Intelligent monitoring system for sewage treatment plant

Abstract

The invention relates to the field of intelligent monitoring devices, in particular to an intelligent monitoring system for a sewage treatment plant, and the system comprises a data collection module which is used for collecting real-time data of the sewage treatment plant; the data preprocessing module is used for preprocessing the real-time data; the intelligent analysis module is used for analyzing and predicting the preprocessed data; the early warning decision module is used for generating early warning information and processing suggestions based on the analysis result of the intelligent analysis module; and the visual display module is used for visually displaying the early warning information and the processing suggestions. Wherein the intelligent analysis module adopts a multi-scale attention residual error recurrent neural network algorithm to analyze and predict the preprocessed data, can early warn the potential water quality standard exceeding problem 4.5 hours in advance, is 9 times that of a traditional PID control system, provides sufficient time for taking prevention measures in time, and reduces the false alarm rate to 3.5%.

Description

Technical Field

[0001] The present invention relates to the field of intelligent detection systems, and more specifically, to an intelligent monitoring system for a sewage treatment plant. Background Art

[0002] With the increasing awareness of environmental protection and increasingly stringent regulatory requirements, the operation and management of sewage treatment plants are facing unprecedented challenges. In recent years, the development of intelligent monitoring systems for sewage treatment plants has evolved from simple data collection to intelligent predictive control.

[0003] Early sewage treatment plant monitoring systems mainly relied on SCADA (Supervisory Control and Data Acquisition) systems, which could only achieve basic data collection and equipment control. Subsequently, the introduction of PID (Proportional-Integral-Differential) control technology improved the automation level of the system, but it was still difficult to cope with complex water quality fluctuations.

[0004] In recent years, with the development of artificial intelligence technology, some researchers have begun to try to apply machine learning algorithms to the monitoring and prediction of sewage treatment processes. Among them, time series analysis methods (such as ARIMA models) and deep learning methods (such as LSTM neural networks) are the two main technical routes.

[0005] However, the existing technology still has the following key problems:

[0006] 1. Insufficient prediction accuracy: Although the traditional ARIMA model can capture the linear trend of time series, it is difficult to deal with the nonlinear relationship and multi-scale time dependence in the sewage treatment process. Taking a municipal sewage treatment plant as an example, when the ARIMA model is used to predict COD, the mean absolute percentage error (MAPE) is as high as 15.8%, which is difficult to meet the needs of precise control.

[0007] 2. Warning lag: Although the single LSTM neural network has improved the prediction accuracy to a certain extent, it is still insufficient in long-term trend prediction. In practical applications, it can often only warn of potential water quality problems 2-3 hours in advance, leaving operators with extremely limited response time.

[0008] 3. High false alarm rate: Due to the complexity and uncertainty of the sewage treatment process, the existing system generally has a high false alarm rate. For example, a sewage treatment plant uses a simple threshold-based early warning system, and the average monthly false alarm rate is as high as 15.3%, which seriously affects the credibility of the system and the work efficiency of operators.

[0009] 4. Poor energy consumption control: The existing system focuses more on meeting water quality standards and does not consider energy consumption optimization enough. Traditional PID control systems often lead to high energy consumption while ensuring water quality, with an average energy consumption index of 0.45kWh / m3 , increasing operating costs.

[0010] 5. Low system integration: The existing intelligent monitoring system of sewage treatment plants is often a simple combination of multiple independent modules. It lacks effective data flow and information integration mechanisms, making it difficult to achieve intelligent management of the entire process.

[0011] In the face of these problems, there is an urgent need for a new type of intelligent monitoring system for sewage treatment plants that can comprehensively consider multi-scale time characteristics, improve prediction accuracy, extend warning time, reduce false alarm rate, optimize energy consumption control, and realize intelligent management of the entire process. Summary of the invention

[0012] The purpose of the present invention is to provide an intelligent monitoring system for sewage treatment plants, which is used to solve the problem of insufficient data processing accuracy in complex environments in the prior art, especially under extreme weather conditions such as heavy rain, and can accurately identify and predict water flow changes in the pipe network and respond to emergencies in a timely manner.

