CN118420012B - Urban sewage treatment aeration control system and method - Google Patents
Urban sewage treatment aeration control system and method Download PDFInfo
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Abstract
The application relates to the aeration control field, and particularly discloses an aeration control system and method for town sewage treatment, which firstly acquire oxygen content of a plurality of preset time points of the aeration process of the town sewage collected by a sensor and aeration images of the aeration process of the town sewage collected by a camera, then utilize a deep learning technology, and carrying out feature extraction and association analysis on the two to obtain an aeration state dynamic feature vector, and finally judging whether the working parameters of the aeration equipment need to be adjusted or not through a classifier, so as to find problems in time and adjust, thereby optimizing the operation of the aeration equipment, improving the treatment efficiency and saving the energy.
Description
Technical Field
The application relates to the field of aeration control, and in particular relates to an aeration control system and method for town sewage treatment.
Background
Urban sewage treatment refers to the process of collecting sewage discharged by urban residents in life, industrial production and the like through a pipeline system, and finally reaching the discharge standard through pretreatment, aeration, precipitation, filtration and other technological processes, thereby protecting the environment and maintaining public health. The aeration is an important link in the sewage treatment process, and the degradation and oxidation of organic matters in the sewage are promoted by injecting air or oxygen into the sewage, so that the content of dissolved oxygen in the sewage is increased, the organic matters and odor in the sewage are effectively removed, and the treatment efficiency is improved.
Conventional aeration systems employ a simple control loop to achieve automatic or manual control of aeration. However, since there is a time delay, that is, a certain time is required from the start of aeration to the reaction change in the tank, it is impossible to perform real-time monitoring. In addition, the conventional method has high power consumption, and the set value of the system is usually kept at a relatively high level to ensure safe operation, which brings about excessive redundancy and waste of resources.
Therefore, an aeration control system and method for town sewage treatment is desired.
Disclosure of Invention
The present application has been made to solve the above-mentioned technical problems. The embodiment of the application provides a town sewage treatment aeration control system and method, which comprises the steps of firstly acquiring oxygen content of a plurality of preset time points in the town sewage aeration process acquired by a sensor and aeration images of the town sewage aeration process acquired by a camera, then carrying out feature extraction and association analysis on the oxygen content and the aeration images by a deep learning technology to obtain dynamic feature vectors of aeration states, and finally judging whether working parameters of aeration equipment need to be regulated or not through a classifier so as to find problems in time and regulate, thereby optimizing the operation of the aeration equipment, improving the treatment efficiency and saving energy.
According to an aspect of the present application, there is provided a town sewage treatment aeration control system, comprising:
The town sewage aeration data acquisition module is used for acquiring oxygen contents of a plurality of preset time points of the town sewage aeration process acquired by the sensor and aeration images of the town sewage aeration process acquired by the camera;
the town sewage aeration data extraction module is used for extracting aeration oxygen content time sequence feature vectors and sewage aeration bubble global feature vectors from oxygen content of a plurality of preset time points of the town sewage aeration process acquired by the sensor and aeration images of the town sewage aeration process acquired by the camera;
And the working parameter adjustment judging module is used for judging whether the working parameters of the aeration equipment need to be adjusted or not based on the aeration oxygen content time sequence characteristic vector and the sewage aeration bubble global characteristic vector.
According to another aspect of the present application, there is provided a town sewage treatment aeration control method, comprising:
Acquiring oxygen content of a plurality of preset time points of the town sewage aeration process acquired by a sensor and aeration images of the town sewage aeration process acquired by a camera;
Extracting aeration oxygen content time sequence feature vectors and sewage aeration bubble global feature vectors from oxygen content of a plurality of preset time points of the town sewage aeration process collected by the sensor and aeration images of the town sewage aeration process collected by the camera;
And judging whether the working parameters of the aeration equipment need to be regulated or not based on the aeration oxygen content time sequence feature vector and the sewage aeration bubble global feature vector.
Compared with the prior art, the urban sewage treatment aeration control system and method provided by the application have the advantages that firstly, the oxygen content of a plurality of preset time points in the urban sewage aeration process collected by the sensor and the aeration image of the urban sewage aeration process collected by the camera are obtained, then, the deep learning technology is utilized to conduct feature extraction and association analysis on the oxygen content and the aeration image to obtain the dynamic feature vector of the aeration state, and finally, the classifier is utilized to judge whether the working parameters of the aeration equipment need to be regulated or not, so that problems are found and regulated in time, and the operation of the aeration equipment is optimized, the treatment efficiency is improved, and the energy is saved.
Drawings
The above and other objects, features and advantages of the present application will become more apparent by describing embodiments of the present application in more detail with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate the application and together with the embodiments of the application, and not constitute a limitation to the application. In the drawings, like reference numerals generally refer to like parts or steps.
Fig. 1 is a block diagram of an aeration control system for town sewage treatment according to an embodiment of the present application.
Fig. 2 is a block diagram of a town sewage aeration data extraction module in a town sewage treatment aeration control system according to an embodiment of the present application.
Fig. 3 is a block diagram of an aeration image feature encoding unit in the town sewage treatment aeration control system according to an embodiment of the present application.
