Intelligent system for mechanical and electrical installation

Abstract

The invention relates to the technical field of electromechanical equipment, in particular to an electromechanical installation intelligent system, which comprises an image acquisition module used for acquiring a real-time image of to-be-installed electromechanical equipment; the data storage module is used for storing historical images of historical electromechanical equipment; the feature extraction module is used for determining a feature extraction mode of the real-time image based on the texture complexity evaluation value; the image analysis module is used for determining a fuzzy type and an accurate type of the real-time image according to the extracted overall features; the equipment determining module is used for determining a specific type according to the shape feature value of the extracted detail feature; installing a simulation module, and determining the qualification of the specific type of simulation installation according to the adaptability evaluation value of the simulation installation; and the optimization adjustment module is used for determining an optimization mode of the data storage module according to a ratio of the adaptability evaluation value to a preset adaptability evaluation value. According to the invention, the installation accuracy and efficiency of the electromechanical equipment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electromechanical equipment, and in particular to an intelligent electromechanical installation system. Background Art

[0002] With the acceleration of urbanization, people have higher and higher requirements for the functionality, comfort and safety of buildings and industrial facilities, and traditional electromechanical installation methods can no longer meet the needs. At the same time, the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence has made it possible for electromechanical installation to be intelligent. Driven by concepts such as smart cities and green buildings, the electromechanical installation intelligent system came into being. It integrates advanced intelligent equipment and management concepts, aiming to achieve efficient, energy-saving, safe and reliable electromechanical installation and operation and maintenance. This system not only improves the intelligence level of buildings and industrial facilities, but also promotes the rational use of resources and the sustainable development of the environment.

[0003] Chinese patent application publication number: CN112989471A discloses a prefabricated intelligent installation system for building electromechanical engineering, including splicing equipment, climbing equipment, identification mechanism and simulation system; construction personnel of the present invention can install pipes at high places through the climbing equipment, and during the installation process, construction personnel can control the splicing equipment through the control handle installed on the climbing equipment, and the splicing unit moves to the material rack under the control of the internal control module to automatically take out the material, and then transport the material to the next installation position, so as to avoid the construction personnel repeatedly climbing up to take the material during the process, and no multiple people are required to lift the pipe, so that single-person operation can be achieved, the labor intensity is greatly improved, and the labor cost is greatly reduced. Through the cooperation of the simulation system with the scanning gun module and the QR code, the pipe is modularly divided and made through the drawings, so that the installer can quickly find the location where the pipe needs to be installed and install it. However, there are the following problems in the prior art: the prior art does not specifically classify electromechanical equipment, nor does it simulate the installation of electromechanical equipment, which may cause mismatching of electromechanical equipment during installation, thereby causing the problem of low accuracy and low efficiency of electromechanical equipment installation. Summary of the invention

[0004] To this end, the present invention provides an intelligent electromechanical installation system to overcome the problems of low accuracy and low efficiency in the installation of electromechanical equipment in the prior art.

[0005] To achieve the above object, the present invention provides an electromechanical installation intelligent system, comprising:

[0006] An image acquisition module, which is used to obtain real-time images of the electromechanical equipment to be installed;

[0007] A data storage module, which is connected to the image acquisition module and is used to store historical images of historical electromechanical equipment;

[0008] A feature extraction module, connected to the image acquisition module, for determining a feature extraction method of the real-time image based on the texture complexity evaluation value of the real-time image, wherein the feature extraction method includes extracting the overall feature of the real-time image or extracting the detail feature of the real-time image;

[0009] An image analysis module connected to the data storage module and the feature extraction module, for determining the blur type of the real-time image based on edge feature values ​​of the extracted overall features under the condition that the overall features of the real-time image are extracted, and for determining the precise type of the real-time image based on shape feature values ​​of the extracted detail features under the condition that the real-time image is a similar image;

[0010] A device determination module, which is connected to the feature extraction module and the image analysis module, and is used to determine the specific type according to the size feature value of the extracted detail feature based on the condition that the precise type of the real-time image is determined to be a similar image;

[0011] An installation simulation module connected to the equipment determination module is used to determine the eligibility of a specific type of simulated installation based on the condition of determining the specific type and the evaluation value of the degree of fit of the simulated installation;

[0012] An optimization and adjustment module is connected to the data storage module and the installation simulation module, and is used to determine the optimization method of the data storage module based on the ratio of the fit evaluation value to the preset fit evaluation value under the condition that a specific type of simulated installation is unqualified, wherein the optimization method includes updating the historical image of the data storage module or updating and adding the historical image of the data storage module.