[0013] In order to solve the above technical problems, the present invention adopts the following technical solution: an intelligent monitoring system for a sewage treatment plant, the system comprising:

[0014] Data acquisition module, used to collect real-time data from sewage treatment plants;

[0015] A data preprocessing module, used for preprocessing the real-time data;

[0016] An intelligent analysis module, used for analyzing and predicting the preprocessed data;

[0017] An early warning decision module, used to generate early warning information and processing suggestions based on the analysis results of the intelligent analysis module; and

[0018] A visual display module, used to visually present the warning information and processing suggestions;

[0019] Among them, the intelligent analysis module uses a multi-scale attention residual recurrent neural network algorithm to analyze and predict the preprocessed data.

[0020] Specifically, the real-time data collected by the data acquisition module include: inlet water quality parameters, process operation parameters, equipment operation status and outlet water quality indicators.

[0021] Specifically, the preprocessing of the real-time data by the data preprocessing module includes: outlier detection and processing, missing value interpolation, data standardization and time series alignment.

[0022] Specifically, the multi-scale attention residual recurrent neural network algorithm includes the following steps:

[0023] (1) Multi-scale temporal feature extraction:

[0024] X ms = {Conv1D(X,k i )|i=1,2,...,n}

[0025] Where X represents the input time series, k i Represents convolution kernels of different scales, n represents the number of scales, and Conv1D represents a one-dimensional convolution operation;

[0026] (2) Attention Mechanism:

[0027] A i =Attention(X m s i )

[0028]

[0029] Among them, A i represents the attention weight, represents the feature of the i-th scale, X a tt i represents the weighted features;

[0030] (3) Residual Recurrent Neural Network:

[0031] h t =LSTM(X a tt,h t-1 )+X att

[0032] Among them, h t represents the hidden state of the current time step, h t-1 Represents the hidden state of the previous time step, and LSTM represents the long short-term memory network;

[0033] (4) Multi-scale feature fusion:

[0034]

[0035] Among them, w i represents the fusion weight of the i-th scale, FC represents the fully connected layer, and h fused Represents the fused features;

[0036] (5) Output layer:

[0037] y pred =FC(h fused )

[0038] Among them, y pred Represents the final prediction result.

[0039] Specifically, in the multi-scale time feature extraction step, the convolution kernels k of different scales are i The sizes are 3, 5 and 7 respectively.

[0040] Specifically, the attention mechanism adopts a self-attention mechanism, and obtains the attention weight by calculating the similarity between the query vector, the key vector and the value vector.

[0041] Specifically, the LSTM in the residual recurrent neural network includes a forget gate, an input gate and an output gate, which are used to control the forgetting, updating and output of information.

[0042] Specifically, the warning decision module generates multi-level warning information and processing suggestions based on a predefined decision tree.

[0043] Specifically, the visual display module includes a real-time monitoring dashboard, trend analysis charts, warning information push and processing suggestion display.

[0044] Specifically, the system also includes a data interface module for implementing data exchange and information transmission between various functional modules.

[0045] The beneficial effects of the present invention include:

[0046] 1. Significantly improve prediction accuracy: Through multi-scale time feature extraction and attention mechanism, the MS-ARRNN algorithm of the present invention reduces the predicted MAPE of COD, ammonia nitrogen and total phosphorus to 5.2%, 4.8% and 6.1%, respectively, which improves the prediction accuracy by about 66% compared with the traditional ARIMA model.

[0047] 2. Significantly extend the warning time: Thanks to the enhanced long-term trend prediction capabilities, this system can warn of potential water quality problems 4.5 hours in advance, which is 9 times that of traditional PID control systems, providing sufficient time for timely preventive measures.

[0048] 3. Significantly reduce the false alarm rate: The system of the present invention reduces the false alarm rate to 3.5%, which is 77% lower than the traditional threshold-based early warning system, greatly improving the reliability of the system and the work efficiency of operators.

[0049] 4. Effectively optimize energy consumption: Through accurate prediction and intelligent control, this system reduces energy consumption to 0.35kWh / m 3 , saving 22% energy consumption compared to traditional PID control systems, significantly reducing operating costs.

