Ion thruster array arc light detection system and method based on openCV

Abstract

The application provides an ion thruster array arc light detection system and method based on OpenCV, which comprises a vacuum chamber, a metal test plate arranged in the vacuum chamber and an annular microcathode ion thruster array, an interface and an observation window arranged on the outer wall of the vacuum chamber, an industrial camera arranged outside the observation window through a mechanical clamp, one end of the annular microcathode ion thruster array connected with the interface, the other end of the interface connected with a matrix switching unit and a driving and control system, the matrix switching unit connected with a current probe and a voltage probe, the current probe and the voltage probe connected with a high-speed oscilloscope, and the high-speed oscilloscope, the driving and control system and the industrial camera connected with a computer. The ion thruster array arc light detection system and method based on OpenCV can comprehensively evaluate the working state of the ion thruster by using computer technology and OpenCV image detection technology and combining pulse voltage and current data.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of aerospace and deep space micro-nano satellite electric propulsion detection, in particular to an ion thruster array arc light detection system and method based on OpenCV. BACKGROUND

[0002] Micro-nano satellite technology is regarded as the commanding point of today's national technology and economic development, and its propulsion system is an important part of micro-nano satellite technology. In the past, satellite platforms mainly used cold gas and chemical propulsion systems, which were limited by conditions and factors such as volume, weight and power. Traditional propulsion systems cannot meet the use requirements of micro-nano satellites. Micro-cathode ion thrusters have the advantages of small size and light weight, which meet the development direction of light weight, modularity and high efficiency of micro-nano satellite propulsion systems, and have good application prospects in tasks such as attitude control and orbit maintenance of micro-nano satellites. At present, many research institutions have made certain achievements in the performance evaluation and functional evaluation testing of micro-cathode ion thrusters in different fields.

[0003] However, the existing ion thruster testing method focuses on the injection driving power, pulse voltage, current data acquisition, and microscopic observation of the ablation state of the surface of the cathode, anode and insulator material, and lacks an automatic visual detection method for the arc light of the thruster. Therefore, it is very necessary to design an ion thruster array arc light detection system and method based on OpenCV. SUMMARY

[0004] The purpose of the present application is to provide an ion thruster array arc light detection system and method based on OpenCV, which uses computer technology and OpenCV image detection technology, and can comprehensively evaluate the working state of the ion thruster by combining pulse voltage and current data.

[0005] To achieve the above purpose, the present application provides the following scheme:

[0006] An ion thruster array arc light detection system based on OpenCV, comprising a computer, an industrial camera, a current probe, a voltage probe, a matrix switching unit, a driving and control system, a vacuum chamber, a high-speed oscilloscope and an industrial camera, a metal test plate is arranged in the inside of the vacuum chamber, and an annular micro-cathode ion thruster array is arranged on the outer wall of the vacuum chamber. An interface and an observation window are arranged on the outer wall of the vacuum chamber, the industrial camera is arranged outside the observation window through a mechanical clamp, one end of the annular micro-cathode ion thruster array is connected to the interface, the other end of the interface is connected to the matrix switching unit and the driving and control system, the matrix switching unit is connected to the current probe and the voltage probe, the current probe and the voltage probe are connected to the high-speed oscilloscope, and the high-speed oscilloscope, the driving and control system and the industrial camera are connected to the computer.

[0007] The drive and control system is used for controlling the pulse ignition of the annular microcathode ion thruster array;

[0008] The matrix switching unit is used for selecting the conducting path for measurement.

[0009] The application further provides an ion thruster array arc light detection method based on OpenCV, applied to the ion thruster array arc light detection system based on OpenCV.

[0010] Step 1: calibrate the industrial camera;

[0011] Step 2: obtain an arc light brightness comparison reference value;

[0012] Step 3: perform arc light detection;

[0013] Step 4: perform information synthesis processing on the detection information and store the information.

