Method and system for measuring parameters of DBD type ozone generator based on image processing

Abstract

The invention provides a DBD (Dielectric Barrier Discharge) type ozone generator parameter measurement method and a DBD (Dielectric Barrier Discharge) type ozone generator parameter measurement system based on image processing. The method specifically comprises the following steps of image preprocessing, calculation of grouping key angular points, preliminary fitting of a quadrangle, secondary fitting of the quadrangle, calculation and output of DBD related parameters and the like. According to the method, the quadrilateral is automatically fitted through a program, manual fitting is replaced, and high-precision measurement of parameters such as the equivalent capacitance, the discharge maintaining voltage and the discharge energy of each period of the DBD type ozone generator is achieved. The method is high in applicability and high in measurement precision, and the problems that an existing manual fitting parameter solving method is long in consumed time and low in precision are solved.

Description

Technical Field

[0001] The present invention belongs to the field of electrical parameter measurement, and specifically relates to a DBD type ozone generator parameter measurement method and system based on image processing. The invention uses a computer program to fit the Lissajous figure, replacing the manual fitting method, to improve the parameter measurement efficiency and accuracy of the DBD type ozone generator. Background Art

[0002] Ozone, as a light blue gas, has strong sterilization and oxidation capabilities. It does not produce pollution or residue during use, so it is widely used in environmental protection, medicine, food and other fields. In industrial production, ozone is mainly generated through dielectric barrier discharge, which has become the main means of industrialized ozone production at home and abroad.

[0003] Existing research on DBD ozone generators focuses on power supply, load characteristics and ozone production rate, while there is less research on the measurement and automatic determination of equivalent circuit parameters of DBD ozone generators. However, accurately obtaining the equivalent parameters of DBD ozone generators is of great significance for optimizing the discharge system and improving the ozone production rate.

[0004] Traditionally, methods for obtaining DBD ozone generator parameters include calculation method and Lissajous figure measurement method. The calculation method is based on geometric structure and physical formula to derive equivalent capacitance, but this method is easily affected by material properties and processing errors, accuracy is difficult to guarantee, and discharge maintenance voltage cannot be measured. In contrast, the Lissajous figure measurement method can measure three important parameters at the same time with high accuracy, and has become the main measurement method. However, most of the existing Lissajous figure measurement methods rely on manual fitting of parallelograms. This method has problems such as low efficiency and poor accuracy, and it is difficult to meet the needs of automation and high-precision measurement.

[0005] In order to solve this problem, it is urgent to develop an automated Lissajous figure measurement method to improve the efficiency and accuracy of the equivalent parameter measurement of DBD type ozone generator. Summary of the invention

[0006] The present invention proposes an automatic DBD ozone generator Lissajous image processing method. The method uses DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering filtering to extract the Lissajous image data points of the DBD ozone generator, complete the preprocessing of the Lissajous image, and determine the four key corner points A, B, C and D of the Lissajous image. Based on these four corner points, the data points are divided into four groups, and straight line fitting is performed on each group of data points and the intersection points of adjacent edges are obtained to obtain a preliminary fitted quadrilateral. After further quadratic fitting, the final Lissajous image fitting quadrilateral is obtained. The method replaces manual fitting with a computer program, which greatly improves the efficiency of parameter fitting while also ensuring the accuracy of fitting.

[0007] The DBD type ozone generator parameter measurement method proposed by the present invention consists of the following main steps:

[0008] Preprocessing the Lissajous image of the DBD type ozone generator, wherein the preprocessed image only contains a valid set of white data points;

[0009] Obtaining grouping key corner points A, B, C, and D based on the white data point set;

[0010] Divide the white data point set into 4 arrays, perform straight line fitting on the data point set of each array, and preliminarily construct a quadrilateral;

[0011] Based on the initially constructed quadrilateral, the boundary line is translated up and down to include a new set of data points, and a secondary fitting is performed to construct a quadrilateral with higher accuracy;

[0012] The equivalent capacitance, discharge maintenance voltage and discharge energy per cycle of the DBD type ozone generator are calculated and output.

[0013] Furthermore, the step of “preprocessing the Lissajous image of the DBD type ozone generator, wherein the preprocessed image only contains a valid set of white data points” includes the following sub-steps:

[0014] intercepting the Lissajous image generated by the oscilloscope to obtain a sub-image containing only a valid data area;

[0015] Convert the sub-image from RGB (Red, Green, Blue) to HSV (Hue, Saturation, Value) to extract color data points, and generate a logical mask image;

[0016] The logic mask image is grayed and binarized to obtain an image that only retains white data points; the image that retains white data points is denoised using a DBSCAN clustering algorithm to obtain a denoised binary image.