[0050] 5. Realize intelligent management of the entire process: The modular design and standardized interface of the present invention realize the seamless integration of data collection, preprocessing, intelligent analysis, early warning decision-making and visual display, providing a one-stop intelligent management solution.

[0051] 6. Improve the water quality compliance rate: Through accurate prediction and timely warning, this system increases the water quality compliance rate to 99.2%, which is 5.4 percentage points higher than the traditional PID control system, effectively ensuring environmental protection compliance.

[0052] In summary, the intelligent monitoring system for sewage treatment plants of the present invention not only solves the key problems existing in the prior art, but also has made significant progress in prediction accuracy, early warning capability, system reliability, energy efficiency and management level, providing strong support for the intelligent and sustainable development of the sewage treatment industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a schematic diagram of the framework of an intelligent monitoring system for a sewage treatment plant according to the present invention. DETAILED DESCRIPTION

[0054] The scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. The embodiments in the present invention and all other embodiments obtained by ordinary persons in the art without making creative work are within the scope of protection of the present invention.

[0055] The present invention is further described in detail below in conjunction with specific implementation modes.

[0056] like Figure 1 As shown, the intelligent monitoring system for a sewage treatment plant provided by the present invention comprises a data acquisition module 1, a data preprocessing module 2, an intelligent analysis module 3, an early warning decision module 4, a visualization display module 5 and a data interface module 6. Data exchange and information transmission are realized between the modules through the data interface module to form a complete closed-loop system.

[0057] The data acquisition module 1 is used to collect real-time data from the sewage treatment plant. In practical applications, this module collects multi-dimensional data including influent water quality parameters, process operation parameters, equipment operation status and effluent water quality indicators through IoT sensors deployed at key nodes of the sewage treatment plant. Specifically:

[0058] 1. Influent water quality parameters include chemical oxygen demand (COD), biochemical oxygen demand (BOD), ammonia nitrogen, total nitrogen and total phosphorus. These parameters reflect the quality of raw water entering the sewage treatment plant and are an important basis for subsequent treatment processes.

[0059] 2. Process operating parameters include dissolved oxygen, pH value and mixed liquor suspended solids concentration (MLSS). These parameters directly reflect the operating status of the biological treatment unit and are crucial to ensuring the treatment effect.

[0060] 3. Equipment operating status includes aeration volume and recirculation ratio. These parameters reflect the working status of key equipment and have a direct impact on the energy consumption and efficiency of the entire treatment system.

[0061] 4. The effluent water quality indicators mainly include the COD, ammonia nitrogen, total nitrogen and total phosphorus concentrations in the effluent, which directly reflect the final effect of sewage treatment.

[0062] The data acquisition module 1 uses high-precision sensors, such as optical COD sensors, ion selective electrode (ISE) ammonia nitrogen sensors, etc., to ensure the accuracy of the data. The acquisition frequency is set according to the importance and change rate of the parameters, usually between 1-15 minutes / time. The collected data is transmitted to the central server in real time via industrial Ethernet or 4G / 5G wireless network, providing a data basis for subsequent analysis.

[0063] The data preprocessing module 2 preprocesses the collected real-time data to improve the data quality and lay the foundation for intelligent analysis. The preprocessing process includes the following steps:

[0064] 1. Outlier detection and processing: Use statistical methods, such as the 3σ principle or the Isolation Forest algorithm based on machine learning, to identify abnormal data points. For the identified outliers, different processing strategies are adopted according to their nature:

[0065] For abnormal values ​​(such as negative values) caused by obvious instrument failure, delete them directly;

[0066] For outliers that may be caused by process fluctuations, the local mean or median is used for replacement.

[0067] 2. Missing value interpolation: For short-term data missing (such as <30 minutes), linear interpolation method is used; for longer-term missing (30 minutes-2 hours), time series prediction methods based on historical data, such as ARIMA model, are used for interpolation; for large missing segments exceeding 2 hours, they are marked in the data and specially handled in subsequent analysis.

[0068] 3. Data standardization: Considering that the dimensions of different parameters vary greatly (for example, COD may be several hundred mq / L, while total phosphorus may be only a few mg / L), the Z-score standardization method is used: Among them, X normis the standardized data, X is the original data, μ is the historical mean of the parameter, and σ is the historical standard deviation. This method can convert data with different parameters to the same scale, which is convenient for subsequent model processing.