Fig. 4 is a flowchart of a town sewage treatment aeration control method according to an embodiment of the present application.
Fig. 5 is a block diagram of an electronic device according to an embodiment of the application.
Detailed Description
Hereinafter, exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
Exemplary System
FIG. 1 is a block diagram of an aeration control system for town sewage treatment in accordance with an embodiment of the present application. As shown in fig. 1, the town sewage treatment aeration control system 100 according to an embodiment of the present application includes: a town sewage aeration data acquisition module 110 for acquiring oxygen contents at a plurality of predetermined time points of the town sewage aeration process acquired by the sensor and aeration images of the town sewage aeration process acquired by the camera; a town sewage aeration data extraction module 120, configured to extract an aeration oxygen content time sequence feature vector and a sewage aeration bubble global feature vector from oxygen contents of a plurality of predetermined time points of the town sewage aeration process collected by the sensor and an aeration image of the town sewage aeration process collected by the camera; and the working parameter adjustment judging module 130 is used for judging whether the working parameters of the aeration equipment need to be adjusted or not based on the aeration oxygen content time sequence characteristic vector and the sewage aeration bubble global characteristic vector.
In the town sewage treatment aeration control system 100 described above, the town sewage aeration data obtaining module 110 may be configured to obtain oxygen contents at a plurality of predetermined time points of the town sewage aeration process collected by the sensor and aeration images of the town sewage aeration process collected by the camera. It should be understood that urban sewage treatment means that sewage discharged from urban residents' life and industrial production is collected through a pipeline system, and is treated through a series of process flows including pretreatment, aeration, precipitation, filtration and the like, so that the discharge standard is finally reached, thereby protecting the environment and maintaining public health. In the processes, aeration is a crucial step, and by injecting air or oxygen into sewage, the degradation and oxidation of organic matters are promoted, the content of dissolved oxygen is increased, the organic matters and odor are effectively removed, and the treatment efficiency is improved. Traditional aeration systems employ simple control loops for automatic or manual control. However, due to the time delay, i.e. the reaction change from the start of aeration to the inside of the tank takes a certain time, real-time monitoring is not possible. In addition, the conventional method has high energy consumption, and in order to ensure safe operation, the system set value is usually kept at a high level, so that excessive redundancy is caused, and resource waste is caused. According to the technical scheme, the sensor is used for acquiring oxygen content data of the town sewage aeration process at a plurality of preset time points, the camera is used for acquiring images of the aeration process, and the deep learning technology is combined, so that whether the working parameters of the aeration equipment need to be regulated or not is judged, the operation of the aeration equipment is optimized, the treatment efficiency is improved, the energy is saved, and the intelligent management and optimization of an aeration system are realized. Specifically, the sensor can monitor the oxygen content in the sewage in real time, which is one of important indexes for evaluating the water quality and the aeration effect. By acquiring oxygen content data at a plurality of preset time points, the change trend of the oxygen content in the aeration process can be drawn, and the analysis of the aeration effect and the sewage treatment condition is facilitated. On the other hand, the aeration image acquired by the camera provides visual information, and the distribution and movement conditions of bubbles in the sewage aeration system can be displayed. These aerated images can be analyzed and feature extracted by image processing techniques, such as identifying information about the size, density, distribution, etc. of the bubbles. By combining the oxygen content data acquired by the sensor and the aeration image acquired by the camera, the system can acquire more comprehensive and multidimensional aeration process information. By analyzing the oxygen content and the aeration image data, the system can timely find out the abnormal operation or water quality problem of the aeration equipment, thereby realizing timely adjustment and optimization of the working parameters of the aeration equipment, improving the sewage treatment efficiency, reducing the energy consumption and guaranteeing the environmental benefit and the economic benefit of town sewage treatment.
In the town sewage treatment aeration control system 100, the town sewage aeration data extraction module 120 is configured to extract an aeration oxygen content time sequence feature vector and a sewage aeration bubble global feature vector from oxygen contents of a plurality of predetermined time points of the town sewage aeration process collected by the sensor and an aeration image of the town sewage aeration process collected by the camera. It should be understood that the process of extracting the timing feature vector of the oxygen content of the aeration may reveal the law of variation of the oxygen content with time during the aeration. These feature vectors may contain information on the average value of the oxygen content, fluctuations, trend changes, etc., to help monitor the stability and efficiency of the aeration effect. By analyzing the characteristic vectors, problems in the aeration system, such as abnormal fluctuation or decline of oxygen content, can be found in time, so that corresponding regulation measures are adopted. The global feature vector of the aeration bubbles of the sewage is extracted to describe and analyze the integral features of the bubbles in the aeration process. The global feature vectors can cover the information such as the size, the distribution density, the motion track and the like of the bubbles, and reflect the overall situation of the bubbles in the aeration system. Through extraction and analysis of the feature vectors, the working state of the aeration system can be evaluated, the problems of uneven bubble distribution, insufficient bubble generation and the like are found, and a reference basis is provided for optimization and adjustment of the system.