[0017] Furthermore, the step of “obtaining grouping key corner points ABCD according to the white data point set” includes the following sub-steps:

[0018] Calculate the centroid of the white data points;

[0019] According to the centroid, the data points are divided into left and right regions, and the average positions of the 10 points farthest from the centroid are selected in the left and right regions respectively to obtain key corner points A and B;

[0020] A straight line AB is constructed from the corner point A and point B, and all the data points are divided into upper and lower areas through the straight line AB. The average positions of the 10 points farthest from the straight line AB are selected in the upper and lower areas respectively to obtain key corner points C and D.

[0021] Furthermore, the step of "dividing the white data point set into four arrays, performing straight line fitting on the data point set of each array, and preliminarily constructing a quadrilateral" includes the following sub-steps:

[0022] Calculate the angle between the line connecting the key corner points A, B, C, D and the center of mass and the horizontal axis of the center of mass;

[0023] According to the angle, the white data point set is divided into four arrays; wherein group 1 is a data point set within the angle range between the centroid to the key point A and the centroid to the key point C, group 2 is a data point set within the angle range between the centroid to the key point C and the centroid to the key point B, group 3 is a data point set within the angle range between the centroid to the key point B and the centroid to the key point D, and group 4 is a data point set within the angle range between the centroid to the key point D and the centroid to the key point A;

[0024] Using the least square method to perform straight line fitting on each data point set, four straight lines are obtained;

[0025] According to the four straight lines, the intersection points of adjacent straight lines are calculated respectively to obtain four vertices E, F, G, and H, and based on them, a preliminary fitting quadrilateral EFGH is constructed.

[0026] Furthermore, the step of "based on the initially constructed quadrilateral, translating the boundary line up and down to include a new data point set, and performing a secondary fitting to construct a quadrilateral with higher accuracy." includes the following sub-steps:

[0027] The straight line corresponding to one side of the initially constructed quadrilateral is translated up and down to form two new translation straight lines;

[0028] Filter out a data point set between the two new translation lines;

[0029] The screened data point set is again fitted using the least square method to obtain an optimized expression for one side of the initially constructed quadrilateral;

[0030] Repeat the optimization expression obtaining process for the remaining three sides of the initially constructed quadrilateral to obtain the optimization expressions of the remaining three sides;

[0031] According to the four optimization expressions, the intersection points of adjacent edges are calculated, and finally the optimized quadrilateral A1B1C1D1 is constructed.

[0032] Furthermore, the step of "calculating and outputting the equivalent capacitance, discharge maintenance voltage and discharge energy per cycle of the DBD type ozone generator" includes the following sub-steps:

[0033] The average slopes of the relative sides of the quadrilateral A1B1C1D1 are calculated, and the equivalent capacitance, dielectric barrier capacitance and air gap capacitance of the DBD type ozone generator are deduced according to the attenuation coefficient in the measurement circuit;

[0034] The discharge maintenance voltage of the DBD type ozone generator is calculated by taking half of the absolute value of the difference between the horizontal straight line where the center of mass is located and the two intersection points of the quadrilateral A1B1C1D1 and combining it with the attenuation coefficient;

[0035] By calculating the area of ​​the quadrilateral A1B1C1D1, the discharge energy per cycle of the DBD type ozone generator is obtained.

[0036] The present invention also discloses a DBD type ozone generator parameter measurement system based on image processing, comprising the following modules:

[0037] Image preprocessing module: used for extracting valid data point areas from the Lissajous image generated by the oscilloscope and performing denoising processing;

[0038] A preliminary fitting module: used to obtain the key corner points A, B, C, and D according to the distribution of the data points, and to group and straight-line fit the data points to construct the preliminary fitting quadrilateral;

[0039] Quadratic fitting and parameter output module: used to construct a quadratic fitting quadrilateral with higher accuracy based on the preliminary fitting quadrilateral, and calculate and output the relevant parameters of the DBD type ozone generator.

[0040] Furthermore, the "image preprocessing module" includes the following submodules:

[0041] Graphics interaction submodule: used to save the captured area image;

[0042] Data point extraction submodule: used for color space conversion, extracting data points and binarizing images;

[0043] Noise point denoising submodule: used to denoise noise points using the DBSCAR clustering algorithm.