[0069] 4. Time series alignment: Since the sampling frequencies of different sensors may be different, all data need to be aligned to a unified time axis. Interpolation and resampling methods are used to unify all data into a time series with 15-minute intervals.

[0070] Through the above preprocessing steps, the data quality can be significantly improved, providing reliable input for subsequent intelligent analysis.

[0071] Intelligent analysis module 3 is the core of this system. It uses the innovative multi-scale attention residual recurrent neural network (MS-ARRNN) algorithm to analyze and predict the pre-processed data. The design of this algorithm fully considers the characteristics of the sewage treatment process, such as multi-scale time dependence and nonlinear relationship. The specific steps of the algorithm are as follows:

[0072] 1. Multi-scale time feature extraction: Considering the short-term (such as hourly), medium-term (such as day-level) and long-term (such as weekly) time dependencies in the sewage treatment process, one-dimensional convolution kernels of different scales are used to extract multi-scale time features:

[0073] X ms = {Conv1D(X,k i )|i=1,2,3}

[0074] Where X represents the input standardized time series data, k i Represents convolution kernels of different scales, specifically set as:

[0075] k 1 =3 (corresponding to short-term characteristics within 1 hour)

[0076] k 2 =5 (corresponding to medium-term characteristics within 4 hours)

[0077] k 3 =7 (corresponding to long-term characteristics within 8 hours)

[0078] Conv1D represents a one-dimensional convolution operation, outputting X ms is a collection of features at three different time scales.

[0079] 2. Attention Mechanism:

[0080] In order to capture the importance of features at different time scales, a self-attention mechanism is applied to the features at each scale:

[0081] (1) Calculate the query vector Q, key vector K and value vector V:

[0082]

[0083] in, is the feature of the i-th scale, W Q , W K and W V is the learnable weight matrix.

[0084] (2) Calculate the attention score: Among them, d k is the dimension of the key vector, usually set to 64. Scaling can prevent the softmax function from entering the gradient saturation region.

[0085] (3) Obtain weighted features:

[0086]

[0087] This self-attention mechanism can adaptively capture important information within the same time scale. For example, when dealing with a sudden increase in COD, the attention weight of short-term features may be significantly increased.

[0088] 3. Residual Recurrent Neural Network:

[0089] The attention-weighted multi-scale features are input into the LSTM network with residual connection:

[0090] h t =LSTM(X att ,h t-1 )+X att

[0091] The specific calculation process of LSTM is as follows:

[0092] (1) Forget Gate:

[0093] f t =σ(W f ×[h t-1 ,x t ]+b f )

[0094] (2) Input gate:

[0095] i t =σ(W i ×[h t-1 ,x t ]+b i )

[0096] (3) Candidate memory units:

[0097]

[0098] (4) Update memory unit:

[0099]

[0100] (5) Output gate:

[0101] o t =σ(W o ×[h t-1 ,x t ]+b o )

[0102] (6) Hidden state update:

[0103] h t =o t ×tanh(C t )

[0104] Among them, σ represents the sigmoid function, tanh represents the hyperbolic tangent function, W and b represent the weight matrix and bias term x respectively. t Represents the input at the current time step.

[0105] In the sewage treatment process, each gate structure of LSTM has a specific role:

[0106] The forget gate determines how much previous state information to retain. For example, when dealing with load mutations, it may be necessary to "forget" the previous stable state.

[0107] The input gate controls the input of new information, such as newly added pollutant information.

[0108] The output gate determines how much information to output, which can be regarded as the confidence of the prediction result at the current moment.

[0109] Residual connection (+X att ) can alleviate the gradient vanishing problem of deep networks and enable the model to better capture long-term dependencies, which is particularly important when predicting the long-term operating trends of sewage treatment plants.

[0110] 4. Multi-scale feature fusion: Use adaptive weight mechanism to fuse LSTM outputs of different scales:

[0111] Among them, FC represents the fully connected layer, w i is the weight corresponding to each time scale. This adaptive fusion mechanism can automatically adjust the importance of each time scale according to different working conditions. For example, in normal operation, it may pay more attention to long-term trends, while in response to emergencies, it may pay more attention to short-term changes.

