CN111275701A - Content analysis system using multi-cloud-end nodes - Google Patents
Content analysis system using multi-cloud-end nodes Download PDFInfo
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Abstract
The present invention relates to a content analysis system using a multi-cloud-end node, including: the first correction network element is arranged at the cloud end and used for performing distortion processing on the received field welding image so as to obtain and output a corresponding first correction image; the second correction network element is arranged at the cloud end and used for executing background blurring processing on the received first correction image so as to obtain and output a corresponding second correction image; the third correction network element is arranged at the cloud end and used for performing directional filtering processing on the received second correction image so as to obtain and output a corresponding third correction image; and the content identification equipment is used for sending out an overhigh welding seam surface signal or a reliable welding seam surface signal. The content analysis system utilizing the multi-cloud-end nodes is reliable in design and stable in operation. Because the state that whether the surface of the welding seam at the welding spot is lower than the surface of the pipeline can be identified by adopting an electronic auxiliary mechanism, the objective analysis of the quality of each welding spot is realized.
Description
Technical Field
The invention relates to the field of cloud processing, in particular to a content analysis system utilizing multiple cloud nodes.
Background
Welding, also known as fusion, welding, is a manufacturing process and technique for joining metals or other thermoplastic materials, such as plastics, in a heated, high temperature or high pressure manner.
The energy sources for modern welding are many, including gas flame, electric arc, laser, electron beam, friction, and ultrasonic, among others. In addition to use in a factory, welding can be performed in a variety of environments, such as the field, underwater, and space. Wherever welding can be dangerous for the operator, appropriate precautions must be taken while welding is being performed. The possible injuries to human body caused by welding include burn, electric shock, visual impairment, toxic gas inhalation, over-irradiation of ultraviolet rays, etc.
In the prior art, the application field of cloud computing does not include on-line analysis of welding quality, so that various advantages of cloud computing cannot be fully exerted.
Disclosure of Invention
The invention has at least the following two important points:
(1) the welding spot quality analysis is carried out on each welding spot by using the characteristic that the welding spot quality requires that the surface of the welding spot is not lower than the surface of the pipeline, so that the manual detection fuzziness is replaced, and the objectivity of the detection result is ensured;
(2) and a remote processing mode of a multi-element end node is introduced, so that the data processing efficiency is improved, and the leakage of key processing data is avoided.
According to an aspect of the present invention, there is provided a content analysis system using a multi-cloud node, the system including:
the embedded camera is arranged at the front end of the portable terminal and used for carrying out field image data acquisition on a welding point in front of the portable terminal under the condition that a worker holds the portable terminal by hand so as to obtain a corresponding field welding image;
the first correction network element is arranged at the cloud end, is connected with the embedded camera through a network, and is used for performing distortion processing on the received field welding image so as to obtain and output a corresponding first correction image;
the second correction network element is arranged at the cloud end, is connected with the first correction network element through a network, and is used for executing background blurring processing on the received first correction image so as to obtain and output a corresponding second correction image;
the third correction network element is arranged at the cloud end, is connected with the second correction network element through a network, and is used for executing directional filtering processing on the received second correction image so as to obtain and output a corresponding third correction image;
the first detection device is positioned in the portable terminal and used for receiving the third correction image through a network, identifying each pipeline pixel point forming a pipeline imaging area from the third correction image based on the color imaging characteristics of the welded pipeline, and acquiring the imaging depth of field of each pipeline pixel point in the third correction image;
the second detection device is positioned near the first detection device and used for identifying each welding line pixel point forming a welding line imaging area from the third correction image based on the color imaging characteristics of the welding line and acquiring the imaging depth of each welding line pixel point in the third correction image;
the content identification equipment is positioned in the portable terminal, is respectively connected with the first detection equipment and the second detection equipment, and is used for sending a welding seam surface overhigh signal when the number of welding seam pixel points with imaging depth of field smaller than the imaging depth of field mean value of each pipeline pixel point in the third correction image is more than or equal to a preset number threshold value in the third correction image;
the content identification equipment is further used for sending out a reliable signal of the surface of the welding seam when the number of the welding seam pixel points with the imaging depth of field smaller than the imaging depth of field mean value of each pipeline pixel point in the third corrected image is smaller than the preset number threshold;
the first correcting network element, the second correcting network element and the second correcting network element are located in the same network topology structure of a cloud.
