CN115410016A - Efficient treatment method for sewage in microbial sewage pool based on image frequency domain analysis - Google Patents
Efficient treatment method for sewage in microbial sewage pool based on image frequency domain analysis Download PDFInfo
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Abstract
The invention relates to the field of sewage treatment, in particular to a high-efficiency sewage treatment method for a microorganism sewage pool based on image frequency domain analysis, which comprises the following steps: setting a first detection point and a second detection point in a microbial sewage treatment pool, and obtaining a first illumination map and a second illumination map according to sewage images of the first detection point and the second detection point; respectively obtaining a water quality change map and an impurity change map of the sewage before and after the microbial treatment by frequency domain analysis of the first illumination map and the second illumination map so as to obtain sewage treatment characteristics, and obtaining average sewage treatment characteristics by combining sewage images of all detection points; and (4) counting the average sewage treatment characteristics at different moments to obtain the sewage treatment effectiveness, and controlling the sewage treatment process according to the sewage treatment effectiveness. The invention accurately and completely describes the change characteristics of the microorganisms on the water body before and after sewage treatment through the water quality change diagram and the impurity change diagram, thereby obtaining the accurate sewage treatment characteristics and ensuring the high efficiency and energy conservation of the sewage treatment process.
Description
Technical Field
The invention relates to the field of sewage treatment, in particular to a high-efficiency sewage treatment method for a microorganism sewage pool based on image frequency domain analysis.
Background
The sewage produced by livestock and poultry breeding or the sewage produced by urban residents often contains a large amount of organic matters and inorganic matters containing elements such as nitrogen, phosphorus and the like, and the sewage is treated by microorganisms generally; the existing method and system for treating sewage by using microorganisms are various, generally comprise the processes of filtering, biochemistry, precipitation and the like, wherein the filtering aims at removing large-particle impurities in the sewage, the biochemistry process aims at treating the sewage by using fillers containing the microorganisms in a sewage tank, so that organic matters are decomposed, the nitrogen and phosphorus removal is realized, and the precipitation process aims at precipitating solid particles in a water body after biochemical treatment; wherein, the biochemical process needs to blast and aerate the water body, improve the oxygen content and need to replace the microbial filler; in the existing sewage treatment method, due to improper control of the operation speed of the blast aeration equipment or the time for replacing the filler, on one hand, energy and materials are wasted, and on the other hand, the sewage treatment efficiency cannot be improved.
In order to improve the efficiency of microbial wastewater treatment, there is a need for an accurate method of wastewater treatment process detection and assessment that automates the control of blast aeration equipment and filler replacement timing.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a high-efficiency treatment method of sewage in a microorganism sewage pool based on image frequency domain analysis, which adopts the following technical scheme:
setting a preset number of first detection points and a preset number of second detection points in a microbial sewage treatment pool, obtaining a low-frequency pixel type and a high-frequency pixel type according to a first sewage image collected at the first detection point and a second sewage image collected at the second detection point for any one of the first detection point pair and the second detection point pair, obtaining an initial water quality distribution map and a target water quality distribution map according to the low-frequency pixel type, and obtaining an initial impurity distribution map and a target impurity distribution map according to the high-frequency pixel type;
matching the pixels in the initial water quality distribution diagram and the target water quality distribution diagram to obtain a first difference value of gray values of any pair of matched paired pixels on a brightness diagram of the second sewage image and a brightness diagram of the first sewage image respectively, and then, referring an image formed by the first difference values of all matched paired pixels as a water quality change diagram;
obtaining an initial impurity density map and a target impurity density map according to the initial impurity distribution map and the target impurity distribution map, matching pixels in the initial impurity density map and the target impurity density map, obtaining second difference values of gray values of any pair of matched pixels on the brightness map of the first sewage image and the brightness map of the second sewage image respectively, and then calling an image formed by the second difference values of all matched pixels as an impurity change map;
obtaining a water quality impurity weight distribution diagram according to the water quality change diagram and the impurity change diagram, then taking the gray value of each pixel on the water quality impurity weight distribution diagram as the weight to perform weighted summation on the gray values of all pixels on the water quality change diagram and the impurity change diagram to obtain a result called as a first detection point and a second detection point pair sewage treatment characteristic, and then calculating the average value of the sewage treatment characteristics of all the first detection point pair and the second detection point pair, called as an average sewage treatment characteristic;
calculating the effectiveness of sewage treatment according to the average sewage treatment characteristics at each moment in a preset time; and controlling the blast rate of the sewage treatment and replacing the microorganism fillers according to the effectiveness of the sewage treatment.
