CN115950886A - Floc state monitoring method based on textural features - Google Patents
Floc state monitoring method based on textural features Download PDFInfo
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Abstract
A floc state monitoring method based on texture features comprises the following steps: taking floc-containing water as a sample, and dividing the floc-containing water into a plurality of equivalent floc-containing water samples; placing them in transparent containers respectively to make them be distributed in suspension state; shooting a plurality of sample images by using a camera; selecting a sample image from the images, preprocessing the sample image, and extracting a gray level co-occurrence matrix of the image; calculating an image texture characteristic value by using the extracted gray level co-occurrence matrix through a texture extraction algorithm; averaging the texture characteristic values of a plurality of images of the same sample to obtain a result; repeating the above operations to obtain texture characteristic values of the floc images at different time periods, and respectively obtaining respective relative variation of the texture characteristic values by using formula calculation; and (3) using the respective relative variation of the texture characteristic values obtained respectively to judge the state of the flocs: the method utilizes the texture characteristic value extracted from the floc image to carry out quantitative analysis on the floc state, so that the result is more accurate and objective, and the method has the advantages of simplicity, convenience, intuition, accuracy, high efficiency and low cost.
Description
Technical Field
The invention belongs to the technical field of water treatment, and particularly relates to a floc state monitoring method based on textural features.
Background
In the daily operation management of some water plants, the reasonable evaluation of the floc state in the water treatment unit is often difficult due to the factors of old process equipment, simple and crude experimental conditions, and good and uneven professional knowledge level of workers. The state change of flocs caused by sudden working conditions such as water inlet turbidity, large rise of flow in a short time, leakage of chemicals in a chemical adding pipe of a flocculating agent and the like can not be mastered in time, so that corresponding adjustment measures can not be taken in time, large and compact flocs can not be generated in a flocculation stage for sedimentation, and the quality of outlet water is further influenced.
In the existing method for monitoring the state of the flocs, most operators adopt visual methods, mainly based on the visual observation of the flocs in form, density and other relevant conditions, and the method is easily influenced by subjective factors and experiences, so the reliability is low.
The patent application with the application number of CN200710144647 proposes a floc state identification method based on morphological characteristics, which identifies the floc state by shooting an image of the floc and calculating morphological characteristic values of the floc, however, the morphological characteristics are individual characteristics of single floc, and have large random errors and large calculation amount.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a floc state monitoring method based on texture features, wherein in the water treatment process, texture feature values such as entropy, correlation and the like are extracted from a floc image, and the entropy and correlation in the texture feature values are analyzed to obtain good correlation with the features such as compactness, shape size, settleability and the like of flocs; the method carries out quantitative analysis on the floc state by utilizing the texture characteristic value extracted from the floc image, so that the result is more accurate and objective, and the method has the advantages of simplicity, convenience, intuition, accuracy, high efficiency and low cost.
In order to achieve the purpose, the invention adopts the technical scheme that:
a floc state monitoring method based on texture features comprises the following steps:
step 1, taking floc-containing water as a sample, and dividing the floc-containing water into a plurality of equivalent floc-containing water samples;
step 3, shooting a plurality of floc water samples distributed in a suspension state obtained in the step 2 by using a camera, and shooting images of a plurality of samples;
step 4, selecting a sample image from the plurality of sample images shot in the step 3 for preprocessing, and extracting a gray level co-occurrence matrix of the image by using image analysis software or an open source vision library;
step 6, averaging the texture characteristic values of a plurality of images of the same sample to obtain a result;
and 7, repeating the steps 1 to 6 to obtain texture characteristic values of the floc images at different time stages, and utilizing a formula
Calculating to respectively obtain the respective relative variation of the texture characteristic values; wherein, R1 and R2 respectively represent texture characteristic values of a previous stage and a next stage of the two continuous monitoring stages;
and 8, using the respective relative variation of the texture characteristic values obtained in the step 7 to judge the state of the flocs:
when the relative variation of the entropies of two adjacent stages is greater than 0 and the relative variation of the correlation is less than 0, the floc compactness is increased, the equivalent grain diameter is increased, and the floc state is improved;
when the relative variation of the entropy of two adjacent stages is less than 0 and the relative variation of the correlation is more than 0, the floc compactness is reduced, the equivalent particle size is reduced, and the floc state is deteriorated.