[0044] Furthermore, the "preliminary fitting module" includes the following submodules:

[0045] Centroid calculation and region segmentation submodule: used to obtain the centroid of the white data point and segment the data point region;

[0046] Key point A, B selection submodule: used to determine the point A and the point B;

[0047] Key point C, D selection submodule: used to determine the point C and the point D;

[0048] Angle calculation and grouping submodule: used to calculate the angles between the vectors from the centroid to the key corner points A, B, C, D and the horizontal axis, and divide the data points into four groups according to these angles;

[0049] Linear fitting submodule: used for performing least squares linear fitting on each set of data points;

[0050] Quadrilateral vertex calculation submodule: used to calculate the intersection of every two adjacent straight lines, obtain the vertices of the quadrilateral, and complete the preliminary fitting.

[0051] Furthermore, the "quadratic fitting and parameter output module" includes the following submodules:

[0052] Line translation and data screening submodule: used to translate each line of the preliminary fitting up and down, and obtain a new set of data points by screening key data points that meet the translation range;

[0053] Quadratic linear fitting submodule: used to perform least squares linear fitting on the screened data point set to calculate a new slope and intercept;

[0054] Optimized vertex calculation submodule: used to calculate the intersection points of adjacent straight lines according to the straight lines after quadratic fitting, obtain the vertices of the quadratic fitting quadrilateral, and obtain the quadratic fitting quadrilateral;

[0055] Equivalent capacitance calculation submodule: used to calculate the equivalent capacitance of DBD type ozone generator, including dielectric barrier capacitance and air gap capacitance;

[0056] Discharge maintenance voltage calculation submodule: used to calculate the discharge maintenance voltage of the DBD type ozone generator;

[0057] Discharge energy calculation submodule: used to calculate the discharge energy of each cycle.

[0058] Beneficial effects:

[0059] The present invention provides a method and system for measuring parameters of a DBD (Dielectric Barrier Discharge) type ozone generator based on image processing. Specifically, the following steps are included: image preprocessing, obtaining key corner points for grouping, preliminary quadrilateral fitting, secondary quadrilateral fitting, and DBD related parameter calculation and output. The method automatically fits quadrilaterals through a program, replacing manual fitting, and realizes high-precision measurement of parameters such as equivalent capacitance, discharge maintenance voltage, and discharge energy per cycle of a DBD type ozone generator. The method has strong applicability and high measurement accuracy, and solves the problem that the existing manual fitting method for obtaining parameters is time-consuming and has low accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of the measurement method;

[0061] Figure 2 To manually capture the interactive interface diagram;

[0062] Figure 3 To complete the preprocessing data point map;

[0063] Figure 4 is a schematic diagram of the centroid position;

[0064] Figure 5 Schematic diagram of the left and right area segmentation of data points;

[0065] Figure 6 Schematic diagram of the upper and lower area segmentation of data points;

[0066] Figure 7 Schematic diagram of the positions of key corner points A, B, C, and D;

[0067] Figure 8 Schematic diagram for grouping data points;

[0068] Fig. 9Schematic diagram of the preliminary fitted quadrilateral EFGH;

[0069] Fig.10 It is a schematic diagram of the data points included in the vertical translation of the straight line;

[0070] Fig.11 Schematic diagram of the final fitted quadrilateral A1B1C1D1. DETAILED DESCRIPTION

[0071] In the following description, many technical details are provided to help readers better understand the present application. However, those skilled in the art can understand that even without these technical details and various changes and modifications based on the following implementations, the technical solutions claimed in the claims of the present application can be implemented. Figure 1 As shown, the following will be discussed in detail.

[0072] Image preprocessing

[0073] This part corresponds to Figure 1 The S0 step in .

[0074] Capture image recognition area

[0075] The image interception step is designed to solve the problem of the original image containing a large amount of irrelevant information. By allowing users to manually or automatically select specific areas in the image that need to be fitted (i.e., regions of interest, ROIs), subsequent processing and analysis can be concentrated on important parts. When performing operations such as centroid calculation and line fitting, appropriate interception areas can significantly improve calculation efficiency and reduce data noise caused by irrelevant areas.

[0076] The following takes manual capture as an example to illustrate the specific operation process of image capture. The operation steps are as follows:

[0077] (1) Image loading: The program first loads the original image data provided by the user.

[0078] (2) Image display: The image is displayed to the user through a graphical interface tool (such as matplotlib) so that the user can view the entire image.

[0079] (3) Manual selection of area by user: The program allows the user to draw a rectangular box on the image with the mouse to define the area (ROI) that he wants to process.

[0080] (4) Determine and extract the selected area: After the user confirms the selected area, the program will save the coordinates of the area and extract the corresponding part from the image. If the user does not select a valid area, the program will prompt an error and require the user to re-capture the valid area. The valid area will be saved as a new image file and used in subsequent processing steps.

