CN112070881A - Electromechanical equipment digital reconstruction method and system based on Internet of things - Google Patents
Electromechanical equipment digital reconstruction method and system based on Internet of things Download PDFInfo
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Abstract
The invention belongs to the technical field of digital processing, and discloses a method and a system for digitally reconstructing electromechanical equipment based on the Internet of things, wherein a data acquisition module is used for acquiring accessory or part structure data, drawings or other related data of the electromechanical equipment; the image acquisition module acquires a complete external contour image of the electromechanical device; the point cloud data acquisition module acquires three-dimensional point cloud data of the electromechanical equipment; the central control module controls the image processing module to process the acquired image to obtain a processed image set; the regional data fusion module acquires regional point cloud data of the electromechanical equipment; the three-dimensional reconstruction module carries out digital three-dimensional reconstruction on the electromechanical equipment; and the display module displays the constructed digital three-dimensional model of the electromechanical equipment. The method can obtain an accurate and comprehensive three-dimensional model of the electromechanical equipment, simultaneously needs less data, processes less data, does not need manual marking or screening, has high reconstruction efficiency and accurate reconstruction result.
Description
Technical Field
The invention belongs to the technical field of digital processing, and particularly relates to a method and a system for digitally reconstructing electromechanical equipment based on the Internet of things.
Background
At present: the physical appearance digitization technology is widely applied in many fields. For example, in our well-known concept of digital museum, virtual collections of art will be presented in the world of digital networks, enabling ordinary viewers to experience equal or even more profound artistic appreciation at or near home, resulting in a more personalized service for the exhibition hall, without the objective limitations of time and space, or even personal economic conditions. Meanwhile, the physical digitization technology has great application potential in industries such as computer games, digital movies, physical simulation, social networks, automobiles, cultural souvenirs, home decorations and the like. The digital three-dimensional modeling technology has wide application prospect, can be deep into the aspects of our life, and can be replaced by digital three-dimensional model display in the prior places where two-dimensional pictures can be displayed.
However, the existing digital reconstruction technology does not provide a reconstruction method for electromechanical devices in the industrial field, and meanwhile, the existing reconstruction method has long scanning time, needs a lot of data, is complicated in data processing, and is not ideal in reconstruction result.
Through the above analysis, the problems and defects of the prior art are as follows: the existing digital reconstruction technology does not aim at a reconstruction method of electromechanical equipment in the industrial field, and meanwhile, the existing reconstruction method has long scanning time, more required data, complex data processing and unsatisfactory reconstruction result.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a system for digitally reconstructing electromechanical equipment based on the Internet of things.
The invention is realized in such a way, and the electromechanical equipment digital reconstruction method based on the Internet of things comprises the following steps:
the method comprises the following steps that firstly, a data acquisition module acquires structural data, drawings or other related data of accessories or parts of electromechanical equipment by using scanning equipment; the image acquisition module acquires a complete external contour image of the electromechanical device by utilizing the rotating camera device;
secondly, a point cloud data acquisition module acquires three-dimensional point cloud data of the electromechanical equipment; the central control module controls the image processing module to process the acquired image to obtain a processed image set;
thirdly, the regional data fusion module acquires regional point cloud data of the electromechanical equipment based on the processed image set and the three-dimensional point cloud data of the electromechanical equipment;
fourthly, the three-dimensional reconstruction module carries out digital three-dimensional reconstruction on the electromechanical equipment based on the acquired contour image of the electromechanical equipment, the regional point cloud data and the structural data of the parts;
and fifthly, displaying the constructed digital three-dimensional model of the electromechanical equipment by the display module.
Further, in the second step, the central control module controls the image processing module to process the acquired image, and obtaining a processed image set includes:
(1) acquiring a collected contour image of the electromechanical equipment, and extracting a brightness component from the acquired image;
(2) carrying out digit lifting on the brightness component, carrying out flat area detection on the image according to the lifted brightness component, and judging whether each pixel in the image is in a flat area;
(3) when the pixel is in a flat area, smoothing the pixel; converting the new brightness value processed by each pixel in the image into a low order through shaking display, and performing brightness component synthesis according to the converted brightness component data;
(4) outputting a smoothed image after synthesizing the brightness components; enhancing, coding and sequencing the smoothed images; and sorting the images according to the sorting order to obtain an image set.
