CN117745622B - Garbage leachate membrane concentrate catalytic oxidation device - Google Patents
Garbage leachate membrane concentrate catalytic oxidation device Download PDFInfo
- Publication number
- CN117745622B CN117745622B CN202410190116.XA CN202410190116A CN117745622B CN 117745622 B CN117745622 B CN 117745622B CN 202410190116 A CN202410190116 A CN 202410190116A CN 117745622 B CN117745622 B CN 117745622B
- Authority
- CN
- China
- Prior art keywords
- category
- point
- saturation
- initial
- line
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000003647 oxidation Effects 0.000 title claims abstract description 32
- 238000007254 oxidation reaction Methods 0.000 title claims abstract description 32
- 239000012141 concentrate Substances 0.000 title claims abstract description 30
- 230000003197 catalytic effect Effects 0.000 title claims abstract description 29
- 239000012528 membrane Substances 0.000 title claims abstract description 20
- 238000004043 dyeing Methods 0.000 claims abstract description 41
- 150000002500 ions Chemical class 0.000 claims abstract description 21
- 238000012216 screening Methods 0.000 claims abstract description 21
- 239000000975 dye Substances 0.000 claims abstract description 7
- 230000011218 segmentation Effects 0.000 claims abstract description 5
- 238000000034 method Methods 0.000 claims description 29
- 230000002708 enhancing effect Effects 0.000 claims description 12
- 239000000149 chemical water pollutant Substances 0.000 claims description 6
- 238000012806 monitoring device Methods 0.000 claims description 6
- 238000012706 support-vector machine Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 abstract description 5
- 239000010865 sewage Substances 0.000 abstract description 2
- 239000007788 liquid Substances 0.000 description 17
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 11
- 238000012545 processing Methods 0.000 description 10
- 239000000356 contaminant Substances 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000005286 illumination Methods 0.000 description 3
- 238000000638 solvent extraction Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000015556 catabolic process Effects 0.000 description 2
- 239000003086 colorant Substances 0.000 description 2
- 238000006731 degradation reaction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000012535 impurity Substances 0.000 description 2
- 238000012797 qualification Methods 0.000 description 2
- 238000003756 stirring Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000006227 byproduct Substances 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 150000003839 salts Chemical class 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Landscapes
- Image Analysis (AREA)
Abstract
The invention relates to the technical field of sewage treatment, in particular to a garbage leachate membrane concentrate catalytic oxidation device, which is used for obtaining a concentrate HSI image after the ions of a concentrate are dyed through a dyeing agent and dividing an initial dyeing area of the concentrate HSI image; intelligently detecting saturation minimum points through analyzing an initial dyeing area, and clustering the saturation minimum points into a class to be selected; screening partial saturation minimum points from the classes to be selected as update classes, and determining a final boundary of the update classes; based on the final dividing line, intelligent segmentation is carried out on the brightness image corresponding to the concentrated solution HSI image, different segmentation areas are enhanced, and the enhanced concentrated solution image is obtained. According to the invention, different partitioned areas are determined through intelligent detection, and are enhanced, so that the problem of local distortion detection is avoided, better original data is obtained, and the accuracy of a detection result is improved, thereby improving the intelligent control performance of the membrane concentrate catalytic oxidation device.
Description
Technical Field
The invention relates to the technical field of sewage treatment, in particular to a garbage leachate membrane concentrate catalytic oxidation device.
Background
After the landfill leachate is treated by a membrane technology, the produced water can be discharged basically up to the standard. The membrane concentrate resulting from membrane entrapment after membrane technology treatment, which is a by-product of water treatment by membrane technology, may contain concentrated contaminants, needs to be treated by an efficient process. Handling membrane concentrates is a challenge because it may contain high concentrations of contaminants, including dissolved salts, organics, and the like. The disposal of these contaminants requires specific techniques to ensure compliance with environmental standards. In treating the membrane concentrate, the treatment may be performed by using a catalytic oxidation process. After detecting the water quality after catalytic oxidation by the catalytic oxidation device, whether the concentrated solution can be discharged is determined according to the qualification rate, and the water is difficult to accurately identify due to the reflection characteristic of water, so that the problem that whether the water quality is qualified or not is avoided due to reflection caused by enhancing a brightness image.
On the one hand, the conventional image enhancement method is difficult to detect by an industrial camera because soluble substances are not obvious, so that the problem that the accurate identification is difficult to realize is caused by the reflection characteristic of water when the water quality after catalytic oxidation is detected in the conventional method, and meanwhile, the problem that the current dyed image is distorted is caused by enhancing the brightness image only by the conventional histogram equalization method in the brightness image enhancement process.