[0081] Since the images exported by oscilloscopes of different models and brands used in the experiment are different, the manual capture operation can accurately obtain the target area and eliminate irrelevant information, thereby ensuring the calculation efficiency and accuracy of subsequent processing. Figure 2 A graphical interactive interface is presented, in which the user can freely select the region of interest.

[0082] Data point extraction

[0083] In this section, we take the Lissajous data point with orange as the object to explain the data point extraction process. The goal of data point extraction is to extract the orange data point from the RGB image through a series of image processing steps and generate a binary image for subsequent data analysis. The specific steps are as follows:

[0084] Step 1: RGB to HSV color space conversion

[0085] Convert the RGB image to HSV color space to make it easier to extract the orange area. In the RGB color space, color is represented by three channels: red, green, and blue, while in the HSV color space, hue represents the color type, saturation represents the color purity, and value represents the brightness. The conversion formula from RGB to HSV is:

[0086] HSV=f(RGB)# (1)

[0087] Among them, function f converts the RGB color space to the HSV color space. The hue range of orange is set to 10 to 25 degrees, the saturation range is 100 to 255, and the brightness range is 100 to 255. By conditionally screening the three HSV channels, the orange pixels are successfully extracted and a logical mask image (orange_mask) is generated.

[0088] Step 2: Generate grayscale image

[0089] After extracting the orange area, the generated logical image has the pixel value of the orange area as True (i.e. 1) and the pixel value of other areas as False (i.e. 0). Multiplying these logical values ​​by 255 generates a grayscale image containing only 0 and 255, with white (255) representing the orange data points and black (0) representing the background.

[0090] Step 3: Binarization

[0091] Binarize the grayscale image, set the threshold to keep only pixels with a grayscale value of 255 as the foreground, and treat all other pixels as the background. The specific expression is as follows:

[0092]

[0093] This step ensures that the orange data points are presented in a clear binary form.

[0094] Through the above steps, the orange data point area is successfully extracted and converted into a binary image, laying the foundation for subsequent denoising analysis.

[0095] Clustering algorithm DBSCAN denoising

[0096] In this part, the extracted white data points need to be filtered to further complete the image preprocessing operation. There are various filtering methods, the most common of which are median filtering and mean filtering. Both algorithms are based on local statistical operations of fixed windows, among which mean filtering achieves image smoothing by calculating the mean of pixels in the neighborhood, but it is easy to cause image blur; median filtering can better retain edges, but when dealing with complex shapes and non-uniform noise, the effect is relatively limited. In contrast, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is a density-based clustering method that can automatically group data points according to the density of the data points. For complex structures such as Lissajous figures, DBSCAN can effectively distinguish dense valid data points from discrete noise points, and automatically adapt to the local density distribution of the data, thereby removing noise while maintaining the structural information of the figure. Therefore, the present invention selects the DBSCAN clustering filtering algorithm.

[0097] DBSCAN is a density-based clustering algorithm that can efficiently handle noisy and irregularly shaped data points. Its core idea is to define clusters by density without pre-specifying the number of clusters. The basic principles of the algorithm include the following points: the definition of a core point is that when the number of neighboring points of a point within the radius ε (eps) is not less than min_samples, the point is a core point; a boundary point refers to a point that has a core point in its neighbors but does not meet the core point conditions; and a noise point refers to a point that does not belong to any cluster. The execution process of the algorithm starts from any data point, finds the core point that meets the conditions and expands the cluster until it can no longer be expanded. The remaining unclassified points are marked as noise points, and the labels of noise points are generally set to -1.

[0098] In the process of image processing, the coordinates of all white data points in the binary image are first extracted, and the DBSCAN algorithm is used for clustering. After experimental debugging, the best effect is achieved when eps=5 and min_samples=6 are set. Then, the noise points with the label of -1 are filtered out, the non-noise points are retained, and the denoised binary image is generated and presented in a visual way to ensure that the interference points in the image are effectively removed.

[0099] After DBSCAN clustering and denoising, purer white data points are obtained, laying the foundation for subsequent centroid calculation and key point selection. At this point, the image preprocessing is completed, and the image exported by the oscilloscope also becomes a white data point diagram with a black background, as shown in the figure below. Figure 2 As shown, the white data points are the target points for the subsequent fitting analysis.

[0100] Determine the key corner points for grouping

[0101] This part corresponds to Figure 1 The S1 step in .

[0102] Centroid calculation and key point selection (points A and B)

[0103] Step 1: Calculate the centroid. First, extract all white data points in the preprocessed image and use them as the basis for centroid calculation. The centroid is determined by the average value of all data point coordinates and represents the geometric center of the entire data point set. The coordinates C of the centroid are calculated using the following formula:

[0104]

[0105] Where: (x i ,y i ) represents the coordinates of the i-th white data point; N is the total number of data points. The calculated centroid position will serve as the basis for grouping and key point selection in subsequent steps. The centroid solution is visualized as follows: Figure 4 shown.

