 110 via the network 120. In some embodiments, the network 120 may be any type of wired or wireless network. For example, network 120 may include a cable network, a wired network, a fiber optic network, a telecommunications network, an internal network, the like, or any combination thereof.
In some embodiments, the user terminal 130 may obtain information or data in the application scenario 100. For example, the user terminal 130 may transmit the fabricated component CAD drawing to the processing device 110 via the network 120. In some embodiments, the user terminal 130 may include one or any combination of a mobile device, a tablet, a laptop, and the like.
In some embodiments, the storage device 140 may be connected to the network 120 to enable communication with one or more components of the application scenario 100 (e.g., the processing device 110, the user terminal 130, etc.). One or more components of the application scenario 100 may access material or instructions stored in the storage device 140 through the network 120. In some embodiments, the storage device 140 may be directly connected or in communication with one or more components (e.g., processing device 110, user terminal 130) in the application scenario 100. In some embodiments, the storage device 140 may be part of the processing device 110.
It should be noted that the foregoing description is provided for illustrative purposes only, and is not intended to limit the scope of the present application. Many variations and modifications will occur to those skilled in the art in light of the teachings herein. The features, structures, methods, and other features of the exemplary embodiments described herein may be combined in various ways to obtain additional and/or alternative exemplary embodiments. For example, the storage device 140 may be a data storage device comprising a cloud computing platform, such as a public cloud, a private cloud, a community and hybrid cloud, and the like. However, such changes and modifications do not depart from the scope of the present application.
FIG. 2 is an exemplary block diagram of a prefabricated component data processing system 200 based on a graphical computing platform according to some embodiments of the present application.
As shown in fig. 2, a graphic computation amount platform-based prefabricated component data processing system 200 may include a database module, a data acquisition module, a data recognition module, a rebar recognition module, and a data population module. In some embodiments, the database module, the data acquisition module, the data identification module, the rebar identification module, and the data population module may be implemented on processing device 110.
In some embodiments, the database module may be configured to build a rebar database, wherein the rebar database stores rebar patterns and accompanying information for a plurality of predetermined rebars.
In some embodiments, the data acquisition module may be configured to acquire a fabricated component CAD drawing, wherein the fabricated component CAD drawing includes a list of rebars.
In some embodiments, the data identification module may be configured to identify the rebar schedule, and obtain rebar data of a plurality of rebars to be identified of the prefabricated component, where the rebar data includes data information of rebars and rebar patterns.
In some embodiments, the rebar identification module may be configured to, for each type of rebar to be identified, obtain a target predetermined rebar corresponding to the rebar to be identified from the plurality of predetermined rebars based on the rebar pattern of the rebar to be identified and the rebar patterns of the plurality of predetermined rebars, and obtain auxiliary information of the rebar to be identified based on the auxiliary information of the target predetermined rebar.
In some embodiments, the data filling module may be configured to fill data information and accompanying information of a plurality of types of rebars to be identified into component attributes of the graphical computation software.
For more description of the database module, the data obtaining module, the data identifying module, the steel bar identifying module and the data filling module, reference may be made to fig. 3 and the related description thereof, which are not described herein again.
FIG. 3 is an exemplary flow diagram of a method for processing prefabricated component data based on a graphical computation load platform according to some embodiments of the present application. As shown in fig. 3, a prefabricated part data processing method based on a graphic computation amount platform includes the following steps. In some embodiments, a method of pre-fabricated part data processing based on a graphical computation volume platform may be implemented on processing device 110.
Step 310, a rebar database is established. In some embodiments, step 310 may be performed by a database module.
In some embodiments, the rebar database is configured to store rebar patterns and accompanying information for a plurality of predetermined rebars. In some embodiments, the rebar pattern can be used to characterize the shape and size of a predetermined rebar. In some embodiments, the auxiliary information may be used to characterize the relevant information of the predetermined rebar, such as at least one or any combination of name, grade, number, manufacturer, price, weight, type, and the like.
And step 320, obtaining a CAD drawing of the assembly type component. In some embodiments, step 310 may be performed by a data acquisition module.