The content analysis system utilizing the multi-cloud-end nodes is reliable in design and stable in operation. Because the state that whether the surface of the welding seam at the welding spot is lower than the surface of the pipeline can be identified by adopting an electronic auxiliary mechanism, the objective analysis of the quality of each welding spot is realized.
Drawings
Embodiments of the invention will now be described with reference to the accompanying drawings, in which:
fig. 1 is a schematic diagram of components of a content analysis system using a multi-cloud node according to the present invention.
Fig. 2 is a block diagram showing a configuration of a content analysis system using a multi-cloud-end node according to a first example of the embodiment of the present invention.
Fig. 3 is a block diagram showing a configuration of a content analysis system using a multi-cloud-end node according to a second example of the embodiment of the present invention.
Detailed Description
Embodiments of a content analysis system using a multi-cloud node according to the present invention will be described in detail with reference to the accompanying drawings.
At present, the welding quality of superior solder joint requires that the welding seam surface must not be less than by welded pipeline surface, and the welding quality's of solder joint detection mainly relies on the manual mode to go on, and when being welded mechanism is bulky, because the solder joint is numerous, it is obviously unrealistic to adopt the manual mode to detect one by one, needs an electronization's alternative to detect in order to accomplish the welding quality of welding point everywhere.
In order to overcome the defects, the invention builds a content analysis system using multiple cloud end nodes, and can effectively solve the corresponding technical problem.
Fig. 1 is a schematic diagram of components of a content analysis system using a multi-cloud node of the present invention, and the technical contents of the present invention will be further embodied and explained from different perspectives using a plurality of different embodiments.
< first embodiment >
Fig. 2 is a block diagram showing a configuration of a content analysis system using a multi-cloud-end node according to a first embodiment of the present invention, the system including:
the embedded camera is arranged at the front end of the portable terminal and used for carrying out field image data acquisition on a welding point in front of the portable terminal under the condition that a worker holds the portable terminal by hand so as to obtain a corresponding field welding image;
the first correction network element is arranged at the cloud end, is connected with the embedded camera through a network, and is used for performing distortion processing on the received field welding image so as to obtain and output a corresponding first correction image;
the second correction network element is arranged at the cloud end, is connected with the first correction network element through a network, and is used for executing background blurring processing on the received first correction image so as to obtain and output a corresponding second correction image;
the third correction network element is arranged at the cloud end, is connected with the second correction network element through a network, and is used for executing directional filtering processing on the received second correction image so as to obtain and output a corresponding third correction image;
the first detection device is positioned in the portable terminal and used for receiving the third correction image through a network, identifying each pipeline pixel point forming a pipeline imaging area from the third correction image based on the color imaging characteristics of the welded pipeline, and acquiring the imaging depth of field of each pipeline pixel point in the third correction image;
the second detection device is positioned near the first detection device and used for identifying each welding line pixel point forming a welding line imaging area from the third correction image based on the color imaging characteristics of the welding line and acquiring the imaging depth of each welding line pixel point in the third correction image;
the content identification equipment is positioned in the portable terminal, is respectively connected with the first detection equipment and the second detection equipment, and is used for sending a welding seam surface overhigh signal when the number of welding seam pixel points with imaging depth of field smaller than the imaging depth of field mean value of each pipeline pixel point in the third correction image is more than or equal to a preset number threshold value in the third correction image;
the content identification equipment is further used for sending out a reliable signal of the surface of the welding seam when the number of the welding seam pixel points with the imaging depth of field smaller than the imaging depth of field mean value of each pipeline pixel point in the third corrected image is smaller than the preset number threshold;
the first correcting network element, the second correcting network element and the second correcting network element are located in the same network topology structure of a cloud.
