CN115282671A - Intelligent control system for backwashing filter - Google Patents
Intelligent control system for backwashing filter Download PDFInfo
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- CN115282671A CN115282671A CN202211211753.8A CN202211211753A CN115282671A CN 115282671 A CN115282671 A CN 115282671A CN 202211211753 A CN202211211753 A CN 202211211753A CN 115282671 A CN115282671 A CN 115282671A
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- 238000011001 backwashing Methods 0.000 title claims abstract description 41
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 232
- 239000012535 impurity Substances 0.000 claims abstract description 163
- 230000009471 action Effects 0.000 claims abstract description 40
- 238000004140 cleaning Methods 0.000 claims abstract description 23
- 230000010365 information processing Effects 0.000 claims abstract description 14
- 238000000034 method Methods 0.000 claims description 27
- 230000008569 process Effects 0.000 claims description 22
- 238000013139 quantization Methods 0.000 claims description 16
- 239000010865 sewage Substances 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 4
- 238000011002 quantification Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 10
- 238000007599 discharging Methods 0.000 description 13
- 239000000126 substance Substances 0.000 description 5
- 238000005406 washing Methods 0.000 description 5
- 239000002351 wastewater Substances 0.000 description 4
- 238000011010 flushing procedure Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005201 scrubbing Methods 0.000 description 2
- BQCADISMDOOEFD-UHFFFAOYSA-N Silver Chemical compound [Ag] BQCADISMDOOEFD-UHFFFAOYSA-N 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 229910052709 silver Inorganic materials 0.000 description 1
- 239000004332 silver Substances 0.000 description 1
- 238000004065 wastewater treatment Methods 0.000 description 1
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D29/00—Filters with filtering elements stationary during filtration, e.g. pressure or suction filters, not covered by groups B01D24/00 - B01D27/00; Filtering elements therefor
- B01D29/60—Filters with filtering elements stationary during filtration, e.g. pressure or suction filters, not covered by groups B01D24/00 - B01D27/00; Filtering elements therefor integrally combined with devices for controlling the filtration
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D29/00—Filters with filtering elements stationary during filtration, e.g. pressure or suction filters, not covered by groups B01D24/00 - B01D27/00; Filtering elements therefor
- B01D29/62—Regenerating the filter material in the filter
- B01D29/66—Regenerating the filter material in the filter by flushing, e.g. counter-current air-bumps
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- Chemical Kinetics & Catalysis (AREA)
- Feedback Control In General (AREA)
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Abstract
The invention relates to the technical field of backwashing filters, in particular to an intelligent control system of a backwashing filter, which acquires a plurality of real-time water quality images and a plurality of real-time filter screen adhesion impurity images at each moment in real time through an information acquisition module; quantifying the real-time water quality image and the real-time filter screen adhesion impurity image according to corresponding characteristics through an information processing module to obtain water quality information and impurity information, and establishing an action parameter equation of water inlet speed and motor rotation speed by using the water quality information and the impurity information; the automatic control module is used for predicting the information by using the historical information of the water quality information and the impurity information, and the intelligent regulation of the water inlet speed and the motor rotating speed is carried out on the backwashing filter based on the action parameter equation and the prediction information. The invention can perform backwashing by utilizing the corresponding water inlet speed and the motor rotating speed based on different water quality conditions and impurity conditions, thereby avoiding incomplete cleaning of the filter screen and achieving better backwashing effect.
Description
Technical Field
The invention relates to the technical field of backwashing filters, in particular to an intelligent control system of a backwashing filter.
Background
In industrial production, a large amount of wastewater is often generated, the wastewater needs to be treated before being discharged, suspended matters and particles in a water body are removed, and the reduction of turbidity is an important step before the wastewater is discharged.
The cleaning process of the back flush filter to the adhesion impurity on the filter screen does: when the pressure difference reaches a set value, corresponding pressure is generated by using a fixed water inlet speed to drive water flow and an internal motor, so that the water flows to clean impurities on the filter screen, and the water stops when the pressure difference reaches another set value. And the in-process of waste water scrubbing, because the distribution of scrubbing impurity on the filter screen is different at every turn, fixed water inlet speed and fixed motor rotation speed also can not reach the effect of filter screen washing and blowdown because of the mutual influence between the two well to the effect of impurity on the filter screen, and long-term filter screen washing and blowdown thoroughly make the filter screen damage easily and cause economic loss.