Fig. 2 is a block diagram of a town sewage aeration data extraction module in a town sewage treatment aeration control system according to an embodiment of the present application. As shown in fig. 2, in a specific embodiment of the present application, the town sewage aeration data extraction module 120 includes: an oxygen content feature encoding unit 121, configured to perform feature encoding on oxygen content at a plurality of predetermined time points in the town sewage aeration process acquired by the sensor to obtain the aeration oxygen content time sequence feature vector; an aeration image feature encoding unit 122, configured to perform feature encoding on the aeration image of the town sewage aeration process acquired by the camera, so as to obtain a sewage aeration bubble large filtering feature map and a sewage aeration bubble small filtering feature map; and the sewage aeration bubble data processing unit 123 is used for performing data processing on the sewage aeration bubble large filtering characteristic diagram and the sewage aeration bubble small filtering characteristic diagram to obtain the sewage aeration bubble global characteristic vector.
It should be appreciated that feature encoding can process and extract raw oxygen content data, mining potential information and patterns from it. Through a feature coding algorithm, the oxygen content data can be converted into a group of representative features, such as mean value, variance, frequency spectrum features and the like, which can better reflect the oxygen content change features in the aeration process and help the system understand and analyze the aeration effect. The aeration oxygen content time sequence feature vector obtained through feature coding can reduce the redundancy of data while keeping important information, and improves the representation capability and analysis efficiency of the data.
Further, the large filtering characteristic diagram and the small filtering characteristic diagram of the sewage aeration bubbles can be extracted by processing the aeration image through the characteristic coding algorithm. These feature maps may contain information about the size, shape, distribution density, etc. of the bubbles, reflecting the overall and local characteristics of the bubbles in the aeration system. The large filtering characteristic diagram can help the system to capture the overall characteristics of the bubbles, the small filtering characteristic diagram is more focused on the details and the local characteristics, and the characteristics of the bubbles in the aeration system can be comprehensively described. The system can more comprehensively understand the characteristics and behaviors of bubbles in the aeration system by comprehensively utilizing the large filtering characteristic diagram and the small filtering characteristic diagram of the sewage aeration bubbles, and realize the fine monitoring and control of the aeration process.
Further, the sewage aeration bubble large filtering characteristic diagram and the sewage aeration bubble small filtering characteristic diagram are considered to contain abundant bubble characteristic information, such as the size, the shape, the distribution density and the like of bubbles. By processing the data of the feature graphs, the global features of the sewage aeration bubbles, such as the number of the whole bubbles, the total volume, the average size and the like, can be extracted, so that the whole features of the bubbles in the sewage aeration system can be more comprehensively described. In particular, the data processing can help the system to quantify and statistically analyze the characteristics of the aeration bubbles of the wastewater, thereby achieving a deeper understanding of the characteristics of the bubbles. By integrating and processing the data in the large filtering characteristic diagram and the small filtering characteristic diagram, a global characteristic vector of the sewage aeration bubble can be obtained, and the vector can contain statistical information of various bubble characteristics, so that a more comprehensive characteristic description and analysis basis is provided for the system.
In a specific embodiment of the present application, the oxygen content feature encoding unit 121 includes: performing data preprocessing on the oxygen content of a plurality of preset time points in the town sewage aeration process acquired by the sensor to obtain an input vector of the town sewage aeration oxygen content; and (3) enabling the aeration oxygen content input vector of the town sewage to pass through an oxygen content time sequence aeration encoder to obtain the aeration oxygen content time sequence characteristic vector.
It should be appreciated that the operating conditions of town sewage aeration systems may have an impact on oxygen content, and that the data collected by the sensors often include noise and uncertainty. Through data preprocessing, operations such as smoothing, abnormal value removal, missing value filling and the like can be performed on the original data, so that the accuracy and the integrity of the data are ensured. Wherein, integrate the oxygen content data of a plurality of predetermined time points into town sewage aeration oxygen content input vector, help to convert time series data into a form which can be processed by machine learning or data analysis model. By constructing the input vector, the change rule and trend of the oxygen content along with time can be captured better, and convenience is provided for modeling and predicting the dynamic change of the oxygen content in the aeration process by the system.
Further, the change of oxygen content with time in the town sewage aeration system reflects the dynamic characteristics of the operation state of the system, and the time series data can be converted into a characteristic vector with more representation and analyzability through the treatment of an oxygen content time series aeration encoder. The encoder can perform dimension reduction, feature extraction and representation learning on the oxygen content data, so that important features and modes of oxygen content change are captured. The obtained aeration oxygen content time sequence feature vector can help the system to better understand and analyze the dynamic change rule of the oxygen content in the town sewage aeration system. The feature vectors can contain information such as trend, periodicity, mutation and the like of oxygen content change, and provide important basis for monitoring, fault diagnosis and optimization of the system operation state. By analyzing the time sequence feature vector, abnormal conditions in the system can be found in time and corresponding measures can be taken. Specifically, full-connection encoding is carried out on the town sewage aeration oxygen content input vector by using a full-connection layer of the oxygen content time sequence aeration encoder so as to extract high-dimensional implicit features of feature values of all positions in the town sewage aeration oxygen content input vector; and carrying out one-dimensional coding on the town sewage aeration oxygen content input vector by using a one-dimensional convolution layer of the oxygen content time sequence aeration coder so as to extract high-dimensional implicit correlation features of correlations among feature values of all positions in the town sewage aeration oxygen content input vector.
