CN114943917A - Algorithm for visually identifying aeration rate of aerobic tank of sewage plant - Google Patents
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Abstract
The invention discloses an algorithm for visually identifying the aeration quantity of an aerobic tank of a sewage plant, which comprises the following steps: the method comprises the steps of firstly, constructing an input sample, including data acquisition and pretreatment, secondly, constructing a training network, adjusting network parameters, and obtaining an optimal model for identifying the aeration amount of the whole picture, specifically adopting a neural network framework based on HRNet, wherein the whole network adopts subnets which are connected with high resolution to low resolution in parallel instead of recovering the resolution through a low-to-high process, and thirdly, checking the feasibility of the model.
Description
Technical Field
The invention relates to the technical field of visual identification analysis, in particular to an algorithm for visually identifying aeration quantity of an aerobic tank of a sewage plant.
Background
The cover-added and buried construction of the sewage treatment plant leads to the fact that the situation of the whole tank cannot be known in time through a manual inspection mode, and the aeration tank with the aeration cost accounting for more than 50% of the operation cost of the whole plant is a main process for removing organic matters in a biochemical section.
But the aeration rate monitoring and control of aeration tank at present only has an aeration rate input value, do not make accurate aeration system's sewage plant, the aeration tank of whole plant only has fan blast volume numerical value, the monitoring and the judgement system of rear end are lacked, can't know the aeration situation in aeration tank different regions, if aeration system goes wrong, can lead to appearing weak exposing to the sun, overexposure, aeration inequality phenomenon such as uneven, and then lead to appearing regional anaerobism, oxygen deficiency environment, reduce this technology section treatment effeciency, cause the play water quality of water not up to standard.
At present, whether the aeration tank has problems or not is observed through visual inspection of inspectors, randomness is high, errors are large, accurate aeration quantity of each area of the aeration tank cannot be analyzed and calculated, and a systematic and intelligent monitoring system cannot be formed.
Disclosure of Invention
The invention aims to provide an algorithm for visually identifying aeration quantity of an aerobic tank of a sewage plant, which is used for independently analyzing videos of all areas of the aeration tank, giving a fitting aeration quantity of each area and a comparison analysis result with a normal aeration quantity, and giving a result judgment whether each area is normal or not.
The purpose of the invention can be realized by the following technical scheme:
an algorithm for visually identifying the aeration rate of an aerobic tank of a sewage plant comprises the following steps:
the method comprises the following steps: constructing an input sample data set, including video acquisition and preprocessing, acquiring an aeration picture by framing the video, and acquiring the aeration amount of the picture in the aeration picture;
step two: constructing a training network, adjusting network parameters, and adopting a neural network framework based on an HRNet network to obtain an optimal model for identifying the aeration amount of the picture in the aeration picture;
step three: and (4) testing the acquired aeration pictures through the optimal model, and outputting aeration values.
As a further scheme of the invention: the video acquisition and preprocessing in the first step comprises the following steps:
s1: a multipoint fixed monitoring mode is set up for a sewage treatment plant, monitoring points are set in an aeration tank, and a collection camera is erected to collect process videos, so that related process videos are obtained;
s2: performing frame processing on the video in the relevant process, and screening the processed picture to obtain an aeration picture;
s3: and collecting the aeration quantities at all the moments by using an SQL data set, reading the aeration quantities at the moments in the aeration pictures, and marking the aeration quantities as the aeration quantities at the moments.
As a further scheme of the invention: the acquisition of the optimal model in the second step comprises the following steps:
w1: putting the data set obtained in the step one into a YOLOV5 training network model, wherein the training of the network model is based on a Pythrch frame, and iterating the video;
w2: establishing a cross entropy loss function;
w3: adding a head of classification output, wherein the input is s response mappings: { X1, X2, …, Xs };
the output is the s-response map: { X1, X2, …, Xs }, where the resolution and width are the same as the input maps, each output map is an aggregation of the input maps, i.e.:
the cross-phase switching unit has an additional output mapping Ys +1, i.e.
Y s+1 ＝a(Y S ,S+1)；
Function a (X) i K) consists of downsampling Xi from resolution i to resolution k, which is convolved with step 3 x 3;
w4: and carrying out a plurality of rounds of training on the data set to obtain the trained optimal model.
As a further scheme of the invention: a YOLOV5 training network model in W1 adopts an Adam optimizer to optimize the training network model, the optimizer is SGD, the training parameters are 512x512, the initial learning rate is 0.05, and the activation function is relu.
As a further scheme of the invention: the number of video iterations in W1 is not less than 100.
As a further scheme of the invention: in the third step, the state judgment is carried out on the recognized result output through model test;
if the state is normal, outputting an aeration value;
and if the state is abnormal, the state is displayed as abnormal.