[0014] Optionally, in step 1, the industrial camera is calibrated, and specifically:

[0015] The annular microcathode ion thruster array is uniformly installed on a metal test plate, wherein the annular microcathode ion thruster array comprises a plurality of ion thrusters, and a channel number ch is set for each ion thruster array i , wherein the value range of i is 1 to N, N is the maximum installable number of ion thrusters that can be set on the metal test plate, the industrial camera is fixed outside the observation window through a mechanical clamp, the computer controls the industrial camera to adjust the camera focal length until the observed annular microcathode ion thruster array region is completely in the camera field of view and in the centered state, and the camera height h1 and the distance s1 of the industrial camera from the observation surface of the annular microcathode ion thruster array are measured and recorded;

[0016] The industrial camera is removed and fixed on a support in front of the observation window, a symmetrical circular checkerboard diffuse reflection correction sheet is used as a calibration board, the size of the calibration board is greater than that of the observed annular microcathode ion thruster array, and the calibration board is vertically fixed between the industrial camera and the annular microcathode ion thruster array, wherein the center height is h1, and the horizontal distance from the industrial camera and the annular microcathode ion thruster array is s1;

[0017] The computer controls the industrial camera to collect the vertical image of the diffuse reflection correction sheet, after the collection is completed, the diffuse reflection correction sheet is adjusted, the center horizontal distance s1 is kept unchanged, the diffuse reflection correction sheet is finely adjusted left and right to form an angle within 10° with the vertical state, after the adjustment is completed, the industrial camera collects multiple images as calibration original pictures;

[0018] The distortion coefficient of the camera is obtained by the cv2.calibrateCamera() function in OpenCV, and the calibration is corrected. The corresponding relationship between the camera pixel size, image pixel size and actual measured object size is established by the cv2.calibrateCamera() function in OpenCV, and the calibration parameters are saved.

[0019] Optionally, in step 2, the arc light brightness comparison reference value is obtained, specifically:

[0020] The computer performs ignition test of the annular microcathode ion thruster array under the set excitation power through the driving and control system, and lasts for 60s, which is used to ensure the normal work of the annular microcathode ion thruster array. The saturation of the industrial camera is adjusted according to different ion thrusters, which is used to avoid overexposure of the industrial camera. After the adjustment is completed, the computer controls the annular microcathode ion thruster array to ignite through the driving and control system. The computer controls the industrial camera to collect multiple groups of images in the normal state, and performs sample storage processing;

[0021] The stored RGB image file is converted to a gray image format file through the cv2.cvtColor function in OpenCV. The circular detection is performed through the cv2.HoughCircles() function in OpenCV. The arc light circular profile center point and radius information of each ion thruster of the annular microcathode ion thruster array are recorded in sequence.

[0022] The stored RGB image file is converted to an HSV format file through the cv2.cvtColor function in OpenCV. The average value Bright_ave of the brightness in the corresponding pixel area is calculated according to the recorded arc light circular profile center point and radius information of the ion thruster, which is used as the arc light brightness comparison reference value under the current driving power. The arc light brightness comparison reference values of ion thrusters of the same type under different powers are recorded, which are used as the normal detection brightness comparison values.

[0023] Optionally, in step 3, the arc light detection is performed, specifically:

[0024] The computer adjusts the pulse excitation voltage, pulse excitation current and excitation interval frequency through the driving and control system. According to the number and installation position of the actual detected ion thrusters, the grouping and order are preset. The computer excites the ion thrusters according to the grouping and order through the driving and control system. Each group of ion thrusters works for 60s. The computer controls the industrial camera to collect images through instructions. Ten images are collected at equal time intervals during the 60s work of each group of ion thrusters. The computer acquires the collected images and saves them according to the preset naming rule.

[0025] Perform contour detection, brightness and color detection on the saved image, obtain the detection results, and save the detection results.