Fig. 3 is a block diagram of an aeration image feature encoding unit in the town sewage treatment aeration control system according to an embodiment of the present application. As shown in fig. 3, in a specific embodiment of the present application, the aerated image feature encoding unit 122 includes: an aeration image convolution coding subunit 1221, configured to pass the aeration image of the town sewage aeration process acquired by the camera through a town sewage aeration feature extractor to obtain a sewage aeration bubble feature map; a sewage aeration large filtering convolution encoding subunit 1222 for passing the sewage aeration bubble signature through a sewage aeration signature encoder having a large filter to obtain the sewage aeration bubble large filtering signature; a sewage aeration small filter convolution encoding subunit 1223 configured to pass the sewage aeration bubble signature through a sewage aeration signature encoder having a small filter to obtain the sewage aeration bubble small filter signature.
It should be understood that the shape, size, distribution and other characteristics of aeration bubbles in the town sewage aeration system are closely related to the aeration effect of the system, and the aeration image acquired by the camera contains abundant visual information. The town sewage aeration characteristic extractor can extract and analyze characteristics of aeration images through an image processing algorithm and a model, so that characteristics of sewage aeration bubbles can be accurately identified and described. The generation of the sewage aeration bubble feature map is beneficial to realizing the real-time monitoring and evaluation of the operation state of the aeration system. The working efficiency and performance of the aeration system can be quantitatively evaluated by extracting the characteristic information of bubbles, such as the number, the size distribution, the movement track and the like of the bubbles.
Further, the characteristic information of bubbles in the sewage aeration system can be further extracted and emphasized by the sewage aeration bubble characteristic diagram through the sewage aeration characteristic encoder with the large filter, and the characteristic of a specific scale is more prominent through the treatment of the filter, so that the key characteristic in the aeration process is more accurately described. The generation of the sewage aeration bubble large filtering characteristic diagram can help to identify and analyze bubble characteristics of different scales in the sewage aeration system, so that dynamic changes of bubbles in the aeration process are more comprehensively understood. The application of the large filter can remove interference information and highlight target characteristics, so that the characteristics of bubbles are more obvious and distinguishable, and the monitoring and evaluation of the system running state are facilitated. Specifically, the input data is subjected to convolution processing, local feature matrix-based mean pooling processing and nonlinear activation processing in forward transfer of layers by using each layer of the sewage aeration characteristic encoder with a large filter so as to output the sewage aeration bubble large filtering characteristic map from the last layer of the sewage aeration characteristic encoder with a large filter, wherein the input of the sewage aeration characteristic encoder with a large filter is the sewage aeration bubble characteristic map.
Furthermore, the sewage aeration bubble feature map is passed through a sewage aeration feature encoder with a small filter to obtain a sewage aeration bubble small filter feature map, so that local detail information in bubble features can be further refined and emphasized, and the fine features in images are more obvious through the treatment of the small-scale filter, so that the detail features in the aeration process are more comprehensively described. Local detail features in the image can be highlighted by the treatment of the sewage aeration feature encoder with a small filter. The small filter is helpful for capturing fine structure and texture information in the image, so that the sewage aeration bubble small filter characteristic diagram is finer and more accurate, and deep analysis and understanding of bubble characteristics are facilitated. By processing the small filter, the detail information such as local texture, edge and the like in the bubble feature map can be highlighted, more accurate and comprehensive feature representation is provided for fine feature analysis of the system, and deeper research on local change and evolution of the bubble feature is facilitated. Specifically, each layer of the sewage aeration characteristic encoder with the small filter is used for respectively carrying out the input data in the forward transmission of the layer: performing convolution processing on the input data based on convolution check to generate a convolution feature map; performing global average pooling processing based on a feature matrix on the convolution feature map to generate a pooled feature map; performing nonlinear activation on the feature values of all positions in the pooled feature map to generate an activated feature map; the output of the last layer of the sewage aeration characteristic encoder with the small filter is the sewage aeration bubble small filtering characteristic diagram, the input from the second layer to the last layer of the sewage aeration characteristic encoder with the small filter is the output of the last layer, and the input of the sewage aeration characteristic encoder with the small filter is the sewage aeration bubble characteristic diagram.
In a specific embodiment of the present application, the sewage aeration bubble data processing unit 123 includes: performing dimension reduction on the sewage aeration bubble large filtering characteristic map to obtain a sewage aeration bubble large filtering characteristic vector; performing dimension reduction on the sewage aeration bubble small filtering characteristic map to obtain a sewage aeration bubble small filtering characteristic vector; and calculating the difference between the large filtering characteristic vector of the sewage aeration bubbles and the small filtering characteristic vector of the sewage aeration bubbles so as to obtain the global characteristic vector of the sewage aeration bubbles.
It should be appreciated that sewage aeration bubble large filter feature maps typically contain a large number of feature dimensions that may contain redundant information or noise, and that dimension reduction may help remove such unnecessary information, preserving the most critical information for describing image features. By the dimension reduction processing, the interpretability and generalization capability of the data can be improved, so that the feature vector is more representative and effective. The dimension reduction of the large filtering feature vector of the sewage aeration bubble can help simplify the tasks of data analysis and pattern recognition. The feature vectors of lower dimensionality are easier to process and understand by machine learning algorithms, helping to increase the training speed and generalization ability of the model. Meanwhile, dimension reduction is also beneficial to reducing the risk of overfitting and improving the stability and reliability of the model.
