CN111611936A - Automatic identification system for similar vector diagrams in CAD drawings - Google Patents

Automatic identification system for similar vector diagrams in CAD drawings Download PDF

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CN111611936A
CN111611936A CN202010442585.8A CN202010442585A CN111611936A CN 111611936 A CN111611936 A CN 111611936A CN 202010442585 A CN202010442585 A CN 202010442585A CN 111611936 A CN111611936 A CN 111611936A
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CN111611936B (en
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陈永宏
张超
王渊博
尹华承
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Qingju Technology Co ltd
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    • G06V30/42—Document-oriented image-based pattern recognition based on the type of document
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Abstract

The invention relates to an automatic identification system of similar vector diagrams in CAD drawings, which comprises an acquisition module used for acquiring a source diagram and acquiring a plurality of diagram feature points in the source diagram, an acquisition module used for determining a feature value of the source diagram according to the feature point distance of each graphic element in the source diagram, a determining module which takes the feature identifier as a feature identifier of the source graph, a retrieving module which is used for searching other primitives with the same feature value as the source graph in the graph layer range of the source graph and grouping according to the primitive types, a calculating module which is used for extracting the combination module which takes the first primitive as the center and the coordinate position in the size range of the source graph as the center from each group and obtains the feature value of the new graph by calculating the feature point distance between the extracted primitives, the comparison module is used for comparing the characteristic value of the new graph serving as the characteristic identifier of the new graph with the characteristic identifier of the source graph; thereby obtaining the sought target figure. The system can automatically identify the primitive information in the CAD file.