In some embodiments, the CAD drawings of the prefabricated components may be recorded with a bar schedule, where the bar schedule may be used to record information about bars of the prefabricated components used by the prefabricated components, such as data information and bar patterns of the bars. In some embodiments, the data information may include the number, diameter, size, number, pattern, etc. of each rebar. In some embodiments, the rebar pattern of the prefabricated steel rebar can be used to characterize the shape and size of the prefabricated steel rebar.
In some embodiments, the data acquisition module may acquire the fabricated component CAD drawings from the processing device 110, the user terminal 130, the storage device 140, and/or an external data source.
And 330, identifying the steel bar list, and acquiring the steel bar data of various steel bars to be identified of the prefabricated part. In some embodiments, step 330 may be performed by a data identification module.
In some embodiments, the rebar data includes data information for the rebar and a rebar pattern.
In some embodiments, the data identification module may obtain data information of a plurality of rebars to be identified of the prefabricated part from the rebar specification table based on a character recognition algorithm (e.g., ctc (connectionist Temporal classification) algorithm, crnn (volumetric recovery Neural network) algorithm, cptn (connectionist Text network) algorithm, multi-tag classification (mutli-label classification) algorithm, etc.). In some embodiments, the data identification module may obtain rebar patterns of a plurality of rebars to be identified of the prefabricated component from the rebar schedule based on a pattern segmentation algorithm (e.g., histogram thresholding, region growing, slack marking region segmentation, etc.).
In some embodiments, the data identification module may obtain rebar data for a plurality of rebars of the prefabricated component to be identified through a machine learning model. The input of the machine learning model can be a steel bar list, and the output of the machine learning model can be steel bar data of various steel bars to be identified of the prefabricated part. In some embodiments, the machine learning model may include, but is not limited to, a combination of one or more of a neural network model, a support vector machine model, a k-nearest neighbor model, a decision tree model, and the like. The neural network model may include one or more of a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), a multilayer neural network (MLP), a ballistic neural network (GAN), and the like.
And 340, for each type of steel bar to be identified, acquiring a target preset steel bar corresponding to the steel bar to be identified from the multiple preset steel bars based on the steel bar graph of the steel bar to be identified and the steel bar graphs of the multiple preset steel bars, and acquiring auxiliary information of the steel bar to be identified based on the auxiliary information of the target preset steel bar. In some embodiments, step 340 may be performed by a rebar identification module.
In some embodiments, the steel bar identification module obtains the target preset steel bar corresponding to the steel bar to be identified from the plurality of preset steel bars based on the steel bar pattern of the steel bar to be identified and the steel bar patterns of the plurality of preset steel bars, and may include:
extracting the steel bar characteristics of the steel bar graph of the steel bar to be identified;
acquiring at least one candidate preset steel bar from a plurality of preset steel bars based on the steel bar characteristics of the steel bar pattern of the steel bar to be identified;
and acquiring a target preset steel bar corresponding to the steel bar to be identified from the at least one first candidate preset steel bar based on the steel bar graph of the steel bar to be identified and the steel bar graph of the at least one first candidate preset steel bar.
In some embodiments, the rebar features can include at least one of a number of bends, a number of hooks, a stirrup shape, a number of each stirrup, a special shape rebar shape, a number of each special shape rebar.
In some embodiments, the rebar database classifies the plurality of predetermined rebars into a plurality of categories, each category including at least one predetermined rebar, based on rebar characteristics of rebar patterns of the plurality of predetermined rebars. In some embodiments, the database module may classify the plurality of predetermined rebar into a plurality of broad categories based on rebar characteristics of the rebar pattern of the predetermined rebar through a clustering algorithm (e.g., a K-means clustering algorithm, a mean shift clustering algorithm, etc.). In some embodiments, for each cluster, the database module may extract the rebar features of the centroid.
In some embodiments, the obtaining, by the rebar identification module, at least one first candidate preset rebar from a plurality of preset rebars based on rebar characteristics of a rebar pattern of a rebar to be identified may include:
and determining a target large class corresponding to the steel bar to be identified from the plurality of large classes based on the steel bar characteristics of the steel bar graph of the steel bar to be identified, and taking the steel bar graph of at least one preset steel bar included in the target large class as at least one first candidate preset steel bar.
