CN111964924A - Tire bulge detection system based on high-speed camera and multi-line laser - Google Patents
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Abstract
The invention discloses a tire bulge detection system based on a high-speed camera and multi-line laser, and relates to the technical field of optical detection. The invention comprises a high-speed camera, a multi-line laser scanning device and an alarm device, wherein a lens is arranged on the front side of the high-speed camera; the high-speed camera is fixedly arranged on the tripod; the high-speed camera is connected with the control terminal through a data line; the multi-line laser scanning device is formed by combining a plurality of lasers; a plurality of lasers are fixed on the laser fixing and adjusting device; the laser fixing and adjusting device is used for adjusting the distance and the direction between the lasers; the alarm device is connected with the computer through a data line; the computer starts a tire detection algorithm, extracts laser lines in the shot image and calculates the curvature change of a plurality of laser lines to judge whether the surface of the tire has faults or not. The invention reminds the user to replace the tire, so that the abnormal tire can be detected in the high-speed running process of the tire, the detection efficiency is improved, and the cost required by detection is reduced.
Description
Technical Field
The invention belongs to the technical field of optical detection, and particularly relates to a tire bulge detection system based on a high-speed camera and multi-line laser.
Background
The tire is one of important components of the automobile, and mainly plays a role in supporting the mass of the automobile and bearing the load of the automobile; traction and braking torque is transmitted, and the adhesion between the wheels and the road surface is ensured; the vibration and impact force of the automobile during running are reduced and absorbed, the automobile parts are prevented from being subjected to severe vibration and early damage, the high-speed performance of the automobile is adapted, the noise during running is reduced, and the running safety, the operation stability, the comfort and the energy-saving economy of the automobile are ensured.
With the development of national economy, the grade and distance of the highway are continuously increased, and the quality problem of the tire is gradually exposed under the harsh use conditions of high speed, long distance and large load. The tire in overload running bears excessive load, the tire is heated greatly, the tire aging is accelerated directly, and the delamination of the endpoint part of the buffer layer is caused.
The tire standard of China is closer to the ISO international standard, belongs to the universal standard range, and is lack of adaptability and practicability compared with the developed national standard. In the aspect of adaptability, by taking the American federal technical standard FM-VSS139 and the European Union ECE30 regulation as examples, the FM-VSS139 fully considers the control of the actual tire by the road speed limit when in use when setting the test condition, and the test speeds of the similar tires are the same and do not exceed the highest speed limit specified by a highway; in countries represented by germany in the european union, the use of highways is limited to the minimum speed per hour, and the upper limit of the running speed is not specified, so that the test speed is strictly adjusted according to the speed level of the tire in the tire test to meet the requirement that the test process is close to the actual use environment as much as possible. The inverse view of the tire standard in China is to design a test at the highest running speed of tire design on the basis of strictly setting the upper running limit of a road, and the test method has larger difference compared with the actual use condition.
From a practical perspective, the federal technical standard FM-VSS139 in the united states is taken as an example, and the standard is added with a low-pressure performance test after the endurance performance test is finished. This is a test item specifically designed for habitual manual handling during actual use of the tire. When a large number of consumers actually use the automobile, the consumers often neglect whether the inflation pressure of the tire is normal, and just this point forms one of the key hidden dangers of tire failure. The tire low-pressure performance test is designed, the use state of the tire in a decompression state is simulated to a great extent, and the safety factor of using the tire in the state is strictly controlled.
However, in the tire detection test, the test is often performed at a high speed, and a normal visual means cannot judge whether the test tire being detected has a fault, so that the operation of the detection machine can be stopped only when the tire explodes or changes significantly, and the reason for the fault is judged by observing the exploded tire.