Further, dimension reduction is performed on the sewage aeration bubble small filter characteristic map to obtain a sewage aeration bubble small filter characteristic vector, so that data can be compressed, and the most representative characteristic can be extracted, so that information in an image can be described and analyzed more efficiently. Considering that the sewage aeration bubble small filtering characteristic diagram may contain a large amount of fine characteristics and local information, and the information may have redundancy or noise, the dimension reduction can help to filter out the unnecessary information, and the most representative characteristics are extracted. Through the dimension reduction process, the original high-dimension feature space can be mapped into a lower-dimension space, so that the data representation is simplified, and the storage and calculation cost is reduced.
Further, calculating the difference between the large filter feature vector of the sewage aeration bubbles and the small filter feature vector of the sewage aeration bubbles can capture the change and the difference of global features in the image, so that the feature information of the image is more comprehensively described. By calculating such differences, the relationship between the overall structure and the feature can be emphasized while preserving local detail, contributing to improved robustness and characterization capabilities of the feature representation. The large filtering features mainly focus on the integral structure of the image and the features with larger scale, and the small filtering features focus on the local details and fine features of the image. By calculating the difference between the two feature vectors, feature changes of the image at different scales and levels can be captured, so that feature information of the image can be more comprehensively described. The calculation of the global feature vector of the sewage aeration bubble can help to extract the global structure and the whole feature in the image, and the relevance and the consistency between different parts in the image are emphasized. The global feature vector can resist the interference of local noise to a certain extent, improves the stability and the robustness of the feature, and is beneficial to improving the effects of image analysis and recognition.
In the town sewage treatment aeration control system 100, the working parameter adjustment judging module 130 is configured to judge whether the working parameter of the aeration equipment needs to be adjusted based on the aeration oxygen content time sequence feature vector and the sewage aeration bubble global feature vector. It should be understood that the comprehensive consideration of the information of the aeration oxygen content time sequence feature vector and the sewage aeration bubble global feature vector can realize the comprehensive monitoring and analysis of the operation state of the aeration system, discover problems in time and take adjusting measures, thereby improving the sewage treatment efficiency, reducing the energy consumption and guaranteeing the treatment quality. The intelligent aeration system management method is beneficial to realizing intelligent water treatment and improving the running efficiency and sustainable development capability of the sewage treatment plant.
In a specific embodiment of the present application, the operation parameter adjustment determination module 130 includes: fusing the aeration oxygen content time sequence feature vector and the sewage aeration bubble global feature vector to obtain an aeration state dynamic feature vector; performing implicit group optimization of space sparsity limitation on the aeration state dynamic feature vector to obtain an optimized aeration state dynamic feature vector; and the dynamic feature vector of the optimized aeration state passes through a classifier to obtain a classification result, and the classification result is used for judging whether the working parameters of the aeration equipment need to be regulated.
It should be understood that the oxygen transmission efficiency of the aeration system and the characteristics of the sewage aeration bubbles can be comprehensively considered by fusing the aeration oxygen content time sequence characteristic vector and the sewage aeration bubble global characteristic vector, so that the dynamic change of the aeration state can be more comprehensively and accurately described. By fusing the two feature vectors, the working state and the performance of the aeration system can be better reflected, and more powerful support is provided for system monitoring, analysis and optimization. The aeration oxygen content time sequence characteristic vector reflects the change condition of the oxygen content in the aeration system along with time, and the oxygen transmission efficiency and the oxygen supply condition of the aeration system in different time periods can be disclosed. The global feature vector of the aeration bubbles of the sewage describes the integral features of the aeration bubbles in the sewage, and reflects the spatial distribution of an aeration system and the statistical information of the bubble features. The global feature vectors can help to understand the overall operation condition of the aeration system, evaluate the uniformity and coverage of bubble distribution, and provide a reference basis for optimizing the system performance. By fusing the aeration oxygen content time sequence feature vector and the sewage aeration bubble global feature vector, the aeration state dynamic feature vector is obtained, and the time change and the space feature of the aeration system can be comprehensively considered, so that the dynamic characteristic of the aeration state can be more comprehensively described. The integration can improve the understanding and monitoring capability of the working state of the aeration system, and provides more effective support for real-time regulation and control, anomaly detection and optimization operation of the system.
In particular, in the technical solution of the present application, it is considered that during the data preprocessing phase, there may be some restrictions or situations of information loss. The preprocessing step of raw data may not fully capture all details and features of the town sewage aeration process, resulting in the resulting input vector lacking some critical information. In the feature extraction stage, each feature extractor may focus on capturing local features rather than global features. For example, oxygen content time series aeration encoders and sewage aeration feature extractors may focus more on local features than global features of the overall sewage aeration process. When the aeration oxygen content time sequence characteristic vector and the sewage aeration bubble global characteristic vector are fused, unbalance or inadequacy in the fusion method may exist. If the fusion method cannot integrate the information of the two, the problem that the information aggregation degree of the finally obtained aeration state dynamic feature vector is not high may be caused. Because the information polymerization degree is not high, the aeration state dynamic feature vector may not fully express the complexity and the full view of the town sewage aeration process, so that the feature representation is incomplete. This may prevent the model from capturing critical information and patterns, affecting the final classification or prediction accuracy. In order to solve the problem, in the technical scheme of the application, the aeration state dynamic characteristic vector is subjected to implicit group optimization with space sparsity limitation.