In some embodiments, the rebar identification module may calculate a similarity of rebar features of a rebar pattern of the rebar to be identified to rebar features of the centroid of each major class based on a similarity algorithm (e.g., euclidean distance algorithm, cosine similarity algorithm, etc.) and take the major class with the greatest similarity as the target major class. In some embodiments, the rebar identification module may determine all preset rebars included in the target category as first candidate preset rebars corresponding to rebars to be identified.
In some embodiments, the obtaining, by the rebar identification module, a target preset rebar corresponding to the rebar to be identified from at least one first candidate preset rebar based on the rebar pattern of the rebar to be identified and the rebar pattern of the at least one first candidate preset rebar may include:
extracting the steel bar characteristics of the steel bar graph of the first candidate preset steel bar;
acquiring at least one second candidate preset steel bar from at least one first candidate preset steel bar based on the steel bar characteristics of the steel bar pattern of the steel bar to be identified and the steel bar characteristics of the steel bar pattern of the first candidate preset steel bar;
and acquiring a target preset steel bar corresponding to the steel bar to be identified from the at least one second candidate preset steel bar through a planar geometric path feature matching algorithm based on the steel bar graph of the steel bar to be identified and the steel bar graph of the at least one second candidate preset steel bar.
In some embodiments, the rebar identification module may calculate a similarity of a rebar feature of the rebar pattern of the rebar to be identified to a rebar feature of the rebar pattern of each first candidate pre-set rebar based on a similarity algorithm (e.g., euclidean distance algorithm, cosine similarity algorithm, etc.), and take the first candidate pre-set rebar with the similarity greater than a similarity threshold (e.g., 90%) as the second candidate pre-set rebar.
In some embodiments, the obtaining, by the rebar identification module, a target preset rebar corresponding to the rebar to be identified from the at least one second candidate preset rebar through a planar geometric path feature matching algorithm based on the rebar pattern of the rebar to be identified and the rebar pattern of the at least one second candidate preset rebar may include:
extracting path nodes of a reinforcement graph of a reinforcement to be identified, and determining the number of the nodes of the reinforcement graph of the reinforcement to be identified, wherein the path nodes of the reinforcement graph of the reinforcement to be identified can be nodes where the reinforcement is bent in the reinforcement graph of the reinforcement to be identified;
extracting path nodes of a second candidate preset steel bar, and determining the node number of the steel bar graph of the second candidate preset steel bar, wherein the path nodes of the second candidate preset steel bar can be nodes where the steel bars in the steel bar graph of the second candidate preset steel bar are bent;
calculating a node difference value between the node number of the rebar graph of the rebar to be identified and the node number of the rebar graph of the second candidate preset rebar, and judging whether the absolute value of the node difference value is smaller than a difference threshold value (for example, 3);
if the absolute value of the node difference is smaller than the difference threshold, extracting the path direction of each path node of the reinforcement graph of the reinforcement to be identified, wherein the path direction of each path node of the reinforcement graph of the reinforcement to be identified can be the bending angle of a node where the reinforcement in the reinforcement graph of the reinforcement to be identified is bent, and extracting the path direction of each path node of the reinforcement graph of a second candidate preset reinforcement, wherein the path direction of each path node of the reinforcement graph of the second candidate preset reinforcement can be the bending angle of a node where the reinforcement in the reinforcement graph of the second candidate preset reinforcement is bent; and acquiring a target preset steel bar corresponding to the steel bar to be identified from at least one second candidate preset steel bar based on the path direction of each path node of the steel bar graph of the steel bar to be identified and the path direction of each path node of the steel bar graph of the second candidate preset steel bar.
In some embodiments, the rebar identification module may calculate a similarity of the path direction of each path node of the rebar pattern of the rebar to be identified and the path direction of each path node of the rebar pattern of the second candidate pre-set rebar based on a similarity algorithm (e.g., euclidean distance algorithm, cosine similarity algorithm, etc.), and take the second candidate pre-set rebar with the similarity greater than a similarity threshold (e.g., 90%) as the second candidate pre-set rebar.