Disclosure of Invention
In order to solve the technical problem that the traditional histogram equalization method only enhances the brightness image and can cause the distortion of the current dyed image, the invention aims to provide a garbage percolate film concentrate catalytic oxidation device, and the adopted technical scheme is as follows:
One embodiment of the present invention provides a landfill leachate membrane concentrate catalytic oxidation device comprising a memory and a processor for processing instructions stored in the memory to implement a monitoring process of:
Acquiring a concentrated solution HSI image after the ions in the concentrated solution are dyed by a dyeing agent;
dividing an initial dyeing region of the concentrated solution HSI image based on the tone value of the concentrated solution HSI image in the tone channel;
Screening out a saturation minimum point from an initial dyeing region based on a saturation value of a pixel point in the initial dyeing region in a saturation channel; clustering the saturation minimum points to obtain a category to be selected; determining the removability of a saturation minimum point in the category to be selected; based on the removability, screening partial saturation minimum points from the category to be selected to obtain an updated category;
Constructing a maximum range line of adjacent update categories, acquiring a maximum hyperplane of the adjacent update categories, and determining an initial boundary; updating the initial boundary based on the maximum range line to obtain a final boundary;
and dividing the brightness image corresponding to the concentrated solution HSI image based on the final dividing line, and enhancing the brightness values of different dividing areas to obtain an enhanced concentrated solution image.
Preferably, the determining the removability of the saturation minimum point in the candidate category includes:
selecting any minimum saturation point on the edge of the class to be selected as a target pixel point, and deleting the target pixel point from the class to be selected to obtain a first class;
Calculating variances of saturation values corresponding to all saturation minimum points in the category to be selected, and marking the variances as initial variances; calculating variances and mean values of saturation values corresponding to all saturation minimum points in a first category corresponding to the category to be selected, and respectively marking the variances and the mean values as first variances and first mean values corresponding to the target pixel points;
and taking the difference between the initial variance and the first variance as a weight of a first mean value of the target pixel point to obtain the removability of the target pixel point.
Preferably, the constructing the maximum range line of the adjacent update category includes:
taking any two adjacent update categories as a first update category and a second update category;
And connecting each saturation minimum point in the first updating category with each saturation minimum point in the second updating category, and marking two line segments with the largest areas formed by the line segments and the saturation minimum points of the edges in the two updating categories as the maximum range line of the updating category after connecting.
Preferably, the acquiring the maximum hyperplane of the adjacent update category, determining the initial dividing line includes:
And obtaining a maximum hyperplane between the first updating category and the second updating category through a support vector machine algorithm, wherein the maximum hyperplane is an initial dividing line.
Preferably, the updating of the initial boundary based on the maximum range line to obtain a final boundary includes:
taking the saturation minimum point screened out from the category to be selected as a discrete minimum point;
Taking a discrete minimum point which is located within the maximum area formed by the maximum range line and is located on the initial demarcation line as a first point; fitting all the first points to obtain an initial fitting line;
performing region growth on the first update category and the second update category to obtain a boundary after region growth;
dividing the dividing line into a plurality of dividing sections through a first point on the dividing line; the normal direction of two endpoints corresponding to each demarcation section is recorded as a first direction; the first direction can be used for obtaining the point of each first direction on the initial fitting line, and the point is marked as a second point; the intersection point of the first direction corresponding to the second point on the demarcation section is marked as a third point; and calculating Euclidean distance between the second point and the third point, replacing the second point with the third point with the Euclidean distance smaller than the preset distance threshold value, combining the first point on the demarcation line to obtain a fitting curve again, and taking the fitting curve as the demarcation line between the first updating category and the second updating category to obtain a final demarcation line.
Preferably, the segmenting the initial dyeing area of the concentrated solution HSI image based on the hue value of the concentrated solution HSI image in the hue channel comprises:
and dividing an initial dyeing area of the concentrated solution HSI image based on the tone value of the concentrated solution HSI image in the tone channel by using a watershed algorithm.
Preferably, the screening the saturation minimum point from the initial dyeing area based on the saturation value of the pixel point in the initial dyeing area in the saturation channel includes:
Sliding windows on the initial dyeing area, and taking the pixel point with the minimum saturation value in the saturation channel in each sliding window as the saturation minimum point.
Preferably, the screening the partial saturation minimum point from the to-be-selected category based on the removability to obtain the updated category includes:
and screening the saturation minimum points with the removability greater than a preset removal threshold value after normalization to obtain an update category.
Preferably, the step of obtaining the enhanced concentrated solution image by the brightness value of the different segmentation areas and enhancing comprises the steps of:
Performing histogram equalization on the brightness values of different segmentation areas to obtain enhanced brightness values; and combining the brightness value, the tone value and the saturation value to obtain the enhanced concentrated solution image.
The device comprises a concentrated liquid catalytic oxidation device, wherein the concentrated liquid catalytic oxidation device comprises an ultraviolet advanced oxidation system, the concentrated liquid catalytic oxidation device further comprises a concentrated liquid monitoring device, the concentrated liquid monitoring device comprises an image collector and a controller, the image collector is connected with the controller, the image collector is used for acquiring a concentrated liquid HSI image after ions in concentrated liquid are dyed through a dye, and the controller is used for dividing an initial dyeing area of the concentrated liquid HSI image based on the tone value of the concentrated liquid HSI image in a tone channel;
Screening out a saturation minimum point from an initial dyeing region based on a saturation value of a pixel point in the initial dyeing region in a saturation channel; clustering the saturation minimum points to obtain a category to be selected; determining the removability of each saturation minimum point in the category to be selected; based on the removability, screening partial saturation minimum points from the category to be selected to obtain an updated category;
Constructing a maximum range line of adjacent update categories, acquiring a maximum hyperplane of the adjacent update categories, and determining an initial boundary; updating the initial boundary based on the maximum range line to obtain a final boundary;
and dividing the brightness image corresponding to the concentrated solution HSI image based on the final dividing line, and enhancing the brightness values of different dividing areas to obtain an enhanced concentrated solution image.