[0106] Step 2: Left and right region segmentation. Based on the centroid C, divide the white data points into two regions, left and right. The left region contains all the points whose horizontal coordinates are less than the horizontal coordinate C of the centroid. x The right area contains points with a horizontal coordinate greater than or equal to C x The mathematical expression is as follows:

[0107] P left ={(x i ,y i )|x i <C x}# (4)

[0108] P right ={(x i ,y i )|x i ≥C x}# (5)

[0109] Among them, P left and P rightRepresent the left and right data point sets respectively. This step of segmentation lays the foundation for the subsequent selection of key points A and B. The left and right area segmentation visualization is as follows Figure 5 shown.

[0110] Step 3: Select key points A and B. In the left and right regions, select the 10 data points farthest from the centroid, and calculate the average position of these points to determine key points A and B. First, use the Euclidean distance formula to calculate the distance from each data point to the centroid:

[0111]

[0112] Among them, d i represents the distance from the i-th point to the centroid, (x i ,y i ) is the coordinate of the data point, C x and C y The 10 data points farthest from the center of mass in the left and right regions are selected respectively, and the average coordinates of these points are calculated to obtain the positions of key points A and B:

[0113]

[0114]

[0115] Among them: A represents the average position of the 10 points on the left that are farthest from the center of mass; B represents the average position of the 10 points on the right that are farthest from the center of mass. This step determines the key points A and B, providing a basis for subsequent linear fitting and quadrilateral construction.

[0116] Region segmentation and key point selection (points C and D)

[0117] 1. Calculate the equation of line AB:

[0118] Determine the equation of the line AB through points A and B. First, calculate the standard form equation of the line using the coordinates of points A and B:

[0119] Ax+By=C#(9)

[0120] Where: A is the slope part, indicating A y -B y ; B is another slope part, indicating B x -A x ; C is the intercept part, through the formula C = A·A x +B·A y Calculate. This equation defines the geometric expression of the straight line AB, laying the foundation for the subsequent division of the upper and lower areas.

[0121] 2. Upper and lower area segmentation:

[0122] Based on the calculated AB line equation, all data points are divided into upper and lower regions. Using the equation A·A x +B·A y -C value to determine the position of the point. If the value is greater than 0, the point is located in the upper area, otherwise, the point is located in the lower area. The formula is:

[0123] P upper ={(x i ,y i )|A·x i +B·y i ―C>0}# (10)

[0124] P lower ={(x i ,y i )|A·x i +B·y i ―C≤0}# (11)

[0125] This segmentation provides a basis for further extracting the key points C and D of the upper and lower parts. Figure 6 A schematic diagram of the upper and lower region segmentation is given.

[0126] 3. Selection of key points C and D:

[0127] After completing the region segmentation, calculate the 10 data points farthest from the AB line in the upper and lower regions. Use the Euclidean distance formula to calculate the distance from the data point to the AB line:

[0128]

[0129] Among them, d i represents the distance from the i-th data point to the AB line, and A, B, and C are the coefficients of the equation of the AB line. Calculate the average coordinates of the 10 points farthest from the AB line in the upper and lower parts respectively:

[0130]

[0131]

[0132] Where: C represents the average position of the 10 farthest points in the upper half; D represents the average position of the 10 farthest points in the lower half; (x i,upper ,y i,upper ) is the coordinate of the upper half of the data point; (x i,lower ,y i,lower ) are the coordinates of the lower half of the data points.

[0133] Figure 7A schematic diagram of the locations of grouped key corner points A, B, C, and D is given.

[0134] Preliminary quadrilateral fitting

[0135] This part corresponds to Figure 1 The S2 step in .

[0136] 1. Calculate the angle of the key point relative to the center of mass:

[0137] After determining the centroid and key points A, B, C, and D, you need to calculate the angle of each key point relative to the centroid. This angle represents the angle between the vector from the centroid to each key point and the x-axis. The formula is as follows:

[0138] θ=arctan 2(y―y c ,x―x c )#(15)

[0139] Where: (x, y) is the coordinates of the point; (x c ,y c ) are the coordinates of the centroid; arctan2 is a function that calculates the angle between the vector from the point to the centroid and the x-axis.

