CN116012701A - Water treatment dosing control method and device based on alum blossom detection - Google Patents
Water treatment dosing control method and device based on alum blossom detection Download PDFInfo
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Abstract
The invention discloses a water treatment dosing control method and device based on alum blossom detection, comprising the following steps: acquiring alum blossom images acquired by an underwater camera; extracting alum characteristic information from the alum blossom image, the alum characteristic information including: the average particle size of alum, the particle size of 10 percent, 50 percent and 90 percent of all the diameters, the density, the number distribution and the small-particle-size alum percentage characteristics; constructing a real-time control model of alum blossom characteristics and dosing amount based on a pre-fitted relation curve of effluent turbidity and alum blossom characteristics; obtaining the required dosage according to the characteristic information of the alum blossom, the characteristic of the alum blossom and a real-time control model of the dosage; and controlling the opening of the dosing valve according to the required dosing amount.
Description
Technical Field
The invention relates to the technical field of visual detection, in particular to a water treatment dosing control method and device based on alum blossom detection.
The background technology is as follows:
flocculation is one of the most important unit processes in the water treatment process, and the quality of flocculation effect directly influences the water quality of subsequent effluent. Therefore, real-time monitoring and detection of the flocculation process is required to control the dosing amount. At present, the common practice of controlling the dosing amount of the sewage plant is to observe the diameter of alum blossom and the distribution condition of the alum blossom in water by naked eyes by experienced staff, but the method has the advantages of subjectivity, great waste of manpower resources and poor real-time performance, and the method for monitoring the state of the alum blossom by utilizing machine vision has the advantages of intuitiveness, high efficiency, universality and the like, and becomes one of the development directions of the automatic control of the future water plant.
Disclosure of Invention
The invention aims to provide a water treatment dosing control method based on alum blossom detection, which is used for alum blossom detection based on a machine vision method of an embedded development board so as to realize automatic dosing control of sewage treatment. The method can detect the characteristics of the average diameter of alum blossom, the diameters of 10%, 50% and 90% of all diameters, density, number distribution, small-particle-size alum blossom ratio and the like, is used for judging the real-time state of the alum blossom, can be stored in a real-time database for monitoring, alarming abnormal conditions, meeting the real-time requirement, fitting a mathematical model according to the relation between a plurality of characteristic parameters of the alum blossom and effluent turbidity, and providing detailed scientific data for controlling the valve opening of a dosing tank.
The technical scheme is as follows: in order to solve the technical problems, the invention adopts the following technical scheme:
in a first aspect, a method for controlling dosing of water treatment based on alum blossom detection is provided, comprising:
acquiring alum blossom images acquired by an underwater camera;
extracting alum characteristic information from the alum blossom image, the alum characteristic information including: the average particle size of alum, the particle size of 10 percent, 50 percent and 90 percent of all the diameters, the density, the number distribution and the small-particle-size alum percentage characteristics;
constructing a real-time control model of alum blossom characteristics and dosing amount based on a pre-fitted relation curve of effluent turbidity and alum blossom characteristics;
obtaining the required dosage according to the characteristic information of the alum blossom, the characteristic of the alum blossom and a real-time control model of the dosage;
and controlling the opening of the dosing valve according to the required dosing amount.
In some embodiments, the underwater camera needs to be calibrated first, and the calibration method includes:
when the scale is stable at the focal length position of the camera, calculating the ratio of the length of the fixed scale to the pixel value occupied by the scale, calculating the ratio for a plurality of times, taking the average value as a calibration coefficient k, and converting the data related to the absolute value of the pixel into actual data after multiplying the calibration coefficient k, thereby completing the calibration process.
In some embodiments, prior to extracting the alum characteristic information from the alum blossom image, further comprising pre-processing the alum blossom image:
noise reduction is carried out on the alum blossom image, a gray histogram of the image after the noise reduction is counted, and an optimal gray threshold value is solved;
based on the optimal gray threshold, the alum is segmented from the background according to the brightness distribution difference between the alum area and the background area in the alum image by adopting a threshold segmentation algorithm.