[0026] Optionally, perform contour detection on the saved image to obtain the detection result and save the detection result, specifically:

[0027] The saved image is read through the relevant functions of OpenCV and saved as an RGB image file. The RGB image file is converted into a grayscale image format file through the cv2.cvtColor function in OpenCV. The grayscale image format file is Gaussian filtered by selecting appropriate convolution kernel parameters through the cv2.GaussianBlur() function in OpenCV to remove noise. The Hough circle contour is detected through the cv2.HoughCircles() function in OpenCV. The coordinates and radius information of the center point of the arc light circular contour of the ion thruster in the array in the recognized image are recorded. According to the number of excitation channels set, the number and position of the contour center points recognized by the image are combined and compared with the ch obtained during the calibration process. i Each channel is compared with the position information in the corresponding picture, and the computer saves the ch i The coordinates of the center point, radius parameters and comparison results of the circular contour of each channel.

[0028] Optionally, perform brightness and color detection on the saved image, obtain the detection results, and save the detection results, specifically:

[0029] The saved image is read through the OpenCV related functions and saved as an RGB image file. The image is processed by calling the cv2.fastNlMeansDenoisingColored() function in OpenCV. The image is denoised by setting the appropriate filter strength parameters. The RGB image file is converted to an HSV format file by calling the cv2.cvtColor() function in OpenCV. The center point coordinates CirCh of the circular contour center point position of each channel obtained in the contour detection stage are obtained. i (x, y) and radius R i , convert it into the corresponding pixel area position of the hue H and brightness V space of the HSV file result, and calculate the coordinates of the center point CirCh in the H space i (x, y) and radius R i The average value of pixels in the area is detected by calling the cv2.inRange() function to detect the color of the measured area and calculate the coordinates of the center point CirCh in the V space i (x, y) and radius R i The average value of pixels in the area Vave_i, the brightness ratio of ion thruster channel i is calculated as:

[0030]

[0031] The computer saves the test results obtained.

[0032] Optionally, in step 3, the detection information is subjected to information synthesis processing and storage, specifically as follows:

[0033] The computer reads the ion thruster excitation pulse voltage, pulse current and pulse width data of each test channel through the high-speed oscilloscope, voltage probe and current probe, and matches the ion thruster configuration information, ion thruster excitation pulse voltage, pulse current, pulse width data, profile visual detection results and brightness and color detection results, and stores them into the database.

[0034] According to the specific embodiments of the present application, the following technical effects are disclosed: the ion thruster array arc light detection system and method based on OpenCV provided by the present application, the system comprising a computer, an industrial camera, a current probe, a voltage probe, a matrix switching unit, a driving and control system, a vacuum chamber, a high-speed oscilloscope and an industrial camera, the arc light information of the ion thruster during operation is collected through the industrial camera, the annular micro-cathode ion thruster array pulse excitation ignition is controlled through the driving and control system, and the current or voltage measurement is performed through the matrix switching unit to select the conduction path; the method comprises calibrating the industrial camera, obtaining the arc light brightness comparison reference value, performing arc light detection, performing information synthesis processing on the detection information, and storing; the system and method can solve the problems of automatic recognition and recording of the arc light color of the plasma thruster, automatic discrimination and recording of the arc light brightness, and arc light profile detection by using computer technology and image processing technology, and solve the problem that the current human eye observation sampling cannot be quantified. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0036] Figure 1 The figure is a structural schematic diagram of the ion thruster array arc light detection system based on OpenCV of the embodiments of the present application.

[0037] Figure 2 The figure is a calibration flowchart of the industrial camera.

[0038] Figure 3 The figure is a schematic diagram of the installation layout of the ion thruster.

[0039] Figure 4 Schematic diagram of the structure of a symmetrical circular chessboard diffuse reflection correction sheet;

[0040] Figure 5 Schematic diagram of the process for obtaining arc brightness comparison benchmark value;

[0041] Figure 6 This is a schematic diagram of the arc detection process;

[0042] Figure 7 Schematic diagram of the detection information synthesis processing flow.