[0140] 2. Grouping of data points:

[0141] All data points are divided into four groups based on the angle between each key point and the centroid. The angle intervals are divided according to the four key points, specifically:

[0142] Group 1: Angle between the centroid and point A and the centroid and point C

[0143] Group 2: Angles between the centroid and point C and between the centroid and point B

[0144] Group 3: Angles between the centroid and point B and between the centroid and point D

[0145] Group 4: Angle between the centroid and point D and the centroid and point A

[0146] After the grouping is completed, the data points of each group represent different areas, preparing for straight line fitting and vertex calculation. The data point grouping visualization is as follows Figure 8 shown.

[0147] 3. Line fitting and quadrilateral vertex calculation:

[0148] For each set of data points, the least squares method is used for linear fitting to fit the best straight line for each set. The equation of the fitted straight line is:

[0149] y=kx+b#(16)

[0150] Where: k is the slope of the line; b is the intercept of the line.

[0151] The calculation formula of the least squares method is:

[0152]

[0153] Where: N is the number of points; ∑xy is the sum of the products of the x-values ​​and y-values ​​of all points; ∑x and ∑y are the sums of the x-values ​​and y-values, respectively; ∑(x 2 ) is the sum of the squares of the x values.

[0154] After completing the linear fitting of each set of data, calculate the intersection of adjacent straight lines and determine the vertices EFGH of the quadrilateral. The calculation formula for the intersection is:

[0155]

[0156] Among them: k1 and k2 are the slopes of the two straight lines; b1 and b2 are the intercepts of the two straight lines.

[0157] Through the intersection of adjacent straight lines, the vertices E, F, G, and H of the quadrilateral are finally determined. At this point, the construction of the preliminary fitting quadrilateral is completed, and the result visualization is as follows Fig. 9 shown.

[0158] Quadratic Fit

[0159] This part corresponds to Figure 1 Step S3 in .

[0160] 1. Linear translation and filtering data points:

[0161] After the four sides of the quadrilateral are obtained by preliminary fitting, these sides need to be translated to improve the fitting accuracy. Assume that the equation of the initial straight line is: y = kx + b#(19)

[0162] Here, k is the slope of the line and b is the intercept of the line.

[0163] To improve the fitting accuracy, the straight line is translated in a direction perpendicular to the straight line, and the translation distance is set to d. Since the unit vector perpendicular to the straight line is (-k, 1), the new straight line intercept can be calculated by the following formula:

[0164] Among them, b1 and b2 represent the intercepts of the two straight lines after upward and translation, respectively, and d is the translation distance. The value used in the study is d=7. After translation, the key data points between the two translation lines are selected, and these points will be used for subsequent fitting. The selection criteria of data points are:

[0165] b2≤y i ―kx i ≤b1#(21)

[0166] The points that satisfy this condition are the set of points located between the translated lines.

[0167] 2. Linear fitting again:

[0168] For the filtered data points {(x i ,y i )}, re-fit the line and use the least squares method to calculate the new slope k new and intercept b new , the formula is as follows:

[0169] Where: N is the number of filtered data points; x i and i They are i The horizontal and vertical coordinates of the point; k new is the slope of the new line; b new is the new intercept of the line.

[0170] By refitting the new data points, the optimized straight line equation can be obtained. Fig.10 A schematic diagram of optimizing the fit of A1B1 is given. This process is repeated for each side of the quadrilateral to obtain the optimized four sides.

[0171] 3. Calculation of optimized vertices:

[0172] After completing the optimized straight line fitting, it is necessary to calculate the intersection of every two adjacent straight lines to determine the optimized quadrilateral vertices. Suppose the equations of the two straight lines are:

[0173] y=k1x+b1#(23)

[0174] y=k2x+b2#(24)

[0175] The intersection point (x0, y 0) It can be obtained by solving the following system of equations:

[0176]

[0177] Among them: k1 and k2 are the slopes of the two straight lines respectively; b1 and b2 are the intercepts of the two straight lines respectively; x0 and y0 are the horizontal and vertical coordinates of the intersection respectively.

[0178] By optimizing the fitting and intersection calculation of the four sides, the optimized quadrilateral vertices A1B1C1D1 are obtained, thereby achieving a more accurate fitting of the quadrilateral. Fig.11 A schematic diagram of the fitted quadrilateral and the intercept PQ between the horizontal line where the centroid is located and the quadrilateral is given.

[0179] Calculation of equivalent parameters of DBD ozone generator

[0180] This part corresponds to Figure 1 Step S4 in .

[0181] The equivalent parameters of DBD ozone generator mainly include: air gap equivalent capacitance, barrier dielectric capacitance, discharge maintenance voltage and discharge energy per cycle. The specific calculation formula is as follows:

[0182] (1) Calculation of equivalent capacitance

[0183] In order to improve the accuracy of equivalent capacitance calculation, the average value of the relative side slope is used as the approximate slope of the Lissajous parallelogram. Assume that the four vertices of quadrilateral A1B1C1D1 are A1(x1,y1), B1(x2,y2), C1(x3,y3) and D1(x4,y4), and formula (16) and formula (17) give the expressions of the average slope of the relative side.

