阅读说明：本技术 基于多模态图像的无损检测装置及其检测方法 (Multi-mode image-based nondestructive testing device and testing method thereof ) 是由 高显亮 戴铮 危荃 王飞 周鹏飞 金翠娥 王文强 郑雪鹏 周建平 吴振成 于 2021-08-23 设计创作，主要内容包括：本发明提供了一种基于多模态图像的无损检测装置及其检测方法,包括：X射线机在开启时放射X射线,X射线穿透被检测物体照射到辐射探测器表面形成射线图像,射线图像反映照射到探测器表面不同区域的辐射强弱,得到被检测物体内部和表面的结构信息；入射侧相机置于X射线机照射被检测物体的入射侧,入射侧相机在入射侧对被检测物体表面进行拍摄,得到物体表面图像,记录射线入射侧被检测物体表面的结构信息；出射侧相机置于X射线出射侧,出射侧相机在出射侧对被检测物体表面进行拍摄,得到物体表面图像,记录射线出射侧被检测物体表面的结构信息。(The invention provides a nondestructive testing device based on multi-mode images and a testing method thereof, wherein the testing method comprises the following steps: the X-ray machine radiates X-rays when being started, the X-rays penetrate through a detected object and irradiate the surface of the radiation detector to form a radiation image, the radiation image reflects the radiation intensity irradiating different areas of the surface of the detector, and the structure information of the inner part and the surface of the detected object is obtained; the incident side camera is arranged at the incident side of the X-ray machine irradiating the detected object, the incident side camera shoots the surface of the detected object at the incident side to obtain an object surface image, and the structural information of the surface of the detected object at the ray incident side is recorded; the emergent side camera is arranged on the X-ray emergent side, shoots the surface of the detected object on the emergent side to obtain an object surface image, and records the structure information of the surface of the detected object on the ray emergent side.)

1. A multi-modal image-based nondestructive inspection apparatus, comprising: the X-ray detection device comprises an X-ray machine (1), a radiation detector (2), an incidence side camera (3), an emergence side camera (4) and a detected object (5);

the X-ray machine (1) radiates X-rays when being started, the X-rays penetrate through the detected object (5) and irradiate the surface of the radiation detector (2) to form a radiation image, the radiation image reflects the intensity of radiation irradiating different areas of the surface of the detector, and the structure information of the inner part and the surface of the detected object is obtained;

the incident side camera (3) is arranged on the incident side of the X-ray machine irradiating the detected object (5), the incident side camera (3) shoots the surface of the detected object on the incident side to obtain an object surface image, and the structural information of the surface of the detected object on the ray incident side is recorded;

the outgoing side camera (4) is arranged on the outgoing side of the X-ray, the outgoing side camera (4) shoots the surface of the detected object on the outgoing side to obtain an object surface image, and the structural information of the surface of the detected object on the outgoing side of the X-ray is recorded.

2. The nondestructive testing device based on the multi-modal images as recited in claim 1, wherein the light emitted from the light source is reflected by the surface of the tested object and then irradiated into the camera to form an object surface image;

the light emitted by the light source comprises uniform visible light or structured light with spatial distribution characteristics.

3. The apparatus according to claim 1, wherein the object surface image is obtained by a camera that captures an image formed by reflecting visible light on the object surface or by other imaging methods.

4. The apparatus according to claim 1, wherein the radiographic image is a projection image obtained by projecting X-rays or other radiation rays onto the radiation detector after passing through the object, and converting the projection image into a projection image containing the surface and internal structural features of the object.

5. A multi-modal image-based nondestructive inspection method, characterized in that the multi-modal image-based nondestructive inspection apparatus according to any one of claims 1 to 4 is used to execute the following steps:

step S1: synchronously acquiring a surface image and a radiographic image of the detected object;

step S2: identifying the ray image by using computer image identification software to obtain an image with defect characteristics, and positioning and marking the defect characteristics;

step S3: performing feature recognition on the surface image at the position corresponding to the defect feature of the object on the radiographic image with the defect feature by using computer image recognition software, and marking and positioning the surface defect feature;

step S4: carrying out position registration on the marked and positioned surface image and the positioned radiographic image, and establishing a corresponding relation of plane coordinate positions of the two images;

step S5: and comparing the positions of the ray image defect identification positioning result and the surface image feature identification result to determine whether the defect features belong to the internal defects.