Wherein, the implicit group optimization of the space sparsity constraint is performed on the aeration state dynamic feature vector to obtain an optimized aeration state dynamic feature vector, which comprises the following steps: calculating a implicit group optimization factor of the space sparsity constraint of the aeration state dynamic feature vector according to the following optimization formula, wherein the optimization formula is as follows:
Wherein v c represents an aeration state dynamic feature vector, σ (v c) represents a variance of a feature value set of the aeration state dynamic feature vector, v i is a feature value of each position in the aeration state dynamic feature vector, L is a length of the aeration state dynamic feature vector, and ||v c||1 represents a norm of the aeration state dynamic feature vector, and w is a implicit group optimization factor of spatial sparsity constraint of the aeration state dynamic feature vector; and weighting the aeration state dynamic feature vector by taking the implicit group optimization factor limited by the space sparsity as a weight to obtain the optimized aeration state dynamic feature vector.
The aeration state dynamic feature vector is subjected to implicit group optimization of space sparsity limitation, the adaptability of each particle in the aeration state dynamic feature vector is evaluated in a high-dimensional feature space, the aeration state dynamic feature vector is updated according to the optimal solution and the group optimal solution of each particle in the aeration state dynamic feature vector so as to meet the limitation of space sparsity of the updated aeration state dynamic feature vector, and therefore the aggregation degree of the feature manifold of the aeration state dynamic feature vector is enhanced in a mode of recovering basic information in a full-precision information representation space, and the expression effect of the aeration state dynamic feature vector is improved.
Further, the dynamic feature vector optimizing the aeration state is input into the classifier for classification, and the working state of the aeration system can be evaluated according to preset standards and rules, so that whether the system is in a normal running state or needs to be adjusted can be rapidly and accurately judged. The classifier is used for classifying the input feature vectors into different classes according to the input feature vectors. In this case, the optimized aeration state dynamic feature vector may be classified into different states such as a normal state and an abnormal state after being processed by the classifier. The classification result can be provided for operation staff or a system controller to be used as a basis for judging the operation condition of the aeration system. The classification result obtained by the classifier can directly indicate whether the working parameters of the aeration equipment need to be adjusted. For example, if the classification result indicates that the system is in an abnormal state, corresponding adjustment measures such as adjusting the aeration amount, changing the bubble size, or adjusting the aeration time may be triggered. The intelligent decision-making system can help to realize automatic management of the aeration system, improve the operation efficiency of the system, reduce the energy consumption and reduce the human intervention.
In summary, the embodiment of the application firstly acquires the oxygen content of a plurality of preset time points of the town sewage aeration process acquired by the sensor and the aeration image of the town sewage aeration process acquired by the camera, then utilizes the deep learning technology to perform feature extraction and association analysis on the oxygen content and the aeration image to obtain the aeration state dynamic feature vector, and finally judges whether the working parameters of the aeration equipment need to be regulated or not through the classifier, so as to find problems in time and adjust, thereby optimizing the operation of the aeration equipment, improving the treatment efficiency and saving the energy.
As described above, the town sewage treatment aeration control system 100 according to the embodiment of the present application may be implemented in various terminal apparatuses, and in one example, the town sewage treatment aeration control system 100 may be integrated into the terminal apparatus as one software module and/or hardware module. For example, the town sewage treatment aeration control system 100 may be a software module in the operating system of the terminal apparatus, or may be an application developed for the terminal apparatus; of course, the town sewage treatment aeration control system 100 may also be one of a plurality of hardware modules of the terminal apparatus.
Alternatively, in another example, the town sewage treatment aeration control system 100 and the terminal apparatus may be separate apparatuses, and the town sewage treatment aeration control system 100 may be connected to the terminal apparatus through a wired and/or wireless network, and transmit the interactive information in a prescribed data format.
Exemplary method
Fig. 4 is a view showing an aeration control method for town sewage treatment according to an embodiment of the present application. As shown in fig. 4, the town sewage treatment aeration control method according to an embodiment of the present application includes: s110, acquiring oxygen content of a plurality of preset time points of the town sewage aeration process acquired by a sensor and aeration images of the town sewage aeration process acquired by a camera; s120, extracting aeration oxygen content time sequence feature vectors and sewage aeration bubble global feature vectors from oxygen content of a plurality of preset time points of the town sewage aeration process acquired by the sensor and an aeration image of the town sewage aeration process acquired by the camera; s130, judging whether the working parameters of the aeration equipment need to be adjusted or not based on the aeration oxygen content time sequence feature vector and the sewage aeration bubble global feature vector.
Here, it will be understood by those skilled in the art that the specific operations of the respective steps in the above-described town sewage treatment aeration control method have been described in detail in the above description of the town sewage treatment aeration control system with reference to fig. 1 to 3, and thus, repetitive descriptions thereof will be omitted.
