AI analysis system for silicon steel plate surface

Abstract

The present invention relates to an AI analysis system for the surface of silicon steel plates, comprising: a steel plate production mechanism, which is used to complete the sequential production of each silicon steel plate, and transmit the latest produced single silicon steel plate to the bottom of the network capture mechanism; a surface identification mechanism, which is used to send a surface roughness identification signal when the current smoothness value corresponding to the intelligently analyzed silicon steel plate is less than or equal to the set smoothness threshold, otherwise, it sends a surface smoothness identification signal. The AI ​​analysis system for the surface of silicon steel plates of the present invention has compact logic and intelligent design. By identifying various visual data of the silicon steel plate and using an AI analysis model to intelligently analyze the current smoothness value corresponding to the silicon steel plate, multiple learning operations are performed on the deep neural network to obtain the corresponding AI analysis model, thereby completing the intelligent analysis of the current smoothness value corresponding to each silicon steel plate.

Description

Technical Field

[0001] The present invention relates to the field of silicon steel plates, and in particular to an AI analysis system for the surface of silicon steel plates. Background Art

[0002] Silicon alloy steel containing 1.0-4.5% silicon and less than 0.08% carbon is called silicon steel. It has the characteristics of high magnetic permeability, low coercive force, and large resistivity, so the hysteresis loss and eddy current loss are small. It is mainly used as a magnetic material in motors, transformers, electrical appliances, and electrical instruments. In order to meet the needs of punching and shearing when manufacturing electrical appliances, a certain plasticity is also required. In order to improve the magnetic induction performance and reduce the hysteresis loss, the content of harmful impurities is required to be as low as possible, and the plate shape is required to be flat and the surface quality is good. Silicon steel plate refers to steel plate products made of silicon steel materials.

[0003] In the production process of silicon steel sheets, due to the deviation in the manufacturing process and the change in the characteristics of the manufacturing materials, the surface smoothness values ​​of the silicon steel sheets produced each time have slight differences in values. Once this difference exceeds the surface smoothness value range corresponding to the silicon steel sheet, the silicon steel sheet products with unqualified surface smoothness values ​​are formed and are difficult to circulate on the market. However, the prior art lacks a technical solution for performing high-precision analysis of the current smoothness values ​​corresponding to the silicon steel sheet. Summary of the invention

[0004] In order to solve the above problems, the present invention provides an AI analysis system for the surface of a silicon steel plate, which identifies the imaging area of ​​the silicon steel plate in a sequential mapping image, obtains the grayscale values ​​corresponding to the various pixel points in the imaging area, and for each pixel point in the imaging area, judges its grayscale value gradient based on its grayscale value and the grayscale values ​​of its surrounding pixels to obtain the grayscale value gradients corresponding to the various pixel points in the imaging area corresponding to the silicon steel plate, thereby providing key data for the intelligent analysis of the current smoothness value corresponding to the silicon steel plate, and when the current smoothness value corresponding to the silicon steel plate of the intelligent analysis is less than or equal to the set smoothness threshold, a surface roughness identification signal is issued, otherwise, a surface smoothness identification signal is issued, thereby realizing intelligent identification processing of the surface smoothness of each newly produced silicon steel plate.

[0005] According to the present invention, there is provided an AI analysis system for the surface of a silicon steel plate, the system comprising:

[0006] The steel plate production mechanism is used to complete the sequential production of each silicon steel plate and transmit the latest produced single silicon steel plate to the bottom of the network capture mechanism;

[0007] A network capture mechanism is used to take the latest produced single silicon steel plate as the target silicon steel plate and perform a visual data capture action of the scene where the target silicon steel plate is located, so as to obtain and output a corresponding production scene image;

[0008] A sequential mapping mechanism, connected to the network capture mechanism, for sequentially performing edge sharpening processing, guided filtering processing and homomorphic filtering processing on the received production scene image to obtain and output a corresponding sequential mapping image;