6. The multi-modality image-based nondestructive inspection method according to claim 5, wherein said step S1 employs: when any position of the detected object is irradiated by X-rays and imaged by a radiation detector, the surface images of the same position are respectively shot by a camera on an X-ray incidence surface and an X-ray emergence surface, the surface area shot by the camera has an inclusion relation with the X-ray incidence surface and the X-ray emergence surface, and an effective detection area is determined according to the image with a smaller area.

7. The multi-modality image-based nondestructive inspection method according to claim 5, wherein said step S2 employs:

step S2.1: preprocessing the ray image to obtain a preprocessed ray image;

step S2.2: performing defect feature identification on the preprocessed radiation image by adopting an image identification algorithm;

step S2.3: and classifying, positioning and marking the identified defect characteristics.

8. The method for nondestructive testing based on multi-modal images as set forth in claim 7, wherein said step S2.1 employs: performing image noise reduction and contrast enhancement processing on the ray image to obtain a preprocessed ray image;

the step S2.2 adopts: and the preprocessed radiographic image is subjected to defect feature recognition by adopting an image recognition algorithm based on image morphology or an image recognition algorithm based on deep learning.

9. The multi-modality image-based nondestructive inspection method according to claim 5, wherein said step S4 employs: and establishing the position corresponding relation between the radiographic image and the surface image of the same position of the object through a space coordinate correction registration and deep learning algorithm, and realizing the matching of corresponding features in the images and the one-to-one corresponding relation.

10. The multi-modality image-based nondestructive inspection method according to claim 5, wherein said step S5 employs: when the defect characteristics are identified in the surface images at the corresponding positions of the radiation images, after the registration of the two images, when the defect characteristics of the radiation images and the defect characteristics of the surface images are at the same corresponding positions, the current defect is judged to be the surface defect of the object; and when the defect characteristics are not identified in the surface image at the corresponding position of the radiographic image, judging that the current defect is an internal defect of the object.

Technical Field

The invention relates to the technical field of nondestructive testing, in particular to a nondestructive testing device based on multi-mode images and a method thereof, and more particularly to a testing method simultaneously having ray testing, surface testing and image recognition technologies.

Background

The ray detection technology is a nondestructive detection technology for detecting internal defects of an object by utilizing the attenuation rule of X-rays in the object and the interaction between the X-rays and an imaging medium. The ray detection technology has the advantages of visual detection result, high detection reliability and the like, and is widely applied to the internal quality detection of key products in the fields of aviation, aerospace, special equipment and the like. The traditional ray detection technology utilizes a film to image, the detection result is recorded by a film analog image, along with the development of an electronic information technology, a digital ray detection technology adopting a radiation detector to image is widely applied, meanwhile, along with the development of an artificial intelligence technology, the research of a digital ray detection image defect identification technology based on a computer image identification algorithm is widely developed at home and abroad at present, the traditional defect identification algorithm only identifies the internal defects of an object according to a ray image, because the ray image is the two-dimensional projection of the object on the plane of the detector along the ray direction, the ray image simultaneously comprises the surface structure information and the internal structure information of the object, and when the identification algorithm is adopted to identify the internal defects of the object, the identification result is easily influenced by the surface structure characteristics, the invention discloses a nondestructive detection method based on a multi-mode image, according to the method, the surface image and the ray detection image of the object are synchronously acquired, the surface image and the ray image are simultaneously subjected to feature recognition by adopting a computer image recognition algorithm, and finally the recognition results of the two images are comprehensively read, so that the influence of the surface features on the internal defect recognition can be effectively eliminated, and the internal defect recognition misjudgment rate is greatly reduced.

Patent document CN112529899A (application number: CN202011584416.4) discloses a nondestructive testing method for a solid rocket engine based on machine learning and computer vision, which divides an input image into a plurality of local images, performs focused learning on a focused part, facilitates subsequent extraction of key features, and makes a preliminary judgment. The invention solves the problems of low manual identification rate, dispersed image data, low data utilization rate and the like. The high-efficiency and quick identification of the nondestructive testing image of the engine is realized. Compared with the method, the method and the device have the advantages that the object ray detection image is identified, meanwhile, the surface structure characteristics of the object are obtained through the shooting of the surface camera, the surface image is identified, the influence of the surface structure of the object on the ray image identification can be eliminated, and the identification accuracy and reliability are higher.