[0013] Furthermore, under the condition of determining to obtain a real-time image of the electromechanical equipment to be installed, the feature extraction module determines to extract overall features of the real-time image based on a comparison result that the texture complexity value of the real-time image is less than or equal to a preset texture complexity value, wherein the overall features include shape features and edge features.

[0014] Furthermore, under the condition of determining to obtain a real-time image of the electromechanical equipment to be installed, the feature extraction module determines to extract detail features of the real-time image based on a comparison result that a texture complexity value of the real-time image is greater than a preset texture complexity value, wherein the detail features include corner features and size features.

[0015] Furthermore, the image analysis module determines that the blur type of the real-time image is a simple electromechanical equipment image based on a comparison result that the edge feature value of the overall feature is less than or equal to the reference edge feature value of the overall feature of the historical image, under the condition of extracting the overall feature of the real-time image.

[0016] Furthermore, the image analysis module determines that the blur type of the real-time image is a complex electromechanical equipment image based on a comparison result that the edge feature value of the overall feature is greater than the reference edge feature value of the overall feature of the historical image, under the condition of extracting the overall feature of the real-time image.

[0017] Furthermore, the image analysis module determines the precise type of the real-time image as a similar image based on the comparison result that the shape feature value of the detail feature is greater than the reference shape feature value of the detail feature of the historical image under the condition of determining the fuzzy type of the real-time image.

[0018] Furthermore, the device determination module determines that the specific type of the similar image is a compact electromechanical device image based on the fact that the size feature value of the detail feature is less than or equal to the reference size feature value of the detail feature of the historical image, under the condition that the real-time image is determined to be a similar image.

[0019] Furthermore, the device determination module determines that the specific type of the similar image is a standard electromechanical device image based on the fact that the size feature value of the detail feature is greater than the reference size feature value of the detail feature of the historical image, under the condition that the real-time image is determined to be a similar image.

[0020] Furthermore, under the condition of determining the specific type of the similar image, the installation simulation module determines that the specific type of simulated installation is unqualified based on the comparison result that the fit evaluation value of the simulated installation is less than or equal to the preset fit evaluation value, and determines to update the historical image of the data storage module at a preset update frequency based on the comparison result that the ratio of the fit evaluation value to the preset fit evaluation value is less than or equal to the preset ratio.

[0021] Furthermore, under the condition of determining the specific type of the similar image, the installation simulation module determines that the specific type of simulated installation is unqualified based on the comparison result that the fit evaluation value of the simulated installation is less than or equal to the preset fit evaluation value, and determines to add other databases storing historical images to the data storage module based on the comparison result that the ratio of the fit evaluation value to the preset fit evaluation value is greater than the preset ratio.

[0022] Compared with the prior art, the beneficial effect of the present invention lies in that the present invention obtains real-time images of electromechanical equipment, determines the precise type through feature extraction and image analysis, and after the equipment determination module identifies the specific type, the installation simulation module evaluates the eligibility of the simulated installation. When it is unqualified, the optimization adjustment module optimizes the historical image of the data storage module according to the matching ratio, thereby realizing the precise type identification of the electromechanical equipment and the efficient judgment of the eligibility of the simulated installation, and at the same time optimizing the data storage module according to the matching ratio, thereby improving the accuracy and efficiency of the installation of the electromechanical equipment.