Further, in step (2), the flat region detection includes:
(2.1) calculating a flatness value of each target pixel;
(2.2) calculating the maximum value of the flatness values of the pixels in the preset area according to the preset area size by taking the target pixel as the center;
(2.3) comparing the maximum flatness value with a preset threshold, wherein if the maximum flatness value is smaller than the threshold, the target pixel needs to be subjected to smoothing, and otherwise, the target pixel does not need to be subjected to smoothing.
Further, in step (4), the enhancing the smoothed image includes:
(4.1) constructing a new histogram array on the basis of the histogram array of the gray level of the smoothed image;
(4.2) replacing the smooth image histogram array with each gray level in the constructed new histogram array in a one-to-one correspondence manner according to the sequence of the gray levels from large to small to obtain a reconstructed histogram array;
and (4.3) carrying out cumulative summation on the reconstructed histogram array, and forming a new enhanced image through the new gray level.
Further, in the second step, the acquiring three-dimensional point cloud data of the electromechanical device by the point cloud data acquiring module specifically includes:
s11, acquiring a complete external contour image of the electromechanical device acquired by the camera device, and performing foreground object segmentation on the external contour image;
s12, calibrating the camera shooting equipment of the foreground target image by adopting an SfM algorithm to obtain initialized camera shooting equipment parameters, and optimizing the initialized camera shooting equipment parameters by adopting a cluster optimization method to obtain camera parameters after the foreground target image is optimized;
s13, matching the same corner features in the foreground target image to obtain a plurality of initial corner feature matching point pairs;
s14, detecting the spot characteristics in the foreground target image by adopting a Gaussian difference operator, and matching the same spot characteristics in the foreground target image to obtain a plurality of initial spot characteristic matching point pairs;
and S15, performing back projection on each diffusion characteristic point pair through the camera equipment parameters after the foreground target image is optimized to obtain three-dimensional space points corresponding to the diffusion characteristic point pairs.
Further, in step S13, the matching the same corner features in the foreground object image includes:
calculating the visual angle similarity of the image to be coded and the similar image after cloud clustering according to the parameters of the camera equipment, and determining a reference image;
generating a matching pixel pair between the reference image and the image to be coded according to the three-dimensional point cloud model corresponding to the reference image;
and generating the prediction of the image to be coded by taking the matched pixel pair as the center of the prediction block.
Further, the step three, the acquiring, by the regional data fusion module, regional point cloud data of the electromechanical device based on the processed image set and the three-dimensional point cloud data of the electromechanical device includes:
1) extracting point cloud data of the electromechanical equipment from the regional point cloud data of the electromechanical equipment, and clustering and curved surface reconstruction are carried out on the point cloud data of the electromechanical equipment to obtain a point cloud model of the electromechanical equipment;
2) acquiring size information of the substation equipment based on the point cloud model of the electromechanical equipment, and comparing and confirming the size information with accessory or part structure data, drawings or other related data to obtain the checked size information of the electromechanical equipment;
3) constructing a mechatronic device model according to the size information of the mechatronic device; acquiring electrical attribute information of the electromechanical equipment, and constructing a three-dimensional digital equipment model based on the electrical attribute information and the corresponding electromechanical equipment model.
Further, in step 1), the clustering and surface reconstruction of the point cloud data of the electromechanical device to obtain a point cloud model of the electromechanical device includes:
1.1) acquiring point cloud data of single substation equipment based on an Euclidean clustering segmentation algorithm;
and 1.2) carrying out triangulation processing on the point cloud data of the single substation equipment based on a greedy projection triangulation algorithm to obtain a point cloud model of the substation equipment.