The embodiment of the invention has at least the following beneficial effects:
in this embodiment, aiming at the problem that the traditional histogram equalization method only enhances the brightness image, which leads to distortion of the currently dyed image, the brightness image is enhanced by combining the tone image, so that on one hand, better original data is obtained, and on the other hand, a more accurate detection result is obtained. In order to avoid the influence of light reflection in concentrated solution on the quality of detected water, the brightness difference of different tone areas is considered to be larger, the local excessive enhancement is caused by the integral enhancement of the channel value of the extracted brightness channel, so that each area corresponds to one tone area and is respectively enhanced by partitioning the concentrated solution HSI image, an initial dyeing area is determined by partitioning the concentrated solution HSI image, a minimum value point is needed to be found in the determination process of a watershed algorithm to determine a watershed line, the area where harmful ions are located in the dyed image is obviously compared with the surrounding environment, the color saturation is higher, meanwhile, the leakage treatment of indissolvable or insoluble impurities is avoided for full mixing before dyeing, stirring is often performed, and the distribution range of different ions is wider. Therefore, the invention screens out the saturation minimum point through the saturation value of the pixel point in the initial dyeing area, further obtains the final dividing line through updating the initial dividing line, divides the brightness image corresponding to the concentrated liquid HSI image based on the final dividing line, and enhances the brightness values of different divided areas to obtain the enhanced concentrated liquid image.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for processing a concentrate HSI image by a controller in a landfill leachate membrane concentrate catalytic oxidation device according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an ion cluster distribution according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following description refers to the specific implementation, structure, characteristics and effects of a landfill leachate membrane concentrate catalytic oxidation device according to the invention in detail with reference to the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The embodiment of the invention provides a garbage leachate membrane concentrated solution catalytic oxidation device, which comprises a concentrated solution catalytic oxidation device, wherein the concentrated solution catalytic oxidation device is a conventional catalytic oxidation device, and comprises an ultraviolet advanced oxidation system. In the embodiment of the invention, the ion in the concentrated solution after degradation and removal by the ultraviolet advanced oxidation system is dyed by the coloring agent, so that the purpose of enhancing the visualization of the image is to be beneficial to subsequent detection, and whether the concentrated solution can be discharged or not is determined by the monitoring result. It will be appreciated that the concentrate catalytic oxidation apparatus also includes associated equipment for effecting the degradation removal of the concentrate.
The concentrated solution catalytic oxidation device further comprises a concentrated solution monitoring device, wherein the concentrated solution monitoring device comprises an image collector and a controller, and the image collector is connected with the controller. The image collector can be a conventional industrial camera and is used for obtaining concentrated solution after dyeing ions in the concentrated solution after catalytic oxidation by using a dyeing agent to shoot, so as to obtain a concentrated solution HSI image. When the image collected by the industrial camera used is an RGB image, the RGB image of the concentrate is obtained and then output to the controller for conversion, so that the RGB image of the concentrate is converted into an HSI image of the concentrate.
The controller may be a data processing chip such as a CPU, MCU, etc., or a data processing device such as a host computer. The image collector signal connection controller can realize wired connection through a data transmission line, and can also realize wireless connection through wireless communication modes such as Bluetooth, wiFi and the like. In addition, the image collector and the controller can be integrated to form a device integrating the functions of image collection and image processing.
The image collector outputs a concentrated solution HSI image obtained after the ions in the concentrated solution are dyed by the colorant to the controller, as shown in fig. 1, which shows a flow chart of steps for processing the concentrated solution HSI image by the controller in the garbage percolate film concentrated solution catalytic oxidation device according to an embodiment of the present invention, and the method comprises the following steps:
Step S100, dividing an initial dyeing area of the concentrated solution HSI image based on the tone value of the concentrated solution HSI image in the tone channel.
In order to avoid the influence of light reflection in the concentrated solution after catalytic oxidation on the quality of detected water, the brightness channel is enhanced by extracting the image of the brightness channel, and the brightness image is enhanced in a whole way in consideration of large brightness difference of different tone areas, so that local excessive enhancement is caused, and the method comprises the steps of firstly partitioning, wherein each area corresponds to one tone area, and then enhancing respectively. In the embodiment of the invention, a common watershed algorithm is adopted to divide the brightness image, and minimum value points are required to be searched in the determination process of the watershed algorithm to determine the watershed line.
Since the concentrate HSI image is partitioned in combination with the hue information, the concentrate HSI image is first partitioned by the hue value. That is, based on the hue value of the concentrated liquid HSI image in the hue channel, the initial dyeing region of the concentrated liquid HSI image is segmented, specifically: and dividing an initial dyeing area of the concentrated solution HSI image based on the tone value of the concentrated solution HSI image in the tone channel by using a watershed algorithm. Namely, dividing a tone image corresponding to the concentrated solution HIS image by using a watershed algorithm to obtain an initial dyeing region in the tone image; and mapping the initial dyeing area in the tone image to the concentrated solution HSI image to obtain the initial dyeing area in the concentrated solution HSI image.