Further, the step of obtaining the low-frequency pixel class and the high-frequency pixel class includes:
respectively obtaining a first illumination image and a second illumination image according to the first sewage image and the second sewage image;
respectively converting a first illumination image and a second illumination image from a spatial domain to a frequency domain, respectively obtaining a first frequency domain image and a second frequency domain image, obtaining all pixels on the second frequency domain image, which are larger than the gray value on the first frequency domain image, obtaining Euclidean distances between all the pixels and the pixel of the central point on the first frequency domain image, carrying out K-Means clustering on all the obtained Euclidean distances to obtain two categories, wherein a corresponding pixel set in the category with the largest mean value of all the Euclidean distances in the two categories is called a high-frequency pixel category, and a corresponding pixel set in the category with the smallest mean value of all the Euclidean distances in the two categories is called a low-frequency pixel category.
Further, the step of obtaining the initial water quality distribution map and the target water quality distribution map according to the low-frequency pixel type and the step of obtaining the initial impurity distribution map and the target impurity distribution map according to the high-frequency pixel type includes:
on the first frequency domain image and the second frequency domain image, keeping the gray values of all pixels in the low-frequency pixel category unchanged, setting the gray values of all pixels outside the low-frequency pixel category to be 0, and then inversely transforming the first frequency domain image and the second frequency domain image from the frequency domain to the space domain to respectively obtain an initial water quality distribution map and a target water quality distribution map;
on the first frequency domain image and the second frequency domain image, keeping the gray values of all pixels in the high-frequency pixel category unchanged, setting the gray values of all pixels outside the high-frequency pixel category to be 0, and then inversely transforming the first frequency domain image and the second frequency domain image from the frequency domain to the spatial domain to respectively obtain an initial impurity distribution map and a target impurity distribution map.
Further, the step of obtaining the water quality impurity weight distribution map according to the water quality change map and the impurity change map comprises:
and obtaining the difference between the water quality change diagram and the impurity change diagram, wherein the result is called a difference diagram, the gray value on the difference diagram is mapped by using a preset mapping curve, and the obtained result is called a water quality impurity weight distribution diagram after normalization processing.
Further, the step of obtaining the effectiveness of the sewage treatment comprises:
fitting a linear model of the average sewage treatment characteristics and time according to the average sewage treatment characteristics at each moment in a preset time period, and obtaining the slope of the linear model, wherein the slope is used as the effectiveness of sewage treatment.
Further, the step of obtaining the initial impurity density map and the target impurity density map comprises:
carrying out binarization processing on the initial impurity density distribution diagram to obtain an initial impurity density binary diagram, constructing a window with a preset size by taking any pixel on the initial impurity density binary diagram as a center, calculating the number of connected domains in the window on the initial impurity density binary diagram, and referring to the number of the impurity distribution densities of any pixel, wherein an image formed by the impurity distribution densities of all the pixels on the initial impurity density binary diagram is referred to as an initial impurity density diagram;
and obtaining a target impurity density map according to the target impurity distribution map.
Further, matching the pixels in the initial water quality distribution map and the target water quality distribution map, wherein the sum of gray value differences between all the obtained matched paired pixels needs to be minimized; matching the pixels in the initial impurity density map and the target impurity density map also requires minimizing the sum of the gray value differences between all the pixels in the matched pair.
Further, the step of obtaining the first illumination image and the second illumination image according to the first sewage image and the second sewage image respectively comprises:
acquiring gray values of any pixel on all channels on the first sewage image, taking the reciprocal of entropy of the gray values on all channels as the illumination abnormal degree of any pixel, and taking an image formed by the illumination abnormal degrees of all pixels as a first illumination image;
and similarly, a second illumination image is obtained according to the illumination abnormal degree of all pixels on the second sewage image.