< second embodiment >
Fig. 3 is a block diagram showing a configuration of a content analysis system using a multi-cloud-end node according to a second embodiment of the present invention, the system including:
the load detection equipment is used for receiving the utilization rate of the current core of the second detection equipment, and the utilization rate is a percentage;
the embedded camera is arranged at the front end of the portable terminal and used for carrying out field image data acquisition on a welding point in front of the portable terminal under the condition that a worker holds the portable terminal by hand so as to obtain a corresponding field welding image;
the first correction network element is arranged at the cloud end, is connected with the embedded camera through a network, and is used for performing distortion processing on the received field welding image so as to obtain and output a corresponding first correction image;
the second correction network element is arranged at the cloud end, is connected with the first correction network element through a network, and is used for executing background blurring processing on the received first correction image so as to obtain and output a corresponding second correction image;
the third correction network element is arranged at the cloud end, is connected with the second correction network element through a network, and is used for executing directional filtering processing on the received second correction image so as to obtain and output a corresponding third correction image;
the first detection device is positioned in the portable terminal and used for receiving the third correction image through a network, identifying each pipeline pixel point forming a pipeline imaging area from the third correction image based on the color imaging characteristics of the welded pipeline, and acquiring the imaging depth of field of each pipeline pixel point in the third correction image;
the second detection device is positioned near the first detection device and used for identifying each welding line pixel point forming a welding line imaging area from the third correction image based on the color imaging characteristics of the welding line and acquiring the imaging depth of each welding line pixel point in the third correction image;
the content identification equipment is positioned in the portable terminal, is respectively connected with the first detection equipment and the second detection equipment, and is used for sending a welding seam surface overhigh signal when the number of welding seam pixel points with imaging depth of field smaller than the imaging depth of field mean value of each pipeline pixel point in the third correction image is more than or equal to a preset number threshold value in the third correction image;
the content identification equipment is further used for sending out a reliable signal of the surface of the welding seam when the number of the welding seam pixel points with the imaging depth of field smaller than the imaging depth of field mean value of each pipeline pixel point in the third corrected image is smaller than the preset number threshold;
the first correcting network element, the second correcting network element and the second correcting network element are located in the same network topology structure of a cloud;
wherein the first detection device and the second detection device share the same quartz oscillator to provide different reference clock signals for the first detection device and the second detection device, respectively.
Next, a detailed structure of the content analysis system using a multi-cloud-end node according to the present invention will be described further.
In the content analysis system using a multi-cloud-end node: the color imaging characteristic of the welded pipeline is the numerical distribution range of each color component of the imaging pixel points of the welded pipeline.
In the content analysis system using a multi-cloud-end node, further comprising: each of the color components is a red-green color component, a black-and-white color component, or a yellow-and-blue color component in an LAB color space.
In the content analysis system using a multi-cloud-end node, further comprising: and the data identification equipment is connected with the load detection equipment and is used for sending a load overfill signal when the utilization rate exceeds the limit.
In the content analysis system using a multi-cloud-end node: the data discrimination apparatus is further arranged to signal a sufficient load when the received utilization is not exceeded.
In the content analysis system using a multi-cloud-end node, further comprising: and the DSP chip is connected with the data identification equipment and is used for entering a working mode from a sleep mode when receiving a load overfill signal.
In the content analysis system using a multi-cloud-end node: and the DSP chip is also used for bearing part of tasks of the second detection equipment when entering the working mode.
In the content analysis system using a multi-cloud-end node: the DSP chip is also used for refusing to undertake part of tasks of the second detection equipment when entering the sleep mode.
In the content analysis system using a multi-cloud-end node, further comprising: and the WIFI communication equipment is connected with the second detection equipment and used for receiving the output data of the second detection equipment and wirelessly transmitting the output data.
In addition, the DSP chip adopts a Harvard structure with separated programs and data, is provided with a special hardware multiplier, widely adopts pipeline operation, provides special DSP instructions and can be used for quickly realizing various digital signal processing algorithms.
According to the requirements of digital signal processing, a DSP chip generally has some main features as follows: (1) one multiply and one add may be done in one instruction cycle. (2) The program and data spaces are separate and instructions and data may be accessed simultaneously. (3) On-chip with fast RAM, it is usually accessible in two blocks simultaneously via separate data buses. (4) Hardware support with low or no overhead loops and jumps. (5) Fast interrupt handling and hardware I/O support. (6) There are multiple hardware address generators operating in a single cycle. (7) Multiple operations may be performed in parallel. (8) And pipeline operation is supported, so that the operations of fetching, decoding, executing and the like can be executed in an overlapping way.
The floating-point formats used by different floating-point DSP chips are not identical, some DSP chips use a custom floating-point format, such as TMS320C3X, and some DSP chips use a standard floating-point format of IEEE, such as MC96002 from Motorola, MB86232 from FUJITSU, and ZR35325 from ZORAN.