Exemplary electronic device
Next, an electronic device according to an embodiment of the present application is described with reference to fig. 5.
Fig. 5 illustrates a hardware structure of an electronic device of another embodiment, the electronic device including:
the processor 11 may be implemented by a general-purpose CPU (central processing unit), a microprocessor, an application-specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), or one or more integrated circuits, etc. for executing related programs, so as to implement the technical solution provided by the embodiments of the present application; memory 12 may be implemented in the form of read-only memory (ReadOnlyMemory, ROM), static storage, dynamic storage, or random access memory (RandomAccessMemory, RAM).
The memory 12 may store an operating system and other application programs, and when the technical scheme provided in the embodiments of the present specification is implemented by software or firmware, relevant program codes are stored in the memory 12, and the processor 11 invokes the town sewage treatment aeration control method according to the embodiments of the present application;
An input/output interface 13 for implementing information input and output;
the communication interface 14 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (such as USB, network cable, etc.), or may implement communication in a wireless manner (such as mobile network, WIFI, bluetooth, etc.);
A bus 15 for transferring information between the various components of the device (e.g., processor 11, memory 12, input/output interface 13, and communication interface 14);
Wherein the processor 11, the memory 12, the input/output interface 13 and the communication interface 14 are in communication connection with each other inside the device via a bus 15.
The embodiment of the application also provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program which realizes the town sewage treatment aeration control method when being executed by a processor.
The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The embodiments described in the embodiments of the present application are for more clearly describing the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application, and those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.
It will be appreciated by persons skilled in the art that the embodiments of the application are not limited by the illustrations, and that more or fewer steps than those shown may be included, or certain steps may be combined, or different steps may be included.
The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the application and in the above figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the above-described division of units is merely a logical function division, and there may be another division manner in actual implementation, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units. The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including multiple instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, or other various media capable of storing a program. The preferred embodiments of the present application have been described above with reference to the accompanying drawings, and are not thereby limiting the scope of the claims of the embodiments of the present application. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the embodiments of the present application shall fall within the scope of the claims of the embodiments of the present application.
Claims (6)
1. An aeration control system for town sewage treatment, comprising:
The town sewage aeration data acquisition module is used for acquiring oxygen contents of a plurality of preset time points of the town sewage aeration process acquired by the sensor and aeration images of the town sewage aeration process acquired by the camera;
the town sewage aeration data extraction module is used for extracting aeration oxygen content time sequence feature vectors and sewage aeration bubble global feature vectors from oxygen content of a plurality of preset time points of the town sewage aeration process acquired by the sensor and aeration images of the town sewage aeration process acquired by the camera;
The working parameter adjusting and judging module is used for judging whether working parameters of the aeration equipment need to be adjusted or not based on the aeration oxygen content time sequence characteristic vector and the sewage aeration bubble global characteristic vector;
Wherein, town sewage aeration data extraction module includes:
The oxygen content characteristic coding unit is used for characteristic coding the oxygen content of a plurality of preset time points in the town sewage aeration process acquired by the sensor so as to obtain the aeration oxygen content time sequence characteristic vector;
the aeration image feature coding unit is used for carrying out feature coding on the aeration image of the town sewage aeration process acquired by the camera so as to obtain a sewage aeration bubble large filtering feature map and a sewage aeration bubble small filtering feature map;
The sewage aeration bubble data processing unit is used for carrying out data processing on the sewage aeration bubble large filtering characteristic diagram and the sewage aeration bubble small filtering characteristic diagram so as to obtain the sewage aeration bubble global characteristic vector;
wherein the oxygen content profile encoding unit comprises:
Performing data preprocessing on the oxygen content of a plurality of preset time points in the town sewage aeration process acquired by the sensor to obtain an input vector of the town sewage aeration oxygen content;
the aeration oxygen content input vector of the town sewage is passed through an oxygen content time sequence aeration encoder to obtain the aeration oxygen content time sequence characteristic vector;
wherein, the aeration image characteristic encoding unit includes:
the aeration image convolution coding subunit is used for enabling the aeration image of the town sewage aeration process acquired by the camera to pass through the town sewage aeration characteristic extractor so as to obtain a sewage aeration bubble characteristic diagram;
A sewage aeration large filtering convolution coding subunit, configured to pass the sewage aeration bubble feature map through a sewage aeration feature coder with a large filter to obtain the sewage aeration bubble large filtering feature map;
and the sewage aeration small-filter convolution coding subunit is used for enabling the sewage aeration bubble characteristic diagram to pass through a sewage aeration characteristic coder with a small filter so as to obtain the sewage aeration bubble small-filter characteristic diagram.
2. The town sewage treatment aeration control system of claim 1, wherein the sewage aeration small filtering convolution encoding sub-unit comprises:
each layer of the sewage aeration characteristic encoder with the small filter is used for respectively carrying out input data in forward transfer of the layer:
performing convolution processing on the input data based on convolution check to generate a convolution feature map;
Performing global average pooling processing based on a feature matrix on the convolution feature map to generate a pooled feature map;
non-linear activation is carried out on the characteristic values of all positions in the pooled characteristic map so as to generate an activated characteristic incremental map;
The output of the last layer of the sewage aeration characteristic encoder with the small filter is the sewage aeration bubble small filtering characteristic diagram, the input from the second layer to the last layer of the sewage aeration characteristic encoder with the small filter is the output of the last layer, and the input of the sewage aeration characteristic encoder with the small filter is the sewage aeration bubble characteristic diagram.