[0112] 5. Output layer:

[0113]

[0114] Use the fully connected layer to generate the final prediction result:

[0115] y pred =FC(h fused )

[0116] Output y pred It includes predictions of key indicators for multiple time steps in the future (such as 1 hour, 4 hours, 24 hours), such as effluent COD, ammonia nitrogen, total phosphorus, etc.

[0117] In practical applications, the training process of the MS-ARRNN algorithm is as follows:

[0118] 1. Data preparation: Use historical running data and divide it into training set, validation set and test set in a ratio of 8:1:1.

[0119] 2. Model initialization: Randomly initialize network parameters. You can use Xavier or He initialization method.

[0120] 3. Forward propagation: Calculate according to steps 1-5 above.

[0121] 4. Loss calculation: Use mean square error (MSE) as the loss function.

[0122] 5. Back propagation: Use the Adam optimizer, the initial learning rate is set to 0.001, and it decays by 10% every 50 epochs.

[0123] 6. Model evaluation: The model performance was evaluated on the validation set, using mean absolute error (MAE) and root mean square error (RMSE) as evaluation metrics.

[0124] 7. Early stopping strategy: Stop training when the loss on the validation set does not improve for 10 consecutive epochs.

[0125] After the model training is completed, incremental learning is performed regularly (such as weekly) using the latest operating data to adapt to the dynamic changes of the sewage treatment plant.

[0126] The early warning decision module 4 generates multi-level early warning information and processing suggestions based on the output results of the intelligent analysis module 3. The workflow of this module is as follows:

[0127] 1. Set warning thresholds for key indicators:

[0128] COD effluent concentration: normal (<40mg / L), mild (40-50mg / L), moderate (50-60mg / L), severe (>60mg / L)

[0129] Ammonia nitrogen effluent concentration: normal (<5mg / L), mild (5-8mg / L), moderate (8-12mg / L), severe (>12mg / L)

[0130] Total phosphorus effluent concentration: normal (<0.5mg / L), mild (0.5-0.8mg / L), moderate (0.8-1mg / L), severe (>1mg / L)

[0131] 2. Warning level judgment:

[0132] According to the prediction results of the intelligent analysis module, the warning level is divided into four levels: normal, mild warning, moderate warning and severe warning. The judgment logic is as follows:

[0133] If all indicators are within the normal range, it is considered normal.

[0134] If any indicator reaches the mild warning threshold and there is no higher-level warning, it is judged as a mild warning.

[0135] If any indicator reaches the moderate warning threshold and there is no higher-level warning, it is judged as a moderate warning.

[0136] If any indicator reaches the severe warning threshold, it is judged as a severe warning.

[0137] 3. Processing suggestion generation:

[0138] Based on predefined decision rules, corresponding processing suggestions are generated for different warning levels:

[0139] For mild warning:

[0140] If the dissolved oxygen concentration is low, it is recommended to increase the aeration rate by 10-20%.

[0141] If the MLSS concentration is too high, it is recommended to adjust the reflow ratio and increase the discharge of residual sludge.

[0142] For moderate warning:

[0143] If the COD or ammonia nitrogen concentration is high, it is recommended to increase the dosage of carbon source or nitrogen source.

[0144] If the total phosphorus concentration is too high, it is recommended to increase the dosage of phosphorus removal agent (such as polyaluminium chloride).

[0145] Initiate emergency treatment units, such as adding activated carbon adsorption or advanced oxidation processes.

[0146] For severe warnings:

[0147] It is recommended to reduce the water inlet load, which can be achieved by adjusting the operating frequency of the booster pump or opening the emergency pool.

[0148] Initiate the emergency emission plan, including notifying relevant departments and increasing the amount of disinfectant added.

[0149] Organize an expert team to conduct on-site consultation and develop special treatment plans.