Disclosure of Invention
The invention aims to provide a tire bulge detection system based on a high-speed camera and multi-line laser, which realizes real-time detection and judgment of whether a bulge exists in a tire in a high-speed rotation process by adopting multi-line laser coverage distribution and utilizing multi-line laser curvature change algorithm processing which is acquired by the high-speed camera and combined, and solves the problems that the judgment of human eyes cannot be realized in the high-speed rotation process of the tire, the detection cost is high and the detection efficiency is low.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a tire bulge detection system based on a high-speed camera and multi-line laser, which comprises the high-speed camera, a multi-line laser scanning device and an alarm device, and is characterized in that:
a lens is arranged on the front side of the high-speed camera; the high-speed camera is fixedly arranged on the tripod; the high-speed camera is connected with the control terminal through a data line; the control terminal is used for setting parameters of the high-speed camera and controlling shooting, image acquisition and storage of the high-speed camera through the computer terminal;
the multi-line laser scanning device is formed by combining a plurality of lasers; a plurality of lasers are fixed on the laser fixing and adjusting device; the laser fixing and adjusting device is used for adjusting the distance and the direction between the lasers;
the alarm device is an acousto-optic alarm device; the sound and light alarm device is connected with the computer through a data line; the computer starts a tire detection algorithm, extracts laser lines in the shot image, calculates the curvature change of a plurality of laser lines, judges whether the surface of the tire has a fault, and timely informs the sound-light alarm device to give an alarm if the fault is generated in the calculation.
Preferably, the tire detection algorithm specifically comprises the steps of:
step T1 acquires image: acquiring a laser line image in a shot image;
step T2 image filtering: performing median filtering, mean filtering and boundary processing on the laser line image;
step T3 centerline extraction: calculating the light bands line by line, and taking the light band gray scale gravity center coordinate calculated by each line as the center coordinate to obtain the gray scale gravity center of the line;
step T4 centerline filtering: processing the extracted central line;
step T5 calculates the radius of curvature: obtaining coordinates of a fixed point on a central line, and calculating the curvature radius of the point through a curvature radius formula;
wherein, the calculation formula of the curvature radius is as follows:
preferably, the work flow of the tire bulge detection system is as follows:
step S1 sets camera parameters: setting the size of a lens aperture, exposure time, an acquisition period, an acquisition frame rate, a video storage initial point and duration;
step S2 laser fixation adjustment: fixing a plurality of lasers by using a firmware frame, and adjusting the distance, the line spacing, the laser line brightness and the angle of the lasers;
step S3 camera and laser mounting: calculating the distance and position of a camera frame according to the selected camera and the test environment, installing a laser firmware frame at a proper position, wherein the camera is over against the tire test area, and a laser line and the camera form a certain angle relatively;
step S4 high-speed picture acquisition: calculating the acquisition frame rate of the high-speed camera according to the tire rotating speed, and entering a high-speed acquisition state when the tire rotating speed is basically stable;
step S5 enables tire detection: starting a tire detection algorithm in a high-speed acquisition state, and quickly extracting and calculating image information acquired at a high speed;
step S6 failure alarm: when a fault is generated on the surface of the tire is captured, an alarm prompt appears on a software interface, a sound-light alarm is externally connected through a USB interface, and when the fault is generated in calculation, a signal is sent to generate a visual sound-light alarm;
step S7 controls the detection algorithm switch: after the alarm is generated, the detection algorithm can be manually closed, and the algorithm can be restarted for detection.
Preferably, in step S5, a threshold and a sensitivity are set for the high-speed camera, the influence of the tire surface pattern and the calculation error is eliminated, the target laser line is selected, the algorithm is started, and the curvature change of the plurality of laser lines in the picture is calculated to determine whether the tire surface has a fault.
Preferably, in step S6, the system determines whether there is a bulge fault according to the output signal of the algorithm result, and if there is a bulge fault, the system may output a signal to generate an audible and visual alarm.
Preferably, in step S7, the tire bulge detection algorithm is manually turned off and turned on in the camera acquisition control software.