Step S200, screening out a saturation minimum point from an initial dyeing region based on a saturation value of a pixel point in the initial dyeing region in a saturation channel; clustering the saturation minimum points to obtain a category to be selected; determining the removability of a saturation minimum point in the category to be selected; and based on the removability, screening partial saturation minimum points from the category to be selected to obtain an updated category.
In this case, the boundary line of the different tone regions contains a large number of minimum value points, but these minimum value points include an unnecessary minimum value point, that is, a minimum value point due to illumination, which causes a large error in the boundary line of the different tone regions, and therefore these minimum value points need to be obtained by screening and further the boundary line is adjusted.
In the dyed image, the area where the harmful ions are located forms obvious contrast with the surrounding environment, the color saturation is higher, meanwhile, in order to fully mix, the leakage treatment of indissolvable or insoluble impurities is avoided, stirring is often carried out, so that the distribution range of different ions is wider, the distribution condition shown in fig. 2 is formed, fig. 2 is a schematic diagram of the distribution of ion clusters in the embodiment of the invention, each small circle in fig. 2 represents various ion clusters, a tone peak value is represented on a tone channel, the surrounding tone value is smaller, each small circle in fig. 2 is corresponding, different ion types are formed due to the fact that the ions are stirred, the corresponding tone value is based, different color areas are formed on the concentrated solution HSI image, and when the area division is carried out by using a watershed algorithm, the minimum value points are often used as area division points, but a plurality of points representing the ions are contained in the minimum value points on the tone channel, so that the minimum value points representing the ions on the tone map are required to be removed, and the process of dividing the area division of the luminance image on the tone channel is not participated in. That is, it is desirable that the middle luminance maximum point of the three class set in fig. 2 is used as a division point in the watershed algorithm, and not a small circle inside each class is also used as a division point of the watershed algorithm, resulting in a large error of the division effect.
Therefore, based on the saturation value of the pixel point in the initial dyeing region in the saturation channel, the saturation minimum point is screened from the initial dyeing region, and the specific point is: sliding windows on the initial dyeing area, and taking the pixel point with the minimum saturation value in the saturation channel in each sliding window as the saturation minimum point. In the embodiment of the invention, the size of the sliding window is 9×9, wherein the sliding process of the sliding window starts from the left upper corner of the concentrated liquid HSI image, and slides from left to right and from top to bottom, namely, slides from the first pixel point of the concentrated liquid HSI image from left to right to the last pixel point of the first line from left to right, then slides from the first pixel point of the second line of the concentrated liquid HSI image from left to right to the last pixel point of the second line from left to right, and so on until the last line of the concentrated liquid HSI image slides from left to right from the first pixel point of the left to right to the last pixel point of the last line from left to right.
Clustering the saturation minimum points to obtain a category to be selected, and specifically: and clustering the saturation minimum points by a density clustering method to obtain a plurality of categories. The distance between the saturation minima in the same category and the coordinate point is closer, and the distance between the saturation minima in different categories and the coordinate point is farther.
The larger the saturation value of the minimum value point in each category is, the smaller the difference of all the saturation minima in the category is, and for some minimum value edge points among a plurality of categories, if the edge points belong to a certain category, the larger the difference change of the saturation value in the category is before and after the edge point is removed, the more likely the point in the category is a noise point caused by illumination; if the edge points do not belong to a certain class, the accuracy of the edge points screened out in the class and the dividing lines formed by the edge points as dividing lines of different tone areas in the brightness image is higher than the edge points which can form very small value edge points of the smoother dividing lines in different classes.
Therefore, the removability of the saturation minimum point in the category to be selected is determined, in particular:
Firstly, selecting any minimum saturation point on the edge of a class to be selected as a target pixel point, and deleting the target pixel point from the class to be selected to obtain a first class. The method for determining the saturation minimum point on the edge of the category to be selected comprises the following steps: and taking the center point of the class to be selected as a ray starting point, starting with a horizontal line which is horizontal to the right, respectively taking one ray every 1 DEG, and obtaining 360 rays in 360 directions, wherein the saturation minimum point with the largest Euclidean distance between each ray and the ray starting point is recorded as the saturation minimum point on the edge of the class to be selected.
Then, calculating variances of saturation values corresponding to all saturation minimum points in the category to be selected, and marking the variances as initial variances; and calculating variances and mean values of saturation values corresponding to all saturation minimum points in the first category corresponding to the category to be selected, and respectively marking the variances and the mean values as first variances and first mean values corresponding to the target pixel points.
And finally, taking the difference between the initial variance and the first variance as a weight of a first mean value of the target pixel point to obtain the removability of the target pixel point.
The calculation formula of the removability is as follows:
; wherein, Is the removability of the target pixel point; initial variance for the class to be selected; a first variance of a first category corresponding to the category to be selected; And the first average value of the first category corresponding to the category to be selected.
The larger the change of the initial variance and the first variance, which correspond to the target pixel before and after the target pixel is removed from the class to be selected, the smaller the membership of the target pixel relative to the class to be selected from the aspect of saturation, and the greater the removability. The larger the first average value of the first class corresponding to the class to be selected, the larger the first average value is, reflecting that other points in the class to be selected are ions, but the larger the probability caused by the influence of illumination is, the larger the removability of the point is.