Therefore, the existing backwashing filter has the problems that the washing speed is fixed, and the adaptive washing cannot be carried out on different use conditions.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent control system of a backwashing filter, which adopts the following technical scheme:
one embodiment of the invention provides an intelligent control system for a backwashing filter, which comprises the following modules:
the information acquisition module is used for acquiring a plurality of real-time water quality images at each moment in the filter screen cleaning and sewage discharge processes in real time and backwashing a plurality of real-time filter screen adhesion impurity images of the filter;
the information processing module is used for quantizing the real-time water quality image and the real-time filter screen adhesion impurity image according to corresponding characteristics to obtain water quality information and impurity information, and establishing an action parameter equation of the water inlet speed and the motor rotation speed by using the water quality information and the impurity information;
and the automatic control module is used for predicting information by using historical information of water quality information and impurity information and intelligently adjusting the water inlet speed and the motor rotating speed of the backwashing filter based on the action parameter equation and the prediction information.
Preferably, the information processing module includes:
and the real-time water quality image quantization unit is used for calculating the information entropy of each pixel point in each real-time water quality image at each moment, the average value of the information entropy of all the pixel points is the quantization result of the corresponding real-time water quality image, and the average value of the quantization results of all the real-time water quality images collected at each moment is the water quality information at the corresponding moment.
Preferably, the information processing module includes:
the real-time filter screen adhesion impurity image quantization unit is used for utilizing super-pixel segmentation to segment an impurity area and a filter screen area in each real-time filter screen adhesion impurity image, calculating the ratio of the size of the impurity area to the size of the filter screen area, taking the gray difference of the impurity area and the filter screen area as the weight of the ratio, calculating the weighted summation result of the ratios of all real-time filter screen adhesion impurity images at each moment, and taking the weighted summation result as the impurity information at the corresponding moment.
Preferably, the process of obtaining the size of the impurity area in the real-time filter screen adhesion impurity image quantization unit is as follows:
and acquiring the number of pixel points of each super-pixel block in the impurity region, wherein the average value of the number of the pixel points of all the super-pixel blocks in the impurity region is the size of the impurity region.
Preferably, the real-time filter screen adhesion impurity image quantization unit includes:
and the weight calculation unit is used for calculating the average gray value of all the superpixel blocks in the filter screen area as the gray value of the filter screen area, and obtaining the gray difference as the weight according to the difference result between the average gray value of each superpixel block in the impurity area and the gray value of the filter screen area.
Preferably, the information processing module includes:
the action parameter equation construction unit is used for taking each moment as a target moment, taking the water quality information at the moment before the target moment as a numerator, taking the product of the water inlet speed and the first action parameter as a denominator, and taking the obtained ratio result as a first water inlet parameter; taking the water quality information at the previous moment of the target moment as a numerator, taking the product of the motor rotation speed and the second action parameter as a denominator, and taking the obtained ratio result as a first motor parameter; taking the sum of the first water inlet parameter and the first motor parameter as water quality information at a target moment;
taking impurity information at the previous moment of the target moment as a numerator, taking the product of the water inlet speed and the first action parameter as a denominator, and taking the obtained ratio result as a second water inlet parameter; taking the impurity information at the previous moment of the target moment as a numerator, taking the product of the motor rotation speed and the second action parameter as a denominator, and taking the obtained ratio result as a second motor parameter; and taking the sum of the second water inlet parameter and the second motor parameter as impurity information of the target moment.
Preferably, the automatic control module includes:
the information prediction unit is used for calculating the sum of the water quality information and the impurity information at each moment as the water body condition at the corresponding moment, and acquiring a first loss amount of the water quality information, a second loss amount of the impurity information and a third loss amount of the water body condition; acquiring fuzzy prediction water quality information at the next moment based on the acquired first loss amount, and acquiring fuzzy prediction impurity information at the next moment based on the acquired second loss amount; acquiring predicted water quality information and predicted impurity information at the next moment according to the fuzzy predicted water quality information, the fuzzy predicted impurity information and the third loss amount; and substituting the predicted water quality information and the predicted impurity information into the action parameter equation to obtain the predicted water inlet speed and the predicted motor rotating speed at the next moment.