The invention has the following beneficial effects:
the invention adopts the mode of combining the high-speed camera and the multiple laser lines, uses the high-speed camera to extract the rotation of the laser lines, judges whether the surface of the tire generates bulges or not through the curvature change of the laser lines, and sends out an alarm signal when detecting the abnormal condition to remind a user to replace the tire, so that the abnormal tire can be detected in the high-speed running process of the tire, the detection efficiency is improved, and the cost required by the detection is reduced.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the operation of a high speed camera and multi-line laser based tire bulge detection system of the present invention;
FIG. 2 is a flow chart of optical zone centerline extraction.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention is a tire bulge detecting system based on a high-speed camera and a multi-line laser, comprising a high-speed camera, a multi-line laser scanning device and an alarm device,
a lens is arranged on the front side of the high-speed camera; the high-speed camera is fixedly arranged on the tripod; the high-speed camera is connected with the control terminal through a data line; the control terminal is used for setting parameters of the high-speed camera and controlling shooting, image acquisition and storage of the high-speed camera through the computer terminal;
the multi-line laser scanning device is formed by combining a plurality of lasers; a plurality of lasers are fixed on the laser fixing and adjusting device; the laser fixing and adjusting device is used for adjusting the distance and the direction between the lasers;
the alarm device is an acousto-optic alarm device; the sound and light alarm device is connected with the computer through a data line; the computer starts a tire detection algorithm, extracts laser lines in the shot image, calculates the curvature change of a plurality of laser lines, judges whether the surface of the tire has a fault, and timely informs an audible and visual alarm device to give an alarm if the fault is generated in the calculation;
calculating the minimum frame rate of a camera meeting the requirement according to the tire rotating speed, uniformly distributing a plurality of laser lines by acquiring information of the tire surface area, covering a camera shooting field of view, and ensuring that a missing detection blind area does not appear in the field of view, wherein the high frame rate ensures that each position of the whole tire circle can be acquired and analyzed in a very short time (1s), thereby realizing the whole tire detection; meanwhile, the software is set to be stored for a period of time before triggering, and when the alarm is given, a signal is triggered to the camera, so that the video of the tire fault process can be collected and stored.
The tire detection algorithm specifically comprises the following steps:
step T1 acquires image: acquiring a laser line image in a shot image;
step T2 image filtering: performing median filtering, mean filtering and boundary processing on the laser line image;
step T3 centerline extraction: calculating the light bands line by line, and taking the light band gray scale gravity center coordinate calculated by each line as the center coordinate to obtain the gray scale gravity center of the line;
step T4 centerline filtering: processing the extracted central line;
step T5 calculates the radius of curvature: obtaining coordinates of a fixed point on a central line, and calculating the curvature radius of the point through a curvature radius formula;
wherein, the calculation formula of the curvature radius is as follows:
the working process of the tire bulge detection system is as follows:
step S1 sets camera parameters: setting the size of a lens aperture, exposure time, an acquisition period, an acquisition frame rate, a video storage initial point, duration and the like;
step S2 laser fixation adjustment: fixing a plurality of lasers by using a firmware frame, and adjusting the distance, the line spacing, the laser line brightness and the angle of the lasers;
step S3 camera and laser mounting: calculating the erection distance and position of a camera according to the selected camera and the test environment, installing a laser firmware frame at a proper position, enabling the camera to be over against a tire test area, enabling a laser line and the camera to form a certain angle relatively, and finding out that the optimal angle is formed when the relative angle of the laser line and the camera is 45 degrees through repeated experiments;
step S4 high-speed picture acquisition: calculating the acquisition frame rate of the high-speed camera according to the tire rotating speed, and entering a high-speed acquisition state when the tire rotating speed is basically stable;
step S5 enables tire detection: starting a tire detection algorithm in a high-speed acquisition state, and quickly extracting and calculating image information acquired at a high speed; setting a threshold value and sensitivity, eliminating the influence of the pattern on the surface of the tire and the calculation error, selecting a target laser line, starting an algorithm, calculating the curvature change of a plurality of laser lines in a picture and judging whether the surface of the tire has a fault or not;
step S6 failure alarm: when a fault is generated on the surface of the tire is captured, an alarm prompt appears on a software interface, a sound-light alarm is externally connected through a USB interface, and when the fault is generated in calculation, a signal is sent to generate a visual sound-light alarm;
step S7 controls the detection algorithm switch: after the alarm is generated, the detection algorithm can be manually closed, and the algorithm can be restarted for detection.