[0009] A first-layer capture mechanism is connected to the sequential mapping mechanism, and is used to identify the imaging area of ​​the silicon steel plate in the sequential mapping image, obtain the grayscale values ​​corresponding to the pixels in the imaging area, and for each pixel in the imaging area, determine its grayscale value gradient based on its grayscale value and the grayscale values ​​of its surrounding pixels, so as to obtain the grayscale value gradients corresponding to the pixels in the imaging area corresponding to the silicon steel plate;

[0010] A sub-layer capture mechanism, for performing multiple learning operations on the deep neural network to obtain a corresponding AI analysis model, wherein the number of learning operations in the multiple learning operations is positively correlated with the area ratio of the imaging area corresponding to the silicon steel plate to the sequential mapping image;

[0011] The last-layer capture mechanism is connected to the first-layer capture mechanism and the second-layer capture mechanism, respectively, and is used to use the AI ​​analysis model to intelligently analyze the current smoothness value corresponding to the silicon steel plate according to the grayscale value gradients corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, the imaging depth data corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, and the area ratio of the imaging area corresponding to the silicon steel plate occupying the sequential mapping image;

[0012] The surface identification mechanism is connected to the last layer capture mechanism and is used to send a surface roughness identification signal when the current smoothness value corresponding to the intelligently analyzed silicon steel plate is less than or equal to the set smoothness threshold, and is also used to send a surface smoothness identification signal when the current smoothness value corresponding to the intelligently analyzed silicon steel plate is greater than the set smoothness threshold.

[0013] Therefore, the present invention has the following four outstanding technical effects:

[0014] First, identify the imaging area of ​​the silicon steel plate in the sequential mapping image, obtain the grayscale values ​​corresponding to the pixels in the imaging area, and for each pixel in the imaging area, determine its grayscale value gradient based on its grayscale value and the grayscale values ​​of its surrounding pixels, so as to obtain the grayscale value gradients corresponding to the pixels in the imaging area corresponding to the silicon steel plate, thereby providing key data for the intelligent analysis of the current smoothness value corresponding to the silicon steel plate;

[0015] Second, performing multiple learning operations on the deep neural network to obtain a corresponding AI analysis model, wherein the number of learning operations in the multiple learning operations is positively correlated with the area ratio of the imaging area corresponding to the silicon steel plate to the sequential mapping image;

[0016] Third: Using the AI ​​analysis model, the current smoothness value of the silicon steel plate is intelligently analyzed according to the grayscale value gradients corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, the imaging depth data corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, and the area ratio of the imaging area corresponding to the silicon steel plate to the sequential mapping image;

[0017] Fourthly, a surface identification mechanism is introduced to send out a surface roughness identification signal when the current smoothness value corresponding to the intelligently analyzed silicon steel plate is less than or equal to the set smoothness threshold, and to send out a surface smoothness identification signal when the current smoothness value corresponding to the intelligently analyzed silicon steel plate is greater than the set smoothness threshold, thereby realizing intelligent identification processing of the surface smoothness of each newly produced silicon steel plate.

[0018] The AI ​​analysis system for the surface of silicon steel plates of the present invention has compact logic and intelligent design. By using the AI ​​analysis model to intelligently analyze the current smoothness value corresponding to the silicon steel plate by identifying various visual data of the silicon steel plate, multiple learning operations are performed on the deep neural network to obtain the corresponding AI analysis model, thereby completing the intelligent analysis of the current smoothness value corresponding to each silicon steel plate. DETAILED DESCRIPTION

[0019] The following is a detailed description of an embodiment of the AI ​​analysis system for the surface of a silicon steel plate of the present invention.