In some embodiments, extracting alum characteristic information from the alum blossom images includes:
extracting the edges of all alum flowers in a single frame alum flower image;
calculating the area and the number of all alum connected domains in a single frame alum image by using a function customized in opencv, regarding the alum approximately as a circle, and using a diameter calculation formula:
wherein S is the area of each alum blossom communicating region to obtain the diameter D, and multiplying the diameter D by a calibration coefficient k to obtain the actual alum blossom particle size; the diameters were ordered and the 10 th, 50 th and 90 th% of the diameters were stored, labeled D10, D50, D90, respectively, while the average alum particle size was stored as follows:
d in i The particle size of the ith Alum blossom communicating domain in a single frame image is that of Alum which is the total number of all Alum blossom communicating domains in the single frame image;
obtaining a threshold value according to the relation between the turbidity of the effluent and the proportion of the small-particle-size alum, counting the number count_s of the small-particle-size alum smaller than the threshold value in a single-frame alum image, and calculating the proportion of the small-particle-size alum to all alum in the single-frame image, wherein the proportion of the small-particle-size alum to the small-particle-size alum is calculated
Wherein count is the total alum blossom number;
the total number of alum flowers is obtained after the image is divided, and the number distribution is calculated:
wherein M is the width of the processed image, N is the height of the processed image, and Alum is the total number of all Alum connected domains in a single frame image;
the calculation formula for calculating the alum blossom density rho is as follows:
wherein M is the width of the processed image, N is the height of the processed image, S i The i-th alum blossom connected domain area in a single frame image.
In some embodiments, a method of fitting a curve of effluent turbidity versus alum blossom characteristics, comprising:
in a sedimentation tank, an underwater camera collects alum blossom images every 10 seconds, and alum blossom characteristic information is extracted from the alum blossom images, wherein the alum blossom characteristic information comprises: the average particle size of alum, the particle size of 10 percent, 50 percent and 90 percent of all the diameters, the density, the number distribution and the small-particle-size alum percentage characteristics;
outputting a plurality of groups of alum blossom characteristic information and corresponding effluent turbidity into an excel table, and fitting to obtain a relation curve of the effluent turbidity and the alum blossom characteristic;
in the relation curve of the turbidity of the discharged water and the characteristic of alum blossom, the turbidity of the discharged water is in direct proportion to the proportion of alum blossom with small particle size, the turbidity of the discharged water is in direct proportion to the density, and the turbidity of the discharged water is in inverse proportion to the particle size of alum blossom.
In some embodiments, constructing a real-time control model of alum characteristics and dosing amount based on pre-fitted relationship curves of effluent turbidity and alum characteristics, comprising:
based on a pre-fitted relation curve of the effluent turbidity and the alum blossom characteristic, carrying out time delay correction on the effluent turbidity of the sedimentation tank, and converting a time delay control model of the effluent turbidity and the dosing amount into a real-time control model of the alum blossom characteristic and the dosing amount.
In some embodiments, the alum blossom detection-based water treatment dosing control method further comprises the step of sending out an instruction to alarm when data abnormality is detected.
In a second aspect, the invention provides a water treatment dosing control device based on alum blossom detection, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to the first aspect.
In a third aspect, the present invention provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of the first aspect.
In a fourth aspect, the invention provides a water treatment dosing control system based on alum blossom detection, which comprises a plurality of underwater cameras and the water treatment dosing control device based on alum blossom detection, wherein the underwater cameras are arranged in a sedimentation tank and are used for collecting alum blossom images and uploading the alum blossom images to the water treatment dosing control device based on alum blossom detection.