Further, the first detection points are arranged at the sewage without microorganisms, and each first detection point is composed of a light source and a multispectral camera; the second detection points are arranged at the sewage with microorganisms, each second detection point is composed of a light source and a multispectral camera, and the light source intensity of all the first detection points is the same as that of the second detection points.
The invention has the following beneficial effects:
1. the invention obtains the effectiveness of sewage treatment by calculating the treatment characteristics of the microorganisms on the sewage at different moments, and controls the sewage treatment process according to the effectiveness of the sewage treatment, so that the sewage treatment is efficient and energy-saving.
2. According to the invention, the frequency domain analysis is carried out through the images of a plurality of detection points, the water quality change diagram and the impurity change diagram are utilized to respectively and accurately and completely describe the change characteristics of the microorganisms to the water body before and after sewage treatment, so that the water quality change diagram and the impurity change diagram of all the detection points are integrated to obtain the accurate sewage treatment characteristics, and the sewage treatment process is ensured to be reasonably and properly controlled.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flow chart of a method for efficiently treating sewage in a microbial wastewater tank based on image frequency domain analysis according to an embodiment of the invention.
Detailed Description
To further illustrate the technical means and effects of the present invention for achieving the predetermined objects, the following detailed description of the method for efficiently treating wastewater in a wastewater tank based on image frequency domain analysis according to the present invention with reference to the accompanying drawings and preferred embodiments will be made below. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
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 specific scheme of the efficient treatment method for the sewage in the microbial sewage pool based on the image frequency domain analysis is specifically described below by combining the attached drawings.
Referring to fig. 1, a method for efficiently treating sewage in a microbial wastewater tank based on image frequency domain analysis according to the present invention is shown, wherein the method comprises the following steps:
and S001, setting a sewage detection point in the microbial sewage pool, and acquiring an illumination image of sewage.
After being filtered, sewage is biochemically treated in a microbial sewage treatment tank, wherein the sewage is generally treated by filling filler containing microbes in the sewage tank; the method comprises the steps of firstly isolating a plurality of areas in a sewage pool, wherein sewage in the areas is not treated by microorganisms, then arranging a plurality of detection points in the areas, designing N detection points, wherein N =5, each detection point is provided with a surface light source, the light source emits parallel white light, a multispectral camera is arranged facing the irradiation direction of the light source, the visual field of the camera is opposite to the light source, the light source and the camera are both positioned in the sewage and are subjected to waterproof treatment, the illumination intensity of all the light sources is ensured to be equal, and all the cameras and the light sources are spaced from pixels.
In addition, N detection points are also arranged in a sewage area after the microbial treatment, the detection points in the area in which the sewage is not treated by the microbes are called first detection points, the detection points in the area in which the sewage is treated by the microbes are called second detection points, and the N first detection points and the N second detection points are arranged; suppose that at the same time, the image obtained at the nth first detection point is
The image before the sewage is treated is shown, and the image obtained by the m-th second detection point is
The invention is based on the image of the treated sewage
And
the change situation before and after sewage treatment is obtained, and the two images have a plurality of channels because the multispectral camera is used in the invention.
Considering that sewage contains a large amount of organic matters, macromolecular inorganic matters and tiny solid particles, the substances can absorb or scatter light emitted by a light source, so that a water body becomes turbid, different substances absorb and scatter light with different colors, so that images acquired by a camera have color difference, and the images acquired by the camera in the clear water body have no color difference; based on this, the present invention obtains
The gray value of each pixel on all channels is calculated, and the entropy of the gray value of each pixel on all channels is calculated
Will be
As the abnormal degree of the illumination of each pixel, the larger the value is, the larger the description is, the smaller the description is, the more different the gray value of each pixel on all channels is, and the fact that each pixel absorbs or scatters a part of illumination is indicated; the smaller the degree of illumination anomaly, the larger H, indicating that the gray value of each pixel is the same across all channels, indicating that a portion of the illumination is not absorbed or scattered at each pixel,
the purpose of (c) is to ensure that the distribution is not 0. Will be provided with
The abnormal degree of illumination of all the pixels forms a single-channel image called a first illumination image
(ii) a The same reason is according to
Obtaining a second illumination image
。
It should be noted that the following invention is not concerned with
And
the gray value of each pixel is measured, and only the frequency domain information contained in the two is taken into consideration.