Another object of the present invention is to provide an internet of things-based electromechanical device digital reconstruction system for implementing the internet of things-based electromechanical device digital reconstruction method, including:
the system comprises a data acquisition module, an image acquisition module, a point cloud data acquisition module, a central control module, an image processing module, a region data fusion module, a three-dimensional reconstruction module and a display module;
the data acquisition module is connected with the central control module and is used for acquiring the structural data, drawings or other related data of accessories or parts of the electromechanical equipment by utilizing the scanning equipment;
the image acquisition module is connected with the central control module and is used for acquiring a complete external contour image of the electromechanical equipment by utilizing the rotating camera equipment;
the point cloud data acquisition module is connected with the central control module and is used for acquiring three-dimensional point cloud data of the electromechanical equipment;
the central control module is connected with the data acquisition module, the image acquisition module, the point cloud data acquisition module, the image processing module, the region data fusion module, the three-dimensional reconstruction module and the display module and is used for controlling each module to normally work based on the controller or the singlechip;
the image processing module is connected with the central control module, comprises an image preprocessing unit, an image coding unit, an image sorting unit and is used for processing the acquired images to obtain a processed image set;
the regional data fusion module is connected with the central control module and used for acquiring regional point cloud data of the electromechanical equipment based on the processed image set and the three-dimensional point cloud data of the electromechanical equipment;
the three-dimensional reconstruction module is connected with the central control module and is used for carrying out digital three-dimensional reconstruction on the electromechanical equipment based on the acquired electromechanical equipment contour image, the acquired regional point cloud data and the acquired part structure data;
and the display module is connected with the central control module and is used for displaying the constructed digital three-dimensional model of the electromechanical equipment.
Further, the image processing module includes:
the image preprocessing unit is used for carrying out denoising and enhancement preprocessing on the acquired image;
an image encoding unit for encoding the enhanced image;
an image sorting unit for sorting the images based on the image coding;
and the image sorting unit is used for sorting the images according to the sorting order to obtain an image set.
By combining all the technical schemes, the invention has the advantages and positive effects that: the invention provides a digital reconstruction method of electromechanical equipment, which is used for carrying out digital reconstruction on the electromechanical equipment based on two-dimensional image data, size, part data and three-dimensional point cloud data of the electromechanical equipment, so that an accurate and comprehensive three-dimensional model of the electromechanical equipment can be obtained, meanwhile, the required data amount is small, the processing aiming at the data is small, manual marking or screening is not needed, the reconstruction efficiency is high, and the reconstruction result is accurate. According to the method, the image sequence after the segmentation of the original scene body is taken as input, the target object is reconstructed on the premise of removing background noise points, so that only the three-dimensional point cloud of the target object is concerned in the reconstruction process, only the related data of the target object is calculated, the defects that in the prior art, not only the data of the target object is calculated, but also a large number of background redundant points are calculated are overcome, and the calculated amount of the reconstruction of the three-dimensional point cloud is effectively reduced.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
Fig. 1 is a flowchart of a digital reconstruction method for an electromechanical device according to an embodiment of the present invention.
Fig. 2 is a flowchart of an embodiment of the present invention, in which a central control module controls an image processing module to process an acquired image, so as to obtain a processed image set.
Fig. 3 is a flowchart of a flat area detection method according to an embodiment of the present invention.
Fig. 4 is a flowchart of enhancing a smoothed image according to an embodiment of the present invention.
Fig. 5 is a flowchart of acquiring, by the regional data fusion module according to the embodiment of the present invention, regional point cloud data of the electromechanical device based on the processed image set and the three-dimensional point cloud data of the electromechanical device.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a method and a system for digitally reconstructing electromechanical equipment based on the internet of things, and the invention is described in detail with reference to the accompanying drawings.