The invention has the advantages that: the method provided by the invention firstly analyzes and summarizes the relation between the turbidity of the effluent and the state of alum, observes the appearance characteristics of the alum, and represents the appearance characteristics as a unified description. And determining an optimal threshold value by utilizing statistical theoretical knowledge, dividing alum flowers from the background, calculating each characteristic of the alum flowers, and storing in real time to obtain more comprehensive, clear and rapid expression of the object. On the basis, the method carries out log printing and prompting on the abnormality in the alum blossom detection process so as to carry out targeted processing. Finally, a real-time control model is constructed to realize the valve opening control of the dosing tank in the sewage treatment plant, and the innovation point is that:
(1) The invention discloses a description method for monitoring the characteristic state of alum blossom in real time
According to the characteristic of alum blossom in the flocculating process of the flocculating agent, the characteristic of the alum blossom is analyzed according to the state of the alum blossom, and parameters describing the characteristic, such as physical geometric characteristics (length, width and the like), image characteristics (brightness, color and the like), space-time characteristics and the like, and the geometric characteristics (average diameter, 10 th percent, 50 th percent, 90 th percent of all diameters, density, number distribution, small-particle-size alum blossom ratio) which can well represent the state of the alum blossom are selected according to the actual situation of the flocculating process of a sewage pool and the purpose of complete detection, omission and quick judgment to be achieved. After the alum blossom images are subjected to statistical analysis, the alum blossom is identified from the background, and the geometric characteristics of the alum blossom are analyzed and calculated, so that the identification speed is greatly improved, and the real-time performance is satisfied. An effective method for describing the characteristics of the alum blossom is established, and a solid foundation is laid for subsequent detection and evaluation.
(2) Alum state detection based on embedded development board
With the development of computer vision, the deployment of models on development boards is becoming increasingly popular. The embedded development board has the advantages of low cost, good compatibility and expandability and AI processing capability of 21TOPS floating point operation, so the invention carries out alum blossom state detection based on the development board.
(3) Real-time storage and abnormality alarm method for alum blossom characteristic data
All alum flowers detected in a single frame image are subjected to average diameter, 10%, 50% and 90% of all the diameters, density, number distribution and small-particle-size alum flower proportion on an embedded development board, and are supplemented into a database through a real-time database, and the state of various transmitted characteristic data and data can be checked in real time after a webpage is logged in the database. Setting a log file to print and output abnormal conditions such as overtime connection cameras or databases and no detected alum blossom, and if the data is not updated for a long time, displaying a data overtime state by a webpage to prompt a user;
(4) Constructing a real-time control model to realize valve opening control of a dosing tank in a sewage treatment plant
According to the detected characteristic indexes of alum blossom and the output water turbidity fitting relation curve, a traditional time delay control model of output water turbidity and dosing quantity is converted into an instant control model for analyzing the characteristic of alum blossom and dosing quantity, and the constructed control model is used for controlling the opening of a dosing water purification valve of a sewage treatment plant, so that the dosing is accurately controlled, and the dosing quantity is saved.
Drawings
FIG. 1 is a flow chart of a water treatment dosing control method based on alum blossom detection in an embodiment of the invention.
Detailed Description
In order that the manner in which the invention is accomplished, as well as the manner in which it is characterized and attained and its efficacy, a better understanding of the invention is obtained, a further description of the invention will be obtained when reference is made to the following detailed description.
In the description of the present invention, the meaning of a number is one or more, the meaning of a number is two or more, and greater than, less than, exceeding, etc. are understood to exclude the present number, and the meaning of a number is understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, the descriptions of the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Example 1
As shown in FIG. 1, the water treatment dosing control method based on alum blossom detection comprises the following steps:
acquiring alum blossom images acquired by an underwater camera;
extracting alum characteristic information from the alum blossom image, the alum characteristic information including: the average particle size of alum, the particle size of 10 percent, 50 percent and 90 percent of all the diameters, the density, the number distribution and the small-particle-size alum percentage characteristics;
constructing a real-time control model of alum blossom characteristics and dosing amount based on a pre-fitted relation curve of effluent turbidity and alum blossom characteristics;
obtaining the required dosage according to the characteristic information of the alum blossom, the characteristic of the alum blossom and a real-time control model of the dosage;
and controlling the opening of the dosing valve according to the required dosing amount.
In some embodiments, the underwater camera needs to be calibrated first, and the calibration method includes:
when the scale is stable at the focal length position of the camera, calculating the ratio of the length of the fixed scale to the pixel value occupied by the scale, calculating the ratio for a plurality of times, taking the average value as a calibration coefficient k, and converting the data related to the absolute value of the pixel into actual data after multiplying the calibration coefficient k, thereby completing the calibration process.
In some embodiments, prior to extracting the alum characteristic information from the alum blossom image, further comprising pre-processing the alum blossom image:
noise reduction is carried out on the alum blossom image, a gray histogram of the image after the noise reduction is counted, and an optimal gray threshold value is solved;
based on the optimal gray threshold, the alum is segmented from the background according to the brightness distribution difference between the alum area and the background area in the alum image by adopting a threshold segmentation algorithm.