In the step 2, the floc-containing water sample in the transparent container is stirred or aerated to be distributed in a suspended state in the transparent container.
And 3, shooting at least 5 sample images of the same batch of floc water samples which are sampled each time and distributed in a suspension state, and keeping camera shooting parameters consistent.
And 4, selecting at least 3 similar sample images for preprocessing.
And 4, when the sample image is selected and analyzed in the step 4, selecting a sampling frame with the same size and position of the floc area in the sample image, and intercepting and analyzing the part.
And the preprocessing in the step 4 comprises the steps of carrying out graying, median filtering and image segmentation on the sample image in sequence and improving the contrast.
The extraction algorithm of the texture feature value in the step 5 is realized by image processing software or an open source vision library.
The average calculation formula of the texture feature values in the step 6 is as follows:
where Xi is each sample value, N is the number of samples, and X is the resulting texture feature value.
The state of the flocs in the step 8 comprises compactness, shape and size and settling property.
Compared with the prior art, the invention has the beneficial effects that:
1. in the prior art, morphological characteristic values are adopted to identify floc states, which only represent individual characteristics of a single floc, and have large random errors and large calculated amount; the texture features adopted by the invention are integral features obtained by traversing floc images, the stability is good, the calculation is easy, the floc state in the water treatment unit is monitored by analyzing the texture feature change of a floc sample, and the texture features are simple and convenient monitoring indexes, so that the method can be suitable for most running water treatment plants, the floc state is characterized by the water treatment plants according to the texture feature value of the floc, the flocculation effect is evaluated and monitored, the current floc state and the flocculation effect are objectively known, the running parameters are timely adjusted, and guiding opinions are provided for optimizing the running, saving energy, reducing consumption and the like of the water treatment plants.
2. Accurate and objective. Different from a visual method, the method applies a machine vision method, and the method carries out quantitative analysis on the floc state by utilizing the texture characteristic value extracted from the floc image, so that the result is more accurate and objective and does not depend on the rich experience of workers.
3. Is beneficial to realizing automation. The operation steps required by the invention, such as image acquisition, image processing, texture characteristic value extraction and other work, can realize automatic operation by certain instrument equipment, can be integrated in on-line monitoring equipment, and is beneficial to realizing the automation and unattended operation of a water plant.
4. Is economical and practical. The parameters applied by the method are based on the texture characteristic values extracted from the floc images, and the images are obtained only by using image shooting equipment with lower price without other expensive detection equipment, so that the cost is reduced.
In conclusion, the invention has the advantages of simplicity, convenience, intuition, accuracy, high efficiency and low cost.
Drawings
FIG. 1 is a graph showing image characteristics of flocs and turbidity change of settled water at different flocculant dosages in an example of the present invention; wherein: FIG. 1 (a) is a graph showing the variation of texture characteristic values and turbidity of settled water at different dosages of flocculant; FIG. 1 (b) is a graph showing morphological characteristic values and turbidity change of settled water at different flocculant dosing amounts.
FIG. 2 is a diagram showing the state of flocs after adding a flocculant in the example of the present invention.
FIG. 3 is a diagram showing pretreatment of floc state after adding a flocculant in the example of the present invention.
FIG. 4 is a graph showing the relative variation of texture characteristic values at different dosages of flocculant in the example of the present invention.
FIG. 5 is a graph showing floc state pretreatment at different flocculant dosages in an example of the present invention; wherein: FIG. 5 (a) is a graph showing pretreatment of flocs in an example of the present invention at a time of insufficient flocculant administration; FIG. 5 (b) is a graph showing pretreatment of flocs at an optimum amount of flocculant in an example of the present invention; FIG. 5 (c) is a graph showing pretreatment of flocs in the case where the amount of a flocculant added is excessive in the example of the present invention.
Detailed Description
The technical solution of the present invention is further illustrated by the following specific examples.
A floc state monitoring method based on texture features comprises the following steps:
step 1, taking floc-containing water in a water treatment unit of a certain water treatment plant as a sample, and dividing the floc-containing water into a plurality of equivalent floc-containing water samples.