As shown in fig. 1, a method for digitally reconstructing an internet-of-things-based electromechanical device according to an embodiment of the present invention includes:
s101, a data acquisition module acquires structural data, drawings or other related data of accessories or parts of the electromechanical equipment by using scanning equipment; the image acquisition module acquires a complete external contour image of the electromechanical device by utilizing the rotating camera device;
s102, a point cloud data acquisition module acquires three-dimensional point cloud data of the electromechanical equipment; the central control module controls the image processing module to process the acquired image to obtain a processed image set;
s103, the regional data fusion module acquires regional point cloud data of the electromechanical equipment based on the processed image set and the three-dimensional point cloud data of the electromechanical equipment;
s104, the three-dimensional reconstruction module carries out digital three-dimensional reconstruction on the electromechanical equipment based on the acquired electromechanical equipment contour image, the acquired regional point cloud data and the acquired part structure data;
and S105, displaying the constructed digital three-dimensional model of the electromechanical device by the display module.
As shown in fig. 2, in step S102, the central control module provided in the embodiment of the present invention controls the image processing module to process the acquired image, and the obtaining of the processed image set includes:
s201, acquiring a collected contour image of the electromechanical device, and extracting a brightness component from the acquired image;
s202, carrying out digit lifting on the brightness component, carrying out flat area detection on the image according to the lifted brightness component, and judging whether each pixel in the image is in a flat area;
s203, when the pixel is in a flat area, smoothing the pixel; converting the new brightness value processed by each pixel in the image into a low order through shaking display, and performing brightness component synthesis according to the converted brightness component data;
s204, outputting a smoothed image after synthesizing the brightness components; enhancing, coding and sequencing the smoothed images; and sorting the images according to the sorting order to obtain an image set.
In step S102 in the embodiment of the present invention, the acquiring three-dimensional point cloud data of the electromechanical device by the point cloud data acquiring module specifically includes:
s11, acquiring a complete external contour image of the electromechanical device acquired by the camera device, and performing foreground object segmentation on the external contour image;
s12, calibrating the camera shooting equipment of the foreground target image by adopting an SfM algorithm to obtain initialized camera shooting equipment parameters, and optimizing the initialized camera shooting equipment parameters by adopting a cluster optimization method to obtain camera parameters after the foreground target image is optimized;
s13, matching the same corner features in the foreground target image to obtain a plurality of initial corner feature matching point pairs;
s14, detecting the spot characteristics in the foreground target image by adopting a Gaussian difference operator, and matching the same spot characteristics in the foreground target image to obtain a plurality of initial spot characteristic matching point pairs;
and S15, performing back projection on each diffusion characteristic point pair through the camera equipment parameters after the foreground target image is optimized to obtain three-dimensional space points corresponding to the diffusion characteristic point pairs.
In step S13 in the embodiment of the present invention, matching the same corner features in the foreground target image includes:
calculating the visual angle similarity of the image to be coded and the similar image after cloud clustering according to the parameters of the camera equipment, and determining a reference image;
generating a matching pixel pair between the reference image and the image to be coded according to the three-dimensional point cloud model corresponding to the reference image;
and generating the prediction of the image to be coded by taking the matched pixel pair as the center of the prediction block.
As shown in fig. 3, in step S202, the flat area detection provided by the embodiment of the present invention includes:
s301, calculating a flatness value of each target pixel;
s302, taking the target pixel as a center, and calculating the maximum value of the flatness values of the pixels in the preset area according to the preset area size;
and S303, comparing the flatness maximum value with a preset threshold, wherein if the flatness maximum value is smaller than the threshold, the target pixel needs to be subjected to smoothing, and otherwise, the target pixel does not need to be subjected to smoothing.
As shown in fig. 4, in step S204, the enhancing the smoothed image according to the embodiment of the present invention includes:
s401, constructing a new histogram array on the basis of the histogram array of the gray level of the smoothed image;
s402, replacing the smooth image histogram array and all gray levels in the constructed new histogram array in a one-to-one correspondence mode according to the sequence of the gray levels from large to small to obtain a reconstructed histogram array;
and S403, performing cumulative summation on the reconstructed histogram array, and forming a new enhanced image through a new gray level.