[0023] Furthermore, the present invention selects a feature extraction method according to the texture complexity of the real-time electromechanical equipment image through a feature extraction module, and further determines the blur type of the image according to the edge feature value of the overall feature and the reference edge feature value of the historical image, and processes the real-time electromechanical equipment image according to the complexity of the image, thereby improving the accuracy and pertinence of feature extraction while ensuring processing efficiency, thereby improving the accuracy and efficiency of electromechanical equipment installation.

[0024] Furthermore, the present invention determines the precise type by comparing the shape feature values ​​of the real-time image and the historical image. Subsequently, the device determination module further determines the specific type by comparing the size feature values ​​based on the identification of similar images, and efficiently and accurately classifies and identifies the real-time image according to the image features, which not only improves the degree of automation of image processing, but also ensures the accuracy and reliability of the classification results.

[0025] Furthermore, the present invention simulates the installation of specific types of similar images and determines their eligibility based on the fit evaluation value. For unqualified cases, the optimization and adjustment module determines the optimization method of the data storage module based on the ratio of the fit evaluation value to the preset fit evaluation value, accurately evaluates the installation adaptability of the specific type of equipment, and adjusts the data storage module according to the ratio, optimizes the historical image library, and improves the accuracy of the simulated installation, thereby improving the accuracy and efficiency of the installation of electromechanical equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a schematic diagram of module connections of an intelligent electromechanical installation system according to an embodiment of the present invention;

[0027] Figure 2 A flow chart of a method for extracting features of a real-time image according to an embodiment of the present invention;

[0028] Figure 3 A flow chart for determining the precise type of a real-time image according to an embodiment of the present invention;

[0029] Figure 4 Flowchart for determining eligibility of a particular type of simulated installation for embodiments of the present invention. DETAILED DESCRIPTION

[0030] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0031] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0032] It should be pointed out that the data in this embodiment are obtained by comprehensive analysis and evaluation of the historical test data and the corresponding historical test results of the present invention in the three months before this test. It can be understood by those skilled in the art that the present invention can determine the above parameters for a single item by selecting the highest proportion of values ​​as the preset standard parameters according to the data distribution, using weighted summation to use the obtained values ​​as the preset standard parameters, substituting each historical data into a specific formula and using the values ​​obtained by the formula as the preset standard parameters or other selection methods, as long as the present invention can clearly define different specific situations in the single determination process through the obtained values.

[0033] See also Figure 1 - Figure 4 As shown, Figure 1 This is a schematic diagram of module connections of an intelligent electromechanical installation system according to an embodiment of the present invention; Figure 2 A flow chart of a method for extracting features of a real-time image according to an embodiment of the present invention; Figure 3 A flow chart for determining the precise type of a real-time image according to an embodiment of the present invention; Figure 4 Flowchart for determining eligibility of a particular type of simulated installation for embodiments of the present invention.

[0034] The electromechanical installation intelligent system according to the embodiment of the present invention comprises:

[0035] An image acquisition module, which is used to obtain real-time images of the electromechanical equipment to be installed;

[0036] A data storage module, which is connected to the image acquisition module and is used to store historical images of historical electromechanical equipment;

[0037] A feature extraction module, connected to the image acquisition module, for determining a feature extraction method of the real-time image based on a texture complexity evaluation value of the real-time image;

[0038] An image analysis module connected to the data storage module and the feature extraction module, for determining the blur type of the real-time image based on edge feature values ​​of the extracted overall features under the condition that the overall features of the real-time image are extracted, and for determining the precise type of the real-time image based on shape feature values ​​of the extracted detail features under the condition that the real-time image is a similar image;

[0039] A device determination module, which is connected to the feature extraction module and the image analysis module, and is used to determine the specific type according to the size feature value of the extracted detail feature based on the condition that the precise type of the real-time image is determined to be a similar image;

[0040] An installation simulation module connected to the equipment determination module is used to determine the eligibility of the specific type of simulated installation based on the fit evaluation value of the simulated installation under the condition of determining the specific type;

[0041] An optimization and adjustment module is connected to the data storage module and the installation simulation module, and is used to determine the optimization method of the data storage module based on the ratio of the fit evaluation value to the preset fit evaluation value under the condition that the specific type of simulated installation is unqualified.