Description

Automatic identification system for similar vector diagrams in CAD drawings
Technical Field
The invention relates to a pattern recognition system, in particular to an automatic recognition system for similar vector diagrams in CAD drawings.
Background
In the field of engineering cost calculation, cost engineers calculate the identified component graphic information in original drawings in a human-eye identification and manual statistics mode in many times. Thus, manual operation of the drawing is very inefficient, time and labor consuming, and recognition accuracy is not high. Especially in large drawings, human eyes are not easy to recognize and search, most of components are composed of discrete primitives, and selection omission is likely to happen when component graphs are selected manually, so that the problem of inaccurate subsequent calculation amount is caused.
The general original drawing is dwg drawing based on two-dimensional CAD software drawing. Most of the graphs drawn by the two-dimensional drawings are composed of primitives, such as straight lines, multiple lines, arcs, circles and the like, to form a component graph. And the graphics at different positions can have problems of rotation, scaling, etc. In addition, the graphics are vector graphics, and the graphics can be formed between every two primitives. In general, if all primitives are combined in sequence, then converted into bitmaps, and then recognized by the existing image recognition method, the efficiency is very low. And because the whole image is large, the relative positions of the primitives are discrete, and the primitives are distorted after being converted into the bitmap, so that the image is difficult to recognize.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides an automatic identification system for similar vector diagrams in CAD drawings. The method combines automatic program identification and statistical data, automatically identifies all similar graphs in an original drawing through the program, and takes the statistical attribute data after the identified graphs are classified as a calculation basis, so that the problem of difficulty in identifying the images in the CAD drawing is solved, and the identification efficiency and accuracy of the similar graphs are improved.
The purpose of the invention is realized by adopting the following technical scheme:
an automatic identification system for similar vector graphics in CAD drawings, said system comprising:
the acquisition module is used for acquiring a source graph and acquiring a plurality of primitive feature points in the source graph;
the determining module is used for determining a characteristic value of the source graph according to the characteristic point distance of each graphic element in the source graph and defining the characteristic value of the source graph as a characteristic identifier of the source graph;
the retrieval module is used for searching other primitives with the same characteristic value as the source graph in the graph layer range of the source graph and grouping the primitives according to the primitive types;
the combination module is used for extracting other primitives with the coordinate position of the first primitive as the center and within the source graph size range from each group to form a new graph;
the calculation module is used for calculating the characteristic point distance between the extracted primitives to obtain the characteristic value of the new graph;
the comparison module is used for comparing the characteristic value of the new graph, which is used as the characteristic identifier of the new graph, with the characteristic identifier of the source graph; if the two images are consistent, obtaining similar images; otherwise, continuing extraction calculation until all the primitives in each group are extracted.
Preferably, the obtaining module includes:
the selection submodule is used for sending the graph source information selected by the user to the CAD graphic service of the host computer;
and the calling submodule is used for matching the CAD graphic service with the corresponding type of the graphic source according to the received graphic source information, calling the uniform service interface corresponding to the graphic source to read the graphic source information and obtaining a plurality of primitive feature points contained in the pre-selected source graphic.
Further, the obtaining module further comprises: and the reading submodule is used for reading a predefined configuration file.
Further, the reading sub-module includes:
the searching unit is used for searching a configuration file path related to the graph source;
the generating unit is used for generating an available graph source information list by reading a predefined configuration file according to the configuration file path;
and the reading unit is used for feeding back the available graph source information list to a front-end interface for selection by a user.
Further, the generation unit includes:
and the loading subunit is used for loading the access dynamic libraries corresponding to the multiple graph sources according to the predefined configuration file by adopting the background graph access service, decoupling the loaded multiple graph sources and the access dynamic libraries corresponding to the multiple graph sources, and generating an available graph source information list.
Preferably, the retrieval module includes:
the traversal submodule is used for traversing all the primitives in the layer range of the source graph;
the definition submodule is used for defining a characteristic value of the source graph according to the characteristic point distance of each graphic element in the source graph;
the calculation submodule is used for calculating the proximity of the characteristic value of the source graph and the characteristic point distance of other primitives;
and the determining submodule is used for determining similar primitives of the source graph based on the distance between the feature points and the proximity.
Preferably, the combination module includes:
the analysis submodule is used for setting the number of the primitive data types, using standard software system SRS software, selecting a coefficient clustering method to perform clustering analysis on other primitives with the same characteristic value as the source graph respectively, or performing clustering analysis on two or more primitives, and checking whether the clustering analysis result conforms to normal distribution: if yes, outputting the grouping result of the graphic elements; otherwise, other systems adopting the coefficient clustering method carry out clustering analysis again until the clustering analysis result obeys normal distribution.
Further, the feature point distance of each primitive in the source graph is calculated by the following formula:
Figure BDA0002504697220000031
in the formula,
Figure BDA0002504697220000032
distance, x, of characteristic points representing a primitiveiAnd yiRespectively representing any two primitives.
Further, the proximity of the feature values of the source graphics to the feature points of other primitives is calculated by:
Figure BDA0002504697220000033
in the formula, ci∈[0,1]The feature values representing the source graphics are in proximity to the feature points of the primitives,
Figure BDA0002504697220000041
a characteristic value representing the source graphic,
Figure BDA0002504697220000042
representing the characteristic point distance of the graphical element.
Further, the determining sub-module includes:
and the sorting unit is used for sorting the similar graphic primitives according to the proximity, wherein the larger the proximity is, the better the selected similar graphic primitives are, and otherwise, the worse the selected similar graphic primitives are.
The invention has the beneficial effects that:
the automatic identification system for the similar vector diagrams in the CAD drawing can automatically identify the primitive information in the CAD file, is convenient and fast to operate and has high identification efficiency. Firstly, obtaining the distances of a plurality of primitive feature points in a source graph, and obtaining the feature value of the source graph through calculation; and then, generating a new graph by using other primitives with the same searching characteristic point distance, and calculating to obtain a characteristic value of the new graph. Finally, the obtained figure characteristic values are used as the basis, and similar new figures are obtained through comparison. The technical scheme of the invention combines automatic program identification and statistical data, automatically identifies all graphs similar to 'long phase' through the program in the original drawing, and takes the attribute data of the identified graphs after classification and statistics as the basis of calculation amount. The method for determining the primitive relation by the space relative position has high speed and high accuracy.
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In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
Fig. 1 is a schematic structural diagram of an automatic identification system for similar vector diagrams in a CAD drawing according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of the automatic identification principle of similar vector graphics in the CAD drawing provided in the specific embodiment of the present invention;