Preferably, the automatic control module includes:
and the intelligent adjusting unit is used for taking the obtained predicted water inlet speed and the predicted motor rotating speed as the water inlet speed and the motor rotating speed at the next moment to finish the intelligent adjustment of the water inlet speed and the motor rotating speed.
Preferably, the information prediction unit includes:
and the third loss amount acquisition unit is used for acquiring the difference value of the water body condition between every two adjacent seconds before the target time by taking each time as the target time, and the average value of all the difference values is used as the third loss amount of the water body condition.
Preferably, the information prediction unit includes:
the predicted water quality information acquisition unit is used for taking the sum of the fuzzy predicted water quality information and the fuzzy predicted impurity information as the fuzzy water body condition and multiplying the ratio of the fuzzy predicted water quality information to the fuzzy water body condition by a third loss amount to obtain predicted water quality loss; calculating the sum of the fuzzy predicted water quality information and the predicted water quality loss as the predicted water quality information at the next time.
The embodiment of the invention at least has the following beneficial effects:
the real-time water quality image and the real-time filter screen adhesion impurity image are quantized according to the corresponding characteristics to obtain water quality information and impurity information, the characteristics of the images reflecting the water quality condition and backwashing the filter screen impurity condition can be extracted as data information, and the water quality condition and the impurity condition can be visually reflected; then, establishing an action parameter equation of the water inlet speed and the motor rotating speed by using the water quality information and the impurity information, and associating the extracted data information with the water inlet speed and the motor rotating speed to facilitate subsequent adjustment; the existing data information is used for predicting the data information, the water inlet speed and the motor rotating speed are adjusted by using the action parameter equation based on the obtained prediction information, the self-adaptive flushing effect is achieved, the back flushing can be performed by using the corresponding water inlet speed and the corresponding motor rotating speed based on different water quality conditions and impurity conditions, the filter screen is prevented from being cleaned incompletely, and the better back flushing effect is achieved.
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 block diagram of an intelligent control system for a backwashing filter according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the intelligent control system for backwashing filter, its specific implementation, structure, features and effects according to the present invention will be provided in conjunction with the accompanying drawings and preferred embodiments. 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 following describes a specific scheme of the intelligent control system for the backwashing filter provided by the invention in detail with reference to the accompanying drawings.
Referring to fig. 1, a system block diagram of an intelligent control system for a backwashing filter according to an embodiment of the present invention is shown, where the system includes the following modules:
an information acquisition module 100, an information processing module 200, and an automatic control module 300.
The information acquisition module 100 is used for acquiring a plurality of real-time water quality images at each moment in the filter screen cleaning and sewage disposal processes and for backwashing a plurality of real-time filter screen adhesion impurity images of the filter.
And collecting real-time information by using a camera, specifically, collecting a plurality of real-time water quality images at each moment in real time and a plurality of real-time filter screen adhesion impurity images of the backwashing filter.
In the embodiment of the invention, each moment is every second, and a plurality of real-time water quality images and real-time filter screen adhesion impurity images are collected every second.
When the backwashing filter is used for wastewater treatment, because the water inlet speed and the motor rotation speed generally have great influence on the cleaning and sewage discharge of the filter screen, real-time data in the cleaning and sewage discharge processes of the filter screen are collected, the water inlet speed and the motor rotation speed are calculated by using an action parameter equation and predicted information in the cleaning and sewage discharge processes of the filter screen, and then the intelligent automatic control of the backwashing water filter is realized by using the water inlet speed and the motor rotation speed.
The information processing module 200 is used for quantifying the real-time water quality image and the real-time filter screen adhesion impurity image according to corresponding characteristics to obtain water quality information and impurity information, and establishing an action parameter equation of the water inlet speed and the motor rotation speed by using the water quality information and the impurity information.