[0020] The AI ​​analysis system for the surface of a silicon steel plate shown in the first embodiment of the present invention includes:

[0021] The steel plate production mechanism is used to complete the sequential production of each silicon steel plate and transmit the latest produced single silicon steel plate to the bottom of the network capture mechanism;

[0022] A network capture mechanism is used to take the latest produced single silicon steel plate as the target silicon steel plate and perform a visual data capture action of the scene where the target silicon steel plate is located, so as to obtain and output a corresponding production scene image;

[0023] For example, a network capture mechanism, for taking a newly produced single silicon steel plate as a target silicon steel plate and performing a visual data capture action of a scene where the target silicon steel plate is located, so as to obtain and output a corresponding production scene image, comprises: the network capture mechanism comprises a network receiving component, and the network receiving component performs a wireless communication operation based on a time division duplex communication link;

[0024] A sequential mapping mechanism, connected to the network capture mechanism, for sequentially performing edge sharpening processing, guided filtering processing and homomorphic filtering processing on the received production scene image to obtain and output a corresponding sequential mapping image;

[0025] A first-layer capture mechanism is connected to the sequential mapping mechanism, and is used to identify the imaging area of ​​the silicon steel plate in the sequential mapping image, obtain the grayscale values ​​corresponding to the pixels in the imaging area, and for each pixel in the imaging area, determine its grayscale value gradient based on its grayscale value and the grayscale values ​​of its surrounding pixels, so as to obtain the grayscale value gradients corresponding to the pixels in the imaging area corresponding to the silicon steel plate;

[0026] A sub-layer capture mechanism, for performing multiple learning operations on the deep neural network to obtain a corresponding AI analysis model, wherein the number of learning operations in the multiple learning operations is positively correlated with the area ratio of the imaging area corresponding to the silicon steel plate to the sequential mapping image;

[0027] The last-layer capture mechanism is connected to the first-layer capture mechanism and the second-layer capture mechanism, respectively, and is used to use the AI ​​analysis model to intelligently analyze the current smoothness value corresponding to the silicon steel plate according to the grayscale value gradients corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, the imaging depth data corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, and the area ratio of the imaging area corresponding to the silicon steel plate occupying the sequential mapping image;

[0028] A surface identification mechanism connected to the last layer capture mechanism, used to send a surface roughness identification signal when the current smoothness value corresponding to the intelligently analyzed silicon steel plate is less than or equal to the set smoothness threshold, and also used to send a surface smoothness identification signal when the current smoothness value corresponding to the intelligently analyzed silicon steel plate is greater than the set smoothness threshold;

[0029] Wherein, the sequential mapping mechanism is connected to the network capture mechanism and is used to sequentially perform edge sharpening processing, guided filtering processing and homomorphic filtering processing on the received production scene image to obtain and output the corresponding sequential mapping image, including: the sequential mapping mechanism includes a front-end mapping component, a mid-end mapping component and a back-end mapping component, which are used to respectively perform edge sharpening processing, guided filtering processing and homomorphic filtering processing on the received image data;

[0030] Among them, using the AI ​​analysis model to intelligently analyze the current smoothness value corresponding to the silicon steel plate according to the grayscale value gradients corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, the imaging depth data corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, and the area ratio of the imaging area corresponding to the silicon steel plate occupying the sequential mapping image includes: synchronously inputting the grayscale value gradients corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, the imaging depth data corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, and the area ratio of the imaging area corresponding to the silicon steel plate occupying the sequential mapping image into the AI ​​analysis model, and running the AI ​​analysis model to obtain the current smoothness value corresponding to the silicon steel plate output by the AI ​​analysis model.

[0031] Compared with the first embodiment of the present invention, the AI ​​analysis system for the surface of a silicon steel plate shown in the second embodiment of the present invention may further include:

[0032] Length sensor devices are respectively connected to the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, and are used to respectively measure the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism;

[0033] Wherein, the length sensor device is respectively connected with the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, and is used to respectively measure the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, including: the length sensor device includes a plurality of length measurement units, which are respectively connected with the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, so as to complete the respective measurement of the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism;

[0034] Wherein, the length sensor device comprises a plurality of length measuring units, which are used to be connected with the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively, so as to complete the respective measurement of the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively, including: the plurality of length measuring units are a plurality of length sensors, which are used to be connected with the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively, so as to complete the respective measurement of the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively;

[0035] Wherein, the plurality of length measuring units are a plurality of length sensors, which are used to be connected to the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively, so as to complete the respective measurement of the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively, including: the structures of the plurality of length sensors are the same;

[0036] Among them, the multiple length measurement units are multiple length sensors, which are used to be connected to the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively, so as to complete the respective measurements of the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, and also include: the multiple length sensors have the same length measurement upper limit value and length measurement lower limit value.