the system comprises a 101 acquisition module, a 102 determination module, a 103 retrieval module, a 104 combination module, a 105 calculation module and a 106 comparison module.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to specifically understand the technical solutions provided by the present invention, the technical solutions of the present invention will be described and illustrated in detail in the following examples. It is apparent that the embodiments provided by the present invention are not limited to the specific details familiar to those skilled in the art. The following detailed description of the preferred embodiments of the invention is intended to provide further embodiments of the invention in addition to those described herein.
The present invention will be described in detail with reference to the accompanying drawings and examples. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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 invention.
As shown in fig. 1, an automatic identification system for similar vector diagrams in CAD drawings disclosed in the embodiments of the present application includes:
the acquisition module 101 is configured to acquire a source graph and acquire a plurality of primitive feature points in the source graph;
the determining module 102 is configured to determine a feature value of the source graph according to a feature point distance of each primitive in the source graph, and define the feature value of the source graph as a feature identifier of the source graph;
the retrieval module 103 is configured to search, within the layer range where the source graph is located, for other primitives that are the same as a feature value of the source graph, and group the primitives according to primitive types;
the combination module 104 is used for extracting other primitives with the coordinate position of the first primitive as the center and within the source graphic size range from each group to form a new graphic;
the calculating module 105 is used for calculating the characteristic point distance between the extracted primitives to obtain the characteristic value of the new graph;
the comparison module 106 is configured to compare the feature value of the new graph with the feature identifier of the source graph, as the feature identifier of the new graph; if the two images are consistent, obtaining similar images; otherwise, continuing extraction calculation until all the primitives in each group are extracted.
Wherein, the acquisition module includes 101 includes: selecting a submodule and calling a submodule;
the selection submodule is used for sending the graph source information selected by the user to cad graph service of the host computer;
and the calling submodule is used for enabling the cad graph service to match with the graph source of the corresponding type according to the received graph source information, calling the uniform service interface corresponding to the graph source to read the graph source information, and obtaining a plurality of primitive feature points contained in the pre-selected source graph.
The acquisition module 101 further comprises: and the reading submodule is used for reading a predefined configuration file.
The reading submodule further comprises: the device comprises a searching unit, a generating unit and a reading unit; wherein,
the searching unit is used for searching a configuration file path related to the graph source;
the generating unit is used for generating an available graph source information list by reading a predefined configuration file according to the configuration file path;
the generation unit further includes: the loading subunit is used for loading the access dynamic libraries corresponding to the multiple graph sources according to a predefined configuration file by adopting a background graph access service, decoupling the multiple loaded graph sources and the access dynamic libraries corresponding to the multiple graph sources, and generating an available graph source information list;
and the reading unit is used for feeding back the available graph source information list to a front-end interface for selection by a user.
The combination module 104 includes: the analysis submodule is used for setting the number of the primitive data types, using standard software system SRS software, selecting a coefficient clustering method to perform clustering analysis on other primitives with the same characteristic value as the source graph respectively, or performing clustering analysis on two or more primitives, and checking whether the clustering analysis result conforms to normal distribution: if yes, outputting the grouping result of the graphic elements; otherwise, other systems adopting the coefficient clustering method carry out clustering analysis again until the clustering analysis result obeys normal distribution.
The retrieval module 103 includes: traversing the submodule, defining the submodule, calculating the submodule and determining the submodule;
the traversal submodule is used for traversing all the primitives in the layer range of the source graph;
the definition submodule is used for defining a characteristic value of the source graph according to the characteristic point distance of each graphic element in the source graph; the feature point distance of each primitive in the source graph recorded in the definition submodule is calculated by the following formula:
Figure BDA0002504697220000061
in the formula,
Figure BDA0002504697220000062
distance, x, of characteristic points representing a primitiveiAnd yiRespectively representing any two primitives.
The calculation submodule is used for calculating the proximity of the characteristic value of the source graph and the characteristic point distance of other primitives;
and calculating the proximity of the characteristic value of the source graph contained in the sub-module to the characteristic point distance of other primitives by the following formula:
Figure BDA0002504697220000071
in the formula, ci∈[0,1]The feature values representing the source graphics are in proximity to the feature points of the primitives,
Figure BDA0002504697220000072
a characteristic value representing the source graphic,
Figure BDA0002504697220000073
representing the characteristic point distance of the graphical element.
And the determining submodule is used for determining similar primitives of the source graph based on the distance between the feature points and the proximity.
The determination sub-module includes: and the sorting unit is used for sorting the similar graphic primitives according to the proximity, wherein the larger the proximity is, the better the selected similar graphic primitives are, and otherwise, the worse the selected similar graphic primitives are.
Correspondingly, the calculating module 105 for obtaining the feature value of the new graph by calculating the feature point distance between the extracted primitives may also calculate the feature point distance between the extracted primitives by the following calculation formula defining sub-modules:
Figure BDA0002504697220000074
in the formula,
Figure BDA0002504697220000075
distance, x, of characteristic points representing the primitive to be extractediAnd yiRespectively representing the extracted primitives.
As shown in fig. 2, based on the same inventive concept, the recognition principle of the automatic recognition system for similar vector diagrams in CAD drawings provided by the present invention can be specifically implemented by the following steps:
1, collecting a source graph (comprising a plurality of primitives)
2, acquiring the characteristic point of each graphic element in the source graph;
calculating the characteristic point distance of the graphic primitive through the characteristic point of the graphic primitive in the source graph, and then calculating the characteristic point distance of the whole graph as a characteristic mark;
4, searching other primitives which are the same as the source primitive feature points in the layer range of the source primitive;
5, grouping the searched primitives according to types;
extracting other primitives of which the coordinates are in a source graphic size area by taking the first primitive as a center from each group, and forming a new graphic;
7, calculating the characteristic value of the primitive in the new graph, and then calculating the characteristic point distance of the new graph to obtain the characteristic value as the characteristic identification of the new graph;
and 8, comparing whether the feature identifier of the new graph is similar to the feature identifier of the source graph, if so, taking the new graph as the found graph, otherwise, continuously extracting and comparing until all the primitives in each group are extracted.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart 1 flow or flows and/or block 1 block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows of FIG. 1 and/or block diagram block or blocks of FIG. 1.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart 1 flow or flows and/or block 1 block or blocks.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application and not for limiting the protection scope thereof, and although the present application is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: numerous variations, modifications, and equivalents will occur to those skilled in the art upon reading the present application and are within the scope of the claims appended hereto.