[0042] In the embodiment of the present invention, the image acquisition module includes but is not limited to a camera, an industrial camera and / or an infrared camera.

[0043] Specifically, the present invention obtains real-time images of electromechanical equipment, determines the precise type through feature extraction and image analysis, and after the equipment determination module identifies the specific type, the installation simulation module evaluates the eligibility of the simulated installation. If it is unqualified, the optimization adjustment module optimizes the historical image of the data storage module according to the matching ratio, thereby realizing the precise type identification of the electromechanical equipment and the efficient judgment of the eligibility of the simulated installation. At the same time, the data storage module is optimized according to the matching ratio, which improves the accuracy and efficiency of the installation of the electromechanical equipment.

[0044] Specifically, the feature extraction module determines the feature extraction method of the real-time image according to the comparison result of the texture complexity value of the real-time image and the preset texture complexity value 50 under the condition of determining to obtain the real-time image of the electromechanical equipment to be installed;

[0045] If the texture complexity value is less than or equal to the preset texture complexity value, determining to extract the overall features of the real-time image;

[0046] If the texture complexity value is greater than the preset texture complexity value, it is determined to extract detail features of the real-time image.

[0047] In the embodiment of the present invention, the preset texture complexity value is 50, but the above value is not limited thereto, and those skilled in the art may also adjust the value according to actual needs.

[0048] In the embodiment of the present invention, the overall features include shape features and edge features, and the detail features include corner features and size features.

[0049] Specifically, the texture complexity value is the standard deviation of the grayscale value on the real-time image.

[0050] Specifically, the feature extraction module determines the blur type of the real-time image based on the comparison result of the edge feature value of the extracted overall feature and the reference edge feature value 70 of the overall feature of the historical image under the condition of determining to extract the overall feature of the real-time image;

[0051] If the edge feature value is less than or equal to the reference edge feature value, determining that the blur type of the real-time image is a simple electromechanical device image;

[0052] If the edge feature value is greater than the reference edge feature value, determining that the blur type of the real-time image is a complex electromechanical device image;

[0053] In the embodiment of the present invention, the reference edge feature value is 70, which is obtained by taking the average value of edge feature values ​​of several historical images. However, the above value is not limited to this, and technicians in this field can also adjust the value according to actual needs.

[0054] Specifically, the edge feature value is an average value of the variation of the pixel grayscale value of the real-time image in the horizontal and vertical directions.

[0055] Specifically, the present invention selects a feature extraction method according to the texture complexity of the real-time electromechanical equipment image through a feature extraction module, and further determines the blur type of the image according to the edge feature value of the overall feature and the reference edge feature value of the historical image, and processes the real-time electromechanical equipment image according to the complexity of the image, thereby improving the accuracy and pertinence of feature extraction while ensuring processing efficiency, thereby improving the accuracy and efficiency of electromechanical equipment installation.

[0056] Specifically, the image analysis module determines the precise type of the real-time image based on a comparison result of the shape feature value of the extracted detail feature with a reference shape feature value of 0.85 of the detail feature of the historical image under the condition of determining the fuzzy type of the real-time image;

[0057] If the shape feature value is less than or equal to the reference shape feature value, determining that the precise type of the real-time image is a difference image;

[0058] If the shape feature value is greater than the reference shape feature value, it is determined that the precise type of the real-time image is a similar image.

[0059] In the embodiment of the present invention, the reference shape feature value is 0.85, and the reference shape feature value is obtained by taking the average value of shape feature values ​​of several historical images, but the above value is not limited to this. Those skilled in the art can also adjust the value according to actual needs.

[0060] Specifically, the shape feature value is a ratio of the distance between a point on the outer contour of the real-time image and the most adjacent point to the average distance.