The invention has the beneficial effects that:
(1) the invention provides a visual identification aeration quantity fitting algorithm, which is characterized in that the aeration quantity of each area of an aeration tank is quantitatively analyzed by a visual identification method to give accurate aeration quantity data, thus finishing the rough and fuzzy operation state of the aeration tank all the time;
(2) the algorithm visual identification aeration quantity fitting algorithm has universality, can be applied to different sewage plants after short-term training is carried out by applying the established algorithm aiming at different sewage plants, and can be applied to different sewage plants by adjusting the model parameters, thereby being suitable for different environments and conditions;
(3) the invention reflects the whole pool condition of the aeration tank by the fitting aeration quantity of a plurality of areas, refines each area, is beneficial to operators to find abnormal areas in time and realizes targeted fixed-point maintenance;
(4) the algorithm analysis result forms a systematic database which is used as a reference index for monitoring the long-term running state of the aeration tank of the sewage plant, is integrated and analyzed with other monitoring data, and assists the sewage plant in energy conservation and emission reduction.
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The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of the structure of the flow chart of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the invention is an algorithm for visually recognizing the aeration rate of an aerobic tank of a sewage plant, which is to output an actual value by fitting the aeration rate value of a picture where the aeration rate value is located, and judge whether the actual state of the area conforms to a normal state;
the system comprises: the video processing module, the statistical analysis module, the data fitting module, the model building module and the result output module;
the video processing is used for processing the collected aeration tank video, firstly, a monitoring real-time video stream is obtained through an upper computer, the real-time video stream is stored by the upper computer system, the real-time video stream is recorded and stored in a DVR hard disk video recording mode in real time, 10S/segment segmentation is obtained according to 10S/segment segmentation, a DVR segmentation video is obtained, 10S/segment is obtained, time coincidence is carried out between segments, and H.246-MP 4 format conversion is carried out on the segmentation video respectively;
the picture screening uses OPENCV to perform framing processing on the video to obtain an available aeration picture;
the statistical analysis can judge through data transmitted in real time on site, the video condition is divided into normal condition and abnormal condition, and the condition of unnecessary detection of aeration concentration state is preferentially eliminated through classifying the video condition judgment, so that the times of model training are reduced, and the training precision is improved;
the data fitting records real-time aeration amount by using SQL, a database of aeration amount is established, so that the data can be conveniently inquired and used, and the aeration amount of the available picture at the specific moment is collected and counted;
because the amount of aeration is determined according to the amount of bubbles in the picture, each actual X has a fixed Y, and the amount of aeration at this time is determined by adopting a linear regression manner, namely the formula is as follows:
y＝Wx+b
w is weight, x is an input image, b is offset, aeration fitting is carried out through the aeration quantity of the input image, finally a model for realizing aeration is formed, and the model is brought into an HRNet network;
model establishment and result output are realized by putting the aeration amount of the picture at each moment into an HRNet model to obtain a linear regression model, and the loss function is continuously reduced in 100 rounds of training in total, so that a model capable of estimating the aeration amount of the picture is finally obtained, and the model is brought into an HRNet network;
outputting the aeration value at the moment, judging whether the moment is in a normal aeration state or not by judging the aeration value at the moment:
if yes, outputting the aeration value at the moment;
otherwise, the output is in an abnormal state.
If the input is a video file, the picture of each second is saved, the aeration amount of the second is output, the aeration values recognized by the single second are added by using the average at the end, and the value recognized as the normal state is divided to obtain an average value of the aeration values. Therefore, the aeration quantity of the whole video is obtained, the precision of the whole identification process is improved, and the aeration quantity at a certain moment is regressed by using the image sequence.
The algorithm for visually identifying the aeration quantity of the aerobic tank of the sewage plant comprises the following steps:
the method comprises the following steps: constructing an input sample data set, including video acquisition and preprocessing, acquiring an aeration picture by performing framing processing on a video, and acquiring the aeration amount of a picture in the aeration picture;
the video acquisition and preprocessing steps are as follows:
s1: a multipoint fixed monitoring mode is set up for a sewage treatment plant, monitoring points are set in an aeration tank, and a collection camera is erected to collect process videos, so that related process videos are obtained;
s2: performing frame processing on the video in the relevant process, and screening the processed picture to obtain an aeration picture;
s3: collecting aeration quantities at all times by using an SQL data set, reading the aeration quantities at the time in the aeration picture, and marking the aeration quantities as the aeration quantities at the time;
step two: constructing a training network, adjusting network parameters, and adopting a neural network framework based on an HRNet network to obtain an optimal model for identifying the aeration amount of the picture in the aeration picture;
w1: putting the data set obtained in the first step into a YOLOV5 training network model, wherein the training of the network model is based on a Pythroch frame, and performing iteration on videos;
the YOLOV5 training network model in W1 adopts an Adam optimizer to optimize the training network model, the optimizer is SGD, the training parameters are 512x512, the initial learning rate is 0.05, and the activation function is relu;
wherein the number of video iterations in W1 is not less than 100;
w2: establishing a cross entropy loss function;
w3: adding a head of classification output, wherein the input is s response mappings: { X1, X2, …, Xs };
the output is an s-response map: { X1, X2, …, Xs }, where the resolution and width are the same as the input maps, each output map is an aggregation of the input maps, i.e.:
the switching unit across stages has an additional output mapping Ys +1, i.e.