In some embodiments, extracting alum characteristic information from the alum blossom images includes:
extracting the edges of all alum flowers in a single frame alum flower image;
calculating the area and the number of all alum connected domains in a single frame alum image by using a function customized in opencv, regarding the alum approximately as a circle, and using a diameter calculation formula:
wherein S is the area of each alum blossom communicating region to obtain the diameter D, and multiplying the diameter D by a calibration coefficient k to obtain the actual alum blossom particle size; the diameters were ordered and the particle sizes of 10%, 50%, 90% of all diameters were stored, respectively identifiedThe average particle diameter of alum blossom is recorded as D10, D50 and D90, and the formula is:
d in i The particle size of the ith Alum blossom communicating domain in a single frame image is that of Alum which is the total number of all Alum blossom communicating domains in the single frame image;
obtaining a threshold value according to the relation between the turbidity of the effluent and the proportion of the small-particle-size alum, counting the number count_s of the small-particle-size alum smaller than the threshold value in a single-frame alum image, and calculating the proportion of the small-particle-size alum to all alum in the single-frame image, wherein the proportion of the small-particle-size alum to the small-particle-size alum is calculated
Wherein count is the total alum blossom number;
the total number of alum flowers is obtained after the image is divided, and the number distribution is calculated:
wherein M is the width of the processed image, N is the height of the processed image, and Alum is the total number of all Alum connected domains in a single frame image;
the calculation formula for calculating the alum blossom density rho is as follows:
wherein M is the width of the processed image, N is the height of the processed image, S i The i-th alum blossom connected domain area in a single frame image.
In some embodiments, a method of fitting a curve of effluent turbidity versus alum blossom characteristics, comprising:
in a sedimentation tank, an underwater camera collects alum blossom images every 10 seconds, and alum blossom characteristic information is extracted from the alum blossom images, wherein the alum blossom characteristic information comprises: the average particle size of alum, the particle size of 10 percent, 50 percent and 90 percent of all the diameters, the density, the number distribution and the small-particle-size alum percentage characteristics;
outputting a plurality of groups of alum blossom characteristic information and corresponding effluent turbidity into an excel table, and fitting to obtain a relation curve of the effluent turbidity and the alum blossom characteristic;
in the relation curve of the turbidity of the discharged water and the characteristic of alum blossom, the turbidity of the discharged water is in direct proportion to the proportion of alum blossom with small particle size, the turbidity of the discharged water is in direct proportion to the density, and the turbidity of the discharged water is in inverse proportion to the particle size of alum blossom.
In some embodiments, constructing a real-time control model of alum characteristics and dosing amount based on pre-fitted relationship curves of effluent turbidity and alum characteristics, comprising:
based on a pre-fitted relation curve of the effluent turbidity and the alum blossom characteristic, carrying out time delay correction on the effluent turbidity of the sedimentation tank, and converting a time delay control model of the effluent turbidity and the dosing amount into a real-time control model of the alum blossom characteristic and the dosing amount.
In some embodiments, the alum blossom detection-based water treatment dosing control method further comprises the step of sending out an instruction to alarm when data abnormality is detected.
In some embodiments, a method for controlling dosing of water treatment based on alum blossom detection comprises: .
(1) Setting up environment and collecting data
With the development of computer vision, the deployment of models on development boards is becoming increasingly popular. The embedded development board has the advantages of low cost, good compatibility and expandability and AI processing capability of 21TOPS floating point operation, so the invention carries out alum blossom state detection based on the development board. On the embedded development board, modifying the configuration file of the embedded development board to build a network bridge, connecting a plurality of paths of underwater cameras (upper limit four paths) and a computer client through POE interfaces of the development board, and arranging the networks of the development board, the plurality of paths of underwater cameras and the computer client in the same network segment; the rtsp real-time streaming transmission protocol is used for carrying out network connection with the multi-path underwater camera, alum blossom images under different dosing amounts are collected, and real-time collection pictures of the multi-path underwater camera can be checked through a computer client.