The information processing module 200 comprises a real-time water quality image quantification unit, a real-time filter screen adhesion impurity image quantification unit and an action parameter equation construction unit.
The characteristics in the image are extracted, the impurities adhered to the filter screen and the impurities in the water are quantified by utilizing the image information.
And the real-time water quality image quantization unit is used for calculating the information entropy of each pixel point in each real-time water quality image at each moment, the average value of the information entropy of all the pixel points is the quantization result of the corresponding real-time water quality image, and the average value of the quantization results of all the real-time water quality images collected at each moment is the water quality information at the corresponding moment.
The muddy degree of water quality information expression water, at the in-process that carries out filter screen washing and blowdown, can wash the impurity on the filter screen to the aquatic through intaking and motor rotation, the gathering degree of impurity is different, and its grey level is different, so utilize the information entropy to ask for the muddy degree of average value as the water to every pixel in every frame of every second.
Water quality information, taking the t-th time, i.e. t-th second as an example
The specific calculation formula of (2) is:
wherein, the first and the second end of the pipe are connected with each other,
is shown as
Second of second
Gray scale value of in frame image
The probability of occurrence of the event is,
is shown as
Second of second
The maximum gray-scale value in the frame image,
is shown as
Second of second
The number of all the pixel points in the frame image,
is shown as
Second is common
And (5) opening an image.
It should be noted that, in the following description,
the information entropy of each pixel point is represented, and the information entropy formula is a known technology.
And the real-time filter screen adhesion impurity image quantization unit is used for utilizing super-pixel segmentation to segment the impurity area and the filter screen area in each real-time filter screen adhesion impurity image, calculating the ratio of the size of the impurity area to the size of the filter screen area, taking the gray difference of the impurity area and the filter screen area as the weight of the ratio, calculating the weighted summation result of the ratios of all real-time filter screen adhesion impurity images at each moment, and taking the weighted summation result as the impurity information at the corresponding moment.
The impurities on the filter screen are gathered in blocks, so that the superpixel blocks are utilized for image segmentation, when the filter screen is cleaned, the impurities on the filter screen at the beginning are more, the superpixel blocks of the impurities are larger than the superpixel blocks of the filter screen, the gray value of the impurities is larger than that of the silver filter screen when the impurities are black, and the comparison between the average superpixel blocks of the impurity area and the average superpixel blocks of the filter screen area is larger.
And acquiring the number of pixels of each superpixel block in the impurity region, wherein the average value of the number of pixels of all superpixel blocks in the impurity region is the size of the impurity region.
Taking the x-th frame of real-time filter screen adhesion impurity image acquired in the t second as an example, calculating the size of the impurity area in the image
：
Wherein the content of the first and second substances,
denotes the first
Second of
Filter screen of frame stuck in impurity
One impurity region super pixel block in common
Each pixel point;
is shown as
Second of second
The total number of the impurity regions in the frame image exceeds the total number of the pixel blocks.
And acquiring the number of the pixels of each superpixel block in the filter screen area, wherein the average value of the number of the pixels of all superpixel blocks in the filter screen area is the size of the filter screen area.
Taking the x-th frame of real-time filter screen adhesion impurity image acquired in the t second as an example, calculating the size of the filter screen area in the image
：
Wherein, the first and the second end of the pipe are connected with each other,
is shown as
Second of second
Time-of-frame filter screen adhesion impurity image
The super pixel blocks in each filter screen area are common
Pixel points;
denotes the first
Second of second
And (4) the total number of the super pixel blocks in the filter screen area in the filter screen adhesion impurity image during the frame.
And the weight calculation unit is used for calculating the average gray value of all the superpixel blocks in the filter screen area as the gray value of the filter screen area, and obtaining the gray difference as the weight according to the difference result between the average gray value of each superpixel block in the impurity area and the gray value of the filter screen area.
Wherein the content of the first and second substances,
is shown as
Second of
Frame image number one
Average gray values of all pixel points in the superpixel blocks of the impurity regions,
is shown as
Second of
And average gray values of all pixel points in the super pixel blocks of all filter screen areas of the frame image.