[0043] Figure numerals: 1. Computer; 2. Industrial camera; 3. Vacuum chamber; 4. Drive and control system; 5. Matrix switching unit; 6. Current probe; 7. Voltage probe; 8. High-speed oscilloscope; 9. Interface; 10. Observation window. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] The purpose of the present invention is to provide an ion thruster array arc detection system and method based on OpenCV, which utilizes computer technology and OpenCV image detection technology, and combines pulse voltage and current data to comprehensively evaluate the working status of the ion thruster.

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] like Figure 1As shown, an ion thruster array arc detection system based on OpenCV provided by an embodiment of the present invention includes: a computer 1, an industrial camera 2, a current probe 6, a voltage probe 7, a matrix switching unit 5, a drive and control system 4, a vacuum chamber 3, a high-speed oscilloscope 8 and the industrial camera 2, wherein a ring-shaped micro-cathode ion thruster array is set on a metal test plate inside the vacuum chamber 3, an interface 9 and an observation window 10 are provided on the outer wall of the vacuum chamber 3, and the industrial camera 2 is set outside the observation window 10 through a mechanical fixture, the ring-shaped micro-cathode ion thruster array is connected to one end of the interface 9, and the other end of the interface 9 is connected to the matrix switching unit 5 and the drive and control system 4, the matrix switching unit 5 is connected to the current probe 6 and the voltage probe 7, the current probe 6 and the voltage probe 7 are connected to the high-speed oscilloscope 8, and the high-speed oscilloscope 8, the drive and control system 4 and the industrial camera 2 are connected to the computer;

[0048] The driving and control system 4 is used to control the pulse excitation ignition of the annular micro-cathode ion thruster array;

[0049] The matrix switching unit 5 is used to select the conductive path for measurement, and realize the measurement of different channel numbers ch i The current and voltage information collection of the ion thruster;

[0050] Among them, the computer and industrial camera are connected through a USB3.0 cable. The industrial camera is used to collect arc information when the ion thruster is working. The current probe and voltage probe are used to collect pulse voltage, pulse current and pulse width information, and display it on the high-speed oscilloscope.

[0051] The present invention also provides an ion thruster array arc detection method based on OpenCV, which is applied to the above-mentioned ion thruster array arc detection system based on OpenCV, and is characterized in that it includes the following steps:

[0052] Step 1: Calibrate the industrial camera;

[0053] Step 2: Obtain arc brightness comparison benchmark value;

[0054] Step 3: Perform arc detection;

[0055] Step 4: Perform information synthesis processing on the detection information and store it.

[0056] like Figure 2 As shown, in step 1, the industrial camera is calibrated, specifically:

[0057] The annular microcathode ion thruster array is evenly mounted on the metal test plate. The annular microcathode ion thruster array includes multiple ion thrusters, and a channel number ch is set for each ion thruster array.i Wherein, the value range of i is 1 to N, N is the maximum installable number of ion thrusters that the metal test board can set, and the specific layout of an embodiment of the present application is as shown in Figure 3 The circular marks 1, 2, …, 24 are the nozzles of the ion thruster, Figure 3 The metal test board detects the channel data range of 1-24 channels;

[0058] The industrial camera is fixed outside the observation window through a mechanical clamp, the computer controls the industrial camera to adjust the camera focal length until the observed area of the annular micro-cathode ion thruster array is completely within the camera field of view and is in a centered state, and the camera height h1 and the distance s1 of the industrial camera from the observation surface of the annular micro-cathode ion thruster array are measured and recorded at this time;

[0059] The industrial camera is removed and fixed on a support in front of the observation window, and the symmetrical circular checkerboard diffuse reflection correction sheet is used as a calibration board, as shown in Figure 4 The size of the calibration board is larger than the observed area of the annular micro-cathode ion thruster array, and it is vertically fixed between the industrial camera and the annular micro-cathode ion thruster array, with a center height of h1 and a horizontal distance of s1 from the industrial camera and the annular micro-cathode ion thruster array;

[0060] The computer controls the industrial camera to collect the vertical image of the diffuse reflection correction sheet, and after collection, the diffuse reflection correction sheet is adjusted to keep the center horizontal distance as s1 unchanged, and the diffuse reflection correction sheet is finely adjusted left and right to form an angle within 10° with the vertical state, and after adjustment, the industrial camera collects multiple groups (10 or more different angle) of images as calibration original pictures;

[0061] The distortion coefficient of the camera is obtained through the cv2.calibrateCamera() function in OpenCV, and calibration is performed, the corresponding relationship between the camera pixel size, image pixel size and actual measured object size is established through the cv2.calibrateCamera() function in OpenCV, and the calibration parameters are saved; wherein, the industrial camera calibration is performed once under the condition that the industrial camera is consistent and the distances s1 and h1 are unchanged, and it is not necessary to perform it every time.