(2) Description of appearance characteristics of alum blossom
Based on the actual requirement of a sewage plant for controlling the dosage of the flocculant, the statistical theoretical knowledge is adopted to analyze a large number of alum blossom images acquired by the underwater camera under different dosage, so as to determine the best characteristic description of the alum blossom, and lay a foundation for subsequent detection and evaluation. Characterization is a prerequisite for detecting the state of alum blossom. Reasonable image characteristics and characteristic description operators are selected, so that the image has good characterization performance. The color, shape, texture and brightness are common characteristics of the descriptive object, and the invention selects the appearance characteristics of the alum blossom with the average diameter, the 10 th percent, the 50 th percent and the 90 th percent of all the diameters, the density, the number distribution, the small-particle-size alum blossom ratio and the like which can well represent the state of the alum blossom in consideration of the actual conditions of the alum blossom under different dosing amounts.
(3) Underwater camera for calibration
The calibration of the underwater camera is to restore the actual alum state in the three-dimensional space by utilizing the alum image shot by the camera, and a linear relation exists between the two: image =k [ object ]. In general, the parameter k is obtained through multiple experiments and calculation, and the process of solving the parameter is called camera calibration. When the scale is stable at the focal length position of the underwater camera (the scale is clear and has no obvious shaking), calculating the ratio of the fixed scale length to the pixel value occupied by the fixed scale length, taking the average value after multiple times of calculation as a calibration coefficient k, and converting the data related to the absolute value of the pixel into actual data after multiplying the coefficient k, thereby completing the calibration process.
(4) Construction of alum blossom detection model
Because alum flowers shot by the camera are dense and fine, and the dosing control needs to be fed back in time to reduce hysteresis, the accuracy of an identification algorithm is required to be higher, and various characteristic index data of the alum flowers needs to be displayed and stored in real time. Thus, the present algorithm achieves the following objectives: not only can identify alum flowers from images as much as possible and extract characteristic information with rich information, but also has better real-time performance of the algorithm. The specific implementation steps are as follows:
(4a) Counting a gray level histogram of the image subjected to noise reduction treatment, and solving an optimal gray level threshold;
(4b) Dividing alum from the background according to the brightness distribution difference between the alum area and the background area in the shot image;
(4c) Extracting the edges of all alum flowers in a single frame image;
(4d) Calculating the area and the number of all alum blossom connected domains in a single frame image by using a function customized in opencv, regarding the alum blossom as a circle approximately, and using a diameter calculation formula:
wherein S is the area of each alum blossom communicating region to obtain a diameter D, and then multiplying the diameter D by a calibration coefficient k obtained in the step (3) to obtain actual alum blossom diameters, sorting the diameters and storing 10%, 50% and 90% of all the diameters, respectively marking the diameters as D10, D50 and D90, and simultaneously storing average diameters, wherein the formula is as follows: />
D in i The diameter of the ith Alum blossom communicating domain in a single frame image is the total number of all Alum blossom communicating domains in the single frame image; through a large number of alum data collection and analysis, a relation between the turbidity of the water and the particle size ratio of the small alum is searched for to obtain a threshold value, the number of the small alum with the particle size smaller than the threshold value in a single frame image is counted, the proportion of the small alum with the particle size in the single frame image to all alum is calculated and stored, and a calculation formula of the small alum with the particle size ratio is provided: />
Wherein count_s is the number of alum blossom particle diameters smaller than a certain threshold value, and count is the total number of alum blossom; the total number of alum flowers is obtained after the image is divided, and a number distribution formula is utilized: />
Wherein M is the width of the processed image, N is the height of the processed image, alum is the total number of all Alum blossom communicating domains in a single frame image, and the number distribution characteristics are stored; finally, calculating the alum blossom density as a subsequent detection index, wherein the density calculation formula is as follows: />
Wherein M is the width of the processed image, N is the height of the processed image, S i The area of the ith alum blossom communicating domain in a single frame image;
(5) And displaying various characteristic indexes of the alum blossom through a real-time database and alarming the abnormal indexes.