[0184]

[0185]

[0186] in represents the average slope of edge A1B1 and edge C1D1, represents the average slope of the edge B1C1 and the edge D1A1, represents the slope of side A1B1, represents the slope of edge C1D1, represents the slope of side B1C1, It represents the slope of side D1A1.

[0187] Once the average slope of the relative side is obtained, the equivalent capacitance, dielectric barrier capacitance and air gap capacitance of the DBD type ozone generator can be calculated based on the attenuation coefficient in the measurement circuit.

[0188] (2) Calculation of discharge maintenance voltage

[0189] The center of mass C(C x ,C y ) is located on the horizontal line y=C y The intercept with the side of the quadrilateral can be used to calculate the discharge maintenance voltage of the dielectric barrier discharge (DBD). Let the intersection of the centroid line and the quadrilateral be P(x P ,C y ) and Q(x Q ,C y ), then the difference in their horizontal coordinates |x P -x Q Half of ∣ is the discharge maintenance voltage U of the DBD ozone generator. Z .

[0190] (3) Discharge energy per cycle

[0191] The discharge energy per cycle of the DBD ozone generator can be obtained by calculating the area of ​​the quadrilateral A1B1C1D1. Assuming that the vertices of the quadrilateral are arranged in counterclockwise order, its area S can be calculated using formula (18):

[0192]

[0193] Among them, (x1,y1), (x2,y2), (x3,y3), and (x4,y4) are the coordinates of the vertices of the quadrilateral respectively.

Claims

1. A DBD ozone generator parameter measurement method based on image processing, It is characterized by the following steps: Preprocessing the Lissajous image of the DBD type ozone generator, wherein the preprocessed image only contains a valid set of white data points; Obtaining grouping key corner points A, B, C, and D based on the white data point set; Divide the white data point set into 4 arrays, perform straight line fitting on the data point set of each array, and preliminarily construct a quadrilateral; Based on the initially constructed quadrilateral, the boundary line is translated up and down to include a new set of data points, and a secondary fitting is performed to construct a quadrilateral with higher accuracy; The equivalent capacitance, discharge maintenance voltage and discharge energy per cycle of the DBD type ozone generator are calculated and output.

2. The method for measuring parameters of a DBD type ozone generator based on image processing according to claim 1 is characterized in that: The step of "preprocessing the Lissajous image of the DBD type ozone generator, wherein the preprocessed image only contains a valid set of white data points" includes the following sub-steps: intercepting the Lissajous image generated by the oscilloscope to obtain a sub-image containing only a valid data area; Convert the sub-image from RGB (Red, Green, Blue) to HSV (Hue, Saturation, Value) to extract color data points, and generate a logical mask image; Gray-scaling and binarization processing is performed on the logical mask image to obtain an image retaining only white data points; The image with the white data points retained is subjected to denoising processing using a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm to obtain a denoised binary image.

3. The method for measuring parameters of a DBD type ozone generator based on image processing according to claim 1 is characterized in that: The step of "obtaining grouped key corner points ABCD based on the white data point set" includes the following sub-steps: Calculate the centroid of the white data points; According to the centroid, the data points are divided into left and right regions, and the average positions of the 10 points farthest from the centroid are selected in the left and right regions respectively to obtain key corner points A and B; A straight line AB is constructed from the corner point A and point B, and all the data points are divided into upper and lower areas through the straight line AB. The average positions of the 10 points farthest from the straight line AB are selected in the upper and lower areas respectively to obtain key corner points C and D.

4. The method for measuring parameters of a DBD type ozone generator based on image processing according to claim 1 is characterized in that: The step of "dividing the white data point set into 4 arrays, performing straight line fitting on the data point set of each array, and preliminarily constructing a quadrilateral" includes the following sub-steps: Calculate the angle between the line connecting the key corner points A, B, C, D and the center of mass and the horizontal axis of the center of mass; According to the angle, the white data point set is divided into four arrays; wherein group 1 is a data point set within the angle range between the centroid to the key point A and the centroid to the key point C, group 2 is a data point set within the angle range between the centroid to the key point C and the centroid to the key point B, group 3 is a data point set within the angle range between the centroid to the key point B and the centroid to the key point D, and group 4 is a data point set within the angle range between the centroid to the key point D and the centroid to the key point A; Using the least square method to perform straight line fitting on each data point set, four straight lines are obtained; According to the four straight lines, the intersection points of adjacent straight lines are calculated respectively to obtain four vertices E, F, G, and H, and based on them, a preliminary fitting quadrilateral EFGH is constructed.