And step 350, filling the data information and the auxiliary information of the steel bars to be identified into the component attributes of the graph computation software. In some embodiments, step 340 may be performed by a data population module.
In other embodiments of the present application, a prefabricated component data processing apparatus based on a graphical computation volume platform is provided, comprising at least one processing device and at least one storage device; the at least one storage device is used for storing computer instructions, and the at least one processing device is used for executing at least part of the computer instructions to realize the prefabricated part data processing method based on the graphic computation amount platform.
In still other embodiments of the present application, a computer-readable storage medium is provided that stores computer instructions that, when executed by a processing device, implement a method of pre-form data processing based on a graphical computation load platform as described above.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.
Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. 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 network format, such as 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), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the present disclosure.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.
Claims (10)
1. A prefabricated part data processing method based on a graph computation amount platform is characterized by comprising the following steps:
establishing a steel bar database, wherein the steel bar database stores steel bar graphs and accessory information of various preset steel bars;
obtaining a CAD drawing of an assembly type component, wherein the CAD drawing of the assembly type component comprises a steel bar list;
identifying the reinforcing steel bar detail table, and acquiring reinforcing steel bar data of a plurality of reinforcing steel bars to be identified of the prefabricated part, wherein the reinforcing steel bar data comprises data information of the reinforcing steel bars and reinforcing steel bar patterns;
for each type of the steel bar to be identified, acquiring a target preset steel bar corresponding to the steel bar to be identified from the plurality of types of preset steel bars based on the steel bar graph of the steel bar to be identified and the steel bar graphs of the plurality of types of preset steel bars, and acquiring auxiliary information of the steel bar to be identified based on the auxiliary information of the target preset steel bar;
and filling the data information and the accessory information of the various steel bars to be identified into the component attributes of the graph computation software.
2. The prefabricated part data processing method based on the graph computation volume platform according to claim 1, wherein the step of obtaining the target preset steel bar corresponding to the steel bar to be identified from the plurality of preset steel bars based on the steel bar graph of the steel bar to be identified and the steel bar graphs of the plurality of preset steel bars comprises the following steps:
extracting the steel bar characteristics of the steel bar graph of the steel bar to be identified;
acquiring at least one candidate preset steel bar from the multiple preset steel bars based on the steel bar characteristics of the steel bar graph of the steel bar to be identified;
and acquiring a target preset steel bar corresponding to the steel bar to be identified from the at least one first candidate preset steel bar based on the steel bar graph of the steel bar to be identified and the steel bar graph of the at least one first candidate preset steel bar.
3. The prefabricated part data processing method based on the graphic computation quantity platform as claimed in claim 2, wherein the reinforcement features comprise at least one of the number of bends, the number of hooks, the shape of stirrups, the number of each stirrup, the shape of reinforcement with special shape, and the number of reinforcement with each special shape.
4. The system of claim 2 or 3, wherein the rebar database classifies the plurality of predetermined rebars into a plurality of categories based on rebar characteristics of rebar patterns of the plurality of predetermined rebars, each of the categories including at least one predetermined rebar;
the acquiring of at least one first candidate preset steel bar from the multiple preset steel bars based on the steel bar features of the steel bar graph of the steel bar to be identified comprises:
and determining a target large class corresponding to the steel bars to be identified from the large classes based on the steel bar characteristics of the steel bar patterns of the steel bars to be identified, and taking the steel bar patterns of at least one preset steel bar included in the target large class as the at least one first candidate preset steel bar.
5. The prefabricated part data processing method based on the graph computation quantity platform according to claim 2 or 3, wherein the step of obtaining the target preset steel bar corresponding to the steel bar to be identified from the at least one first candidate preset steel bar based on the steel bar graph of the steel bar to be identified and the steel bar graph of the at least one first candidate preset steel bar comprises the following steps:
extracting the steel bar characteristics of the steel bar graph of the first candidate preset steel bar;
acquiring at least one second candidate preset steel bar from the at least one first candidate preset steel bar based on the steel bar characteristics of the steel bar pattern of the steel bar to be identified and the steel bar characteristics of the steel bar pattern of the first candidate preset steel bar;
and acquiring a target preset steel bar corresponding to the steel bar to be identified from the at least one second candidate preset steel bar through a planar geometric path feature matching algorithm based on the steel bar graph of the steel bar to be identified and the steel bar graph of the at least one second candidate preset steel bar.