[0061] Specifically, the device determination module determines the specific type of the similar image based on a comparison result of the size feature value of the extracted detail feature with a reference size feature value of 0.9 of the detail feature of the historical image under the condition that the real-time image is determined to be a similar image;

[0062] If the size characteristic value is less than or equal to the reference size characteristic value, determining that the specific type of the similar image is a compact electromechanical device image;

[0063] If the size characteristic value is greater than the reference size characteristic value, it is determined that the specific type of the similar image is a standard electromechanical equipment image.

[0064] In the embodiment of the present invention, the reference size characteristic value is 0.9, and the reference size characteristic value is obtained by taking the average value of the size characteristic values ​​of several historical images, but the above value is not limited to this, and those skilled in the art can also adjust the value according to actual needs.

[0065] Specifically, the size characteristic value is obtained by constructing a feature area with a preset radius of 0.5 cm with each detail feature point extracted from the real-time image as the center and obtaining the size characteristic value by the average distance of all feature points in the feature area.

[0066] Specifically, the present invention determines the precise type by comparing the shape feature values ​​of the real-time image and the historical image. Subsequently, the device determination module further determines the specific type by comparing the size feature values ​​based on the identification of similar images. The real-time image is efficiently and accurately classified and identified according to the image features, which not only improves the degree of automation of image processing, but also ensures the accuracy and reliability of the classification results.

[0067] Specifically, the installation simulation module performs simulated installation on the specific type under the condition of determining the specific type of the similar image, and determines the eligibility of the simulated installation of the specific type according to the comparison result of the fit evaluation value of the simulated installation and the preset fit evaluation value of 0.98;

[0068] If the fit evaluation value is less than or equal to the preset fit evaluation value, it is determined that the specific type of simulated installation is unqualified;

[0069] If the fit evaluation value is greater than the preset fit evaluation value, it is determined that the specific type of simulated installation is qualified;

[0070] In the embodiment of the present invention, the preset matching evaluation value is 0.98, but the above value is not limited thereto, and those skilled in the art may also adjust the value according to actual needs.

[0071] Specifically, the fit evaluation value is the ratio of the preset maximum size deviation to the actual size deviation multiplied by the ratio of the preset maximum position deviation to the actual position deviation.

[0072] Specifically, the adjustment module determines the optimization mode of the data storage module according to the comparison result of the ratio of the fit evaluation value to the preset fit evaluation value and the preset ratio 0.75 under the condition that the specific type of simulated installation is determined to be unqualified;

[0073] If the ratio is less than or equal to the preset ratio, determining to update the historical image of the data storage module at a preset update frequency;

[0074] If the ratio is greater than the preset ratio, determining to add other databases storing historical images to the data storage module;

[0075] The ratio is the ratio of the fit evaluation value to a preset fit evaluation value.

[0076] In the embodiment of the present invention, the preset ratio is 0.75, but the above value is not limited thereto, and those skilled in the art may also adjust the value according to actual needs.

[0077] In the embodiment of the present invention, the ratio of the preset update frequency coordination evaluation value to the preset coordination evaluation value and the difference between the preset ratio are determined, and the value range is 1.05-1.10, preferably 1.07.

[0078] Specifically, the present invention simulates the installation of specific types of similar images and determines their eligibility based on the fit evaluation value. For unqualified cases, the optimization and adjustment module determines the optimization method of the data storage module based on the ratio of the fit evaluation value to the preset fit evaluation value, accurately evaluates the installation adaptability of the specific type of equipment, and adjusts the data storage module according to the ratio, optimizes the historical image library, and improves the accuracy of the simulated installation, thereby improving the accuracy and efficiency of the installation of electromechanical equipment.