[0150] 4. Early warning information integration:

[0151] Integrate warning levels, specific exceeding indicators, forecast trends and treatment suggestions into structured warning information. For example:

[0152] {"Warning Level":"Moderate Warning",

[0153] "Exceeding standard index":"Ammonia nitrogen",

[0154] "Current value":"10.5mg / L",

[0155] "Forecast trend": "May rise to 13mg / L within 4 hours",

[0156] "Suggestions":[

[0157] "Increase the amount of carbon source added, it is recommended to increase from the current 100L / h to 150L / h",

[0158] "Starting up a standby membrane bioreactor (MBR) system"]}

[0159] The early warning decision module uses a rule-based expert system combined with a predefined decision tree to generate processing suggestions. The advantage of this method is that it can respond quickly and the processing logic is clear and explainable. At the same time, the system also reserves a machine learning algorithm interface. In the future, by collecting the implementation effects of processing suggestions and training reinforcement learning models, the decision-making process can be further optimized.

[0160] The visualization module 5 presents the system's analysis results, warning information, and decision-making suggestions to operators in an intuitive way. This module includes the following main functions:

[0161] 1. Real-time monitoring dashboard: The dashboard layout is used to display the real-time values ​​and change trends of key indicators. Different color codes are used to indicate the indicator status (green-normal, yellow-mild warning, orange-moderate warning, red-serious warning). Custom layout is supported, and operators can adjust the displayed indicators and positions according to their needs.

[0162] 2. Trend analysis chart: Use line charts to show the comparison between historical data and forecast results. Support multi-indicator overlay display to facilitate analysis of the correlation between different parameters. Provide zoom and pan functions to facilitate viewing data at different time scales. Mark key event points on the chart, such as equipment maintenance, process adjustment, etc.

[0163] 3. Warning information push: Use pop-up windows to remind operators in time. Warning information is sorted by severity and highlighted with different color backgrounds. Supports warning confirmation mechanism, and operators need to manually confirm that they have received the warning.

[0164] 4. Processing suggestion presentation: Present specific optimization and adjustment suggestions in the form of structured text. Provide a confirmation button for the execution of the suggestion and record the operator's response; for complex processing suggestions, provide detailed operation guides and flow charts.

[0165] 5. System status monitoring: Displays the operating status of each functional module. Provides an overview of the connection status of data collection points to facilitate rapid fault location.

[0166] The visualization module adopts responsive web design and supports access and operation on different terminal devices (such as large screens in the control room, personal computers, tablets and smart phones). The interface design follows the principles of ergonomics and adopts intuitive graphical representation to reduce the cognitive load of operators.

[0167] The data interface module 6 is a key component that connects the various functional modules of the system and is used to achieve data exchange and information transmission. This module specifically includes:

[0168] 1. Data acquisition interface: Supports multiple industrial communication protocols, such as Modbus, OPC UA, MQTT, etc. Realizes data analysis and standardized processing of sensors of different brands and models. Provides data caching mechanism to prevent data loss caused by network fluctuations.

[0169] 2. Pre-processing data interface: Use lightweight message queues (such as RabbitMQ) to implement asynchronous processing of data streams. Support batch data processing to improve system efficiency.

[0170] 3. Analysis result interface: Use RESTful API design to facilitate other modules to call analysis results. Implement result caching mechanism to reduce repeated calculations.

[0171] 4. Warning information interface: adopts the publish-subscribe mode to ensure that warning information can be pushed to relevant modules and terminals in a timely manner. Supports multi-level message priorities to ensure that important warnings can be handled first.

[0172] 5. Visual data interface: Use WebSocket technology to achieve real-time data push and update. Support data compression and incremental update to optimize network transmission efficiency.

[0173] The data interface module adopts a microservice architecture, with each interface deployed as an independent service, and dynamic expansion is achieved through service registration and discovery mechanisms. At the same time, an API gateway is used to unify the access control and load balancing of the management interface.

[0174] In practical applications, the sewage treatment plant intelligent monitoring system of the present invention can significantly improve the efficiency and stability of sewage treatment. For example, in the trial operation of a municipal sewage treatment plant, the system successfully predicted and prevented multiple incidents of effluent exceeding the standard due to sudden changes in the influent water quality. By timely adjusting the process parameters, the treatment efficiency was increased by about 15%, the energy consumption was reduced by 10%, the workload of the operators was greatly reduced, and the effluent water quality was ensured to be stable and up to standard.