[0062] As shown in Figure 5 In step 2, the arc light brightness comparison reference value is obtained, specifically:

[0063] The computer drives and controls the system to perform annular micro-cathode ion thruster array ignition test at a set excitation power, and lasts for 60s, which is used to ensure the normal work of the annular micro-cathode ion thruster array;

[0064] Different ion thrusters have differences, and the brightness is inconsistent under the same excitation power. The saturation of the industrial camera is adjusted to avoid overexposure of the industrial camera. After adjustment, the computer controls the ignition of the annular microcathode ion thruster array through the driving and control system. The computer controls the industrial camera to collect multiple groups of images in the normal state for sample storage and processing.

[0065] The stored RGB image file is converted to a gray image format file through the cv2.cvtColor function in OpenCV (image, cv2.COLOR_BGRA2GRAY). The cv2.HoughCircles() function in OpenCV is used for circle detection. According to the attached Figure 3 In the middle, the recorded channel number ch i , the arc light circular profile center point and radius information of each ion thruster of the annular microcathode ion thruster array are recorded in sequence.

[0066] The stored RGB image file is converted to an HSV format file through the cv2.cvtColor function in OpenCV. The average value Bright_ave of the brightness in the corresponding pixel area is calculated based on the recorded arc light circular profile center point and radius information of the ion thruster, which is used as the arc light brightness comparison reference value under the current driving power. The arc light brightness comparison reference values of ion thrusters of the same type under different powers are recorded as normal detection brightness comparison values.

[0067] As shown in Figure 6 , in step 3, arc light detection is performed, specifically:

[0068] The computer adjusts the pulse excitation voltage, pulse excitation current, and excitation interval frequency through the driving and control system. According to the actual number and installation position of the ion thrusters, the grouping and order are preset. The computer excites the ion thrusters according to the grouping and order through the driving and control system. Each group of ion thrusters works for 60s. The computer controls the industrial camera to collect images through instructions. During the 60s of continuous work of each group of ion thrusters, 10 images are collected at equal time intervals. The computer acquires the collected images and saves them according to the preset naming rules. The file name contains the collection time and excitation channel mode.

[0069] The saved image is subjected to contour detection and brightness and color detection to obtain a detection result, and the detection result is saved, wherein the contour detection stage includes RGB to grayscale image conversion, image filtering and denoising, and Hough circle contour detection steps, and the processed image object is a grayscale image; the brightness and color detection stage includes color image denoising, RGB to HSV conversion, target region determination, and detection target brightness and color acquisition steps, and the processed image object is an HSV format color picture.

[0070] The saved image is subjected to contour detection, and the detection result is saved, specifically as follows:

[0071] The contour detection stage prevents destructive modification of the original file, the saved image is read through OpenCV related functions, and is saved as an RGB image file, the RGB image file is converted to a grayscale image format file through the cv2.cvtColor function in OpenCV, the grayscale image format file is subjected to Gaussian filtering through the cv2.GaussianBlur() function in OpenCV by selecting appropriate kernel parameters, for removing noise and reducing interference, Hough circle contour detection is performed through the cv2.HoughCircles() function in OpenCV, the ion thruster arc light circular contour center point coordinates and radius information in the array of the recognized picture are recorded, the number of excitation channels is set in combination with the number and position of the contour center points recognized by the image, and ch i The position information in the picture corresponding to each channel is compared, and the computer saves the ch i The circular contour center point coordinates, radius parameters and comparison results of each channel.