It is to be understood that while the present invention has been described in conjunction with the preferred embodiments thereof, it is not intended to limit the invention to those embodiments. It will be apparent to those skilled in the art from this disclosure that many changes and modifications can be made, or equivalents modified, in the embodiments of the invention without departing from the scope of the invention. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the scope of the protection of the technical solution of the present invention, unless the contents of the technical solution of the present invention are departed.
Claims (10)
1. A content analysis system using a multi-cloud-end node, comprising:
the embedded camera is arranged at the front end of the portable terminal and used for carrying out field image data acquisition on a welding point in front of the portable terminal under the condition that a worker holds the portable terminal by hand so as to obtain a corresponding field welding image;
the first correction network element is arranged at the cloud end, is connected with the embedded camera through a network, and is used for performing distortion processing on the received field welding image so as to obtain and output a corresponding first correction image;
the second correction network element is arranged at the cloud end, is connected with the first correction network element through a network, and is used for executing background blurring processing on the received first correction image so as to obtain and output a corresponding second correction image;
the third correction network element is arranged at the cloud end, is connected with the second correction network element through a network, and is used for executing directional filtering processing on the received second correction image so as to obtain and output a corresponding third correction image;
the first detection device is positioned in the portable terminal and used for receiving the third correction image through a network, identifying each pipeline pixel point forming a pipeline imaging area from the third correction image based on the color imaging characteristics of the welded pipeline, and acquiring the imaging depth of field of each pipeline pixel point in the third correction image;
the second detection device is positioned near the first detection device and used for identifying each welding line pixel point forming a welding line imaging area from the third correction image based on the color imaging characteristics of the welding line and acquiring the imaging depth of each welding line pixel point in the third correction image;
the content identification equipment is positioned in the portable terminal, is respectively connected with the first detection equipment and the second detection equipment, and is used for sending a welding seam surface overhigh signal when the number of welding seam pixel points with imaging depth of field smaller than the imaging depth of field mean value of each pipeline pixel point in the third correction image is more than or equal to a preset number threshold value in the third correction image;
the content identification equipment is further used for sending out a reliable signal of the surface of the welding seam when the number of the welding seam pixel points with the imaging depth of field smaller than the imaging depth of field mean value of each pipeline pixel point in the third corrected image is smaller than the preset number threshold;
the first correcting network element, the second correcting network element and the second correcting network element are located in the same network topology structure of a cloud.
2. The content analysis system using multi-cloud nodes of claim 1, wherein:
the color imaging characteristic of the welded pipeline is the numerical distribution range of each color component of the imaging pixel points of the welded pipeline.
3. The content analysis system using multi-cloud-end nodes according to claim 2, wherein:
each of the color components is a red-green color component, a black-and-white color component, or a yellow-and-blue color component in an LAB color space.
4. The content analysis system utilizing multiple cloud nodes as claimed in claim 3, wherein said system further comprises:
the load detection equipment is used for receiving the utilization rate of the current core of the second detection equipment, and the utilization rate is a percentage;
wherein the first detection device and the second detection device share the same quartz oscillator to provide different reference clock signals for the first detection device and the second detection device, respectively.
5. The content analysis system utilizing multiple cloud nodes as claimed in claim 4, wherein said system further comprises:
and the data identification equipment is connected with the load detection equipment and is used for sending a load overfill signal when the utilization rate exceeds the limit.
6. The content analysis system using multi-cloud nodes of claim 5, wherein:
the data discrimination apparatus is further arranged to signal a sufficient load when the received utilization is not exceeded.
7. The content analysis system utilizing multiple cloud nodes as claimed in claim 6, wherein said system further comprises:
and the DSP chip is connected with the data identification equipment and is used for entering a working mode from a sleep mode when receiving a load overfill signal.
8. The content analysis system using multi-cloud nodes of claim 7, wherein:
and the DSP chip is also used for bearing part of tasks of the second detection equipment when entering the working mode.
9. The content analysis system using multi-cloud nodes of claim 8, wherein:
the DSP chip is also used for refusing to undertake part of tasks of the second detection equipment when entering the sleep mode.
10. The content analysis system utilizing multiple cloud nodes of claim 9, wherein said system further comprises:
and the WIFI communication equipment is connected with the second detection equipment and used for receiving the output data of the second detection equipment and wirelessly transmitting the output data.
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