6. A prefabricated component data processing system based on a graphical computation volume platform, comprising:
the system comprises a database module, a database module and a database module, wherein the database module is used for establishing a steel bar database, and the steel bar database stores steel bar graphs and accessory information of various preset steel bars;
the data acquisition module is used for acquiring an assembly type member CAD drawing, and the assembly type member CAD drawing comprises a steel bar detail list;
the data identification module is used for identifying the reinforcing steel bar list and acquiring reinforcing steel bar data of various reinforcing steel bars to be identified of the prefabricated part, wherein the reinforcing steel bar data comprises data information of the reinforcing steel bars and reinforcing steel bar patterns;
the steel bar identification module is used for acquiring a target preset steel bar corresponding to the steel bar to be identified from the various preset steel bars based on the steel bar graph of the steel bar to be identified and the steel bar graphs of the various preset steel bars for each type of the steel bar to be identified, and acquiring the auxiliary information of the steel bar to be identified based on the auxiliary information of the target preset steel bar;
and the data filling module is used for filling the data information and the auxiliary information of the various steel bars to be identified into the component attributes of the graph computation software.
7. The system of claim 6, wherein the rebar identification module is further configured to:
extracting the steel bar characteristics of the steel bar graph of the steel bar to be identified;
acquiring at least one candidate preset steel bar from the multiple preset steel bars based on the steel bar characteristics of the steel bar graph of the steel bar to be identified;
and acquiring a target preset steel bar corresponding to the steel bar to be identified from the at least one first candidate preset steel bar based on the steel bar graph of the steel bar to be identified and the steel bar graph of the at least one first candidate preset steel bar.
8. The system of claim 7, wherein the rebar features include at least one of a number of bends, a number of hooks, a stirrup shape, a number of each stirrup, a shape of a special-shaped rebar, and a number of each special-shaped rebar.
9. The system of claim 7 or 8, wherein the rebar database classifies the plurality of predetermined rebars into a plurality of categories based on rebar characteristics of rebar patterns of the plurality of predetermined rebars, each of the categories including at least one predetermined rebar;
the rebar identification module is further configured to:
and determining a target large class corresponding to the steel bars to be identified from the large classes based on the steel bar characteristics of the steel bar patterns of the steel bars to be identified, and taking the steel bar patterns of at least one preset steel bar included in the target large class as the at least one first candidate preset steel bar.
10. The system of claim 7 or 8, wherein the rebar identification module is further configured to:
extracting the steel bar characteristics of the steel bar graph of the first candidate preset steel bar;
acquiring at least one second candidate preset steel bar from the at least one first candidate preset steel bar based on the steel bar characteristics of the steel bar pattern of the steel bar to be identified and the steel bar characteristics of the steel bar pattern of the first candidate preset steel bar;
and acquiring a target preset steel bar corresponding to the steel bar to be identified from the at least one second candidate preset steel bar through a planar geometric path feature matching algorithm based on the steel bar graph of the steel bar to be identified and the steel bar graph of the at least one second candidate preset steel bar.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115168960A (en) * | 2022-07-19 | 2022-10-11 | 中国建筑西南设计研究院有限公司 | Automatic checking method based on plate reinforcement and expression mapping table |
| CN115796805A (en) * | 2023-02-09 | 2023-03-14 | 中建安装集团有限公司 | Method and device for calculating electromechanical installation items and storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115168960A (en) * | 2022-07-19 | 2022-10-11 | 中国建筑西南设计研究院有限公司 | Automatic checking method based on plate reinforcement and expression mapping table |
| CN115796805A (en) * | 2023-02-09 | 2023-03-14 | 中建安装集团有限公司 | Method and device for calculating electromechanical installation items and storage medium |
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