[0079] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent electromechanical installation system, characterized in that: include: An image acquisition module, which is used to obtain real-time images of the electromechanical equipment to be installed; A data storage module, which is connected to the image acquisition module and is used to store historical images of historical electromechanical equipment; A feature extraction module, connected to the image acquisition module, for determining a feature extraction method of the real-time image based on the texture complexity evaluation value of the real-time image, wherein the feature extraction method includes extracting the overall feature of the real-time image or extracting the detail feature of the real-time image; An image analysis module connected to the data storage module and the feature extraction module, for determining the blur type of the real-time image based on edge feature values ​​of the extracted overall features under the condition that the overall features of the real-time image are extracted, and for determining the precise type of the real-time image based on shape feature values ​​of the extracted detail features under the condition that the real-time image is a similar image; A device determination module, which is connected to the feature extraction module and the image analysis module, and is used to determine the specific type according to the size feature value of the extracted detail feature based on the condition that the precise type of the real-time image is determined to be a similar image; An installation simulation module connected to the equipment determination module is used to determine the eligibility of a specific type of simulated installation based on the condition of determining the specific type and the evaluation value of the degree of fit of the simulated installation; An optimization and adjustment module is connected to the data storage module and the installation simulation module, and is used to determine the optimization method of the data storage module based on the ratio of the fit evaluation value to the preset fit evaluation value under the condition that a specific type of simulated installation is unqualified, wherein the optimization method includes updating the historical image of the data storage module or updating and adding the historical image of the data storage module.

2. The electromechanical installation intelligent system according to claim 1, characterized in that: The feature extraction module determines to extract the overall features of the real-time image based on the comparison result that the texture complexity value of the real-time image is less than or equal to a preset texture complexity value under the condition of determining to obtain the real-time image of the electromechanical equipment to be installed, wherein the overall features include shape features and edge features.

3. The electromechanical installation intelligent system according to claim 1, characterized in that: The feature extraction module determines to extract detail features of the real-time image based on a comparison result that a texture complexity value of the real-time image is greater than a preset texture complexity value under the condition of obtaining a real-time image of the electromechanical equipment to be installed, wherein the detail features include corner features and size features.

4. The electromechanical installation intelligent system according to claim 2, characterized in that: The image analysis module determines that the blur type of the real-time image is a simple electromechanical equipment image based on a comparison result that the edge feature value of the overall feature is less than or equal to the reference edge feature value of the overall feature of the historical image under the condition of extracting the overall feature of the real-time image.

5. The electromechanical installation intelligent system according to claim 2, characterized in that: The image analysis module determines that the blur type of the real-time image is a complex electromechanical equipment image based on a comparison result that the edge feature value of the overall feature is greater than the reference edge feature value of the overall feature of the historical image under the condition of extracting the overall feature of the real-time image.

6. The electromechanical installation intelligent system according to claim 3, characterized in that: The image analysis module determines the precise type of the real-time image as a similar image based on a comparison result that the shape feature value of the detail feature is greater than the reference shape feature value of the detail feature of the historical image, under the condition of determining the fuzzy type of the real-time image.

7. The electromechanical installation intelligent system according to claim 3, characterized in that: The device determination module determines that the specific type of the similar image is a compact electromechanical device image based on the fact that the size feature value of the detail feature is less than or equal to the reference size feature value of the detail feature of the historical image, under the condition that the real-time image is determined to be a similar image.

8. The electromechanical installation intelligent system according to claim 7, characterized in that: The device determination module determines that the specific type of the similar image is a standard electromechanical device image based on the fact that the size feature value of the detail feature is greater than the reference size feature value of the detail feature of the historical image, under the condition that the real-time image is determined to be a similar image.

9. The electromechanical installation intelligent system according to claim 8, characterized in that: The installation simulation module determines that the specific type of simulated installation is unqualified based on the comparison result that the fit evaluation value of the simulated installation is less than or equal to the preset fit evaluation value under the condition of determining the specific type of the similar image, and determines to update the historical image of the data storage module at a preset update frequency based on the comparison result that the ratio of the fit evaluation value to the preset fit evaluation value is less than or equal to the preset ratio.

10. The electromechanical installation intelligent system according to claim 8, characterized in that: Under the condition of determining the specific type of the similar image, the installation simulation module determines that the specific type of simulated installation is unqualified based on the comparison result that the fit evaluation value of the simulated installation is less than or equal to the preset fit evaluation value, and determines to add other databases storing historical images to the data storage module based on the comparison result that the ratio of the fit evaluation value to the preset fit evaluation value is greater than the preset ratio.

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