As shown in fig. 5, the acquiring, by the regional data fusion module according to the embodiment of the present invention, regional point cloud data of the electromechanical device based on the processed image set and the three-dimensional point cloud data of the electromechanical device includes:
s501, extracting point cloud data of the electromechanical equipment from regional point cloud data of the electromechanical equipment, and clustering and surface reconstruction are carried out on the point cloud data of the electromechanical equipment to obtain a point cloud model of the electromechanical equipment;
s502, acquiring size information of the substation equipment based on the point cloud model of the electromechanical equipment, and comparing and confirming the size information with accessory or part structure data, drawings or other related data to obtain the checked size information of the electromechanical equipment;
s503, constructing a mechatronic device model according to the size information of the mechatronic device; acquiring electrical attribute information of the electromechanical equipment, and constructing a three-dimensional digital equipment model based on the electrical attribute information and the corresponding electromechanical equipment model.
In step S501, the clustering and surface reconstruction of the point cloud data of the electromechanical device provided in the embodiment of the present invention to obtain a point cloud model of the electromechanical device includes:
1.1) acquiring point cloud data of single substation equipment based on an Euclidean clustering segmentation algorithm;
and 1.2) carrying out triangulation processing on the point cloud data of the single substation equipment based on a greedy projection triangulation algorithm to obtain a point cloud model of the substation equipment.
The electromechanical equipment digital reconstruction system based on the Internet of things provided by the embodiment of the invention comprises:
the data acquisition module is connected with the central control module and is used for acquiring the structural data, drawings or other related data of accessories or parts of the electromechanical equipment by utilizing the scanning equipment;
the image acquisition module is connected with the central control module and is used for acquiring a complete external contour image of the electromechanical equipment by utilizing the rotating camera equipment;
the point cloud data acquisition module is connected with the central control module and is used for acquiring three-dimensional point cloud data of the electromechanical equipment;
the central control module is connected with the data acquisition module, the image acquisition module, the point cloud data acquisition module, the image processing module, the region data fusion module, the three-dimensional reconstruction module and the display module and is used for controlling each module to normally work based on the controller or the singlechip;
the image processing module is connected with the central control module, comprises an image preprocessing unit, an image coding unit, an image sorting unit and is used for processing the acquired images to obtain a processed image set;
the regional data fusion module is connected with the central control module and used for acquiring regional point cloud data of the electromechanical equipment based on the processed image set and the three-dimensional point cloud data of the electromechanical equipment;
the three-dimensional reconstruction module is connected with the central control module and is used for carrying out digital three-dimensional reconstruction on the electromechanical equipment based on the acquired electromechanical equipment contour image, the acquired regional point cloud data and the acquired part structure data;
and the display module is connected with the central control module and is used for displaying the constructed digital three-dimensional model of the electromechanical equipment.
The image processing module provided by the embodiment of the invention comprises:
the image preprocessing unit is used for carrying out denoising and enhancement preprocessing on the acquired image;
an image encoding unit for encoding the enhanced image;
an image sorting unit for sorting the images based on the image coding;
and the image sorting unit is used for sorting the images according to the sorting order to obtain an image set.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, and any modification, equivalent replacement, and improvement made by those skilled in the art within the technical scope of the present invention disclosed herein, which is within the spirit and principle of the present invention, should be covered by the present invention.