Disclosure of Invention

Aiming at the defects in the prior art, the invention aims to provide a nondestructive testing device based on multi-mode images and a testing method thereof.

The invention provides a nondestructive testing device based on multi-mode images, which comprises: the X-ray machine 1, the radiation detector 2, the incident side camera 3, the emergent side camera 4 and the detected object 5;

the X-ray machine 1 radiates X-rays when being started, the X-rays penetrate through the detected object 5 and irradiate the surface of the radiation detector 2 to form a radiation image, the radiation image reflects the radiation intensity irradiating different areas of the surface of the detector, and the structure information of the inner part and the surface of the detected object is obtained;

the incident side camera 3 is arranged at the incident side of the X-ray machine irradiating the detected object 5, the incident side camera 3 shoots the surface of the detected object at the incident side to obtain an object surface image, and the structure information of the surface of the detected object at the ray incident side is recorded;

the emergent side camera 4 is arranged on the X-ray emergent side, the emergent side camera 4 shoots the surface of the detected object on the emergent side to obtain an object surface image, and the structural information of the surface of the detected object on the ray emergent side is recorded.

Preferably, light emitted by the light source is reflected by the surface of the detected object and then is irradiated into the camera to form an object surface image;

the light emitted by the light source comprises uniform visible light or structured light with spatial distribution characteristics.

Preferably, the object surface image is obtained by taking an image formed by reflecting visible light on the object surface through a camera or by other imaging methods, wherein the image can reflect the surface structure of the object.

Preferably, the radiographic image is a projected image including X-rays or other radiation rays projected through the object onto the radiation detector, and transformed by the radiation detector to form a projection image containing the surface and internal structural features of the object.

According to the nondestructive testing method based on the multi-modal images, the nondestructive testing device based on the multi-modal images is used for executing the following steps:

step S1: synchronously acquiring a surface image and a radiographic image of the detected object;

step S2: identifying the ray image by using computer image identification software to obtain an image with defect characteristics, and positioning and marking the defect characteristics;

step S3: performing feature recognition on the surface image at the position corresponding to the defect feature of the object on the radiographic image with the defect feature by using computer image recognition software, and marking and positioning the surface defect feature;

step S4: carrying out position registration on the marked and positioned surface image and the positioned radiographic image, and establishing a corresponding relation of plane coordinate positions of the two images;

step S5: and comparing the positions of the ray image defect identification positioning result and the surface image feature identification result to determine whether the defect features belong to the internal defects.

Preferably, the step S1 adopts: when any position of the detected object is irradiated by X-rays and imaged by a radiation detector, the surface images of the same position are respectively shot by a camera on an X-ray incidence surface and an X-ray emergence surface, the surface area shot by the camera has an inclusion relation with the X-ray incidence surface and the X-ray emergence surface, and an effective detection area is determined according to the image with a smaller area.

Preferably, the step S2 adopts:

step S2.1: preprocessing the ray image to obtain a preprocessed ray image;

step S2.2: performing defect feature identification on the preprocessed radiation image by adopting an image identification algorithm;

step S2.3: and classifying, positioning and marking the identified defect characteristics.

Preferably, said step S2.1 employs: performing image noise reduction and contrast enhancement processing on the ray image to obtain a preprocessed ray image;

the step S2.2 adopts: and the preprocessed radiographic image is subjected to defect feature recognition by adopting an image recognition algorithm based on image morphology or an image recognition algorithm based on deep learning.

Preferably, the step S4 adopts: and establishing the position corresponding relation between the radiographic image and the surface image of the same position of the object through a space coordinate correction registration and deep learning algorithm, and realizing the matching of corresponding features in the images and the one-to-one corresponding relation.

Preferably, the step S5 adopts: when the defect characteristics are identified in the surface images at the corresponding positions of the radiation images, after the registration of the two images, when the defect characteristics of the radiation images and the defect characteristics of the surface images are at the same corresponding positions, the current defect is judged to be the surface defect of the object; and when the defect characteristics are not identified in the surface image at the corresponding position of the radiographic image, judging that the current defect is an internal defect of the object.