[0072] The saved image is subjected to brightness and color detection, and the detection result is saved, specifically as follows:

[0073] The color brightness detection stage prevents destructive modification of the original file, the saved image is read through OpenCV related functions, and is saved as an RGB image file, the image is processed through the cv2.fastNlMeansDenoisingColored() function in OpenCV by setting appropriate filter strength parameters, image denoising is performed, the RGB image file is converted to an HSV format file through the cv2.cvtColor() function in OpenCV, the center point coordinates CirCh i (x, y) and the radius R i in the circular contour center point position obtained in the contour detection stage are converted to the corresponding pixel region position in the hue H and brightness V space of the HSV file result, the center point coordinates CirCh i(x, y) and radius R i The pixel average value in the region, the color of the measured region is detected by calling the cv2.inRange() function, and the center point coordinate CirCh in the V space is calculated i (x, y) and radius R i The pixel average value Vave_i in the region, the brightness ratio of the ion thruster channel i is calculated as:

[0074]

[0075] The computer saves the test results obtained.

[0076] As Figure 7 shown, in step 4, the detection information is subjected to information synthesis processing and stored, specifically:

[0077] The ion thruster configuration information is calculated and obtained, including excitation power, excitation channel information, excitation interval and other parameters, the computer reads the ion thruster excitation pulse voltage, pulse current and pulse width data of each test channel through a high-speed oscilloscope, a voltage probe and a current probe, and matches the ion thruster configuration information, the ion thruster excitation pulse voltage, the pulse current, the pulse width data, the profile visual detection result and the brightness and color detection result, and stores them in the database.

[0078] The present application adopts computer technology and OpenCV image recognition technology to identify and record the arc light color and brightness of the micro-cathode ion thruster during testing, as a means of directly judging whether the thruster is working stably, and the results of pulse voltage, pulse current and pulse width measurement can be used to evaluate the working state of the ion thruster. The image acquisition and recording of the industrial camera and the computer processing have the characteristics of high efficiency, good consistency and strong reliability compared with the human eye recognition.

[0079] The arc light profile of the ion thruster contains the center position and the profile radius as a kind of intuitive arc light feature, which can represent the working state of the ion thruster. There is no precedent for image recognition recording of the present technology. The OpenCV image recognition technology used in the present application can effectively solve the problem that manual vision cannot effectively judge the arc light profile of the ion thruster array, and has the characteristics of fast recognition speed, good consistency and automatic recording for easy saving.

[0080] Current ion thruster working state evaluation depends on pulse voltage, pulse current and pulse width measurement, and single means inevitably causes inaccurate evaluation of thruster working state, and the color, brightness and contour of arc light can be used to intuitively judge whether the thruster works stably. At present, there is still lack of effective means and automatic test technology for ion thruster arc light monitoring, and the test personnel cannot continuously observe for a long time, and the test personnel adopts interval observation and records whether the arc light color, brightness and contour are normal, and manually records the results. The present application uses computer technology and image recognition technology to complete automatic discrimination and record results in the whole ion thruster test process, and the detection efficiency is greatly improved. Through the functions related to OpenCV, the arc light brightness and contour size of the ion thruster are measured, and the problem that manual measurement cannot be quantified is overcome.

[0081] The present application provides an ion thruster array arc light detection system and method based on OpenCV, which comprises a computer, an industrial camera, a current probe, a voltage probe, a matrix switching unit, a driving and control system, a vacuum chamber, a high-speed oscilloscope and an industrial camera. The arc light information of the ion thruster during operation is collected by the industrial camera, the annular microcathode ion thruster array pulse excitation ignition is controlled by the driving and control system, and the current or voltage measurement is performed by selecting the conduction path of the matrix switching unit. The method comprises calibrating the industrial camera, obtaining the arc light brightness comparison reference value, performing arc light detection, information synthesis processing and storage of the detection information. The system and method can use computer technology and image processing technology to solve the problems of automatic recognition and recording of arc light color, automatic discrimination and recording of arc light brightness, and arc light contour detection, and solve the problem that the current eye observation cannot be quantified.