[0037] Compared with the first embodiment of the present invention, the AI ​​analysis system for the surface of a silicon steel plate shown in the third embodiment of the present invention may further include:

[0038] A length display mechanism, connected to the multiple length measuring units of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, respectively, for synchronously displaying the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively;

[0039] Wherein, the length display mechanism is respectively connected to the plurality of length measuring units of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, and is used to synchronously display the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively, including: the length display mechanism is a touch display screen;

[0040] And wherein, the length display mechanism is respectively connected to multiple length measuring units of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, and is used to synchronously display the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, including: the length display mechanism is a non-touch display screen.

[0041] In addition, in the AI ​​analysis system for the surface of silicon steel plates, the AI ​​analysis model is used to intelligently analyze the current smoothness value corresponding to the silicon steel plate according to the grayscale value gradients corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, the imaging depth data corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, and the area ratio of the imaging area corresponding to the silicon steel plate to the sequential mapping image, which also includes: using a numerical simulation mode to realize the simulation and test processing of the data processing process of intelligently analyzing the current smoothness value corresponding to the silicon steel plate according to the grayscale value gradients corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, the imaging depth data corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, and the area ratio of the imaging area corresponding to the silicon steel plate to the sequential mapping image.

[0042] In the above description, the present invention has been described with reference to specific embodiments. However, it will be appreciated by those skilled in the art that various modifications and changes may be made without departing from the scope defined by the claims appended hereto. Therefore, the description should be considered illustrative rather than restrictive, and it is intended that all such modifications are included within the scope of the present invention.

[0043] The benefits, other advantages and solutions to the problems have been described above in conjunction with specific embodiments. However, the benefits, advantages, solutions to the problems, and any other elements that may produce any benefit, advantage, or solution or make any benefit, advantage, or solution more apparent should not be interpreted as key, necessary, or basic features or elements of any or all claims. The term "comprises," "comprising," or any other variation thereof as used herein is intended to cover non-exclusive inclusions, such as processes, methods, articles, or devices, which include not only those elements but also a series of elements that are not explicitly listed or are inherent to such processes, methods, articles, or devices.

Claims

1. An AI analysis system for silicon steel plate surface, characterized in that: The system comprises: The steel plate production mechanism is used to complete the sequential production of each silicon steel plate and transmit the latest produced single silicon steel plate to the bottom of the network capture mechanism; A network capture mechanism is used to take the latest produced single silicon steel plate as the target silicon steel plate and perform a visual data capture action of the scene where the target silicon steel plate is located, so as to obtain and output a corresponding production scene image; A sequential mapping mechanism, connected to the network capture mechanism, for sequentially performing edge sharpening processing, guided filtering processing and homomorphic filtering processing on the received production scene image to obtain and output a corresponding sequential mapping image; A first-layer capture mechanism is connected to the sequential mapping mechanism, and is used to identify the imaging area of ​​the silicon steel plate in the sequential mapping image, obtain the grayscale values ​​corresponding to the pixels in the imaging area, and for each pixel in the imaging area, determine its grayscale value gradient based on its grayscale value and the grayscale values ​​of its surrounding pixels, so as to obtain the grayscale value gradients corresponding to the pixels in the imaging area corresponding to the silicon steel plate; A sub-layer capture mechanism, for performing multiple learning operations on the deep neural network to obtain a corresponding AI analysis model, wherein the number of learning operations in the multiple learning operations is positively correlated with the area ratio of the imaging area corresponding to the silicon steel plate to the sequential mapping image; The last-layer capture mechanism is connected to the first-layer capture mechanism and the second-layer capture mechanism, respectively, and is used to use the AI ​​analysis model to intelligently analyze the current smoothness value corresponding to the silicon steel plate according to the grayscale value gradients corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, the imaging depth data corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, and the area ratio of the imaging area corresponding to the silicon steel plate occupying the sequential mapping image; The surface identification mechanism is connected to the last layer capture mechanism and is used to send a surface roughness identification signal when the current smoothness value corresponding to the intelligently analyzed silicon steel plate is less than or equal to the set smoothness threshold, and is also used to send a surface smoothness identification signal when the current smoothness value corresponding to the intelligently analyzed silicon steel plate is greater than the set smoothness threshold.