Compared with the prior art, the invention has the following beneficial effects:

1. in the traditional detection system for automatically identifying the radiographic image, surface defects and internal defects cannot be distinguished, so that the surface topography characteristics are easily judged as the internal defects, the automatic identification result is wrong, the identification accuracy is low, the automatic identification result needs to be confirmed again manually, and the detection efficiency is low; after the method is adopted, the detection system can simultaneously identify the radiographic image and the surface image of the object, so that the internal structure information and the surface structure information of the object are effectively distinguished, and the influence of the surface characteristics on the internal defect identification result is automatically eliminated, so that the identification accuracy is improved, the number of images needing to be manually and repeatedly confirmed is reduced, and the detection efficiency is greatly improved;

2. the method can increase the criterion of defect identification software, solves the problem that the surface characteristic and the internal characteristic of a single radiographic image cannot be distinguished through the identification and registration of the radiographic image and the surface image, and greatly improves the accuracy rate of internal defect identification.

Drawings

Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:

FIG. 1 is a schematic diagram of hardware components of a nondestructive testing method based on multi-modal images.

FIG. 2 is a diagram of software modules of a nondestructive testing method based on multi-modal images.

FIG. 3 is a flow chart of a nondestructive testing method based on multi-modal images.

Detailed Description

The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.

Example 1

The invention aims to solve the technical problems that the existing ray detection image defect identification algorithm only takes a ray image as an object to carry out defect detection, the identification result is easily influenced by surface characteristics and surface defects, and the identification accuracy is low.

The ray detection image in the traditional ray detection process is a two-dimensional projection of the structural characteristics of the detected object on the imaging equipment, the surface and the interior cannot be distinguished, and the surface appearance characteristics are easily judged as internal defects when the image is manually evaluated, so that misjudgment is caused;

in order to solve the technical problems, the invention provides a multi-modal image-based nondestructive testing device and a multi-modal image-based nondestructive testing method, which can simultaneously acquire an object ray detection image and a surface image, thereby effectively distinguishing internal structure information and surface structure information of an object and greatly improving the identification accuracy of a defect identification algorithm.

The invention provides a nondestructive testing device based on multi-mode images, which comprises: the X-ray machine 1, the radiation detector 2, the incident side camera 3, the emergent side camera 4, the detected object 5 and auxiliary equipment matched with the hardware;

the X-ray machine 1 radiates X-rays when being started, the X-rays penetrate through the detected object 5 and irradiate the surface of the radiation detector 2 to form a radiation image, the radiation image reflects the radiation intensity irradiating different areas of the surface of the detector, and the structure information of the inner part and the surface of the detected object is obtained;

the incident side camera 3 is arranged at the incident side of the X-ray machine irradiating the detected object 5, the incident side camera 3 shoots the surface of the detected object at the incident side to obtain an object surface image, and the structure information of the surface of the detected object at the ray incident side is recorded;

the emergent side camera 4 is arranged on the X-ray emergent side, the emergent side camera 4 shoots the surface of the detected object on the emergent side to obtain an object surface image, and the structural information of the surface of the detected object on the ray emergent side is recorded.

According to the nondestructive testing method based on the multi-modal images, the nondestructive testing device based on the multi-modal images is used for executing the following steps:

step S1: synchronously acquiring a surface image and a radiographic image of the detected object;

step S2: identifying the ray image by using computer image identification software to obtain an image with defect characteristics, and positioning and marking the defect characteristics;

step S3: performing feature recognition on the surface image at the position corresponding to the defect feature of the object on the radiographic image with the defect feature by using computer image recognition software, and marking and positioning the surface defect feature if the surface defect exists;

step S4: carrying out position registration on the marked and positioned surface image and the positioned radiographic image, and establishing a corresponding relation of plane coordinate positions of the two images;

step S5: and comparing the positions of the ray image defect identification positioning result and the surface image feature identification result to determine whether the defect features belong to the internal defects.