In step S5, a threshold and sensitivity need to be set for the high-speed camera, the influence of tire surface pattern and calculation error is eliminated, a target laser line is selected, an algorithm is started, and curvature change of multiple laser lines in a picture is calculated to determine whether a failure occurs on the tire surface.
In step S6, it is determined whether there is a bulge fault according to the algorithm result output signal, and if there is a bulge fault, the system may output a signal to generate an audible and visual alarm.
In step S7, the tire bulge detection algorithm can be manually turned off and on in the camera acquisition control software.
One specific application of this embodiment is:
the tire detection algorithm mainly adopts a light band center line extraction algorithm to calculate the curvature radius; as shown in fig. 2, the optical tape centerline extraction mainly includes image filtering, centerline extraction, centerline filtering and curvature radius calculation processes.
The image filtering can adopt a median filtering method, a mean filtering method and a boundary processing method;
median filtering is a non-linear smoothing technique, which sets the pixel value of each pixel point as the median of all pixel values of pixel points in a certain neighborhood window of the point.
Defining the size of a neighborhood window of a certain point (x, y) as I x j (usually j I and I is an odd number), arranging the pixel values I (I, j) corresponding to each point in the neighborhood window in ascending or descending order, calculating the median value of the neighborhood window, and replacing the value of the center point G (x, y) of the neighborhood window with the median value.
G(x,y)＝median[I(i,j)]；
The mean filtering is a linear filtering, and the basic principle is to set the pixel value of each pixel point as the mean value of a certain neighborhood window at the point.
The size of the neighborhood window defining a certain point (x, y) is i x j (usually j i and i is an odd number), and the values of all pixels in the neighborhood window are averaged and used to replace the value of the center point G (x, y) of the neighborhood window.
No matter the median filtering method or the mean filtering method is used for image filtering, a boundary can be lack of a side field. There are currently four methods for border handling.
(1) Do not do boundary processing
The image boundary is not processed, namely the filter is not applied to the periphery of the image when the image is filtered, so that the periphery of the image is not changed;
(2) filling 0
Expanding the image boundary, and filling 0 in the expanded boundary;
(3) filling in recent pixel values
Similar to fill 0, except that the place filled with 0 is filled with the pixel value of the nearest pixel;
(4) filling in pixel values of the other side
Similar to the first two fills, the existing dots are copied to the corresponding locations on the other side when filling the data.
The time gray scale gravity center method adopted by the central line extraction: the gray scale gravity center method is to calculate the light band row by row, and the coordinates of the gray scale gravity center of the light band calculated by each row are used as the coordinates of the center of the light band.
The light band in the image is arranged in the horizontal direction and the x-th row coordinate of the image is set to be (x, y) along the direction perpendicular to the light band (corresponding to the vertical direction of the image)i) The gray value corresponding to each point coordinate in the column is f (x, y)i) Where the variable i 1,2, 3. Let the threshold be T, all satisfy f (x, y)i) The set of i values > T is denoted ROI. Let the gray scale center of gravity of the column be (x, y)k)，ykThe calculation method of (c) is as follows:
the threshold T can be set in two ways, one is a fixed threshold, and the other is a dynamic threshold. The fixed threshold is a fixed value in which T is set to 0 to 255, and may be set to 50 in general. The dynamic threshold is to find the maximum gray value of the k column
Will be provided with
80% (which can be adjusted according to actual needs) of the column is taken as a threshold value of the column, and is marked as Tk. If TkWhen the value is less than a preset value (the value is between 0 and 255), the gray scale gravity center of the row can be directly obtained; otherwise, the gray center of gravity of this column needs to be calculated.