[0082] The principles and embodiments of the present application are described by specific examples in this paper, and the above examples are only used to help understand the method and core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific embodiments and application range will be changed. In view of the above, the content of the specification should not be understood as a limitation of the present application.

Claims

1. An ion thruster array arc detection method based on OpenCV, applied to an ion thruster array arc detection system based on OpenCV, characterized in that: The system includes: a computer, an industrial camera, a current probe, a voltage probe, a matrix switching unit, a drive and control system, a vacuum chamber, a high-speed oscilloscope, and an industrial camera. A metal test plate inside the vacuum chamber is provided with an annular micro-cathode ion thruster array, an interface and an observation window are provided on the outer wall of the vacuum chamber, and the industrial camera is provided outside the observation window via a mechanical fixture. The annular micro-cathode ion thruster array is connected to one end of the interface, and the other end of the interface is connected to the matrix switching unit and the drive and control system. The matrix switching unit is connected to the current probe and the voltage probe, and the current probe and the voltage probe are connected to the high-speed oscilloscope. The high-speed oscilloscope, the drive and control system, and the industrial camera are connected to the computer. The drive and control system is used to control the pulse excitation ignition of the annular microcathode ion thruster array; The matrix switching unit is used to select a conductive path for measurement; The method comprises the following steps: Step 1: Calibrate the industrial camera; Step 2: Obtain arc brightness comparison benchmark value; Step 3: Perform arc detection; Step 4: Perform information synthesis processing on the detection information and store it; In step 1, the industrial camera is calibrated as follows: The annular microcathode ion thruster array is evenly mounted on the metal test plate. The annular microcathode ion thruster array includes multiple ion thrusters, and a channel number ch is set for each ion thruster array. i , where i ranges from 1 to N, and N is the maximum number of ion thrusters that can be installed on the metal test plate. An industrial camera is fixed to the outside of the observation window using a mechanical fixture. The computer controls the industrial camera to adjust the camera focal length until the area of ​​the annular microcathode ion thruster array being observed is completely within the camera's field of view and centered. The camera height h1 and the distance s1 between the industrial camera and the observation surface of the annular microcathode ion thruster array are measured and recorded at this time. Remove the industrial camera and secure it to the bracket in front of the observation window. Use a symmetrical circular checkerboard diffuse reflection correction plate as a calibration plate. The plate is larger than the observed area of ​​the annular micro-cathode ion thruster array and is fixed vertically between the industrial camera and the annular micro-cathode ion thruster array. The center height is h1, and the horizontal distance from the industrial camera and the annular micro-cathode ion thruster array is s1. The computer controls the industrial camera to collect vertical images of the diffuse reflection correction sheet. After the collection is completed, the diffuse reflection correction sheet is adjusted to keep the center horizontal distance s1 unchanged. The diffuse reflection correction sheet is fine-tuned left and right to keep the angle within 10° from the vertical state. After the adjustment is completed, the industrial camera collects multiple sets of images as the calibration original images; The camera distortion coefficient is obtained and calibrated through the cv2.calibrateCamera() function in OpenCV. The correspondence between the camera pixel size, image pixel size and the actual object size is established through the cv2.calibrateCamera() function in OpenCV, and the calibration parameters are saved.