Specifically, in the detection process, an X-ray machine and a radiation detector are respectively placed on two sides of an object to be detected, a camera and a light source are respectively installed on the X-ray machine side and the radiation detector side, and X-rays emitted by the X-ray machine penetrate through the object and then irradiate the surface of the radiation detector to form a radiographic image; the light emitted by the light source can be uniform visible light or structural light with spatial distribution characteristics, and the light emitted by the light source is reflected by the surface of an object and then is irradiated into the camera to form an image of the surface image of the object.

Specifically, the acquired object surface image may be an image formed by reflecting visible light captured by a camera on the object surface, or an image obtained by other imaging methods and capable of reflecting the object surface structure. The ray detection image is a projection image which is formed by projecting X rays or other radiation rays on a radiation detector after penetrating through an object and is converted by the radiation detector and contains the surface and internal structure characteristics of the object.

Specifically, the step S1 employs: when any position of the detected object is irradiated by X-rays and imaged by a radiation detector, the surface images of the same position are respectively shot by a camera on an X-ray incidence surface and an X-ray emergence surface, the surface area shot by the camera has an inclusion relation with the X-ray incidence surface and the X-ray emergence surface, and an effective detection area is determined according to the image with a smaller area.

Specifically, the step S2 employs: the computer image recognition software is designed according to the characteristics of a recognition object and can automatically recognize specific details in an image. For a radiographic inspection image, object surface defect features and internal defect features contained in the image can be identified, and for a surface image, object surface defect features contained in the image can be identified.

Specifically, the step S2 employs:

step S2.1: preprocessing the ray image to obtain a preprocessed ray image;

step S2.2: performing defect feature identification on the preprocessed radiation image by adopting an image identification algorithm;

step S2.3: and classifying, positioning and marking the identified defect characteristics.

Specifically, the step S2.1 employs: performing image noise reduction and contrast enhancement processing on the ray image to obtain a preprocessed ray image;

the step S2.2 adopts: and the preprocessed radiographic image is subjected to defect feature recognition by adopting an image recognition algorithm based on image morphology or an image recognition algorithm based on deep learning.

Specifically, the step S3 employs: the surface image of the corresponding position of the radiation image refers to the image of the surface of the object, which is acquired by the ray incidence surface and the ray emergence surface at the same position of the object for each radiation image.

Specifically, the step S4 employs: and establishing the position corresponding relation between the radiographic image and the surface image of the same position of the object through a space coordinate correction registration and deep learning algorithm, and realizing the matching of corresponding features in the images and the one-to-one corresponding relation.

Specifically, the step S5 employs: when the defect characteristics are identified in the surface images at the corresponding positions of the radiation images, after the registration of the two images, when the defect characteristics of the radiation images and the defect characteristics of the surface images are at the same corresponding positions, the current defect is judged to be the surface defect of the object; and when the defect characteristics are not identified in the surface image at the corresponding position of the radiographic image, judging that the current defect is an internal defect of the object.

Example 2

Example 2 is a preferred example of example 1

The invention discloses a nondestructive testing method based on multi-mode images, which can effectively eliminate the influence of surface characteristics on internal defect identification and greatly reduce the false judgment rate of internal defect identification by synchronously acquiring surface images and ray detection images of an object, simultaneously performing characteristic identification on the surface images and the ray images by adopting a computer image identification algorithm and finally comprehensively interpreting the identification results of the two images.

As shown in fig. 1, the hardware used in the system for detecting and identifying internal defects of a weld joint provided by the present invention mainly includes: the X-ray machine 1, the radiation detector 2, the camera 3, the camera 4, and a welding part (the detected object 5). The X-ray machine 1 can radiate X-rays when being started, the X-rays can penetrate through the welding part, the rays with uniform intensity can generate intensity attenuation with different degrees after penetrating through the welding part according to the difference of the thickness, the density and the like of an object in the direction of a ray propagation path, and structural information of the welding part along the direction of the rays can be obtained by detecting the change of the intensity of the rays penetrating through the welding part. The radiation detector 2 is an electronic device which utilizes X-rays to perform imaging, when the X-rays with different intensities irradiate the surface of the detector, the detector converts the radiation into a digital image, the image can reflect the intensity of the radiation irradiating different areas of the surface of the detector, so that the structural information of the inside and the surface of the welding part can be obtained, and whether the welding part has defects can be judged through the image. The camera 3 and the ray machine 1 are arranged on the incident side of the X-ray irradiation welding part, and the camera 3 can shoot the surface of the welding part, so that the structural information of the surface of the welding part on the ray incident side is recorded. The camera 4 and the radiation detector 2 are arranged on the X-ray emergent side, and the camera 4 can shoot the surface of the welding piece, so that the structural information of the surface of the welding piece on the X-ray emergent side is recorded.