3. The town sewage treatment aeration control system of claim 2, wherein the sewage aeration bubble data processing unit comprises:
Performing dimension reduction on the sewage aeration bubble large filtering characteristic map to obtain a sewage aeration bubble large filtering characteristic vector;
Performing dimension reduction on the sewage aeration bubble small filtering characteristic map to obtain a sewage aeration bubble small filtering characteristic vector;
And calculating the difference between the large filtering characteristic vector of the sewage aeration bubbles and the small filtering characteristic vector of the sewage aeration bubbles so as to obtain the global characteristic vector of the sewage aeration bubbles.
4. A town sewage treatment aeration control system according to claim 3, wherein the operating parameter adjustment judging module comprises:
fusing the aeration oxygen content time sequence feature vector and the sewage aeration bubble global feature vector to obtain an aeration state dynamic feature vector;
performing implicit group optimization of space sparsity limitation on the aeration state dynamic feature vector to obtain an optimized aeration state dynamic feature vector;
And the dynamic feature vector of the optimized aeration state passes through a classifier to obtain a classification result, and the classification result is used for judging whether the working parameters of the aeration equipment need to be regulated.
5. The town sewage treatment aeration control system of claim 4, wherein performing implicit group optimization of the aeration state dynamic feature vector with spatial sparsity constraint to obtain an optimized aeration state dynamic feature vector, comprises:
calculating a implicit group optimization factor of the space sparsity constraint of the aeration state dynamic feature vector according to the following optimization formula, wherein the optimization formula is as follows:
;
Wherein, Represents the dynamic characteristic vector of the aeration state,Representing the variance of the set of eigenvalues of the aeration state dynamic eigenvector,Is the characteristic value of each position in the aeration state dynamic characteristic vector, andIs the length of the aeration state dynamic characteristic vector, andRepresents a norm of the aeration state dynamic characteristic vector,Is a implicit group optimization factor of the space sparsity limit of the aeration state dynamic feature vector;
and weighting the aeration state dynamic feature vector by taking the implicit group optimization factor limited by the space sparsity as a weight to obtain the optimized aeration state dynamic feature vector.
6. The town sewage treatment aeration control method is characterized by comprising the following steps:
Acquiring oxygen content of a plurality of preset time points of the town sewage aeration process acquired by a sensor and aeration images of the town sewage aeration process acquired by a camera;
Extracting aeration oxygen content time sequence feature vectors and sewage aeration bubble global feature vectors from oxygen content of a plurality of preset time points of the town sewage aeration process collected by the sensor and aeration images of the town sewage aeration process collected by the camera;
judging whether working parameters of aeration equipment need to be adjusted or not based on the aeration oxygen content time sequence feature vector and the sewage aeration bubble global feature vector;
wherein, draw aeration oxygen content time sequence eigenvector and sewage aeration bubble global eigenvector from oxygen content of a plurality of predetermined time points of town sewage aeration process that is gathered by the sensor and the aeration image of town sewage aeration process that is gathered by the camera, include:
Performing feature coding on oxygen content of a plurality of preset time points in the town sewage aeration process acquired by the sensor to obtain a time sequence feature vector of the aeration oxygen content;
Performing feature coding on the aeration image of the town sewage aeration process acquired by the camera to obtain a sewage aeration bubble large filtering feature map and a sewage aeration bubble small filtering feature map;
Performing data processing on the sewage aeration bubble large filtering characteristic diagram and the sewage aeration bubble small filtering characteristic diagram to obtain the sewage aeration bubble global characteristic vector;
Wherein, the feature coding is carried out on the oxygen content of a plurality of preset time points of the town sewage aeration process acquired by the sensor so as to obtain the aeration oxygen content time sequence feature vector, and the feature vector comprises the following steps:
Performing data preprocessing on the oxygen content of a plurality of preset time points in the town sewage aeration process acquired by the sensor to obtain an input vector of the town sewage aeration oxygen content;
the aeration oxygen content input vector of the town sewage is passed through an oxygen content time sequence aeration encoder to obtain the aeration oxygen content time sequence characteristic vector;
The feature coding is performed on the aeration image of the town sewage aeration process acquired by the camera to obtain a sewage aeration bubble large filtering feature map and a sewage aeration bubble small filtering feature map, and the feature coding comprises the following steps:
the aeration image of the town sewage aeration process acquired by the camera is passed through a town sewage aeration characteristic extractor to obtain a sewage aeration bubble characteristic map;
The sewage aeration bubble characteristic diagram passes through a sewage aeration characteristic encoder with a large filter to obtain the sewage aeration bubble large filter characteristic diagram;
And the sewage aeration bubble characteristic diagram is passed through a sewage aeration characteristic encoder with a small filter to obtain the sewage aeration bubble small filter characteristic diagram.
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| CN116854300A (en) * | 2023-07-23 | 2023-10-10 | 杭州回水科技股份有限公司 | Sewage treatment system with activated carbon adsorption filtration function |
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