And S002, obtaining a water quality change diagram and an impurity change diagram according to the first illumination image and the second illumination image.
For the sewage before the microbial treatment, organic matters and macromolecular inorganic matters after the microbial treatment are decomposed, the water quality is improved a little, the light absorption of a water body is little, but more solid particles appear in the sewage after the microbial treatment, and the solid particles block light from entering a camera; in particular to
And
compared with
In the case of a composite material, for example,
the medium and low frequency components are more, as the water quality becomes better, the absorption of the water body to light is reduced by one point, the light transmission is enhanced, and the low frequency information is more; and is
The high frequency components will be a bit more, because the solid impurities will be more, and the high frequency information will be more. Based on this, the invention distributes pairs
And
performing fast Fourier transform to transform the two images from space domain to frequency domain to obtain first frequency domain images
And a second frequency domain image
(ii) a According to the principle of Fourier transform, the following method is known:
and
the pixel above corresponds to a frequency, the gray value of the pixel represents the content of the signal containing the corresponding frequency,
and
the closer the upper pixel is to the center, the smaller the corresponding frequency, and the farther the upper pixel is from the center, the larger the corresponding frequency.
Obtaining
Upper ratio of
All pixel sets S with large upper gray value are obtained
Or
Calculating the Euclidean distance between a certain pixel in the S and the pixel at the central point of the upper image, namely each pixel in the S corresponds to one Euclidean distance; pixel mappingThe larger the Euclidean distance is, the higher the frequency corresponding to the pixel is, and the smaller the Euclidean distance is, the lower the frequency corresponding to the pixel is; the Euclidean distances corresponding to all pixels in a pixel set S are clustered, the Euclidean distances of the pixels are divided into two categories by using a K-Means algorithm, the mean value of all the Euclidean distances in each category is obtained, the category with the maximum mean value is obtained, the pixels corresponding to all the Euclidean distances in the category are called a high-frequency pixel category, the category with the minimum mean value is obtained, and the pixels corresponding to all the Euclidean distances in the category are called a low-frequency pixel category.
Therefore, the water quality of the water body at all the pixels in the low-frequency pixel category is improved, and the light absorption of the water body is reduced; and the impurities of the water body at all the pixels in the high-frequency pixel category are increased, and the pollutants in the water are treated into solid precipitates.
Order to
The gray values of the pixels above and in the low-frequency pixel class remain unchanged, so that
Set the gray value of a pixel outside the low frequency pixel class of (1) to 0 and then use the inverse fourier transform algorithm to transform the gray value to 0
Transforming the frequency domain to the space domain, and recording the obtained result as an initial water quality distribution diagram
(ii) a In the same way, order
The gray values of the pixels above and in the low-frequency pixel class remain unchanged, so that
The gray value of the pixels outside the low-frequency pixel class of (1) is set to 0, howeverThen using inverse Fourier transform algorithm to convert
Transforming the frequency domain to the space domain, and recording the obtained result as a target water quality distribution diagram
。
The gray value of each pixel represents the absorption condition of the water before sewage treatment to the light, which is referred to as the water quality characteristic before the treatment for short,
the gray value of each pixel represents the absorption condition of the water after sewage treatment to light, and is called water quality characteristic for short; then
And
the difference of the gray values on the same pixel can represent the water quality change before and after sewage treatment, but the water bodies of the nth first detection point and the mth second detection point are different, namely
Water quality characteristic at a certain pixel position and
the water quality characteristics at the same pixel position are not the same organic matter or inorganic matter, and the change of the water quality before and after the microbial treatment cannot be directly compared, namely, the calculation is carried out
And
the difference in gray values over the same pixel is meaningless; the invention is to pass
And
calculating the change condition of the water quality, and performing the following treatment: by using KM algorithm
And
are matched such that the difference in gray values between all matched pairs of pixels is minimized, said minimum value being
And
the smallest difference that can be achieved, i.e. the invention cannot be based on
And
the difference value of the water quality is used for obtaining the effective water quality change condition, but the invention can know the minimum value of the water quality change condition, so the invention represents the water quality change condition through the minimum value, but considers that