In the detection process, the X-ray machine 1 emits X-rays, the radiation detector converts the X-rays which penetrate through the welding piece and irradiate the surface of the detector to obtain a ray image, meanwhile, the camera 3 and the camera 4 shoot the surface of the welding piece in the ray irradiation area at the same time to obtain a surface image of the welding piece, and the acquisition of a position multi-mode image is completed. Because the radiographic image is a two-dimensional projection of an object on a detector plane along the ray propagation direction and simultaneously contains the surface structure information and the internal structure information of the welding seam, the camera 3 and the camera 4 respectively acquire images of the X-ray incident side and the X-ray emergent side of the welding part, so that the structure information of the incident surface and the structure information of the emergent surface in the X-ray propagation direction are recorded. Through the detection system, at least two types of images containing the internal and external structural features of the welding part can be obtained, and the internal defects and the surface structural features contained in the radiographic image of the welding part can be effectively distinguished.

As shown in FIG. 2, the software system of the nondestructive testing method based on the multi-modal images comprises a radiation image acquisition module, a surface image acquisition module, a radiation image preprocessing module, a surface image preprocessing module, a radiation image defect identification module, a surface image feature identification module, an image registration module and a comprehensive interpretation module. Further, the radiation image acquisition module is connected with the X-ray machine 1 and the radiation detector 2 to acquire a radiation image; the surface image acquisition module is connected with the camera 3 and the camera 4 to acquire a surface image; the ray image preprocessing module carries out noise reduction and contrast enhancement processing on the ray image, so that different features in the image can obtain higher contrast, and defect feature identification by a computer identification algorithm is facilitated; the surface image preprocessing module carries out image noise reduction, contrast enhancement and other processing on the surface image, and is more beneficial to carrying out surface defect feature identification by a computer identification algorithm; the method comprises the steps that a radiographic image defect recognition module carries out defect feature recognition by adopting an image recognition algorithm based on image morphology or an image recognition algorithm based on deep learning, furthermore, the image recognition algorithm based on the deep learning also comprises the contents of defect feature image database establishment, model training and the like, and the radiographic image recognition module can screen radiographic images containing defect features and classify, position and mark the defect features; the surface image feature recognition module carries out defect feature recognition by adopting an image recognition algorithm based on image morphology or an image recognition algorithm based on deep learning, further, the image recognition algorithm based on the deep learning also comprises the contents of establishing a defect feature image database, model training and the like, and the surface image recognition module can screen surface images containing defect features and classify, position and mark the defect features; the image registration module adopts a coordinate transformation and feature alignment method to register the radiographic image and the surface image synchronously acquired in the same area of the object, so that the surface image and the radiographic image are completely overlapped in the ray direction; and the comprehensive interpretation module judges whether the defect characteristics exist in the interior or on the surface of the welding part according to the recognition result of the radiation image and the recognition result of the surface image, judges that the defect is a surface defect if the defect characteristics exist in the radiation image and similar defect characteristics exist in the same position in the surface image after image registration, and judges that the defect is an interior defect of the welding part if the defect characteristics are not recognized in the same position in the surface image.

As shown in fig. 3, the method for nondestructive testing based on multi-modal images of the present invention specifically includes the following steps: (1) synchronously acquiring an object surface image and a ray detection image; (2) adopting computer image recognition software to recognize the ray detection image, selecting an image with defect characteristics, and positioning and marking the defect characteristics; (3) performing feature recognition on the surface image of the corresponding position of the object by using computer image recognition software on the radiographic image with the defect features, and marking and positioning the surface defect features; (4) carrying out position registration on the surface image and the radiographic image, and establishing a corresponding relation of plane coordinate positions of the two images; (5) and comparing the positions of the ray image defect identification positioning result and the surface image feature identification result to determine whether the defect features belong to the internal defects.

In the description of the present application, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience in describing the present application and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present application.

Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.

The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.