After the centerline extraction, it may be further processed, i.e., the centerline may be filtered. Centerline filtering currently provides two modes: mean filtering and median filtering. The principle of the two filtering modes is basically the same as that of image filtering, except that the size of a neighborhood window of a certain point (x, y) on a central line is set to be i multiplied by 1, and the original y is replaced by the mean value or the median value of the ordinate of the i point.
Calculating the curvature radius: after the tire surface centerline coordinates are obtained, they are fitted to a one-dimensional quadratic equation:
Y＝a*X2+b*X+c；
and parameters a, b, c are obtained.
Coordinate values at fixed points on the curve (depending on the shooting angle using Xmax, or Ymax) are acquired, and by a curvature radius formula:
the radius of curvature of this point is calculated, and the radius of curvature reflects the unevenness of the tire surface, and can be used as a criterion for judging whether or not a bulge exists.
It should be noted that, in the above system embodiment, each included unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
In addition, it is understood by those skilled in the art that all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing associated hardware, and the corresponding program may be stored in a computer-readable storage medium.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. The utility model provides a tire bulge detecting system based on high-speed camera and multi-line laser, includes high-speed camera, multi-line laser scanning device and alarm device, its characterized in that:
a lens is arranged on the front side of the high-speed camera; the high-speed camera is fixedly arranged on the tripod; the high-speed camera is connected with the control terminal through a data line; the control terminal is used for setting parameters of the high-speed camera and controlling shooting, image acquisition and storage of the high-speed camera through the computer terminal;
the multi-line laser scanning device is formed by combining a plurality of lasers; a plurality of lasers are fixed on the laser fixing and adjusting device; the laser fixing and adjusting device is used for adjusting the distance and the direction between the lasers;
the alarm device is an acousto-optic alarm device; the sound and light alarm device is connected with the computer through a data line; the computer starts a tire detection algorithm, extracts laser lines in the shot image, calculates the curvature change of a plurality of laser lines, judges whether the surface of the tire has a fault, and timely informs the sound-light alarm device to give an alarm if the fault is generated in the calculation.
2. The system for detecting a tire bulge based on a high-speed camera and a multi-line laser as claimed in claim 1, wherein the tire detection algorithm specifically comprises the steps of:
step T1 acquires image: acquiring a laser line image in a shot image;
step T2 image filtering: performing median filtering, mean filtering and boundary processing on the laser line image;
step T3 centerline extraction: calculating the light bands line by line, and taking the light band gray scale gravity center coordinate calculated by each line as the center coordinate to obtain the gray scale gravity center of the line;
step T4 centerline filtering: processing the extracted central line;
step T5 calculates the radius of curvature: obtaining coordinates of a fixed point on a central line, and calculating the curvature radius of the point through a curvature radius formula;
wherein, the calculation formula of the curvature radius is as follows:
3. the high-speed camera and multi-line laser based tire bulge detection system according to claim 1, wherein the work flow of the tire bulge detection system is as follows:
step S1 sets camera parameters: setting the size of a lens aperture, exposure time, an acquisition period, an acquisition frame rate, a video storage initial point and duration;
step S2 laser fixation adjustment: fixing a plurality of lasers by using a firmware frame, and adjusting the distance, the line spacing, the laser line brightness and the angle of the lasers;
step S3 camera and laser mounting: calculating the distance and position of a camera frame according to the selected camera and the test environment, installing a laser firmware frame at a proper position, wherein the camera is over against the tire test area, and a laser line and the camera form a certain angle relatively;
step S4 high-speed picture acquisition: calculating the acquisition frame rate of the high-speed camera according to the tire rotating speed, and entering a high-speed acquisition state when the tire rotating speed is basically stable;
step S5 enables tire detection: starting a tire detection algorithm in a high-speed acquisition state, and quickly extracting and calculating image information acquired at a high speed;
step S6 failure alarm: when a fault is generated on the surface of the tire is captured, an alarm prompt appears on a software interface, a sound-light alarm is externally connected through a USB interface, and when the fault is generated in calculation, a signal is sent to generate a visual sound-light alarm;
step S7 controls the detection algorithm switch: after the alarm is generated, the detection algorithm can be manually closed, and the algorithm can be restarted for detection.