Based on the removability, partial saturation minimum points are screened from the classes to be selected, and updated classes are obtained, specifically: and screening the saturation minimum points with the removability greater than a preset removal threshold value after normalization to obtain an update category. In the embodiment of the present invention, the preset removal threshold value is 0.8, and in other embodiments, the practitioner adjusts the value according to the actual situation. And (3) marking saturation minimum points larger than a preset removal threshold value as discrete minimum points, wherein the discrete minimum points and the discrete minimum points among different categories form a discrete minimum point set. At the same time, the update of each category is completed and is recorded as an update category.
Step S300, constructing a maximum range line of adjacent update categories, acquiring a maximum hyperplane of the adjacent update categories, and determining an initial boundary; and obtaining a final boundary based on the updating of the maximum range line to the initial boundary.
In order to ensure the accuracy of the obtained parting lines of different categories, the angle range of the reliable edge points of the parting line is set through the maximum range angle of the edge points of each category, the parting points in the range have larger credibility, and then the parting points with larger credibility are taken as starting points, and the tone parting line obtained by a watershed algorithm is adjusted by combining the connectivity of the parting points.
First, for the updated update category, a maximum range line of the adjacent update category is constructed, specifically: taking any two adjacent update categories as a first update category and a second update category;
And connecting each saturation minimum point in the first updating category with each saturation minimum point in the second updating category, and marking two line segments with the largest areas formed by the line segments and the saturation minimum points of the edges in the two updating categories as the maximum range line of the updating category after connecting. The method for acquiring the saturation minimum point of the edge in the update category is the same as the method for acquiring the saturation minimum point of the edge in the candidate category.
Acquiring the maximum hyperplane of the adjacent update category, and determining an initial dividing line, specifically: the maximum hyperplane between the first update category and the second update category is obtained by a support vector machine algorithm (Support Vector Machine, SVM), which is a line, i.e. the maximum hyperplane is the initial dividing line. Taking a discrete minimum point which is located within the maximum area formed by the maximum range line and is located on the initial demarcation line as a first point; i.e. discrete minimum points that fall on the initial boundary and are inside the line of maximum sound range, as the first point, the probability that the first point belongs to the boundary of these two update categories is relatively large.
Fitting all the first points to obtain an initial fitting line. Meanwhile, a boundary line between the first update category and the second update category is acquired, and the method is specifically: the dividing line is a dividing line obtained through region growth, namely, region growth is carried out on the first updating category and the second updating category, and the dividing line after the region growth is obtained; the dividing line is divided into a plurality of dividing lines by the first point on the dividing line, and if there is no first point on the dividing line, the line connecting the two first points having the smallest sag distance from the dividing line is used as the target line, the sag distance between the dividing line and the target line is calculated, the target line is translated along the sag distance toward the dividing line, and the dividing line is non-partitioned based on the translated target line, so as to obtain a plurality of dividing lines.
The normal direction of two endpoints of each demarcation section is marked as a first direction; the points of each first direction on the initial fitting line can be obtained through the first directions and marked as second points a; and marking a point, corresponding to the second point a, of the first direction line on the demarcation line segment as a third point b, calculating the Euclidean distance between the second point a and the third point b, replacing the second point a with the third point b with the Euclidean distance smaller than 5, combining the first point on the demarcation line to obtain a fitting curve again, taking the fitting curve as a demarcation line between the first updating category and the second updating category to obtain a final demarcation line, and finishing the adjustment of the demarcation line. In the embodiment of the invention, the common watershed algorithm is adopted to divide the brightness image, and the minimum value point is needed to be searched in the determination process of the watershed algorithm to determine the watershed line, so that the final dividing line obtained in the process is the watershed line in the watershed algorithm.
And step S400, dividing the brightness image corresponding to the concentrated solution HSI image based on the final dividing line, and enhancing the brightness values of different division areas to obtain an enhanced concentrated solution image.
After the final dividing line between any two updating categories is obtained, the luminance image is divided by the final dividing line by utilizing a watershed algorithm to obtain a plurality of divided areas, each divided area is subjected to histogram equalization to obtain an enhanced luminance image, the hue image and the saturation image are combined to obtain an original RGB image, the original RGB image is recorded as the enhanced RGB image, the luminance values of different divided areas are subjected to histogram equalization to obtain an enhanced luminance value, and the enhanced concentrated solution image is obtained by combining the luminance value, the hue value and the saturation value.
The enhanced RGB image is identified through a neural network to obtain the water quality qualification rate, a VGG-NET network is adopted, a loss function adopts a mean square error loss function, and network training is an existing process and is not repeated.
A landfill leachate membrane concentrate catalytic oxidation device according to example two:
the system comprises a processor and a memory, wherein the processor is used for processing instructions stored in the memory to realize the following monitoring process:
Acquiring a concentrated solution HSI image after the ions in the concentrated solution are dyed by a dyeing agent;
dividing an initial dyeing region of the concentrated solution HSI image based on the tone value of the concentrated solution HSI image in the tone channel;
Screening out a saturation minimum point from an initial dyeing region based on a saturation value of a pixel point in the initial dyeing region in a saturation channel; clustering the saturation minimum points to obtain a category to be selected; determining the removability of a saturation minimum point in the category to be selected; based on the removability, screening partial saturation minimum points from the category to be selected to obtain an updated category;
Constructing a maximum range line of adjacent update categories, acquiring a maximum hyperplane of the adjacent update categories, and determining an initial boundary; updating the initial boundary based on the maximum range line to obtain a final boundary;
and dividing the brightness image corresponding to the concentrated solution HSI image based on the final dividing line, and enhancing the brightness values of different dividing areas to obtain an enhanced concentrated solution image.