And
the method shows that the absorption condition of the water body to light can only show the distribution condition of the water quality in the water body, and the difference value of the two can not be directly used as the evaluation index of the water quality change in the water body, which is knownThe visual characteristic is that the brightness of the light becomes smaller when the water absorbs the light, so the invention represents and evaluates the change of the water quality according to the change of the brightness graph of the image, and the specific method is as follows:
obtaining
And
a luminance map of, said
Is equal to the gray value of each pixel on the luminance map
The maximum value of each pixel in all channels,
the same applies to the brightness map; then for any pair of matched pixels obtained above (p, q), where p is
A pixel point of (1), q is
The pixel point of (1) is obtained, and the pixel q is
The gray value on the luminance graph of (1) and p are
The difference value of the gray values on the luminance graph of (1) is recorded as a first difference value, and when the difference value is smaller than 0, the difference value is forced to be set to 0, so that any pair of matched pixels (p, q) corresponds to a first difference value, or a pixel q corresponds to a first difference value, and then all the first difference value differences corresponding to all the matched pixel pairs are determined according to the corresponding first difference value differences of all the matched pixel pairsForming a single-channel gray scale map by the first difference values corresponding to all q, and referring to the single-channel gray scale map as a water quality change map
The method is used for describing the water quality change conditions of different positions before and after the sewage is subjected to the microbial treatment, and the water quality change condition is not the real water quality change but the minimum water quality change condition.
On the other hand, let
The gray values of the pixels above and in the high-frequency pixel class remain unchanged, so that
The gray value of the pixels outside the high-frequency pixel class of (1) is set to 0 and then the inverse fourier transform algorithm is used to convert the gray value of the pixels outside the high-frequency pixel class of (1)
Transforming the frequency domain to the space domain, and recording the obtained result as an initial impurity distribution diagram
(ii) a In the same way, order
The gray values of the pixels above and in the high-frequency pixel class remain unchanged, so that
Set the gray value of the pixels outside the high frequency pixel class to 0 and then use the inverse fourier transform algorithm to perform the gray value detection
Converting the frequency domain into the space domain, and recording the obtained result as a target impurity distribution diagram
。
The gray value of each pixel represents the shielding and scattering condition of impurities before sewage treatment to light, which is referred to as the characteristics of the impurities before treatment for short,
the gray value of each pixel represents the shielding and scattering conditions of impurities after sewage treatment on light, and is referred to as the characteristics of the impurities after treatment for short; then
And
the difference of the gray values on the same pixel can represent the impurity change before and after sewage treatment, but the water bodies of the nth first detection point and the mth second detection point are considered to be different, namely
Impurity characteristics at a certain pixel position and
the impurity characteristics at the same pixel position aim at impurities which are not formed by the same organic matter or inorganic matter, and the change situation of the impurities before and after microbial treatment cannot be directly compared, namely calculation
And
the difference in gray values on the same pixel is meaningless; the invention is to pass
And
calculating the change condition of the water quality, and performing the following treatment:
to initial impurity profile
Performing binarization processing to obtain an initial impurity density binary image, constructing a 21 × 21 window by taking any pixel on the initial impurity density binary image as a center, and calculating the number of connected domains in the window on the initial impurity density binary image, namely the number of impurity particles, which is called the impurity distribution density of any pixel, and the initial impurity distribution map
The image formed by the impurity distribution density of all the pixels is called an initial impurity density map
Representing the density of the impurity particles around each location; according to the target impurity distribution diagram
Obtaining a target impurity density map
。
By using KM algorithm
And
are matched such that the difference in gray values between all matched pairs of pixels is minimized, said minimum value being
And
the smallest difference that can be achieved, i.e. the invention cannotAccording to
And
the invention is able to know the minimum value of the impurity variation, so the invention characterizes the impurity variation by the minimum value, but takes into account
And
the method represents the blocking or scattering condition of impurities to light, only represents the distribution condition of impurities in the water body, the difference value of the two conditions cannot be directly used as an evaluation index of the change of the impurities in the water body, and the known visual characteristic of the blocking or scattering of the impurities to the light is that the brightness of the light is reduced, so the change of the impurities is represented and evaluated according to the change of a brightness graph of an image, and the specific method comprises the following steps: for any pair of matched pixels (p, q) obtained as described above, where p is again
Q is still the pixel point of
To obtain the pixel p at
The gray value on the luminance graph of (a) and q are
The difference of the gray values on the luminance graph is denoted as a second difference, and when the difference is smaller than 0, the difference is forced to be 0, so that any pair of matched pixels (p, q) corresponds to a second difference, and it can be said that one pixel q corresponds to one pixel qAnd a second difference value, forming a single-channel gray scale image by using all the second difference values corresponding to all the matched pixel pairs according to all the second difference values corresponding to all the matched pixel pairs, and referring to the single-channel gray scale image as an impurity change image
The method is used for describing the change situation of impurities around different positions before and after the sewage is subjected to the microbial treatment, and the change situation of the impurities is not real change of the impurities but minimum change situation of the impurities.
And S003, obtaining average sewage treatment characteristics according to the water quality change diagram and the impurity change diagram.
For water quality change chart
In other words, a pixel or a region having a larger gradation value on the graph indicates that an organic substance or an inorganic substance at the pixel or the region is decomposed by microorganisms, and the water quality becomes better as the water transmittance is better; for water quality change chart
In other words, the larger the gray value of the pixel or the area on the graph, the more insoluble impurity particles are present, the more obvious the shielding and scattering effect of the impurities on the light is, and the more pollutants in the water are removed; to summarize the description
Or
The larger the gray value is, the more effective the sewage treatment is; however, it is considered that the position where the light transmittance is improved has less impurities, and the position where the impurities are more has lower light transmittance, that is, the position where the impurities are more has
Pixels with large gray scale values
The upper gray value should be small;
pixels with small gray scale values
The upper gray value should be large; but due to the complex sewage environment, the
And
the process of performing analytical calculations introduces errors and noise, resulting in
Pixels with large gray scale values
The gray value of (2) is also large
Pixels with small gray scale values
The above gray scale value is also small, and the calculation of the present invention is disturbed for this case, so the present invention needs to eliminate this case, and then the following processing is performed:
to pair
And
respectively performing normalization processing, and then calculating
And
the difference between the two is recorded as an image
，
The upper gray value is greater than or equal to-1 and less than or equal to 1; for the
Is assumed to be at the image g
X, the gray value of the pixel is mapped, and the mapping result is
The mapping method has the following characteristics: when in use
The closer to 0 the gray value x above is, then
The faster it approaches 0 if
The closer the gray value x above is to 1 or-1, then
The faster the approach to 1; in addition, need to be
Is normalized so that
The sum of all gray values is 1
To pair
The gray values of all the pixels are mapped, and the mapping result is still recorded as an image
Can enable
And
the pixel with large difference of upper gray value
Also has a large gray scale value of
And
pixels with small upper gray value difference
The gray value of (a) approaches 0.
Then pair
The gray values of all the pixels are normalized, so that
Will be provided with
Referred to as the sewage treatment characteristic between the nth first detection point and the mth second sampling point, wherein
To represent
An upper arbitrary pixel g;
to represent
The gray value at the upper pixel g,
to represent
The gray value at the upper pixel g,
the sum of the two gray values is expressed, the larger the value is, the more the water quality or impurities change before and after the microbial treatment is performed, and the better the sewage treatment effect is;
represents the gray value at the upper pixel g, a larger value being indicative of
And
the greater the difference is, the more the description is
The larger the size
The smaller, or vice versa, then
The more accurate and therefore more interesting
The size of (d);
the smaller the description
And
the smaller the difference is, the more the description is made
The larger the size
Also larger, or vice versa, then
The more likely there is noise interference and the introduction of uncertainty, and thus the less of a concern
The size of (2).
And step S004, obtaining the high-efficiency sewage treatment method according to the average sewage treatment characteristics.
In summary, the average sewage treatment characteristic is obtained according to the images acquired by all the detection points at the same time, and then the time-dependent change sequence of the average sewage treatment characteristic is obtained at the latest T times, wherein one minute is used as one time, and T =10; fitting a linear model of average sewage treatment characteristics and time according to a least square method, and obtaining the slope of the linear model, wherein the slope is used as the effectiveness of sewage treatment, the greater the effectiveness of sewage treatment is, the higher the sewage treatment capacity of the microbial sewage pool in T moments is indicated, when the effectiveness of sewage treatment is less than or equal to 0, the sewage treatment capacity is decreased, at the moment, the operating power of the blast aeration equipment is increased, and if the effectiveness of sewage treatment is still less than or equal to 0 at the latest T moments, the microbial filler is replaced; when the effectiveness of sewage treatment is more than 0, the operation of the blast aeration equipment at lower power can be properly maintained, and the electric power is saved. The invention controls the sewage treatment process by the effectiveness of the sewage treatment at the latest T moments, and realizes the purposes of high efficiency and energy saving of the sewage treatment.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And that specific embodiments have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
Claims (9)
1. The efficient treatment method of the sewage of the microorganism sewage pool based on image frequency domain analysis is characterized by comprising the following steps:
setting a preset number of first detection points and a preset number of second detection points in a microbial sewage treatment pool, obtaining a low-frequency pixel type and a high-frequency pixel type according to a first sewage image collected at the first detection point and a second sewage image collected at the second detection point for any one of the first detection point pair and the second detection point pair, obtaining an initial water quality distribution map and a target water quality distribution map according to the low-frequency pixel type, and obtaining an initial impurity distribution map and a target impurity distribution map according to the high-frequency pixel type;
matching the pixels in the initial water quality distribution diagram and the target water quality distribution diagram to obtain a first difference value of gray values of any pair of matched paired pixels on a brightness diagram of the second sewage image and a brightness diagram of the first sewage image respectively, and then, referring an image formed by the first difference values of all matched paired pixels as a water quality change diagram;
obtaining an initial impurity density map and a target impurity density map according to the initial impurity distribution map and the target impurity distribution map, matching pixels in the initial impurity density map and the target impurity density map, and obtaining second difference values of gray values of any pair of matched paired pixels on the brightness map of the first sewage image and the brightness map of the second sewage image respectively, so that an image formed by the second difference values of all matched paired pixels is called an impurity change map;
obtaining a water quality impurity weight distribution diagram according to the water quality change diagram and the impurity change diagram, then taking the gray value of each pixel on the water quality impurity weight distribution diagram as the weight to perform weighted summation on the gray values of all pixels on the water quality change diagram and the impurity change diagram to obtain a result called as a first detection point and a second detection point pair sewage treatment characteristic, and then calculating the average value of the sewage treatment characteristics of all the first detection point pair and the second detection point pair, called as an average sewage treatment characteristic;
calculating the effectiveness of sewage treatment according to the average sewage treatment characteristics at each moment in preset time; controlling the blast rate of the sewage treatment and replacing the microorganism filling material according to the effectiveness of the sewage treatment.
2. The method for efficiently treating the sewage of the microbial sewage pool based on the image frequency domain analysis as claimed in claim 1, wherein the step of obtaining the low-frequency pixel class and the high-frequency pixel class comprises:
respectively obtaining a first illumination image and a second illumination image according to the first sewage image and the second sewage image;
respectively converting a first illumination image and a second illumination image from a spatial domain to a frequency domain, respectively obtaining a first frequency domain image and a second frequency domain image, obtaining all pixels on the second frequency domain image, which are larger than the gray value on the first frequency domain image, obtaining Euclidean distances between all the pixels and the pixel of the central point on the first frequency domain image, carrying out K-Means clustering on all the obtained Euclidean distances to obtain two categories, wherein a corresponding pixel set in the category with the largest mean value of all the Euclidean distances in the two categories is called a high-frequency pixel category, and a corresponding pixel set in the category with the smallest mean value of all the Euclidean distances in the two categories is called a low-frequency pixel category.
3. The method for efficiently treating sewage in a microbial sewage pool based on image frequency domain analysis as claimed in claim 1, wherein the step of obtaining the initial water quality distribution map and the target water quality distribution map according to the low-frequency pixel type and the step of obtaining the initial impurity distribution map and the target impurity distribution map according to the high-frequency pixel type comprise:
on the first frequency domain image and the second frequency domain image, keeping the gray values of all pixels in the low-frequency pixel category unchanged, setting the gray values of all pixels outside the low-frequency pixel category to be 0, and then inversely transforming the first frequency domain image and the second frequency domain image from the frequency domain to the space domain to respectively obtain an initial water quality distribution map and a target water quality distribution map;
on the first frequency domain image and the second frequency domain image, keeping the gray values of all pixels in the high-frequency pixel category unchanged, setting the gray values of all pixels outside the high-frequency pixel category to be 0, and then inversely transforming the first frequency domain image and the second frequency domain image from the frequency domain to the spatial domain to respectively obtain an initial impurity distribution map and a target impurity distribution map.
4. The efficient wastewater treatment method for the microorganism sewage pool based on the image frequency domain analysis as claimed in claim 1, wherein the step of obtaining the water quality impurity weight distribution map according to the water quality change map and the impurity change map comprises:
and obtaining the difference between the water quality change diagram and the impurity change diagram, wherein the result is called a difference diagram, the gray value on the difference diagram is mapped by using a preset mapping curve, and the obtained result is called a water quality impurity weight distribution diagram after normalization processing.
5. The method for efficiently treating the sewage of the microbial sewage pool based on the image frequency domain analysis as claimed in claim 1, wherein the step of obtaining the effectiveness of sewage treatment comprises:
fitting a linear model of the average sewage treatment characteristics and time according to the average sewage treatment characteristics of each moment in a preset time period, and obtaining the slope of the linear model, wherein the slope is used as the effectiveness of sewage treatment.
6. The method for efficiently treating the sewage of the microbial sewage pool based on the image frequency domain analysis as claimed in claim 3, wherein the step of obtaining the initial impurity density map and the target impurity density map comprises:
carrying out binarization processing on the initial impurity density distribution diagram to obtain an initial impurity density binary diagram, constructing a window with a preset size by taking any pixel on the initial impurity density binary diagram as a center, calculating the number of connected domains in the window on the initial impurity density binary diagram, and referring to the number of the impurity distribution densities of any pixel, wherein an image formed by the impurity distribution densities of all the pixels on the initial impurity density binary diagram is referred to as an initial impurity density diagram;
and obtaining a target impurity density map according to the target impurity distribution map.
7. The method for efficiently treating the sewage of the microbial sewage pool based on the image frequency domain analysis as claimed in claim 1, wherein the pixels in the initial water quality distribution map and the target water quality distribution map are matched, and the sum of the gray value differences between all the matched paired pixels is required to be minimized; matching the pixels in the initial impurity density map and the target impurity density map also requires minimizing the sum of the gray value differences between all the pixels in the matched pair.
8. The method for efficient wastewater treatment in a microbial wastewater basin based on image frequency domain analysis of claim 2, wherein the step of obtaining the first illumination image and the second illumination image from the first wastewater image and the second wastewater image respectively comprises:
acquiring gray values of any pixel on all channels on the first sewage image, taking the reciprocal of entropy of the gray values on all channels as the illumination abnormal degree of any pixel, and calling an image formed by the illumination abnormal degrees of all pixels as a first illumination image;
and similarly, a second illumination image is obtained according to the illumination abnormal degree of all pixels on the second sewage image.
9. The method for efficient wastewater treatment in a microorganism wastewater pool based on image frequency domain analysis as claimed in claim 1, wherein the first detection points are arranged at wastewater without microorganisms, and each first detection point is composed of a light source and a multispectral camera; the second detection points are arranged at the sewage with microorganisms, each second detection point is composed of a light source and a multispectral camera, and the light source intensity of all the first detection points is the same as that of the second detection points.
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