The method comprises the steps of taking the comparison condition of the average size of the super-pixel blocks of the impurity area and the average size of the super-pixel blocks of the filter screen area in each frame of image as a main body, taking the difference degree of the average gray value of the super-pixel blocks of the integral impurity area and the gray value of the super-pixel blocks of the integral filter screen area in each frame as a weight to quantify the filter screen adhesion impurities of each frame, and then solving the average value of all frames per second to quantify the impurities of the filter screen per second.
Obtaining impurity information of the tth second by mathematical modeling based on the logic
Comprises the following steps:
wherein the content of the first and second substances,
denotes the first
And the total frame number of all real-time filter screen adhesion impurity images collected in seconds.
The action parameter equation construction unit is used for taking each moment as a target moment, taking the water quality information at the moment before the target moment as a numerator, taking the product of the water inlet speed and the first action parameter as a denominator, and taking the obtained ratio result as a first water inlet parameter; taking the water quality information at the previous moment of the target moment as a numerator, taking the product of the motor rotation speed and the second action parameter as a denominator, and taking the obtained ratio result as a first motor parameter; taking the sum of the first water inlet parameter and the first motor parameter as water quality information at a target moment; taking impurity information at the previous moment of the target moment as a numerator, taking the product of the water inlet speed and the first action parameter as a denominator, and taking the obtained ratio result as a second water inlet parameter; taking the impurity information at the previous moment of the target moment as a numerator, taking the product of the motor rotation speed and the second action parameter as a denominator, and taking the obtained ratio result as a second motor parameter; and taking the sum of the second water inlet parameter and the second motor parameter as impurity information of the target moment.
In the process of cleaning and discharging the filter screen of the back flush filter, the water inlet speed and the motor rotation speed drive the water body in the cabin of the back flush filter to move to clean and discharge the filter screen, and in the process of cleaning and discharging the filter screen, the water inlet speed and the motor rotation speed have different directions of force applied to the water body, so that the filter screen is cleaned and discharged with different degrees of action, theoretically, the water inlet speed and the motor rotation speed have equal effects on the filter screen, and the optimal filter screen cleaning and discharging effect can be achieved in the process of cleaning and discharging the filter screen.
Therefore, an action parameter equation about the water inlet speed and the motor rotation speed is established through the quantized water quality information and impurity information, and the filter screen cleaning and sewage discharging process of t seconds is taken as an example:
first, the
Water quality information of second
Is at the first
Water quality information of second
Through the first step
Water inlet speed of second
And a first
Second motor rotation speed
Obtained under different degrees of influence; in the same way
Is also at
And
in pair
Obtained by different degrees of influence; all changes in the whole process are in the same system, so that the
Second and third
In seconds no matter whether
Or also
Self-in-pair
Or
The degree of influence is constant, so the action parameter is also constant within these two seconds, so the logic is established for
And
in pair
Second acting parameter for filter screen cleaning and sewage discharge process
And
the equation of (c).
By using the first
Second and fourth
Water quality information and impurity information of second and the third
Second water flow rate and
the motor rotation speed in seconds is used for establishing an action parameter equation as follows:
wherein the content of the first and second substances,
is shown as
The water quality information of the second is obtained,
is shown as
Water quality information of seconds;
is shown as
The information on the impurities in the second of the series,
is shown as
Second impurity information;
denotes the first
Water intake rate of seconds;
is shown as
Second motor rotation speed;
to represent
In the first place
The first action parameter of the second on the filter screen cleaning and sewage discharging process;
to represent
In the first place
And the second action parameter is applied to the filter screen cleaning and sewage discharging process.
The automatic control module 300 is used for performing information prediction by using historical information of water quality information and impurity information, and intelligently adjusting the water inlet speed and the motor rotating speed of the backwashing filter based on an action parameter equation and the prediction information.
The automatic control module 300 includes an information prediction unit and an intelligent adjustment unit.