Therefore, the garbage leachate membrane concentrate catalytic oxidation device provided in this embodiment is essentially a processor device, and is implemented by an internal data processing process, and the data processing process is described in detail in the foregoing embodiment of the garbage leachate membrane concentrate catalytic oxidation device, which is not repeated.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (5)
1. A landfill leachate membrane concentrate catalytic oxidation device comprising a processor and a memory, the processor being configured to process instructions stored in the memory to effect a monitoring process comprising:
Acquiring a concentrated solution HSI image after the ions in the concentrated solution are dyed by a dyeing agent;
dividing an initial dyeing region of the concentrated solution HSI image based on the tone value of the concentrated solution HSI image in the tone channel;
Screening out a saturation minimum point from an initial dyeing region based on a saturation value of a pixel point in the initial dyeing region in a saturation channel; clustering the saturation minimum points to obtain a category to be selected; determining the removability of a saturation minimum point in the category to be selected; based on the removability, screening partial saturation minimum points from the category to be selected to obtain an updated category;
Constructing a maximum range line of adjacent update categories, acquiring a maximum hyperplane of the adjacent update categories, and determining an initial boundary; updating the initial boundary based on the maximum range line to obtain a final boundary;
Dividing a brightness image corresponding to the concentrated solution HSI image based on the final dividing line, and enhancing brightness values of different dividing areas to obtain an enhanced concentrated solution image;
wherein the determining the removability of the saturation minimum point in the candidate category includes:
selecting any minimum saturation point on the edge of the class to be selected as a target pixel point, and deleting the target pixel point from the class to be selected to obtain a first class;
Calculating variances of saturation values corresponding to all saturation minimum points in the category to be selected, and marking the variances as initial variances; calculating variances and mean values of saturation values corresponding to all saturation minimum points in a first category corresponding to the category to be selected, and respectively marking the variances and the mean values as first variances and first mean values corresponding to the target pixel points;
Taking the difference between the initial variance and the first variance as a weight of a first mean value of the target pixel point to obtain the removability of the target pixel point;
wherein, based on the removability, the method screens out partial saturation minimum points from the category to be selected to obtain an updated category, including: screening the saturation minimum points with the removability greater than a preset removal threshold value after normalization to obtain an update category;
Wherein said constructing a maximum extent line for adjacent update categories comprises:
taking any two adjacent update categories as a first update category and a second update category;
connecting each saturation minimum point in the first updating category with each saturation minimum point in the second updating category, and marking two line segments with the largest area formed by the line segments and the saturation minimum points of the edges in the two updating categories as the maximum range line of the updating category after connecting;
The obtaining the maximum hyperplane of the adjacent update category, determining the initial dividing line includes:
obtaining a maximum hyperplane between the first updating category and the second updating category through a support vector machine algorithm, wherein the maximum hyperplane is an initial boundary;
the updating of the initial boundary based on the maximum range line to obtain a final boundary comprises the following steps:
taking the saturation minimum point screened out from the category to be selected as a discrete minimum point;
Taking a discrete minimum point which is located within the maximum area formed by the maximum range line and is located on the initial demarcation line as a first point; fitting all the first points to obtain an initial fitting line;
performing region growth on the first update category and the second update category to obtain a boundary after region growth;
Dividing the dividing line into a plurality of dividing sections through a first point on the dividing line; the normal direction of two endpoints corresponding to each demarcation section is recorded as a first direction; obtaining points of each first direction on the initial fitting line through the first directions, and marking the points as second points; the intersection point of the first direction corresponding to the second point on the demarcation section is marked as a third point; and calculating Euclidean distance between the second point and the third point, replacing the second point with the third point with the Euclidean distance smaller than the preset distance threshold value, combining the first point on the demarcation line to obtain a fitting curve again, and taking the fitting curve as the demarcation line between the first updating category and the second updating category to obtain a final demarcation line.
2. The device of claim 1, wherein the segmenting the initial stain region of the concentrate HSI image based on the hue value of the concentrate HSI image in the hue channel comprises:
and dividing an initial dyeing area of the concentrated solution HSI image based on the tone value of the concentrated solution HSI image in the tone channel by using a watershed algorithm.
3. The device of claim 1, wherein the means for screening out minimal saturation points from an initial dyeing zone based on saturation values of pixels in saturation channels in the initial dyeing zone comprises:
Sliding windows on the initial dyeing area, and taking the pixel point with the minimum saturation value in the saturation channel in each sliding window as the saturation minimum point.
4. The device of claim 1, wherein said means for enhancing the brightness values of the various segmented regions provides enhanced concentrate images comprising:
Performing histogram equalization on the brightness values of different segmentation areas to obtain enhanced brightness values; and combining the brightness value, the tone value and the saturation value to obtain the enhanced concentrated solution image.