Claims (10)
1. The electromechanical equipment digital reconstruction method based on the Internet of things is characterized by comprising the following steps of:
the method comprises the following steps that firstly, a data acquisition module acquires structural data, drawings or other related data of accessories or parts of electromechanical equipment by using scanning equipment; the image acquisition module acquires a complete external contour image of the electromechanical device by utilizing the rotating camera device;
secondly, a point cloud data acquisition module acquires three-dimensional point cloud data of the electromechanical equipment; the central control module controls the image processing module to process the acquired image to obtain a processed image set;
the central control module controls the image processing module to process the acquired image, and the obtaining of the processed image set comprises the following steps:
(1) acquiring a collected contour image of the electromechanical equipment, and extracting a brightness component from the acquired image;
(2) carrying out digit lifting on the brightness component, carrying out flat area detection on the image according to the lifted brightness component, and judging whether each pixel in the image is in a flat area;
the flat area detection includes:
(2.1) calculating a flatness value of each target pixel;
(2.2) calculating the maximum value of the flatness values of the pixels in the preset area according to the preset area size by taking the target pixel as the center;
(2.3) comparing the maximum flatness value with a preset threshold, wherein if the maximum flatness value is smaller than the threshold, the target pixel needs to be subjected to smoothing, and otherwise, the target pixel does not need to be subjected to smoothing;
(3) when the pixel is in a flat area, smoothing the pixel; converting the new brightness value processed by each pixel in the image into a low order through shaking display, and performing brightness component synthesis according to the converted brightness component data;
(4) outputting a smoothed image after synthesizing the brightness components; enhancing, coding and sequencing the smoothed images; arranging the images according to the sorting sequence to obtain an image set;
thirdly, the regional data fusion module acquires regional point cloud data of the electromechanical equipment based on the processed image set and the three-dimensional point cloud data of the electromechanical equipment;
fourthly, the three-dimensional reconstruction module carries out digital three-dimensional reconstruction on the electromechanical equipment based on the acquired contour image of the electromechanical equipment, the regional point cloud data and the structural data of the parts;
and fifthly, displaying the constructed digital three-dimensional model of the electromechanical equipment by the display module.
2. The internet of things-based electromechanical device digital reconstruction method according to claim 1, wherein in the second step, the acquiring the three-dimensional point cloud data of the electromechanical device by the point cloud data acquiring module specifically comprises:
s11, acquiring a complete external contour image of the electromechanical device acquired by the camera device, and performing foreground object segmentation on the external contour image;
s12, calibrating the camera shooting equipment of the foreground target image by adopting an SfM algorithm to obtain initialized camera shooting equipment parameters, and optimizing the initialized camera shooting equipment parameters by adopting a cluster optimization method to obtain camera parameters after the foreground target image is optimized;
s13, matching the same corner features in the foreground target image to obtain a plurality of initial corner feature matching point pairs;
s14, detecting the spot characteristics in the foreground target image by adopting a Gaussian difference operator, and matching the same spot characteristics in the foreground target image to obtain a plurality of initial spot characteristic matching point pairs;
and S15, performing back projection on each diffusion characteristic point pair through the camera equipment parameters after the foreground target image is optimized to obtain three-dimensional space points corresponding to the diffusion characteristic point pairs.
3. The internet-of-things-based electromechanical device digital reconstruction method according to claim 2, wherein the step S13, the matching the same corner features in the foreground object image includes:
calculating the visual angle similarity of the image to be coded and the similar image after cloud clustering according to the parameters of the camera equipment, and determining a reference image;
generating a matching pixel pair between the reference image and the image to be coded according to the three-dimensional point cloud model corresponding to the reference image;
and generating the prediction of the image to be coded by taking the matched pixel pair as the center of the prediction block.
4. The digital reconstruction method for electromechanical device based on internet of things as claimed in claim 1, wherein in step (4), the enhancing the smoothed image comprises:
(4.1) constructing a new histogram array on the basis of the histogram array of the gray level of the smoothed image;
(4.2) replacing the smooth image histogram array with each gray level in the constructed new histogram array in a one-to-one correspondence manner according to the sequence of the gray levels from large to small to obtain a reconstructed histogram array;
and (4.3) carrying out cumulative summation on the reconstructed histogram array, and forming a new enhanced image through the new gray level.
5. The internet of things-based electromechanical device digital reconstruction method of claim 1, wherein the step three, the obtaining, by the regional data fusion module, regional point cloud data of the electromechanical device based on the processed image set and three-dimensional point cloud data of the electromechanical device comprises:
1) extracting point cloud data of the electromechanical equipment from the regional point cloud data of the electromechanical equipment, and clustering and curved surface reconstruction are carried out on the point cloud data of the electromechanical equipment to obtain a point cloud model of the electromechanical equipment;
2) acquiring size information of the substation equipment based on the point cloud model of the electromechanical equipment, and comparing and confirming the size information with accessory or part structure data, drawings or other related data to obtain the checked size information of the electromechanical equipment;
3) constructing a mechatronic device model according to the size information of the mechatronic device; acquiring electrical attribute information of the electromechanical equipment, and constructing a three-dimensional digital equipment model based on the electrical attribute information and the corresponding electromechanical equipment model.