The information prediction unit is used for calculating the sum of the water quality information and the impurity information at each moment as the water body condition at the corresponding moment, and acquiring a first loss amount of the water quality information, a second loss amount of the impurity information and a third loss amount of the water body condition; acquiring fuzzy prediction water quality information at the next moment based on the acquired first loss amount, and acquiring fuzzy prediction impurity information at the next moment based on the acquired second loss amount; acquiring the predicted water quality information and the predicted impurity information at the next moment according to the fuzzy predicted water quality information, the fuzzy predicted impurity information and the third loss amount; and substituting the predicted water quality information and the predicted impurity information into an action parameter equation to obtain the predicted water inlet speed and the predicted motor rotating speed at the next moment.
In the process of cleaning and discharging the filter screen, the quantified real-time water quality information and the filter screen adhesion information have certain relationThat is, the longer the time of cleaning and discharging the sewage, the less and less the adhering impurities on the filter screen, and the more and more the impurities exist in the water body. However, there may be shielding or other factors affecting the information acquisition process, so the average loss of the real-time information is calculated through the historical real-time quantitative information, and then the average loss of the real-time information is calculated through the real-time loss of the real-time information and the historical real-time information
And predicting real-time quantitative information of seconds.
The information prediction unit includes: a first loss amount acquisition unit, a second loss amount acquisition unit, a third loss amount acquisition unit, a fuzzy prediction water quality information acquisition unit, a fuzzy prediction impurity information acquisition unit, a prediction water quality information acquisition unit, a prediction impurity information acquisition unit, and a prediction speed acquisition unit.
And the first loss obtaining unit is used for obtaining the difference value of the water quality information between every two adjacent seconds before the target time by taking each time as the target time, and the average value of all the difference values is used as the first loss of the water quality information.
In the process of cleaning and discharging the filter screen, under the condition of not considering information loss, the change of the water quality information between two continuous seconds has certain regularity, so the average value of the difference value of the water quality information between two adjacent seconds is used as the quantization of the regularity:
. Where T represents the target time.
And the second loss amount acquisition unit is used for acquiring the difference value of the impurity information between every two adjacent seconds before the target time, and the average value of all the difference values is used as the second loss amount of the impurity information.
In the process of cleaning and discharging the filter screen, under the condition of not considering information loss, the impurity information change between two continuous seconds has certain regularity, so the average value of the difference values of the impurity information between two adjacent seconds is used as the quantization of the regularity:
。
and the third loss amount acquisition unit is used for acquiring the difference value of the water body condition between every two adjacent seconds before the target moment, and the average value of all the difference values is used as the third loss amount of the water body condition.
The impurity information and the water quality information of each second are used for representing the water body condition of each second in the process of cleaning and discharging the filter screen, then the information loss amount of two continuous seconds is reflected by using the difference of the water body conditions of two adjacent seconds, and the average value obtained by using all the information loss amounts is used for representing the average loss amount of the real-time information as the third loss amount of the water body condition.
The third loss at the target time is:
wherein T represents a target time,
showing the condition of the water body in the t second,
showing the water condition at the t-1 second.
A fuzzy prediction water quality information acquisition unit for taking the sum of the water quality information at the target time and the first loss amount as fuzzy prediction water quality information at the next time and recording the sum as the fuzzy prediction water quality information at the next time
。
And using the water quality information at the target time and the first loss amount without considering the information loss as the water quality information predicted value without considering the information loss at the next time.
A fuzzy prediction impurity information acquisition unit for recording the sum of the impurity information of the target time and the second loss as the fuzzy prediction impurity information of the next time
。
Similarly, the impurity information at the target time and the second loss amount of the unaccounted information loss are used as the predicted impurity information value of the unaccounted information loss at the next time.
The predicted water quality information acquisition unit is used for taking the sum of the fuzzy predicted water quality information and the fuzzy predicted impurity information as the fuzzy water body condition and multiplying the ratio of the fuzzy predicted water quality information to the fuzzy water body condition by a third loss amount to obtain predicted water quality loss; and calculating the sum of the fuzzy predicted water quality information and the predicted water quality loss as predicted water quality information at the next moment.
Average loss of real-time information
Is obtained through the water body condition which is obtained through the impurity information and the water quality information, so the method is used for obtaining the water body condition
Second impurity information in
The ratio of the whole water body in second is used as a weight to the third loss
And (4) bearing information loss by using the adjustment idea to obtain the predicted loss of the water quality information. Then obtaining the predicted water quality information at the next moment
：
Wherein the content of the first and second substances,
in order to obscure the water body situation,
representing the predicted water quality loss.