5. The method for measuring parameters of a DBD type ozone generator based on image processing according to claim 1 is characterized in that: The step of "based on the initially constructed quadrilateral, translating the boundary line up and down to include a new data point set, and performing secondary fitting to construct a quadrilateral with higher accuracy." includes the following sub-steps: The straight line corresponding to one side of the initially constructed quadrilateral is translated up and down to form two new translation straight lines; Filter out a data point set between the two new translation lines; The screened data point set is again fitted using the least square method to obtain an optimized expression for one side of the initially constructed quadrilateral; Repeat the optimization expression obtaining process for the remaining three sides of the initially constructed quadrilateral to obtain the optimization expressions of the remaining three sides; According to the four optimization expressions, the intersection points of adjacent edges are calculated, and finally the optimized quadrilateral A1B1C1D1 is constructed.

6. The method for measuring parameters of a DBD type ozone generator based on image processing according to claim 1, characterized in that: The step of "calculating and outputting the equivalent capacitance, discharge maintenance voltage and discharge energy per cycle of the DBD type ozone generator" comprises the following sub-steps: The average slopes of the relative sides of the quadrilateral A1B1C1D1 are calculated, and the equivalent capacitance, dielectric barrier capacitance and air gap capacitance of the DBD type ozone generator are deduced according to the attenuation coefficient in the measurement circuit; The discharge maintenance voltage of the DBD type ozone generator is calculated by taking half of the absolute value of the difference between the horizontal straight line where the center of mass is located and the two intersection points of the quadrilateral A1B1C1D1 and combining it with the attenuation coefficient; By calculating the area of ​​the quadrilateral A1B1C1D1, the discharge energy per cycle of the DBD type ozone generator is obtained.

7. A DBD type ozone generator parameter measurement system based on image processing, characterized in that: Includes the following modules: Image preprocessing module: used for extracting valid data point areas from the Lissajous image generated by the oscilloscope and performing denoising processing; A preliminary fitting module: used to obtain the key corner points A, B, C, and D according to the distribution of the data points, and to group and straight-line fit the data points to construct the preliminary fitting quadrilateral; Quadratic fitting and parameter output module: used to construct a quadratic fitting quadrilateral with higher accuracy based on the preliminary fitting quadrilateral, and calculate and output the relevant parameters of the DBD type ozone generator.

8. The DBD type ozone generator parameter measurement system based on image processing according to claim 7 is characterized in that: The "image preprocessing module" includes the following submodules: Graphics interaction submodule: used to save the captured area image; Data point extraction submodule: used for color space conversion, extracting data points and binarizing images; Noise point denoising submodule: used to denoise noise points using the DBSCAR clustering algorithm.

9. The DBD type ozone generator parameter measurement system based on image processing according to claim 7 is characterized in that: The "preliminary fitting module" includes the following submodules: Centroid calculation and region segmentation submodule: used to obtain the centroid of the white data point and segment the data point region; Key point A, B selection submodule: used to determine the point A and the point B; Key point C, D selection submodule: used to determine the point C and the point D; Angle calculation and grouping submodule: used to calculate the angles between the vectors from the centroid to the key corner points A, B, C, D and the horizontal axis, and divide the data points into four groups according to these angles; Linear fitting submodule: used for performing least squares linear fitting on each set of data points; Quadrilateral vertex calculation submodule: used to calculate the intersection of every two adjacent straight lines, obtain the vertices of the quadrilateral, and complete the preliminary fitting.

10. A DBD type ozone generator parameter measurement system based on image processing according to any one of claims 7 to 9, characterized in that: The "quadratic fitting and parameter output module" includes the following submodules: Line translation and data screening submodule: used to translate each line of the preliminary fitting up and down, and obtain a new set of data points by screening key data points that meet the translation range; Quadratic linear fitting submodule: used to perform least squares linear fitting on the screened data point set to calculate a new slope and intercept; Optimized vertex calculation submodule: used to calculate the intersection points of adjacent straight lines according to the straight lines after quadratic fitting, obtain the vertices of the quadratic fitting quadrilateral, and obtain the quadratic fitting quadrilateral; Equivalent capacitance calculation submodule: used to calculate the equivalent capacitance of DBD type ozone generator, including dielectric barrier capacitance and air gap capacitance; Discharge maintenance voltage calculation submodule: used to calculate the discharge maintenance voltage of the DBD type ozone generator; Discharge energy calculation submodule: used to calculate the discharge energy of each cycle.

Images