6. The method for digitally reconstructing the electromechanical device based on the internet of things as claimed in claim 5, wherein in the step 1), the clustering and surface reconstruction of the electromechanical device point cloud data to obtain the electromechanical device point cloud model comprises:
1.1) acquiring point cloud data of single substation equipment based on an Euclidean clustering segmentation algorithm;
and 1.2) carrying out triangulation processing on the point cloud data of the single substation equipment based on a greedy projection triangulation algorithm to obtain a point cloud model of the substation equipment.
7. An internet-of-things-based electromechanical device digital reconstruction system for implementing the internet-of-things-based electromechanical device digital reconstruction method according to claims 1 to 6, wherein the internet-of-things-based electromechanical device digital reconstruction system comprises:
the system comprises a data acquisition module, an image acquisition module, a point cloud data acquisition module, a central control module, an image processing module, a region data fusion module, a three-dimensional reconstruction module and a display module;
the data acquisition module is connected with the central control module and is used for acquiring the structural data, drawings or other related data of accessories or parts of the electromechanical equipment by utilizing the scanning equipment;
the image acquisition module is connected with the central control module and is used for acquiring a complete external contour image of the electromechanical equipment by utilizing the rotating camera equipment;
the point cloud data acquisition module is connected with the central control module and is used for acquiring three-dimensional point cloud data of the electromechanical equipment;
the central control module is connected with the data acquisition module, the image acquisition module, the point cloud data acquisition module, the image processing module, the region data fusion module, the three-dimensional reconstruction module and the display module and is used for controlling each module to normally work based on the controller or the singlechip;
the image processing module is connected with the central control module, comprises an image preprocessing unit, an image coding unit, an image sorting unit and is used for processing the acquired images to obtain a processed image set;
the regional data fusion module is connected with the central control module and used for acquiring regional point cloud data of the electromechanical equipment based on the processed image set and the three-dimensional point cloud data of the electromechanical equipment;
the three-dimensional reconstruction module is connected with the central control module and is used for carrying out digital three-dimensional reconstruction on the electromechanical equipment based on the acquired electromechanical equipment contour image, the acquired regional point cloud data and the acquired part structure data;
and the display module is connected with the central control module and is used for displaying the constructed digital three-dimensional model of the electromechanical equipment.
8. The internet-of-things-based electromechanical device digital reconstruction system of claim 7, wherein the image processing module comprises:
the image preprocessing unit is used for carrying out denoising and enhancement preprocessing on the acquired image;
an image encoding unit for encoding the enhanced image;
an image sorting unit for sorting the images based on the image coding;
and the image sorting unit is used for sorting the images according to the sorting order to obtain an image set.
9. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface for implementing the method of digitally reconstructing an internet of things based electromechanical device as claimed in any one of claims 1 to 6 when executed on an electronic apparatus.
10. A computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method for digitally reconstructing an internet-of-things-based electromechanical device as claimed in any one of claims 1 to 6.
Priority Applications (1)
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| US20190073825A1 (en) * | 2017-09-01 | 2019-03-07 | Vangogh Imaging, Inc. | Enhancing depth sensor-based 3d geometry reconstruction with photogrammetry |
| CN110517348A (en) * | 2019-07-23 | 2019-11-29 | 西安电子科技大学 | Target object three-dimensional point cloud method for reconstructing based on display foreground segmentation |
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| US20190073825A1 (en) * | 2017-09-01 | 2019-03-07 | Vangogh Imaging, Inc. | Enhancing depth sensor-based 3d geometry reconstruction with photogrammetry |
| CN110517348A (en) * | 2019-07-23 | 2019-11-29 | 西安电子科技大学 | Target object three-dimensional point cloud method for reconstructing based on display foreground segmentation |
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| 章秀华;白浩玉;李毅;: "多目立体视觉三维重建系统的设计", 武汉工程大学学报, no. 03 * |
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