Y s+1 ＝a(Y S ,S+1)；
Function a (X) i K) consists of downsampling Xi from resolution i to resolution k, which is convolved with step 3 x 3;
where a stride 3 x 3 convolution of one stride2 is used for 2x downsampling and a stride 3 x 3 convolution of two consecutive stride2 is used for 4 x downsampling); if i-k, a (·) is just one identity (identity) connection, then: a (X) i ,k)＝X i ；
W4: carrying out a plurality of rounds of training on the data set to obtain an optimal model for the training;
step three: through the optimal model, the acquired aeration picture passes through a model test, and the state judgment is carried out on the recognized result output through the model test;
if the state is normal, outputting an aeration value;
if the state is abnormal, the display is abnormal.
Although one embodiment of the present invention has been described in detail, the description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (6)
1. An algorithm for visually identifying the aeration rate of an aerobic tank of a sewage plant is characterized by comprising the following steps of:
the method comprises the following steps: constructing an input sample data set, including video acquisition and preprocessing, acquiring an aeration picture by framing the video, and acquiring the aeration amount of the picture in the aeration picture;
step two: constructing a training network, adjusting network parameters, and adopting a neural network framework based on an HRNet network to obtain an optimal model for identifying the aeration amount of the picture in the aeration picture;
step three: and (4) through an optimal model, carrying out model test on the acquired aeration pictures, and outputting aeration values.
2. The algorithm for visually identifying the aeration rate of the aerobic tank of the sewage plant as claimed in claim 1, wherein the video acquisition and preprocessing in the first step comprises the following steps:
s1: a multipoint fixed monitoring mode is set up for a sewage treatment plant, monitoring points are set in an aeration tank, and a collection camera is erected to collect process videos, so that related process videos are obtained;
s2: performing frame processing on the video in the relevant process, and screening the processed picture to obtain an aeration picture;
s3: and collecting the aeration quantities at all the moments by using an SQL data set, reading the aeration quantities at the moments in the aeration pictures, and marking the aeration quantities as the aeration quantities at the moments.
3. The algorithm for visually recognizing the aeration rate of the aerobic tank of the sewage plant as claimed in claim 1, wherein the obtaining of the optimal model in the second step comprises the following steps:
w1: putting the data set obtained in the first step into a YOLOV5 training network model, wherein the training of the network model is based on a Pythroch frame, and performing iteration on videos;
w2: establishing a cross entropy loss function;
w3: adding a head of classification output, wherein the input is s response mappings: { X1, X2, …, Xs };
the output is an s-response map: { X1, X2, …, Xs }, where the resolution and width are the same as the input maps, each output map is an aggregation of the input maps, i.e.:
the switching unit across stages has an additional output mapping Ys +1, i.e.
Y s+1 ＝a(Y S ,S+1)；
Function a (X) i K) consists of down-samples Xi of resolution i to k, which are convolved with steps 3 x 3;
w4: and carrying out a plurality of rounds of training on the data set to obtain the trained optimal model.
4. The algorithm for visually recognizing the aeration rate of the aerobic tank of the sewage plant as claimed in claim 3, wherein the Yolov5 trained network model in W1 adopts an Adam optimizer to optimize the trained network model, the optimizer is SGD, the trained parameters are 512x512, the initial learning rate is 0.05, and the activation function is relu.
5. The algorithm for visually identifying the aeration rate of the aerobic tank of the sewage plant as claimed in claim 3, wherein the number of video iterations in W1 is not less than 100.
6. The algorithm for visually recognizing the aeration rate of the aerobic tank of the sewage plant as claimed in claim 1, wherein the state judgment is performed on the recognized result output through a model test in the third step;
if the state is normal, outputting an aeration value;
and if the state is abnormal, the state is displayed as abnormal.
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| CN116135797A (en) * | 2023-04-19 | 2023-05-19 | 江苏海峡环保科技发展有限公司 | Intelligent control system for sewage treatment |
| CN116693075A (en) * | 2023-07-27 | 2023-09-05 | 杭州回水科技股份有限公司 | Aeration device of activated carbon biological filter |
| CN117466501A (en) * | 2023-12-28 | 2024-01-30 | 山东公用环保科技集团有限公司 | Sewage aeration method with deodorization function |
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| CN116693075A (en) * | 2023-07-27 | 2023-09-05 | 杭州回水科技股份有限公司 | Aeration device of activated carbon biological filter |
| CN116693075B (en) * | 2023-07-27 | 2023-11-21 | 杭州回水科技股份有限公司 | Aeration device of activated carbon biological filter |
| CN117466501A (en) * | 2023-12-28 | 2024-01-30 | 山东公用环保科技集团有限公司 | Sewage aeration method with deodorization function |
| CN117466501B (en) * | 2023-12-28 | 2024-03-29 | 山东公用环保科技集团有限公司 | Sewage aeration method with deodorization function |
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