And 2, respectively placing a plurality of floc-containing water samples into transparent containers, and stirring to ensure that flocs are distributed in a suspended state in the transparent containers, wherein the stirring strength is not too high during stirring to prevent the flocs from being broken.
And 3, shooting a plurality of floc water samples distributed in a suspension state by using a camera, namely shooting more than 5 images of the floc water samples distributed in the suspension state of the same sample sampled every time, and controlling all shooting parameters to be consistent during shooting so that the images can truly reflect the texture information of the flocs.
Step 4, selecting at least three images with similar floc distribution conditions, intercepting the area part where the flocs exist and have no impurity interference by using sampling frames with the same size and position, performing preprocessing steps such as graying, median filtering, image segmentation and contrast improvement on each image by using image analysis software, and then extracting a gray level co-occurrence matrix of each image;
step 6, averaging the texture characteristic values of a plurality of images of the same sample to obtain a result, wherein the average value of the texture characteristic values is calculated according to the formula
Wherein X i For each sample value, N is the number of samples, and X is the texture characteristic value finally obtained;
and 7, repeating the steps 1 to 6 to obtain texture characteristic values of floc images in water treatment plants at different time stages, and utilizing a formula
Calculating to respectively obtain the respective relative variation of the texture characteristic values; wherein, R1, R2 represent the texture characteristic value of the former stage and the latter stage of two stages of continuous monitoring respectively, and the time interval of sampling and taking a picture is 1 day, and the time interval can also be adjusted according to actual need.
The texture feature value in step 7 includes correlation and entropy.
Step 8, the respective relative variation of the texture characteristic values obtained respectively is used for judging the state of the flocs, wherein the state of the flocs comprises compactness, shape and size and settling property;
when the relative variation of the entropy of two adjacent stages is greater than 0 and the relative variation of the correlation is less than 0, the density of the flocs is increased, the equivalent particle size is increased, and the state of the flocs is improved;
when the relative variation of the entropy of two adjacent stages is less than 0 and the relative variation of the correlation is more than 0, the floc compactness is reduced, the equivalent particle size is reduced, and the floc state is deteriorated.
Further, in the step 1, the floc-containing water can be obtained from water treatment plants, i.e. tap water plants, sewage treatment plants, and the like; the floc-containing water can be taken from different types of flocculation tanks, clarification tanks, flocculation equipment and other water treatment units in which the floc-containing water exists.
Furthermore, when the images are analyzed by professional image analysis software, the areas with flocs and without impurity interference are selected.
The advantageous effects of the present invention are further illustrated by the following specific examples.
Examples
Step 1, taking raw water in a pretreatment pool of a water plant in Gansu province as a sample, dividing the raw water into 3 parts with the same amount, and placing the 3 parts into a beaker, wherein the volume of each part is 1L;
step 3, when stirring is stopped, continuously shooting 5 images of each water sample by using a camera with the same shooting parameters, and obtaining 3 × 5=15 floc images as shown in fig. 2;
step 4, 3 images with similar and clear floc distribution conditions are respectively selected from the images shot by each group of water samples, 3X 3=9 images are used, sampling frames with the same size and position are used for intercepting the area part where the floc exists and where no impurity interference exists, then image analysis software is used for sequentially carrying out preprocessing operations such as graying, median filtering, image segmentation and contrast improvement on each image, and the preprocessed images are shown in fig. 3;
step 6, changing the flocculant dosage, repeating the steps 1 to 5 to obtain floc image texture characteristic values under different flocculant dosage, and passing each texture characteristic index through a filter as shown in figure 1 (a)
Calculating, wherein R1 and R2 respectively represent texture characteristic values of a previous stage and a next stage of two continuous stages, and obtaining a relative variation graph of texture characteristics under different flocculant dosing amounts, as shown in FIG. 4;
step 7, after stopping stirring each time, standing and settling the floc suspension for 20min, then taking a supernatant, and measuring the turbidity of the supernatant by using a turbidity meter, wherein the turbidity is the turbidity of settled water after flocculation of the raw water at each dosage, and is shown in fig. 1 (a);
and 8, sequentially carrying out graying and binarization processing on the floc image obtained under each dosage by using image processing software, and then measuring morphological characteristics such as floc particle size, floc quantity, fractal dimension and the like to obtain morphological characteristic data under each dosage stage as shown in fig. 1 (b).