Claims (10)

1. An automatic identification system for similar vector graphics in CAD drawings, said system comprising:
the acquisition module is used for acquiring a source graph and acquiring a plurality of primitive feature points in the source graph;
the determining module is used for determining a characteristic value of the source graph according to the characteristic point distance of each graphic element in the source graph and defining the characteristic value of the source graph as a characteristic identifier of the source graph;
the retrieval module is used for searching other primitives with the same characteristic value as the source graph in the graph layer range of the source graph and grouping the primitives according to the primitive types;
the combination module is used for extracting other primitives with the coordinate position of the first primitive as the center and within the source graph size range from each group to form a new graph;
the calculation module is used for calculating the characteristic point distance between the extracted primitives to obtain the characteristic value of the new graph;
the comparison module is used for comparing the characteristic value of the new graph, which is used as the characteristic identifier of the new graph, with the characteristic identifier of the source graph; if the two images are consistent, obtaining similar images; otherwise, continuing extraction calculation until all the primitives in each group are extracted.
2. The system of claim 1, wherein the acquisition module comprises:
the selection submodule is used for sending the graph source information selected by the user to the CAD graphic service of the host computer;
and the calling submodule is used for matching the CAD graphic service with the corresponding type of the graphic source according to the received graphic source information, calling the uniform service interface corresponding to the graphic source to read the graphic source information and obtaining a plurality of primitive feature points contained in the pre-selected source graphic.
3. The system of claim 2, wherein the acquisition module further comprises: and the reading submodule is used for reading a predefined configuration file.
4. The system of claim 3, wherein the read submodule comprises:
the searching unit is used for searching a configuration file path related to the graph source;
the generating unit is used for generating an available graph source information list by reading a predefined configuration file according to the configuration file path;
and the reading unit is used for feeding back the available graph source information list to a front-end interface for selection by a user.
5. The system of claim 4, wherein the generating unit comprises:
and the loading subunit is used for loading the access dynamic libraries corresponding to the multiple graph sources according to the predefined configuration file by adopting the background graph access service, decoupling the loaded multiple graph sources and the access dynamic libraries corresponding to the multiple graph sources, and generating an available graph source information list.
6. The system of claim 1, wherein the retrieval module comprises:
the traversal submodule is used for traversing all the primitives in the layer range of the source graph;
the definition submodule is used for defining a characteristic value of the source graph according to the characteristic point distance of each graphic element in the source graph;
the calculation submodule is used for calculating the proximity of the characteristic value of the source graph and the characteristic point distance of other primitives;
and the determining submodule is used for determining similar primitives of the source graph based on the distance between the feature points and the proximity.
7. The system of claim 1, wherein the combining module comprises:
the analysis submodule is used for setting the number of the primitive data types, using standard software system SRS software, selecting a coefficient clustering method to perform clustering analysis on other primitives with the same characteristic value as the source graph respectively, or performing clustering analysis on two or more primitives, and checking whether the clustering analysis result conforms to normal distribution: if yes, outputting the grouping result of the graphic elements; otherwise, other systems adopting the coefficient clustering method carry out clustering analysis again until the clustering analysis result obeys normal distribution.
8. The system of claim 6, wherein the feature point distance for each primitive in the source graph is calculated by:
Figure FDA0002504697210000021
in the formula,
Figure FDA0002504697210000023
distance, x, of characteristic points representing a primitiveiAnd yiRespectively representing any two primitives.
9. The system of claim 6, wherein the proximity of feature values of the source graphics to feature points of other primitives is calculated by:
Figure FDA0002504697210000022
in the formula, ci∈[0,1]The feature values representing the source graphics are in proximity to the feature points of the primitives,
Figure FDA0002504697210000024
a characteristic value representing the source graphic,
Figure FDA0002504697210000025
representing the characteristic point distance of the graphical element.
10. The system of claim 6, wherein the determination submodule comprises:
and the sorting unit is used for sorting the similar graphic primitives according to the proximity, wherein the larger the proximity is, the better the selected similar graphic primitives are, and otherwise, the worse the selected similar graphic primitives are.
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