The predicted impurity information acquisition unit is used for multiplying the third loss by the ratio of the fuzzy predicted impurity information to the fuzzy water body condition to obtain predicted impurity loss; the sum of the fuzzy predicted impurity information and the predicted impurity loss is calculated as predicted impurity information at the next time.
Similarly, predicted impurity information at the next time is obtained
：
And the predicted speed obtaining unit is used for substituting the predicted water quality information and the predicted impurity information into the action parameter equation to obtain the predicted water inlet speed and the predicted motor rotating speed at the next moment.
Will be first
The predicted water quality information and the predicted impurity information of the second are substituted into an action parameter equation as follows:
through the assignment of the water inlet speed and the motor rotation speed
At this time correspond to
And
is the first
Water intake speed in seconds and motor rotation speed.
And the intelligent adjusting unit is used for taking the obtained predicted water inlet speed and the predicted motor rotating speed as the water inlet speed and the motor rotating speed at the next moment to finish the intelligent adjustment of the water inlet speed and the motor rotating speed.
Using the obtained predicted water intake speed
And predicting the rotational speed of the motor
And adjusting the water inlet speed of the backwashing water filter and the rotating speed of the motor.
In summary, the information of the embodiment of the present invention includes the following modules: an information acquisition module 100, an information processing module 200, and an automatic control module 300.
Specifically, a plurality of real-time water quality images and a plurality of real-time filter screen adhesion impurity images of a backwashing filter are acquired in real time at each moment in the filter screen cleaning and sewage discharging processes through an information acquisition module; respectively quantifying the real-time water quality image and the real-time filter screen adhesion impurity image according to corresponding characteristics through an information processing module to obtain water quality information and impurity information, and establishing an action parameter equation of water inlet speed and motor rotation speed by utilizing the water quality information and the impurity information; the automatic control module is used for predicting information by using historical information of water quality information and impurity information, and the intelligent regulation of water inlet speed and motor rotation speed is carried out on the backwashing filter based on an action parameter equation and prediction information. The embodiment of the invention can perform backwashing by utilizing the corresponding water inlet speed and the motor rotating speed based on different water quality conditions and impurity conditions, thereby avoiding incomplete cleaning of the filter screen and achieving better backwashing effect.
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 specific embodiments thereof have been described above. 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 or similar parts in the embodiments are referred to each other, and each embodiment focuses on differences from other embodiments.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; modifications of the technical solutions described in the foregoing embodiments, or equivalents of some technical features thereof, are not essential to the spirit of the technical solutions of the embodiments of the present application, and are all included in the scope of the present application.
Claims (10)
1. The intelligent control system for the backwashing filter is characterized by comprising the following modules:
the information acquisition module is used for acquiring a plurality of real-time water quality images at each moment in the filter screen cleaning and sewage discharge processes in real time and backwashing a plurality of real-time filter screen adhesion impurity images of the filter;
the information processing module is used for quantizing the real-time water quality image and the real-time filter screen adhesion impurity image according to corresponding characteristics to obtain water quality information and impurity information, and establishing an action parameter equation of the water inlet speed and the motor rotation speed by using the water quality information and the impurity information;
and the automatic control module is used for predicting information by using historical information of water quality information and impurity information and intelligently adjusting the water inlet speed and the motor rotating speed of the backwashing filter based on the action parameter equation and the prediction information.
2. The intelligent control system for the backwashing filter of claim 1, wherein the information processing module comprises:
and the real-time water quality image quantization unit is used for calculating the information entropy of each pixel point in each real-time water quality image at each moment, the average value of the information entropy of all the pixel points is the quantization result of the corresponding real-time water quality image, and the average value of the quantization results of all the real-time water quality images collected at each moment is the water quality information at the corresponding moment.