5. The device is characterized by further comprising a concentrated solution monitoring device, wherein the concentrated solution monitoring device comprises an image collector and a controller, the image collector is connected with the controller, the image collector is used for acquiring a concentrated solution HSI image after the ions in the concentrated solution are dyed through a dye, and the controller is used for dividing an initial dyeing area of the concentrated solution HSI image based on the tone value of the concentrated solution HSI image in a tone channel;
Screening out a saturation minimum point from an initial dyeing region based on a saturation value of a pixel point in the initial dyeing region in a saturation channel; clustering the saturation minimum points to obtain a category to be selected; determining the removability of each saturation minimum point in the category to be selected; based on the removability, screening partial saturation minimum points from the category to be selected to obtain an updated category;
Constructing a maximum range line of adjacent update categories, acquiring a maximum hyperplane of the adjacent update categories, and determining an initial boundary; updating the initial boundary based on the maximum range line to obtain a final boundary;
Dividing a brightness image corresponding to the concentrated solution HSI image based on the final dividing line, and enhancing brightness values of different dividing areas to obtain an enhanced concentrated solution image;
wherein the determining the removability of the saturation minimum point in the candidate category includes:
selecting any minimum saturation point on the edge of the class to be selected as a target pixel point, and deleting the target pixel point from the class to be selected to obtain a first class;
Calculating variances of saturation values corresponding to all saturation minimum points in the category to be selected, and marking the variances as initial variances; calculating variances and mean values of saturation values corresponding to all saturation minimum points in a first category corresponding to the category to be selected, and respectively marking the variances and the mean values as first variances and first mean values corresponding to the target pixel points;
Taking the difference between the initial variance and the first variance as a weight of a first mean value of the target pixel point to obtain the removability of the target pixel point;
wherein, based on the removability, the method screens out partial saturation minimum points from the category to be selected to obtain an updated category, including: screening the saturation minimum points with the removability greater than a preset removal threshold value after normalization to obtain an update category;
Wherein said constructing a maximum extent line for adjacent update categories comprises:
taking any two adjacent update categories as a first update category and a second update category;
connecting each saturation minimum point in the first updating category with each saturation minimum point in the second updating category, and marking two line segments with the largest area formed by the line segments and the saturation minimum points of the edges in the two updating categories as the maximum range line of the updating category after connecting;
The obtaining the maximum hyperplane of the adjacent update category, determining the initial dividing line includes:
obtaining a maximum hyperplane between the first updating category and the second updating category through a support vector machine algorithm, wherein the maximum hyperplane is an initial boundary;
the updating of the initial boundary based on the maximum range line to obtain a final boundary comprises the following steps:
taking the saturation minimum point screened out from the category to be selected as a discrete minimum point;
Taking a discrete minimum point which is located within the maximum area formed by the maximum range line and is located on the initial demarcation line as a first point; fitting all the first points to obtain an initial fitting line;
performing region growth on the first update category and the second update category to obtain a boundary after region growth;
Dividing the dividing line into a plurality of dividing sections through a first point on the dividing line; the normal direction of two endpoints corresponding to each demarcation section is recorded as a first direction; obtaining points of each first direction on the initial fitting line through the first directions, and marking the points as second points; the intersection point of the first direction corresponding to the second point on the demarcation section is marked as a third point; and calculating Euclidean distance between the second point and the third point, replacing the second point with the third point with the Euclidean distance smaller than the preset distance threshold value, combining the first point on the demarcation line to obtain a fitting curve again, and taking the fitting curve as the demarcation line between the first updating category and the second updating category to obtain a final demarcation line.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410190116.XA CN117745622B (en) | 2024-02-21 | 2024-02-21 | Garbage leachate membrane concentrate catalytic oxidation device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410190116.XA CN117745622B (en) | 2024-02-21 | 2024-02-21 | Garbage leachate membrane concentrate catalytic oxidation device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117745622A CN117745622A (en) | 2024-03-22 |
| CN117745622B true CN117745622B (en) | 2024-05-07 |
Family
ID=90261331
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410190116.XA Active CN117745622B (en) | 2024-02-21 | 2024-02-21 | Garbage leachate membrane concentrate catalytic oxidation device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117745622B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118388089B (en) * | 2024-06-18 | 2024-08-23 | 湖南北控威保特环境科技股份有限公司 | Full quantification treatment process for percolate based on Fenton treatment |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6535632B1 (en) * | 1998-12-18 | 2003-03-18 | University Of Washington | Image processing in HSI color space using adaptive noise filtering |