Example analysis:
as can be seen from fig. 1 (b), in the monitoring stage when the flocculant dosage is insufficient (dosage is less than 25 mg/L), along with the addition of the flocculant, the fractal dimension of flocs and the particle size of flocs gradually increase, and the turbidity of settled water gradually decreases, which indicates that the flocs in water gradually increase and become more dense, the boundary between the flocs in the image and the image background becomes more obvious, the state of the flocs gradually becomes better, and the floc image at the dosage insufficiency stage is as shown in fig. 5 (a). At this time, as can be seen from fig. 1 (a) and 4, the correlation of the floc image gradually decreases, and the relative variation of the correlation is smaller than 0, because as the dosage increases, a large number of fine particles distributed throughout the water gradually collide and aggregate to form flocs, and the floc image characteristics change from being full of fine particles to a small number of uneven distribution of formed flocs, so that the correlation of the image decreases; the entropy of the floc image is gradually increased, and the relative variation of the entropy is larger than 0, which is caused by the fact that the complexity of image information is increased due to the gradual increase and compaction of the floc, so that the entropy is increased.
As can be seen from fig. 1 (b), when the flocculant dosage is the optimal dosage (25 mg/L), the fractal dimension of flocs and the particle size of flocs reach the maximum, and the turbidity of settled water reaches the minimum, which indicates that the flocs are the largest and dense in multiple stages and have the best settleability, and the image of the flocs at the optimal dosage is shown in fig. 5 (b). At this time, as can be seen from fig. 1 (a), the entropy in the texture feature value of the floc image reaches the peak value, and the correlation reaches the lowest value.
As can be seen from fig. 1 (b), in the monitoring stage of the overdosing of the flocculant (the dosage is more than 25 mg/L), as the flocculant is added, the fractal dimension of the floc and the particle size of the floc gradually decrease, and the turbidity of the settled water gradually increases, because the repulsive force between the flocs gradually increases, the re-stabilization phenomenon of the colloid particles occurs, the large floc gradually becomes loose and is crushed under the shearing force of the water flow to form a plurality of small flocs, so that the compactness of the floc decreases, the particle size decreases, the state of the floc gradually becomes worse, and the image of the floc in the overdosing stage is shown in fig. 5 (c). At this time, as can be seen from fig. 1 (a) and 4, the correlation of the floc images gradually increases, and the relative change amount of the correlation is greater than 0, because the fine-crushed flocs make the flocs more uniformly distributed, so that the correlation increases; while the entropy of the floc image gradually decreases, the relative change amount of the entropy is less than 0, because small and loose flocs reduce the complexity of the image, thereby causing the entropy to decrease.
In addition, any one of the floc images preprocessed in step 4 is taken, the proportion of the image analysis area is gradually reduced on the basis of the image, 5 floc images with different area sizes are obtained, and the morphological characteristic value and the texture characteristic value of each image are respectively extracted, as shown in table 1 below.
TABLE 1 morphological and textural feature values for each image
The extraction of the floc image characteristics is carried out by framing a certain area range of flocs in the floc image, and for the same image, the total amount of the flocs for analysis is reduced along with the reduction of the framing proportion of the image. As can be seen from Table 1, when the frame selection ratio of the image is lower than 60%, the morphological features of the floc image are gradually distorted, the dispersion coefficient is larger, the texture features are more stable, and the dispersion coefficient is smaller. The texture features are not the features of a single floc, but are comprehensive features obtained by analyzing the distribution and the compactness of all flocs in an image, and the comprehensive features have the characteristics of strong integrity and high stability and cannot be greatly changed due to different extraction ranges of the floc images. As can be seen from the above examples, monitoring the floc state using the texture features of the floc is not only economical, practical, stable and objective, but also independent of the rich experience and subjective judgment of the operator.
The invention is therefore an effective method for monitoring the state of a floe by its textural features.