4. The system of claim 3, wherein in step S5, threshold and sensitivity are required to be set for the high-speed camera, the influence of tire surface pattern and calculation error is eliminated, the target laser line is selected, an algorithm is started, and the curvature change of the laser lines in the picture is calculated to determine whether the tire surface is faulty.
5. The system for detecting the bulge of the tire based on the high-speed camera and the multi-line laser as claimed in claim 3, wherein in step S6, the system outputs a signal according to the algorithm result to determine whether there is a bulge fault, and if there is a bulge fault, the system can output a signal to generate an audible and visual alarm.
6. The high-speed camera and multi-line laser based tire bulge detection system as claimed in claim 3, wherein in step S7, the tire bulge detection algorithm is in the camera acquisition control software and can be manually turned off and on.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113008158A (en) * | 2021-03-25 | 2021-06-22 | 烟台大学 | Multi-line laser tyre pattern depth measuring method |
| CN113267137A (en) * | 2021-05-28 | 2021-08-17 | 北京易航远智科技有限公司 | Real-time measurement method and device for tire deformation |
| CN114862854A (en) * | 2022-07-07 | 2022-08-05 | 上海群乐船舶附件启东有限公司 | Ship electrical accessory defect detection method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101986143A (en) * | 2010-03-17 | 2011-03-16 | 燕山大学 | Machine vision belt tear detection and protective device |
| CN102495026A (en) * | 2011-11-23 | 2012-06-13 | 天津大学 | Acquiring method of optical zone central line used in linear laser scanning vision measurement system |
| CN104657587A (en) * | 2015-01-08 | 2015-05-27 | 华中科技大学 | Method for extracting center line of laser stripe |
| CN107150701A (en) * | 2017-03-24 | 2017-09-12 | 东莞市诺丽电子科技有限公司 | Train wheel physical dimension measuring method and its detection means |
-
2020
- 2020-08-12 CN CN202010808325.8A patent/CN111964924A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101986143A (en) * | 2010-03-17 | 2011-03-16 | 燕山大学 | Machine vision belt tear detection and protective device |
| CN102495026A (en) * | 2011-11-23 | 2012-06-13 | 天津大学 | Acquiring method of optical zone central line used in linear laser scanning vision measurement system |
| CN104657587A (en) * | 2015-01-08 | 2015-05-27 | 华中科技大学 | Method for extracting center line of laser stripe |
| CN107150701A (en) * | 2017-03-24 | 2017-09-12 | 东莞市诺丽电子科技有限公司 | Train wheel physical dimension measuring method and its detection means |
Non-Patent Citations (1)
| Title |
|---|
| 李杰: "基于激光三角法检测轮胎表面形貌", 《中国优秀博硕士学位论文全文数据库（硕士） 信息科技辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113008158A (en) * | 2021-03-25 | 2021-06-22 | 烟台大学 | Multi-line laser tyre pattern depth measuring method |
| CN113267137A (en) * | 2021-05-28 | 2021-08-17 | 北京易航远智科技有限公司 | Real-time measurement method and device for tire deformation |
| CN113267137B (en) * | 2021-05-28 | 2023-02-03 | 北京易航远智科技有限公司 | Real-time measurement method and device for tire deformation |
| CN114862854A (en) * | 2022-07-07 | 2022-08-05 | 上海群乐船舶附件启东有限公司 | Ship electrical accessory defect detection method |
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