The method comprises the steps of obtaining average diameters of all alum flowers in a single frame image, diameter data of 10%, 50% and 90% of all the diameters, alum flower density, number distribution, small-particle-size alum flower occupation ratio and other data on an embedded development board through real-time detection and calculation, supplementing the data into a real-time library, and simultaneously carrying out real-time visual display on the transmitted data on a browser; the abnormal conditions encountered in the detection process are printed into a log file, so that the later-stage problem investigation is facilitated, the state of the data is displayed in a database, and the data which is not updated for a long time is subjected to alarm prompt; and finally, combining with practical experience, carrying out instant quantitative and qualitative evaluation according to each index of the alum blossom, thereby controlling the dosage and meeting the requirement of automatic control of a water plant.
(6) Constructing a real-time control model to realize valve opening control of a dosing tank in a sewage treatment plant
Because the sedimentation tank is far away from the dosing equipment, the problem of time lag exists in the turbidity correction of the water discharged from the sedimentation tank, the sedimentation time of the sedimentation tank is changed to about 120 minutes after actual measurement, and no monitoring equipment is arranged on site, so that the dosing condition can not be known for the first time. By analyzing the relation between the characteristic of alum blossom and the turbidity of the effluent, the traditional delay control model of the turbidity of the effluent and the dosage is converted into an instant control model for analyzing the characteristic of alum blossom and the dosage, and a set of dosage standard is formed for controlling the opening degree of a valve of a dosing tank in a sewage treatment plant.
Example 2
In a second aspect, the present embodiment provides a water treatment dosing control device based on alum blossom detection, including a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is operative according to the instructions to perform the steps of the method according to embodiment 1.
Example 3
In a third aspect, the present embodiment provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method described in embodiment 1.
Example 4
In a fourth aspect, the embodiment provides a water treatment dosing control system based on alum blossom detection, which comprises a plurality of underwater cameras and the water treatment dosing control device based on alum blossom detection, wherein the underwater cameras are arranged in a sedimentation tank and are used for collecting alum blossom images and uploading the alum blossom images to the water treatment dosing control device based on alum blossom detection.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be appreciated by those skilled in the art that the present invention can be carried out in other embodiments without departing from the spirit or essential characteristics thereof. Accordingly, the above disclosed embodiments are illustrative in all respects, and not exclusive. All changes that come within the scope of the invention or equivalents thereto are intended to be embraced therein.
Claims (10)
1. The water treatment dosing control method based on alum blossom detection is characterized by comprising the following steps of:
acquiring alum blossom images acquired by an underwater camera;
extracting alum characteristic information from the alum blossom image, the alum characteristic information including: the average particle size of alum, the particle size of 10 percent, 50 percent and 90 percent of all the diameters, the density, the number distribution and the small-particle-size alum percentage characteristics;
constructing a real-time control model of alum blossom characteristics and dosing amount based on a pre-fitted relation curve of effluent turbidity and alum blossom characteristics;
obtaining the required dosage according to the characteristic information of the alum blossom, the characteristic of the alum blossom and a real-time control model of the dosage;
and controlling the opening of the dosing valve according to the required dosing amount.
2. The method for controlling dosing of water treatment based on alum blossom detection according to claim 1, wherein the underwater camera is required to be calibrated first, and the calibration method comprises the following steps:
when the scale is stable at the focal length position of the camera, calculating the ratio of the length of the fixed scale to the pixel value occupied by the scale, calculating the ratio for a plurality of times, taking the average value as a calibration coefficient k, and converting the data related to the absolute value of the pixel into actual data after multiplying the calibration coefficient k, thereby completing the calibration process.
3. The method for controlling the dosing of water treatment based on the detection of alum blossom according to claim 1, wherein the method further comprises the step of pre-treating the alum blossom image before extracting the alum blossom characteristic information from the alum blossom image:
noise reduction is carried out on the alum blossom image, a gray histogram of the image after the noise reduction is counted, and an optimal gray threshold value is solved;
based on the optimal gray threshold, the alum is segmented from the background according to the brightness distribution difference between the alum area and the background area in the alum image by adopting a threshold segmentation algorithm.