The present invention is not limited to the above embodiments, and based on the technical solutions disclosed in the present invention, those skilled in the art can make some substitutions and modifications on some technical features without creative efforts based on the disclosed technical contents, and these substitutions and modifications are all within the protection scope of the present invention.
Claims (9)
1. A floc state monitoring method based on texture features is characterized by comprising the following steps: the method comprises the following steps:
step 1, taking floc-containing water as a sample, and dividing the floc-containing water into a plurality of equivalent floc-containing water samples;
step 2, respectively placing a plurality of floc-containing water samples obtained in the step 1 into a transparent container, so that the floc-containing water samples are distributed in a suspended state in the transparent container, and obtaining a plurality of floc water samples distributed in a suspended state;
step 3, shooting a plurality of floc water samples distributed in a suspension state obtained in the step 2 by using a camera, and shooting images of a plurality of samples;
step 4, selecting a sample image from the plurality of sample images shot in the step 3 for preprocessing, and extracting a gray level co-occurrence matrix of the image by using image analysis software or an open source vision library;
step 5, calculating an image texture characteristic value by using the gray level co-occurrence matrix extracted in the step 4 through a texture extraction algorithm, wherein the texture characteristic value comprises entropy and correlation;
step 6, averaging the texture characteristic values of a plurality of images of the same sample to obtain a result;
and 7, repeating the steps 1 to 6 to obtain texture characteristic values of the floc images at different time stages, and utilizing a formula
Calculating outRespectively obtaining respective relative variation of the texture characteristic values; wherein, R1 and R2 respectively represent texture characteristic values of a previous stage and a next stage of the two stages of continuous monitoring;
and 8, using the respective relative variation of the texture characteristic values obtained in the step 7 to judge the state of the flocs:
when the relative variation of the entropy of two adjacent stages is greater than 0 and the relative variation of the correlation is less than 0, the density of the flocs is increased, the equivalent particle size is increased, and the state of the flocs is improved;
when the relative variation of the entropy of two adjacent stages is less than 0 and the relative variation of the correlation is more than 0, the floc compactness is reduced, the equivalent grain diameter is reduced, and the floc state is deteriorated.
2. The method for monitoring floc status based on texture features of claim 1, wherein: in the step 2, the floc-containing water sample in the transparent container is stirred or aerated to be distributed in a suspension state in the transparent container.
3. The method for monitoring floc status based on texture features of claim 1, wherein: and 3, shooting at least 5 sample images of the same batch of floc water samples which are sampled each time and distributed in a suspension state, and keeping camera shooting parameters consistent.
4. The method for monitoring floc status based on texture features of claim 1, wherein: and 4, selecting at least 3 similar sample images for preprocessing.
5. A method for monitoring floc state based on texture features as claimed in claim 1 or 4, wherein: when the sample image is selected and analyzed, a sampling frame with the same size and position of the floc area in the sample image is selected to intercept and analyze the part.
6. A method for monitoring floc state based on texture features as claimed in claim 1 or 4, wherein: and the preprocessing in the step 4 comprises the steps of carrying out graying, median filtering and image segmentation on the sample image in sequence and improving the contrast.
7. The method for monitoring floc state based on texture features as claimed in claim 1, wherein: the extraction algorithm of the texture feature value in the step 5 is realized by image processing software or an open source vision library.
8. The method for monitoring floc state based on texture features as claimed in claim 1, wherein: the average calculation formula of the texture feature values in the step 6 is as follows:
where Xi is each sample value, N is the number of samples, and X is the resulting texture feature value. />
9. The method for monitoring floc status based on texture features of claim 1, wherein: the state of the flocs in the step 8 comprises compactness, shape and size and settling property.
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| CN117036250A (en) * | 2023-07-14 | 2023-11-10 | 小鲲智能技术(广州)有限公司 | Method and device for judging floc sedimentation performance based on visual algorithm |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117036250A (en) * | 2023-07-14 | 2023-11-10 | 小鲲智能技术(广州)有限公司 | Method and device for judging floc sedimentation performance based on visual algorithm |
| CN117036250B (en) * | 2023-07-14 | 2023-12-26 | 小鲲智能技术(广州)有限公司 | Method and device for judging floc sedimentation performance based on visual algorithm |
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