| CN101477630A (en) * | 2009-02-17 | 2009-07-08 | 吴俊� | System and method for intelligent water treatment micro-organism machine vision identification |
| CN105461151A (en) * | 2014-08-19 | 2016-04-06 | 青岛炜烨锻压机械有限公司 | Landfill leachate treatment technology |
| CN106228172A (en) * | 2016-09-20 | 2016-12-14 | 西安工程大学 | damaged insulator image extraction method based on cluster analysis |
| CN106530313A (en) * | 2016-11-16 | 2017-03-22 | 上海大学 | Sea-sky line real-time detection method based on region segmentation |
| CN109272480A (en) * | 2017-07-18 | 2019-01-25 | 杭州领克信息科技有限公司 | A kind of detection method in immiscible solution line of demarcation |
| CN112633296A (en) * | 2020-12-28 | 2021-04-09 | 常州微亿智造科技有限公司 | Visual industrial part light and shade level dividing method based on cluster analysis |
| CN113452969A (en) * | 2020-03-26 | 2021-09-28 | 华为技术有限公司 | Image processing method and device |
| CN113989299A (en) * | 2021-10-27 | 2022-01-28 | 北方魏家峁煤电有限责任公司 | Open-pit mine rock stratum image segmentation method based on k-means clustering |
| CN114612346A (en) * | 2022-04-02 | 2022-06-10 | 杨海鹰 | X-ray backscatter image pseudo-color processing algorithm |
| CN114708464A (en) * | 2022-06-01 | 2022-07-05 | 广东艺林绿化工程有限公司 | Municipal sanitation cleaning garbage truck cleaning method based on road garbage classification |
| CN116665051A (en) * | 2023-05-29 | 2023-08-29 | 山东睿政信息科技有限公司 | Method for rescreening metals in garbage based on RGB image reconstruction hyperspectral image |
-
2024
- 2024-02-21 CN CN202410190116.XA patent/CN117745622B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6535632B1 (en) * | 1998-12-18 | 2003-03-18 | University Of Washington | Image processing in HSI color space using adaptive noise filtering |
| CN101477630A (en) * | 2009-02-17 | 2009-07-08 | 吴俊� | System and method for intelligent water treatment micro-organism machine vision identification |
| CN105461151A (en) * | 2014-08-19 | 2016-04-06 | 青岛炜烨锻压机械有限公司 | Landfill leachate treatment technology |
| CN106228172A (en) * | 2016-09-20 | 2016-12-14 | 西安工程大学 | damaged insulator image extraction method based on cluster analysis |
| CN106530313A (en) * | 2016-11-16 | 2017-03-22 | 上海大学 | Sea-sky line real-time detection method based on region segmentation |
| CN109272480A (en) * | 2017-07-18 | 2019-01-25 | 杭州领克信息科技有限公司 | A kind of detection method in immiscible solution line of demarcation |
| CN113452969A (en) * | 2020-03-26 | 2021-09-28 | 华为技术有限公司 | Image processing method and device |
| CN112633296A (en) * | 2020-12-28 | 2021-04-09 | 常州微亿智造科技有限公司 | Visual industrial part light and shade level dividing method based on cluster analysis |
| CN113989299A (en) * | 2021-10-27 | 2022-01-28 | 北方魏家峁煤电有限责任公司 | Open-pit mine rock stratum image segmentation method based on k-means clustering |
| CN114612346A (en) * | 2022-04-02 | 2022-06-10 | 杨海鹰 | X-ray backscatter image pseudo-color processing algorithm |
| CN114708464A (en) * | 2022-06-01 | 2022-07-05 | 广东艺林绿化工程有限公司 | Municipal sanitation cleaning garbage truck cleaning method based on road garbage classification |
| CN116665051A (en) * | 2023-05-29 | 2023-08-29 | 山东睿政信息科技有限公司 | Method for rescreening metals in garbage based on RGB image reconstruction hyperspectral image |
Non-Patent Citations (1)
| Title |
|---|
| 分割暗通道先验邻域的单幅图像去雾算法;黄黎红;;地球信息科学学报;20180228(02);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117745622A (en) | 2024-03-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117745622B (en) | Garbage leachate membrane concentrate catalytic oxidation device | |
| CN101794434B (en) | Image processing apparatus and image processing method | |
| CN103116751B (en) | A kind of Method of Automatic Recognition for Character of Lcecse Plate | |
| CN113723573B (en) | Tumor tissue pathological classification system and method based on adaptive proportion learning | |
| CN101599175B (en) | Detection method for determining alteration of shooting background and image processing device | |
| CN111611643A (en) | Family type vectorization data obtaining method and device, electronic equipment and storage medium | |
| CN101710425B (en) | Self-adaptive pre-segmentation method based on gray scale and gradient of image and gray scale statistic histogram | |
| CN111461165A (en) | Image recognition method, recognition model training method, related device and equipment | |
| CN102999886B (en) | Image Edge Detector and scale grating grid precision detection system | |
| CN107506760A (en) | Traffic signals detection method and system based on GPS location and visual pattern processing | |
| CN116597392B (en) | Hydraulic oil impurity identification method based on machine vision | |
| CN112132206A (en) | Image recognition method, training method of related model, related device and equipment | |
| CN109975196B (en) | Reticulocyte detection method and system | |
| CN116310845B (en) | Intelligent monitoring system for sewage treatment | |
| CN116091421A (en) | Method for automatically dividing and calculating area of blastomere image of in-vitro fertilized embryo | |
| CN105701491A (en) | Method for making fixed-format document image template and application thereof | |
| CN117522719B (en) | Bronchoscope image auxiliary optimization system based on machine learning | |
| CN109190742A (en) | A kind of coding/decoding method of the coding characteristic point based on gray feature | |
| CN110910372A (en) | Deep convolutional neural network-based uniform light plate defect detection method | |
| CN112598002B (en) | License plate recognition method under influence of fog and noise | |
| CN113989771A (en) | Traffic signal lamp identification method based on digital image processing | |
| CN117197064A (en) | Automatic non-contact eye red degree analysis method | |
| CN103324931A (en) | Method and device for image color binarization | |
| CN110930358A (en) | Solar panel image processing method based on self-adaptive algorithm | |
| CN113469939B (en) | HER-2 immunohistochemical automatic interpretation system based on characteristic curve |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |