CN114849316A - Automatic control system for intelligent backwashing filtration - Google Patents
Automatic control system for intelligent backwashing filtration Download PDFInfo
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Abstract
The invention discloses an automatic control system for intelligent backwashing filtration, and relates to the field of intelligent control. The method comprises the following steps: acquiring a raw water image, a standard filter element image and a filter element image at a water inlet end, and obtaining a gray level co-occurrence matrix of the raw water gray level image and a gray level difference matrix of the standard filter element image and the filter element image at the water inlet end through an image processing unit; the data processing unit calculates the clutter and the correlation of the raw water image and the correlation coefficient of the standard filter element image and the filter element image at the water inlet end to obtain the turbidity degree of the sewage at the water inlet and the blockage degree of the filter element at the current moment, corrects the blockage degree of the filter element at the current moment, and utilizes the final blockage degree of the filter element at the current moment and the controller to carry out automatic control. According to the invention, the blockage degree of the filter element at the current moment is predicted by detecting the turbidity degree in the wastewater treatment process by using the filtering device, and whether the backwashing pump carries out filter element cleaning or not is controlled according to the obtained real-time filter element blockage degree, so that the filtering efficiency of the device is ensured.
Description
Technical Field
The application relates to the field of intelligent control, in particular to an automatic control system for intelligent backwashing filtration.
Background
Because the country attaches importance to the environmental protection theory, especially attaches importance to the water resource that lacks, the problem that the water resource is deficient can be alleviated to a great extent to the cyclic utilization of water. However, in the recycling of water, a large amount of impurities are inevitably brought into the water body due to natural circulation and social circulation of water. Aiming at civil domestic water, the quality requirement on water is higher, so when the water is recycled, the quality of the recycled water needs to be ensured before the recycled water is recycled, raw water which is not subjected to filtration treatment is firstly subjected to filtration treatment, impurities in the recycled water are removed, and the water is divided into inorganic matters, organic matters and aquatic organisms according to the chemical structure of the impurities; the impurities can be classified into suspended matters, colloids and dissolved impurities according to the size of the impurities.
In order to meet the requirements of life and production of people, the water treatment work is very important, and the filtration is a key link in the water treatment work. The backwashing filter treats pollutants in water, including suspended matters, particles and the like, through a filter screen, so as to achieve the purpose of purifying water quality. During the process of filtering by utilizing backwashing filtration, suspended matters and the like in raw water are intercepted and adsorbed by the filter material layer and are continuously accumulated in the filter material layer, so that the pores of the filter layer are gradually blocked by dirt, filter residues are formed on the surface of the filter layer, and the loss of a filter head is continuously increased. When a certain limit is reached, the filter material needs to be cleaned, so that the filter layer recovers the working performance and continues to work; because head loss increases during the filtration, rivers are to the shearing force grow of the filth of adsorbing on the filter material surface, and wherein some granules move down to the lower floor filter material in the impact of rivers and go, finally can make the suspended solid content in aquatic constantly rise, and muddy degree grow, when impurity sees through the filtering layer, the filter loses the filter effect. In certain industries, there are high demands on the quality of the filtered water and the efficiency with which it is filtered. Therefore, when the filtration reaches a certain degree, the filter element may be blocked and the filtration work cannot be continued, and the filter material needs to be cleaned so as to recover the dirt holding capacity of the filter material layer.
When the filter element is cleaned by utilizing the backwashing filter in the prior art, the filter element is generally cleaned at a fixed time, sometimes the cleaning opportunity is too early, the filter element is not blocked, the time and the resource are wasted, sometimes the filter element is blocked seriously due to the phenomenon of untimely cleaning caused by too poor impurity of turbidity degree, and the filtering work cannot be carried out, so that the problem of low filtering efficiency is caused.
Disclosure of Invention
In view of the above technical problems, the present invention provides an automatic control system for intelligent backwash filtration, comprising:
an image acquisition unit: the system is used for acquiring a filter element image at the water inlet end of the filter at the current moment and a standard filter element image before filtering;
an image processing unit: acquiring a gray level difference matrix of the standard filter element image and the filter element image at the water inlet end by utilizing gray levels of pixel points at corresponding positions in the standard filter element image and the filter element image at the water inlet end, which are acquired by an image acquisition unit;
a data acquisition unit: the device is used for collecting the water inlet pressure at the front end of the filter, the water outlet pressure at the rear end of the filter at the current moment, the water inlet flow velocity at the front end of the filter and the water outlet flow velocity at the rear end of the filter at the current moment;
a data processing unit: calculating the gray difference value of the filter element image at the water inlet end and the standard filter element image according to the element value of each position in the gray difference matrix obtained by the image processing unit;
acquiring a row vector and a column vector of the gray difference matrix, calculating the correlation between rows and columns according to the row vector and the column vector of the gray difference matrix, and calculating the correlation coefficient of the gray difference matrix according to the correlation between rows and columns;
calculating the blockage degree of the filter element at the current moment by using the obtained correlation coefficient of the gray difference matrix and the acquired water inlet pressure, water outlet pressure, water inlet flow velocity and water outlet flow velocity;
a controller: the received data processing unit obtains the blockage degree of the filter element at the current moment, and the received blockage degree of the filter element at the current moment is used for controlling the backwashing pump of the filter.
The automatic control system for intelligent backwashing filtration further comprises:
acquiring an image of raw water at a water inlet by using an image acquisition unit to obtain a raw water image;
carrying out graying processing on the raw water image by using an image processing unit to obtain a raw water gray image of the water inlet, obtaining a gray value of each pixel point of the raw water gray image, counting the frequency of each gray value of the raw water gray image, and generating a gray level co-occurrence matrix of the raw water gray image according to each gray value and the frequency of each gray value in the raw water gray image;
calculating the turbidity degree of raw water at the water inlet by using a data processing unit according to the gray level co-occurrence matrix of the raw water gray level image and the gray level value of each pixel point of the raw water gray level image;
and correcting the blockage degree at the current moment by using the turbidity degree of raw water at the water inlet to obtain the final blockage degree, and sending the final blockage degree to the controller.
The calculation method of the turbidity degree of raw water at the water inlet comprises the following steps:
the calculation formula is as follows:
in the formula:
is the turbidity degree of raw water at the water inlet,
showing the degree of disorder of the raw water image,
showing the correlation of the raw water image,
indicating the brightness of the raw water image.
The method for acquiring the brightness of the raw water image comprises the following steps: extracting the gray value of each pixel point of the raw water gray image, calculating the mean value of the gray values of each pixel point of the raw water gray image to obtain the mean value of the gray values of the raw water gray image, and taking the mean value of the gray values as the brightness of the raw water image.
The calculation process of the clutter of the raw water image comprises the following steps:
acquiring a gray level co-occurrence matrix of a raw water gray level image, calculating the mean value of elements in the gray level co-occurrence matrix of the raw water gray level image, performing difference processing on the element value of each position in the gray level co-occurrence matrix of the raw water gray level image and the mean value of the element values of the gray level co-occurrence matrix of the raw water gray level image, and accumulating the absolute value of the difference value between the element value of each position of the gray level co-occurrence matrix of the raw water gray level image and the mean value of the element values of the gray level co-occurrence matrix of the raw water gray level image;
and obtaining the dispersion degree of the gray level co-occurrence matrix of the raw water gray level image by taking the quotient of the addition result of the absolute value of the difference value of the element value of each position of the gray level co-occurrence matrix of the raw water gray level image and the element number of the gray level co-occurrence matrix of the raw water gray level image, and taking the obtained dispersion degree as the disorder degree of the raw water image.
The method for calculating the correlation of the raw water image comprises the following steps:
the calculation formula is as follows:
in the formula:
is the relevance of the original water image,
expressing original water gray level image gray level co-occurrence matrix
Go to the first
The value of the element of the column,
、
respectively the serial numbers of the rows and columns of the gray level co-occurrence matrix,
gray level co-occurrence matrix for representing gray level images of raw water
The average of the values of the row elements,
gray level co-occurrence matrix for representing gray level images of raw water
The average of the values of the column elements,
gray level co-occurrence matrix for representing gray level images of raw water
The standard deviation of the values of the row elements,
gray level co-occurrence matrix for representing gray level images of raw water
Standard deviation of column element values.
The process of correcting the blockage degree at the current moment by using the turbidity degree of raw water at the water inlet to obtain the final blockage degree is as follows:
wherein the content of the first and second substances,
in order to achieve the final degree of clogging,
indicating the degree of clogging of the filter element at the present moment,
representing the turbidity degree of raw water at the water inlet;
the calculation formula of the blockage degree of the filter element at the current moment is as follows:
In the formula:
the degree of clogging of the filter element at the present moment,
the difference between the flow velocity of the water inlet at the front end of the filter and the flow velocity of the water outlet at the rear end of the filter at the current moment,
is the pressure-bearing value of the filter element,
is the gray difference value of the filter element image at the water inlet end and the standard filter element image,
is the correlation coefficient of the gray difference matrix of the standard filter element image and the filter element image at the water inlet end.
The correlation between the row and the column is calculated according to the row vector and the column vector of the gray level difference matrix, and the process of calculating the correlation coefficient of the gray level difference matrix according to the correlation between the row and the column is as follows:
acquiring a gray matrix of a standard filter element image and a filter element image at the water inlet end, and obtaining a gray difference matrix of the standard filter element image and the filter element image at the water inlet end by subtracting gray values of the standard filter element image and the filter element image at the water inlet end;
accumulating element values in the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end to obtain a gray level difference value of the standard filter element image and the filter element image at the water inlet end;
extracting row vectors and column vectors in a gray level difference matrix of a standard filter element image and a filter element image at the water inlet end, transposing the row vectors in the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end to enable the row vectors to be in the same format as the column vectors in the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end, calculating the correlation between the row vectors in the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end and the corresponding positions of the column vectors in the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end to represent the uniformity of the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end, and taking the sum of the correlation between the row vectors in the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end and the corresponding positions of the column vectors in the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end as the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end Correlation coefficients of the degree difference matrix.
The process of controlling the backwashing pump of the filter by using the clogging degree of the filter element at the current moment is as follows:
setting a blocking threshold, if the final blocking degree of the filter element after correction at the current moment is greater than or equal to the blocking threshold, which indicates that the filter element is seriously blocked, performing backwashing operation to clean the filter element, and controlling a backwashing pump to clean the filter element through a controller;
if the final blocking degree of the filter element after correction at the current moment is smaller than the blocking threshold value, the fact that the filter element has few impurities is indicated, the filtering work can be continued, and the back washing operation is not needed for cleaning the filter element.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
according to the invention, the blocking degree of the filter device is judged by combining the water pressure and the flow velocity of the water inlet and the water outlet with the condition of the filter element, whether the filter device needs to be cleaned by backwashing is determined according to the blocking degree of the filter device, the turbidity degree of raw water is determined by analyzing the raw water image, the blocking degree of the filter element in the filter device at the current moment is predicted by using the obtained turbidity degree of the raw water, and the backwashing pump is controlled to intelligently and automatically clean the filter device according to the predicted blocking degree of the filter element, so that the blocking condition of the filter device can be monitored in real time, and the filter device can be ensured to constantly keep a good filtering effect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a system provided by an automatic control system for intelligent backwash filtration according to embodiment 2 of the present invention;
FIG. 2 is a schematic view of a backwash filter device provided in an automatic control system for intelligent backwash filtration according to an embodiment of the present invention;
FIG. 3 is a flow chart of a data processing unit provided in an automatic control system for intelligent backwash filtration according to embodiment 2 of the present invention;
in the figure: 1. a water inlet; 2. a water outlet; 3. a waterproof camera; 4. a filter element; 5. a controller; 6. a backwash pump; 7. back flushing the impurity removing port; 8. a speed sensor; 9. a pressure sensor.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature; in the description of the present embodiment, "a plurality" means two or more unless otherwise specified.
Example 1
The embodiment of the invention provides an automatic control system for intelligent backwashing filtration, which is shown in fig. 1 and 2 and comprises:
in the embodiment, a water speed control device is arranged at a water inlet 1 of the filter, and the blockage degree of the filter element 4 at the current time of the filter is comprehensively judged according to the detected blockage degree of the filter element 4 at the current time and the turbidity degree of raw water at the water inlet 1; respectively placing a speed sensor 8 and a speed sensor 9 at the water inlet 1 and the water outlet 2 to monitor the flow speed and the pressure at the water inlet 1 and the water outlet 2 in real time; a waterproof camera 3 is arranged right in front of the filter element 4 and used for shooting an image of the filter element 4 and an image of raw water at the water inlet 1; the back-flushing pump 6 is connected to the pipeline at the water outlet 2 and used for back-flushing the filter element 4, the back-flushed filter residues are discharged through the back-flushing impurity discharging port 7, the controller 5 is connected to the filter, and the data of the data processing unit is transmitted to the controller 5 through wireless signal transmission, so that the automatic control of intelligent back-flushing filtering is realized. The backwashing filter device of the embodiment is schematically shown in figure 2.
An image acquisition unit: the intelligent backwashing device comprises a waterproof camera 3 which is used for collecting partial images in the filtering device to analyze and carrying out intelligent backwashing operation on a filter element 4 of the filtering device.
In the civil field, certain requirements are made on recycled domestic water, unfiltered raw water which needs to be recycled often needs to be treated again for recycling, impurities in water are removed through a large-scale filtering device, and in the embodiment, a waterproof camera 3 is arranged at a water inlet 1 of a filter to obtain partial images in the filtering device for analysis, so that an intelligent backwashing operation is carried out on a filter element 4 of the filtering device.
An image processing unit: the waterproof camera 31 is used for processing the standard filter element 4 image before filtering and the filter element 4 image at the water inlet 1 end collected by the waterproof camera.
A data acquisition unit: including a speed sensor 9 and a speed sensor 8.
The speed sensor 9: the speed sensor 9(Pressure Transducer) is a device or apparatus capable of sensing a Pressure signal, generally composed of a Pressure sensing element and a signal processing unit, and converting the Pressure signal into a usable output electric signal according to a certain rule.
Utilize speed sensor 9 to acquire the water pressure that filter core 4 bore in the filter in this embodiment, filter core 4 pressure-bearing value promptly, synthesize filter equipment's jam degree according to water pressure to whether need clear up filter equipment and judge, the jam degree that can real-time supervision filter in time washs when needs are cleared up, improves filter equipment's filter effect.
The speed sensor 8: the increment of displacement per unit time is the velocity. The velocity includes a linear velocity and an angular velocity, and there are a linear velocity sensor 8 and an angular velocity sensor 8 corresponding thereto, which are collectively referred to as a velocity sensor 8. In the case of robot automation, the rotational speed is measured more frequently, and the linear speed is often measured indirectly via the rotational speed. For example: the tachogenerator can convert the rotation speed into an electrical signal, a speed sensor 8.
In this embodiment, the speed sensors 8 are respectively placed at the front end and the rear end of the filter element 4, the speed sensors 8 are used for respectively acquiring the flow rate of the water inlet 1 before filtration and the flow rate of the water outlet 2 after filtration, the flow rates of the water at the front end and the rear end of the filter element 4 are also used as the judgment standard of the filtration condition, and the impurity content of the unfiltered raw water is reflected according to the flow rates of the water at the front section and the rear end of the filter element 4, so that the turbidity degree of the unfiltered raw water is reflected.
A data processing unit: data processing is a basic link of system engineering and automatic control, the data processing runs through various fields of social production and social life, and the data is an expression form of facts, concepts or instructions and can be processed by manual or automatic devices. Data processing is the process of extracting valuable information from a large amount of raw data, i.e., converting data into information. The input data in various forms are mainly processed and sorted, and the process comprises the whole process of evolution and derivation of collection, storage, processing, classification, merging, calculation, sorting, conversion, retrieval and propagation of the data.
In this embodiment, the gray value of the image of the standard filter element 4 and the gray value of the image of the filter element 4 at the water inlet 1 end are subtracted to obtain a gray difference matrix, the gray difference and the correlation coefficient between the image of the standard filter element 4 and the image of the filter element 4 at the water inlet 1 end are calculated according to the element values in the gray difference matrix, and the gray difference, the correlation coefficient, the pressure-bearing value of the filter element 4 and the flow velocity difference between the water inlet 1 and the water outlet 2 are sent to the data processing unit to be processed to obtain the blocking degree of the filtering device.
The controller 5: the controller 5 is a master device for controlling the starting, speed regulation, braking and reversing of the motor by changing the wiring of the main circuit or the control circuit and changing the resistance value in the circuit according to a preset sequence. The system consists of a program counter, an instruction register, an instruction decoder, a time sequence generator and an operation controller 5, and is a decision mechanism for issuing commands, namely, the system completes coordination and commands the operation of the whole computer system.
In the embodiment, the data sent to the controller 5 by the data processing unit is used for sending a command to the automatic backwashing filter control system to control the backwashing pump 6 to flush the filter element 4 of the filter device. Whether the back flushing operation is required or not is determined by the clogging degree of the filtering apparatus received by the controller 5, and the filter element 4 of the filter is cleaned, so that the filtering effect of the filtering apparatus is optimized.
A backwashing pump 6: the back flushing pump 6 is a water pump for providing back flushing water for the filter chamber, so that the filter chamber recovers the filtering capacity. The technology applied to the site of the backwashing water pump is not needed, the selected water pump types are different, the backwashing pump 6 is often used for lifting, conveying and the like in raw water treatment, the initial function can be recovered after backwashing, the filtering precision is improved, the flow or the filtering period is not influenced, and the filtering efficiency is improved under the condition that other indexes are not influenced.
Example 2
The embodiment of the invention provides an automatic control system for intelligent backwashing filtration, which is shown in fig. 1 and 2 and comprises the following specific contents:
large-scale filtering equipment often has many filtering devices, the initial filtering is to remove larger impurities, and then gradually reduces, so the filtering effect after each filtering needs to be detected, and the filtering effect is poor because the existence of impurities can make the filter element surrounded by impurities. Therefore, the filter element needs to be cleaned according to the situation, and the necessity of cleaning the filter element needs to be determined according to the pressure and the water flow speed before and after the filter element is cleaned.
Because the calculation of the filter element blockage degree by using the image acquired at the image acquisition time has a certain delay, the blockage degree of the filter element at the current time is corrected by combining the turbidity degree of the raw water at the water inlet, and the influence degree of the filter element blockage is larger as the turbidity degree of the raw water at the water inlet is worse, so that the turbidity degree of the raw water at the water inlet is required to be calculated by acquiring the raw water image.
In summary, the present embodiment needs to continuously detect the turbidity degree and the water flow speed and pressure, so a sampling device and a sensor device need to be arranged.
The water source initially entering the filtering device contains various impurities, the filtering device is used for performing water layer-by-layer grading treatment to finally reach the use standard, and the filtering device equipment contains a plurality of filtering devices, and different filtering devices are used for treating different impurities. If the current level is not good for the turbidity degree treatment, the residual impurities can be accumulated on the filter element of the next level, because the size of the impurities penetrating through the filter element is reduced along with the deepening of the filtration, the larger impurities which are not well treated in the previous level are blocked on the filter element of the next level, and because the filter element of the next level is refined, the large impurities easily block the filter element to cause the filter element to have long filtration time, the purification speed of the final water is influenced, and the filter element needs to be cleaned under the condition.
The water flow speed is adjusted according to the condition of the turbidity degree and the condition of the filter element to the embodiment controls the filtering effect of the filtering device, controls the automatic cleaning of the filter element according to the pressure of the filter element and the change of the water flow speed, and realizes the high-efficiency intellectualization of the filtering equipment.
S1: calculating the blockage degree of the filter element at the current moment
In the traditional backwashing filter device, the filter element is generally cleaned only according to pressure or directly fixed at regular time for a long time, the former only considers the pressure and does not consider the speed of water flow and the color depth of the filter element, so that the problems that the cleaning is too late, the filter element is seriously blocked, the speed of filtering water is slow for a long time, and impurities on the filter element enter the next layer of filter due to overlarge pressure, and the filtering effect is reduced; the latter washing with fixed time is completely fixed, and the washing time is possibly too early or too late, which is not preferable. The present embodiment determines the degree of clogging by impurities on the filter element by pressure, water flow rate and color texture on the filter element.
Firstly, the filter element image of the filtered water inlet end and the standard filter element image before filtering are analyzed, the filter element is originally silver or white, the filter element is wrapped by impurities after being filtered by a large amount of water, and the color of the filter element is changed greatly, so that the blocking condition of the filter element is determined according to the change degree of the color.
Acquiring a gray level difference matrix of a filter element image at the water inlet end after filtering and a standard filter element image before filtering:
in the formula:
is a gray level difference matrix of a standard filter element image and a filter element image at the water inlet end,
a gray scale matrix representing an image of the filter element at the inlet end,
a grayscale matrix representing the image of a standard filter element,
in the gray scale difference matrix representing the standard filter image and the filter image at the water inlet end
Go to the first
The value of the element of the column,
the number of rows/columns in the gray scale difference matrix representing the standard filter element image and the filter element image at the water inlet end.
The gray level difference value of the standard filter element image and the filter element image at the water inlet end is obtained by accumulating the element values in the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end, and the calculation formula is as follows:
in the formula:
is the gray difference value of the standard filter element image and the filter element image at the water inlet end,
the number of rows/columns in the gray scale difference matrix representing the standard filter image and the filter image at the inlet end,
is a standard filter core image and a filter core image of the water inlet endIn the gray difference matrix of
Go to the first
The value of the element of the column,
、
is the serial number of the row/column in the gray difference matrix of the standard filter element image and the filter element image at the water inlet end.
Unlike the common metering method, only the difference is considered during the common metering difference, and the uniformity of the gray difference matrix of the whole standard filter element image and the filter element image at the water inlet end is also considered here, because the local blockage of the filter element as the filtering device does not affect the whole use.
The correlation analysis refers to the analysis of two or more variable elements with correlation, so as to measure the degree of closeness of correlation of the two variable elements. Different from the correlation of the conventional calculation matrix, the embodiment further calculates the correlation of the standard filter element image and the gray level difference matrix of the filter element image at the water inlet end by calculating the correlation between rows and columns in the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end, and takes the correlation of the gray level difference matrix as the correlation coefficient of the standard filter element image and the filter element image at the water inlet end.
In the above formula, the first and second carbon atoms are,
、
the column vector and the transposed row vector are respectively the gray level difference vector, and the function of transposing the row vector is to make the formats of the row vector and the column vector the same, facilitate subsequent calculation and reduce the calculation amount.
The present embodiment characterizes the uniformity of the gray difference matrix by calculating the correlation between the column vector and the row vector of the gray difference vector:
is shown as
Amount of line travel
The correlation of the column-column vector is,
/
is shown as
Column/row vector-
Is shown as
The variance of the column/row vector is,
is shown as
Amount of line travel
Covariance of column vectors.
Then the finally obtained correlation coefficient of the standard filter element image and the filter element image at the water inlet end is as follows:
in the above formula
And (3) representing the correlation coefficient of the standard filter element image and the filter element image at the water inlet end, wherein the size of the correlation coefficient finally represents the uniformity degree of the gray difference matrix of the standard filter element image and the filter element image at the water inlet end.
The filter element blockage degree at the current moment is obtained from the angle of image color characteristics by combining the difference degree and the uniformity degree of the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end:
comprehensively judging the blocking condition of the filter element according to the flow velocity of the water inlet of the filter element, the flow velocity of the water outlet, the pressure-bearing value of the filter element and the difference information of the standard filter element image and the filter element image at the water inlet end, wherein the calculation formula is as follows:
in the formula:
the degree of clogging of the filter element at the present moment,
the difference between the flow rate of the water inlet at the front end of the filter and the flow rate of the water outlet at the rear end of the filter at the current moment,
is the pressure-bearing value of the filter element,
is the gray difference value of the filter element image at the water inlet end and the standard filter element image,
is the correlation coefficient of the gray difference matrix of the standard filter element image and the filter element image at the water inlet end.
The expression of the above formula means that the gray level difference value of the filter element image at the water inlet end and the standard filter element image
Larger and more severe plugging, and a measure of the uniformity of the filter image at the inlet end and the standard filter image
The more uniform the clogging of the filter element at the present moment on this basis, the more severe.
The difference between the flow speed of the water inlet at the front end of the filter and the flow speed of the water outlet at the rear end of the filter at the current moment is the difference between the flow speed of the water inlet at the front end of the filter and the flow speed of the water outlet at the rear end of the filter, which is obtained by the speed sensor, the filter element pressure-bearing value is the pressure value obtained by the pressure sensor at the filter element, and the pressure value is used as the filter element pressure-bearing value.
S2, calculating the turbidity degree of the raw water at the water inlet
In the filtering process, impurities on the filter element are more and more along with the entering of raw water, the influence of the turbidity degree on the filtering is the largest, the influence of the turbidity degree on the filter element is larger, an error exists in the process of actually calculating the filter element blockage degree due to various reasons, the error is related to the turbidity degree, in the actual calculation, the filtering pressure of the filter element is larger as the turbidity degree is worse, the actual filter element blockage degree is larger, and the calculated blockage degree is delayed for a certain time, so that the filter element blockage degree in the actual filter element cleaning process is larger than the calculated value, and the value of the turbidity degree is larger, so that an error correction term brought by the turbidity degree is added in the final filter element cleaning process.
Generally, the more impurities in the water, the darker the color of the water appears on the image, and the more particles exist in the water, the more disordered the image appears, so the turbidity degree of the water can be obtained by analyzing the turbidity degree image.
The brightness degree of water can be expressed as the change of gray value, the larger the gray value is, the brighter the image is, the better the clarity of the corresponding water is; on the contrary, due to the effect of the impurities, the more the impurities are, the darker the brightness of the water is, and the darker the brightness of the photographed image is.
Therefore, the raw water gray image is obtained by performing gray processing on the raw water image, and the brightness of the raw water image is calculated according to the raw water gray image, wherein the calculation formula is as follows:
in the formula:
the brightness of the original water image is shown,
expressing the gray scale image of raw water
The gray value of each pixel point is calculated,
the serial numbers of the pixel points in the raw water gray level image,
the number of pixel points in the raw water gray level image is shown.
The quality of water is characterized from the brightness of the raw water image, and meanwhile, when impurities including particle color change suspension and the like in the water appear, the images show different texture edges and contain different information amounts. The gray level co-occurrence matrix of the image can analyze some detail characteristics of the image itself, so the embodiment performs correlation analysis on the obtained gray level co-occurrence matrix of the raw water gray level image.
The more the types of impurities are, the richer the edge information and the detailed information are, the more disordered the element distribution in the gray level co-occurrence matrix is reflected in the gray level co-occurrence matrix, mathematically, the disorder of data, i.e. the dispersion degree is measured by the variance, and the dispersion degree of the gray level co-occurrence matrix of the raw water gray level image is calculated:
obtaining the mean value of elements in the gray level co-occurrence matrix of the raw water gray level image
Performing difference processing on each element in the gray level co-occurrence matrix and the mean value of the gray level co-occurrence matrix, and accumulating the absolute values of the difference values of all the elements and the mean value; the dispersion degree of the gray level co-occurrence matrix is obtained by quotient of the accumulated result of the absolute values of the difference values of all the elements and the average value and the number of the elements, the obtained dispersion degree is used as the disorder degree of the raw water image, and the calculation formula is as follows:
in the formula:
is the disorder degree of the raw water gray level image,
、
respectively representing the serial numbers of rows and columns in the gray level co-occurrence matrix of the raw water gray level image,
representing raw water grayscale imagesThe number of rows/columns in the gray level co-occurrence matrix of (a),
gray level co-occurrence matrix for representing gray level images of raw water
Go to the first
The value of the element of the column,
the mean value of elements in the gray level co-occurrence matrix of the raw water gray level image is obtained.
The self-correlation of the gray level co-occurrence matrix of the raw water gray level image reflects the consistency of image textures, and the more and more the impurities are, the poorer the correlation of the gray level co-occurrence matrix of the raw water gray level image is due to the fact that various impurities are different in size, color and shape on the filter element, and the correlation of the raw water gray level image is calculated to reflect the disorder degree of the raw water image.
The calculation formula is as follows:
in the formula:
is the relevance of the original water image,
expressing original water gray level image gray level co-occurrence matrix
Go to the first
The value of the element of the column,
、
respectively the serial numbers of the rows and columns of the gray level co-occurrence matrix,
gray level co-occurrence matrix for representing gray level images of raw water
The average of the values of the row elements,
gray level co-occurrence matrix for representing gray level images of raw water
The average of the values of the column elements,
gray level co-occurrence matrix for representing gray level images of raw water
The standard deviation of the values of the row elements,
gray level co-occurrence matrix for representing gray level images of raw water
Standard deviation of column element values.
In the above formula, the correlation of the raw water images is quantified from the perspective of the raw water gray level image gray level co-occurrence matrix, and a larger correlation of the raw water images indicates that the image characteristics of the raw water images are more similar, and the turbidity degree reflected on the turbidity degree indicates that the turbidity degree of the raw water at the water inlet is smaller.
In summary, in consideration of the brightness, the clutter and the correlation of the raw water image, the method for calculating the turbidity degree of the raw water at the water inlet is obtained by:
in the formula:
is the turbidity degree of raw water at the water inlet,
showing the degree of disorder of the raw water image,
showing the correlation of the raw water image,
indicating the brightness of the raw water image.
Above-mentioned in-process has carried out relevant evaluation to the muddy degree of current water inlet raw water, when filtering, the filter effect of filter core is constantly reducing, because along with filterable going on, impurity on the filter core is more and more for the permeability variation of filter core, ultimate performance is that the pressure on the filter core is bigger and bigger, and the velocity of flow of water around the filter core can change. The filter element should be cleaned in time after being blocked, otherwise the efficiency and the quality of the filtration can be influenced.
S3: calculating the final blockage degree of the filter element at the current moment
Because the influence of the turbidity degree on the filtration is the largest, the influence of the turbidity degree on the filter element is larger, an error exists in the process of actually calculating the filter element blockage degree due to various reasons, and the size of the error is related to the turbidity degree.
Wherein the content of the first and second substances,
the final degree of clogging of the filter element at the present moment,
indicating the degree of clogging of the filter element at the present moment,
representing the turbidity degree of raw water at the water inlet;
the calculation formula of the blockage degree of the filter element at the current moment is as follows:
in the formula:
the degree of clogging of the filter element at the present moment,
the difference between the flow rate of the water inlet at the front end of the filter and the flow rate of the water outlet at the rear end of the filter at the current moment,
is the pressure-bearing value of the filter element,
is the gray difference value of the filter element image at the water inlet end and the standard filter element image,
for standard filter element image and water inlet endCorrelation coefficients of the gray level difference matrix of the filter element image.
The final degree of clogging of the filter element, i.e. the corrected clogging degree of the filter element, is therefore:
s4: automatically controlling according to the final blockage degree of the filter element at the current moment after correction
In the filtering process, the filter element has more and more impurities along with the entering of raw water, the process finally determines the final blockage degree of the filter element after correction at the current moment
The value is worth calculating to obtain the final blockage degree of the filter element at the current time, and different from the traditional cleaning depending on pressure or timing, the calculation of the final blockage degree of the filter element at the current time in the embodiment considers multiple factors.
And controlling the controller according to the final blockage degree of the filter element at the current moment transmitted by the data processing unit, wherein the working content of the data processing unit, namely a flow chart of the data processing unit is shown in fig. 3.
Setting occlusion threshold
Then, there are:
if the final blockage degree of the filter element at the current moment is not less than the blockage threshold value, the filter element is seriously blocked, the filter element needs to be cleaned by back washing operation, and the back washing pump is controlled by the controller to clean the filter element according to the turbidity degree of raw water at the water inlet and the final blockage degree corrected by the filter element at the current moment;
if the final blockage degree of the filter element at the current moment is smaller than the blockage threshold value, the fact that the filter element is not provided with a large amount of impurities indicates that the filter element can continue to carry out filtering work and does not need back washing operation to clean the filter element.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. The utility model provides an automatic control system that filterable of intelligent back flush which characterized in that includes:
an image acquisition unit: the system is used for acquiring a filter element image at the water inlet end of the filter at the current moment and a standard filter element image before filtering;
an image processing unit: acquiring a gray level difference matrix of the standard filter element image and the filter element image at the water inlet end by utilizing gray levels of pixel points at corresponding positions in the standard filter element image and the filter element image at the water inlet end, which are acquired by an image acquisition unit;
a data acquisition unit: the device is used for collecting the water inlet pressure at the front end of the filter, the water outlet pressure at the rear end of the filter at the current moment, the water inlet flow velocity at the front end of the filter and the water outlet flow velocity at the rear end of the filter at the current moment;
a data processing unit: calculating the gray difference value of the filter element image at the water inlet end and the standard filter element image according to the element value of each position in the gray difference matrix obtained by the image processing unit;
acquiring a row vector and a column vector of the gray difference matrix, calculating the correlation between rows and columns according to the row vector and the column vector of the gray difference matrix, and calculating the correlation coefficient of the gray difference matrix according to the correlation between rows and columns;
calculating the blockage degree of the filter element at the current moment by using the obtained correlation coefficient of the gray difference matrix and the acquired water inlet pressure, water outlet pressure, water inlet flow velocity and water outlet flow velocity;
a controller: the received data processing unit obtains the blockage degree of the filter element at the current moment, and the received blockage degree of the filter element at the current moment is used for controlling the backwashing pump of the filter.
2. The intelligent automatic control system for backwashing filtration of claim 1, further comprising:
acquiring an image of raw water at a water inlet without being filtered by using an image acquisition unit to obtain a raw water image;
carrying out graying processing on the raw water image by using an image processing unit to obtain a raw water gray image of the water inlet, obtaining a gray value of each pixel point of the raw water gray image, counting the frequency of each gray value of the raw water gray image, and generating a gray co-occurrence matrix of the raw water gray image according to each gray value of the raw water gray image and the frequency of each gray value;
calculating the turbidity degree of raw water at the water inlet by using a data processing unit according to the gray level co-occurrence matrix of the raw water gray level image and the gray level value of each pixel point of the raw water gray level image;
and correcting the blockage degree at the current moment by using the turbidity degree of raw water at the water inlet to obtain the final blockage degree, and sending the final blockage degree to the controller.
3. The automatic control system for intelligent backwashing filtration of claim 1, wherein the turbidity degree of raw water at the water inlet is calculated by the following method:
the calculation formula is as follows:
in the formula:
is the turbidity degree of raw water at the water inlet,
showing the degree of disorder of the raw water image,
showing the correlation of the raw water image,
indicating the brightness of the raw water image.
4. The automatic control system for intelligent backwashing filtration of claim 3, wherein the brightness of the raw water image is obtained by the following method: extracting the gray value of each pixel point of the raw water gray image, calculating the mean value of the gray values of each pixel point of the raw water gray image to obtain the mean value of the gray values of the raw water gray image, and taking the mean value of the gray values as the brightness of the raw water image.
5. The automatic control system for intelligent backwashing filtration of claim 3, wherein the calculation process of the clutter of the raw water image is as follows:
acquiring a gray level co-occurrence matrix of a raw water gray level image, calculating the mean value of elements in the gray level co-occurrence matrix of the raw water gray level image, performing difference processing on the element value of each position in the raw water gray level image gray level co-occurrence matrix and the mean value of the element values of the raw water gray level image gray level co-occurrence matrix, and accumulating the absolute value of the difference value between the element value of each position of the raw water gray level image gray level co-occurrence matrix and the mean value of the element values of the raw water gray level image gray level co-occurrence matrix;
and obtaining the dispersion degree of the raw water gray level image gray level co-occurrence matrix by taking the quotient of the addition result of the absolute value of the difference value of the element value of each position of the raw water gray level image gray level co-occurrence matrix and the element number of the raw water gray level image gray level co-occurrence matrix as the disorder degree of the raw water image.
6. The automatic control system for intelligent backwashing filtration of claim 3, wherein the correlation of the raw water image is calculated by:
the calculation formula is as follows:
in the formula:
is the relevance of the original water image,
expressing original water gray level image gray level co-occurrence matrix
Go to the first
The value of the element of the column,
、
respectively the serial numbers of the rows and columns of the gray level co-occurrence matrix,
gray level co-occurrence matrix for representing gray level images of raw water
The average of the values of the row elements,
gray level co-occurrence matrix for representing gray level images of raw water
The average of the values of the column elements,
representation sourceGray level co-occurrence matrix of water gray level image
The standard deviation of the values of the row elements,
gray level co-occurrence matrix for representing gray level images of raw water
Standard deviation of column element values.
7. The automatic control system for intelligent backwashing filtration of claim 2, wherein the process of correcting the clogging degree at the current time by using the turbidity degree of raw water at the water inlet to obtain the final clogging degree is as follows:
wherein the content of the first and second substances,
in order to achieve the final degree of clogging,
indicating the degree of clogging of the filter element at the present moment,
representing the turbidity degree of raw water at the water inlet;
the calculation formula of the blockage degree of the filter element at the current moment is as follows:
in the formula:
the degree of clogging of the filter element at the present moment,
the difference between the flow rate of the water inlet at the front end of the filter and the flow rate of the water outlet at the rear end of the filter at the current moment,
is the pressure-bearing value of the filter element,
is the gray difference value of the filter element image at the water inlet end and the standard filter element image,
is the correlation coefficient of the gray difference matrix of the standard filter element image and the filter element image at the water inlet end.
8. The automatic control system for intelligent backwash filtering as claimed in claim 1, wherein the process of calculating the correlation coefficient of the grey scale difference matrix according to the correlation between the row vector and the column vector of the grey scale difference matrix is:
acquiring a gray matrix of a standard filter element image and a filter element image at the water inlet end, and obtaining a gray difference matrix of the standard filter element image and the filter element image at the water inlet end by subtracting gray values of the standard filter element image and the filter element image at the water inlet end;
accumulating element values in a gray level difference matrix of the standard filter element image and the filter element image at the water inlet end to obtain a gray level difference value of the standard filter element image and the filter element image at the water inlet end;
extracting row vectors and column vectors in a gray level difference matrix of a standard filter element image and a filter element image at the water inlet end, transposing the row vectors in the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end to enable the row vectors to be in the same format as the column vectors in the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end, calculating the correlation between the row vectors in the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end and the corresponding positions of the column vectors in the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end to represent the uniformity of the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end, and taking the sum of the correlation between the row vectors in the gray level difference matrix of the standard filter element image and the gray level difference matrix of the filter element image at the water inlet end and the corresponding positions of the column vectors in the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end as the gray level difference matrix of the standard filter element image and the filter element image at the water inlet end Correlation coefficients of the degree difference matrix.
9. The automatic control system for intelligent backwashing filtration of claim 1, wherein the process of controlling the backwashing pump of the filter by using the received clogging degree of the filter element at the current time is as follows:
setting a blockage threshold, if the final blockage degree of the filter element at the current moment is greater than or equal to the blockage threshold, indicating that the filter element is seriously blocked, needing a back flushing operation to clean the filter element, and controlling a back flushing pump to clean the filter element through a controller;
if the final blockage degree of the filter element at the current moment is smaller than the blockage threshold value, the fact that the filter element is not provided with a large amount of impurities indicates that the filter element can continue to carry out filtering work and does not need back washing operation to clean the filter element.
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| CN115282671A (en) * | 2022-09-30 | 2022-11-04 | 南通润厚设备工程有限公司 | Intelligent control system for backwashing filter |
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