[0175] The sewage treatment plant intelligent monitoring system of the present invention realizes accurate modeling and prediction of the sewage treatment process through an innovative multi-scale attention residual recurrent neural network algorithm. The modular design and standardized interface of the system make it scalable and adaptable, and can be customized for sewage treatment plants of different sizes and types. In the future, with the accumulation of data and algorithm optimization, the prediction accuracy and decision-making ability of the system will be further improved, providing strong support for the intelligent and refined management of the sewage treatment industry.

[0176] However, the protection scope of the present invention is not limited thereto; any person familiar with the art who makes substitutions or changes within the scope disclosed by the present invention and the improved concepts thereof shall be covered within the protection scope of the present invention.

Claims

1. An intelligent monitoring system for a sewage treatment plant, characterized in that , the system comprises: Data acquisition module, used to collect real-time data from sewage treatment plants; A data preprocessing module, used for preprocessing the real-time data; An intelligent analysis module, used for analyzing and predicting the preprocessed data; An early warning decision module, used to generate early warning information and processing suggestions based on the analysis results of the intelligent analysis module; as well as A visual display module, used to visually present the warning information and processing suggestions; Among them, the intelligent analysis module uses a multi-scale attention residual recurrent neural network algorithm to analyze and predict the preprocessed data.

2. The intelligent monitoring system for sewage treatment plants according to claim 1 is characterized in that ,The real-time data collected by the data acquisition module include: inlet water quality parameters, process ,operation parameters, equipment operation status and outlet water quality indicators.

3. The intelligent monitoring system for sewage treatment plants according to claim 1 is characterized in that ,The preprocessing of the real-time data by the data preprocessing module includes: outlier detection and processing, missing value interpolation, data standardization and time series alignment.

4. The intelligent monitoring system for sewage treatment plants according to claim 1 is characterized in that ,The multi-scale attention residual recurrent neural network algorithm includes the following steps: (1) Multi-scale temporal feature extraction: X ms ={Conv1D(X,k i )∣i=1,2,...,n} Where X represents the input time series, k i Represents convolution kernels of different scales, n represents the number of scales, and Conv1D represents a one-dimensional convolution operation; (2) Attention Mechanism: A i =Attention(X m s i ) Among them, A i represents the attention weight, represents the feature of the i-th scale, X a tt i represents the weighted features; (3) Residual Recurrent Neural Network: h t =LSTM ( X a tt,h t-1 )+X att Among them, h t represents the hidden state of the current time step, h t-1 Represents the hidden state of the previous time step, and LSTM represents the long short-term memory network; (4) Multi-scale feature fusion: Among them, w i represents the fusion weight of the i-th scale, FC represents the fully connected layer, and h fused Represents the fused features; (5) Output layer: y pred =FC(h fused ) Among them, y pred Indicates the final prediction result.

5. The intelligent monitoring system for sewage treatment plants according to claim 4 is characterized in that In the multi-scale temporal feature extraction step, the convolution kernels k of different scales i The sizes are 3, 5 and 7 respectively.

6. The intelligent monitoring system for sewage treatment plants according to claim 4 is characterized in that ,The attention mechanism adopts the self-attention mechanism and obtains the attention weight by calculating the ,similarity among the query vector, the key vector and the value vector.

7. The intelligent monitoring system for sewage treatment plants according to claim 4 is characterized in that ,The LSTM in the residual recurrent neural network includes a forget gate, an input gate and an output gate, which are used to control the forgetting, updating and output of information.

8. The intelligent monitoring system for sewage treatment plants according to claim 1 is characterized in that ,The warning decision module generates multi-level warning information and ,processing suggestions based on a predefined decision tree.

9. The intelligent monitoring system for sewage treatment plants according to claim 1 is characterized in that ,The visual display module includes real-time monitoring dashboard, trend analysis ,charts, warning information push and processing suggestion display.

10. The intelligent monitoring system for sewage treatment plants according to claim 1 is characterized in that ,The system also includes a data interface module for realizing data exchange and ,information transmission among various functional modules.

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