2. The AI ​​analysis system for silicon steel plate surface according to claim 1, characterized in that: A sequential mapping mechanism, connected to the network capture mechanism, is used to sequentially perform edge sharpening processing, guided filtering processing and homomorphic filtering processing on the received production scene image to obtain and output a corresponding sequential mapping image, including: the sequential mapping mechanism includes a front-end mapping component, a mid-end mapping component and a back-end mapping component, which are used to respectively perform edge sharpening processing, guided filtering processing and homomorphic filtering processing on the received image data; Among them, using the AI ​​analysis model to intelligently analyze the current smoothness value corresponding to the silicon steel plate according to the grayscale value gradients corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, the imaging depth data corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, and the area ratio of the imaging area corresponding to the silicon steel plate occupying the sequential mapping image includes: synchronously inputting the grayscale value gradients corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, the imaging depth data corresponding to each pixel point in the imaging area corresponding to the silicon steel plate, and the area ratio of the imaging area corresponding to the silicon steel plate occupying the sequential mapping image into the AI ​​analysis model, and running the AI ​​analysis model to obtain the current smoothness value corresponding to the silicon steel plate output by the AI ​​analysis model.

3. The AI ​​analysis system for silicon steel plate surface according to claim 2, characterized in that: The system further comprises: Length sensor devices are respectively connected to the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, and are used to respectively measure the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism; Among them, the length sensor device is respectively connected with the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, and is used to respectively measure the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism. The length sensor device includes multiple length measurement units, which are respectively connected with the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism to complete the respective measurements of the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism.

4. The AI ​​analysis system for silicon steel plate surface according to claim 3, characterized in that: The length sensor device includes a plurality of length measuring units, which are used to be connected to the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively, so as to complete the measurement of the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively. The plurality of length measuring units are a plurality of length sensors, which are used to be connected to the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively, so as to complete the measurement of the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively.

5. The AI ​​analysis system for silicon steel plate surface according to claim 4, characterized in that: The multiple length measurement units are multiple length sensors, which are used to be connected to the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively, so as to complete the measurement of the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively, including: the structures of the multiple length sensors are the same.

6. The AI ​​analysis system for silicon steel plate surface according to claim 5, characterized in that: The multiple length measurement units are multiple length sensors, which are used to be connected to the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism respectively, so as to complete the respective measurements of the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, and also include: the multiple length sensors have the same length measurement upper limit value and length measurement lower limit value.

7. The AI ​​analysis system for silicon steel plate surface according to any one of claims 3 to 6, characterized in that: The system further comprises: The length display mechanism is respectively connected to multiple length measuring units of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, and is used to synchronously display the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism.

8. The AI ​​analysis system for silicon steel plate surface according to claim 7, characterized in that: The length display mechanism is respectively connected to multiple length measuring units of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, and is used to synchronously display the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism. The length display mechanism is a touch display screen.

9. The AI ​​analysis system for silicon steel plate surface according to claim 7, characterized in that: The length display mechanism is respectively connected to multiple length measuring units of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, and is used to synchronously display the current real-time length values ​​of the first-layer capture mechanism, the second-layer capture mechanism, the last-layer capture mechanism and the surface identification mechanism, including: the length display mechanism is a non-touch display screen.