2. The ion thruster array arc detection method based on OpenCV according to claim 1, characterized in that: In step 2, the arc brightness comparison benchmark value is obtained, specifically: The computer, through the drive and control system, performs an ignition test of the annular microcathode ion thruster array at a set excitation power for 60 seconds to ensure the normal operation of the annular microcathode ion thruster array. The saturation of the industrial camera is adjusted according to different ion thrusters to avoid overexposure of the industrial camera. After the adjustment is completed, the computer controls the ignition of the annular microcathode ion thruster array through the drive and control system. The computer controls the industrial camera to collect multiple sets of images under normal conditions and perform sample storage and processing. The stored RGB image file is converted into a grayscale image format file using the cv2.cvtColor function in OpenCV. Circle detection is performed using the cv2.HoughCircles() function in OpenCV. The arc circle outline center point and radius information of each ion thruster in the identified annular microcathode ion thruster array are recorded in turn. The stored RGB image file is converted into an HSV format file using the cv2.cvtColor function in OpenCV. The average brightness value Bright_ave in the corresponding pixel area is calculated by recording the center point and radius information of the arc circular outline of the ion thruster. This value is used as the arc brightness comparison benchmark value under the current driving power. The arc brightness comparison benchmark values ​​of the same model of ion thruster under different powers are recorded as the brightness comparison value for normal detection.

3. The ion thruster array arc detection method based on OpenCV according to claim 2, characterized in that: In step 3, arc detection is performed, specifically: The computer adjusts the pulse excitation voltage, pulse excitation current, and excitation interval frequency through the drive and control system. The computer presets the grouping and sequence based on the number and installation positions of the ion thrusters actually detected. The computer excites the ion thrusters according to the grouping and sequence through the drive and control system. Each group of ion thrusters operates continuously for 60 seconds. The computer controls the industrial camera through instructions to perform image acquisition. During the 60-second period when each group of ion thrusters operates continuously, 10 images are acquired at equal time intervals. The computer acquires the acquired images and names and saves them according to a preset naming rule. Perform contour detection, brightness and color detection on the saved image, obtain the detection results, and save the detection results.

4. The ion thruster array arc detection method based on OpenCV according to claim 3, characterized in that: Perform contour detection on the saved image, obtain the detection results, and save the detection results. Specifically: The saved image is read through the relevant functions of OpenCV and saved as an RGB image file. The RGB image file is converted into a grayscale image format file through the cv2.cvtColor function in OpenCV. The grayscale image format file is Gaussian filtered by selecting appropriate convolution kernel parameters through the cv2.GaussianBlur() function in OpenCV to remove noise. The Hough circle contour is detected through the cv2.HoughCircles() function in OpenCV. The coordinates and radius information of the center point of the arc light circular contour of the ion thruster in the array in the recognized image are recorded. According to the number of excitation channels set, the number and position of the contour center points recognized by the image are combined and compared with the ch obtained during the calibration process. i Each channel is compared with the position information in the corresponding picture, and the computer saves the ch i The coordinates of the center point, radius parameters and comparison results of the circular contour of each channel.

5. The ion thruster array arc detection method based on OpenCV according to claim 4, characterized in that: Perform brightness and color detection on the saved image, obtain the detection results, and save the detection results. Specifically: The saved image is read through the OpenCV related functions and saved as an RGB image file. The image is processed by calling the cv2.fastNlMeansDenoisingColored() function in OpenCV. The image is denoised by setting the appropriate filter strength parameters. The RGB image file is converted to an HSV format file by calling the cv2.cvtColor() function in OpenCV. The center point coordinates CirCh of the circular contour center point position of each channel obtained in the contour detection stage are obtained. i (x, y) and radius R i , convert it into the corresponding pixel area position of the hue H and brightness V space of the HSV file result, and calculate the coordinates of the center point CirCh in the H space i (x, y) and radius R i The average value of pixels in the area is checked by calling the cv2.inRange() function. Measure the color of the measured area and calculate the coordinates of the center point CirCh in the V space i (x, y) and radius R i The average value of pixels in the area Vave_i, the brightness ratio of ion thruster channel i is calculated as: ； The computer saves the obtained test results.

6. The ion thruster array arc detection method based on OpenCV according to claim 5, characterized in that: In step 4, the detection information is synthesized and stored, specifically: The computer reads the ion thruster excitation pulse voltage, pulse current, and pulse width data of each test channel through a high-speed oscilloscope, voltage probe, and current probe, and matches the ion thruster configuration information, ion thruster excitation pulse voltage, pulse current, pulse width data, contour visual detection results, and brightness and color detection results, and stores them in the database.

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