4. The method for controlling the dosing of water treatment by alum detection according to claim 1 or 3, wherein the step of extracting alum characteristic information from the alum image comprises:
extracting the edges of all alum flowers in a single frame alum flower image;
calculating the area and the number of all alum connected domains in a single frame alum image by using a function customized in opencv, regarding the alum approximately as a circle, and using a diameter calculation formula:
wherein S is the area of each alum blossom communicating region to obtain the diameter D, and multiplying the diameter D by a calibration coefficient k to obtain the actual alum blossom particle size; sorting the diameters and storing 10%, 50% and 90% of the diameters, respectively labeled as D10, D50 and D90, and storing the average particle size of alum blossom with the formula +.>
D in i The particle size of the ith Alum blossom communicating domain in a single frame image is that of Alum which is the total number of all Alum blossom communicating domains in the single frame image;
obtaining a threshold value according to the relation between the turbidity of the effluent and the proportion of the small-particle-size alum, counting the number count_s of the small-particle-size alum smaller than the threshold value in a single-frame alum image, and calculating the proportion of the small-particle-size alum to all alum in the single-frame image, wherein the proportion of the small-particle-size alum to the small-particle-size alum is calculated
Wherein count is the total alum blossom number;
the total number of alum flowers is obtained after the image is divided, and the number distribution is calculated:
wherein M is the width of the processed image, N is the height of the processed image, and Alum is the total number of all Alum connected domains in a single frame image;
the calculation formula for calculating the alum blossom density rho is as follows:
wherein M is the width of the processed image, N is the height of the processed image, S i The i-th alum blossom connected domain area in a single frame image.
5. The method for controlling dosing of water treatment based on alum detection according to claim 1, wherein the method for fitting a relation curve between turbidity of effluent and characteristics of alum, comprises:
in a sedimentation tank, an underwater camera collects alum blossom images every 10 seconds, and alum blossom characteristic information is extracted from the alum blossom images, wherein the alum blossom characteristic information comprises: the average particle size of alum, the particle size of 10 percent, 50 percent and 90 percent of all the diameters, the density, the number distribution and the small-particle-size alum percentage characteristics;
outputting a plurality of groups of alum blossom characteristic information and corresponding effluent turbidity into an excel table, and fitting to obtain a relation curve of the effluent turbidity and the alum blossom characteristic;
in the relation curve of the turbidity of the discharged water and the characteristic of alum blossom, the turbidity of the discharged water is in direct proportion to the proportion of alum blossom with small particle size, the turbidity of the discharged water is in direct proportion to the density, and the turbidity of the discharged water is in inverse proportion to the particle size of alum blossom.
6. The method for controlling dosing of water treatment based on alum detection according to claim 1, wherein the construction of a model for controlling the characteristics of alum and the dosing amount in real time based on a pre-fitted relationship curve between turbidity of effluent and characteristics of alum, comprises:
based on a pre-fitted relation curve of the effluent turbidity and the alum blossom characteristic, carrying out time delay correction on the effluent turbidity of the sedimentation tank, and converting a time delay control model of the effluent turbidity and the dosing amount into a real-time control model of the alum blossom characteristic and the dosing amount.
7. The method for controlling medication dosing of water treatment based on detection of alum blossom according to claim 1, further comprising, when an abnormality in data is detected, issuing an instruction to alarm.
8. The water treatment dosing control device based on alum blossom detection is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1to 7.
9. A storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the method according to any of claims 1to 7.
10. The water treatment dosing control system based on alum blossom detection is characterized by comprising a plurality of paths of underwater cameras and the water treatment dosing control device based on alum blossom detection as claimed in claim 8, wherein the underwater cameras are arranged in a sedimentation tank and are used for collecting alum blossom images and uploading the alum blossom images to the water treatment dosing control device based on alum blossom detection.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116621386A (en) * | 2023-07-03 | 2023-08-22 | 南京国荣环保科技有限公司 | Method and system for treating fecal sewage by microorganisms |
| CN116768346A (en) * | 2023-08-23 | 2023-09-19 | 四川省每文环保科技有限公司 | Sewage treatment process control method based on pumping flocculation filtration |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116621386A (en) * | 2023-07-03 | 2023-08-22 | 南京国荣环保科技有限公司 | Method and system for treating fecal sewage by microorganisms |
| CN116768346A (en) * | 2023-08-23 | 2023-09-19 | 四川省每文环保科技有限公司 | Sewage treatment process control method based on pumping flocculation filtration |
| CN116768346B (en) * | 2023-08-23 | 2023-12-12 | 四川省每文环保科技有限公司 | Sewage treatment process control method based on pumping flocculation filtration |
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