3. The intelligent control system for the backwashing filter of claim 1, wherein the information processing module comprises:
the real-time filter screen adhesion impurity image quantization unit is used for utilizing super-pixel segmentation to segment an impurity region and a filter screen region in each real-time filter screen adhesion impurity image, calculating the ratio of the size of the impurity region to the size of the filter screen region, taking the gray difference of the impurity region and the filter screen region as the weight of the ratio, calculating the weighted summation result of the ratios of all real-time filter screen adhesion impurity images at each moment, and taking the weighted summation result as the impurity information at the corresponding moment.
4. The intelligent control system for the backwashing filter of claim 3, wherein the obtaining process of the size of the impurity area in the real-time filter screen adhesion impurity image quantification unit is as follows:
and acquiring the number of pixel points of each super-pixel block in the impurity region, wherein the average value of the number of the pixel points of all the super-pixel blocks in the impurity region is the size of the impurity region.
5. The intelligent control system for the backwashing filter of claim 3, wherein the real-time filter screen adhesion impurity image quantifying unit comprises:
and the weight calculation unit is used for calculating the average gray value of all the superpixel blocks in the filter screen area as the gray of the filter screen area, and obtaining the gray difference as the weight according to the difference result between the average gray value of each superpixel block in the impurity area and the gray of the filter screen area.
6. The intelligent control system for the backwashing filter of claim 1, wherein the information processing module comprises:
the action parameter equation construction unit is used for taking each moment as a target moment, taking the water quality information at the moment before the target moment as a numerator, taking the product of the water inlet speed and the first action parameter as a denominator, and taking the obtained ratio result as a first water inlet parameter; taking the water quality information at the previous moment of the target moment as a numerator, taking the product of the motor rotation speed and the second action parameter as a denominator, and taking the obtained ratio result as a first motor parameter; taking the sum of the first water inlet parameter and the first motor parameter as water quality information at a target moment;
taking impurity information at the previous moment of the target moment as a numerator, taking the product of the water inlet speed and the first action parameter as a denominator, and taking the obtained ratio result as a second water inlet parameter; taking the impurity information at the previous moment of the target moment as a numerator, taking the product of the motor rotation speed and the second action parameter as a denominator, and taking the obtained ratio result as a second motor parameter; and taking the sum of the second water inlet parameter and the second motor parameter as impurity information of the target moment.
7. The intelligent control system for the backwashing filter of claim 1, wherein the automatic control module comprises:
the information prediction unit is used for calculating the sum of the water quality information and the impurity information at each moment as the water body condition at the corresponding moment, and acquiring a first loss amount of the water quality information, a second loss amount of the impurity information and a third loss amount of the water body condition; acquiring fuzzy prediction water quality information at the next moment based on the acquired first loss amount, and acquiring fuzzy prediction impurity information at the next moment based on the acquired second loss amount; acquiring predicted water quality information and predicted impurity information at the next moment according to the fuzzy predicted water quality information, the fuzzy predicted impurity information and the third loss amount; and substituting the predicted water quality information and the predicted impurity information into the action parameter equation to obtain the predicted water inlet speed and the predicted motor rotating speed at the next moment.
8. The intelligent control system for the backwashing filter of claim 7, wherein the automatic control module comprises:
and the intelligent adjusting unit is used for finishing intelligent adjustment of the water inlet speed and the motor rotating speed by taking the obtained predicted water inlet speed and the predicted motor rotating speed as the water inlet speed and the motor rotating speed at the next moment.
9. The intelligent control system for the backwashing filter of claim 7, wherein the information prediction unit comprises:
and the third loss amount acquisition unit is used for acquiring the difference value of the water body condition between every two adjacent seconds before the target time by taking each time as the target time, and the average value of all the difference values is used as the third loss amount of the water body condition.
10. The intelligent control system for the backwashing filter of claim 7, wherein the information prediction unit comprises:
the predicted water quality information acquisition unit is used for taking the sum of the fuzzy predicted water quality information and the fuzzy predicted impurity information as a fuzzy water body condition and multiplying the ratio of the fuzzy predicted water quality information to the fuzzy water body condition by a third loss amount to obtain predicted water quality loss; calculating the sum of the fuzzy predicted water quality information and the predicted water quality loss as the predicted water quality information at the next time.
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