Intelligent sewage treatment plant operation management system

Abstract

The invention discloses an intelligent sewage treatment plant operation management system, which integrates advanced technologies such as Internet of Things, big data analysis and artificial intelligence, and realizes comprehensive monitoring, intelligent analysis and optimal scheduling of a sewage treatment process. The system monitors water quality, equipment state and energy consumption data in real time, applies a machine learning algorithm to carry out water quality prediction and equipment fault early warning, and automatically adjusts the agent dosage, the aeration rate and an equipment scheduling scheme, so as to improve the processing efficiency and quality and reduce the operation cost; meanwhile, the system has an adaptive learning capability, and can continuously optimize a prediction model and an optimization strategy according to historical data and real-time feedback. The intelligent sewage treatment plant operation management system has the characteristics of high efficiency, stability and reliability, and provides powerful support for environmental protection and sustainable development.

Description

Technical Field

[0001] The present invention relates to the field of sewage treatment technology, and in particular to an intelligent sewage treatment plant operation and management system that integrates modern information technology, big data analysis, artificial intelligence algorithms and other advanced technologies, aiming to improve sewage treatment efficiency, optimize resource allocation, reduce operating costs, and achieve environmentally friendly management. Background Art

[0002] With the acceleration of urbanization and the improvement of environmental awareness, sewage treatment plants play a vital role in protecting water resources and the ecological environment. At present, sewage treatment technology has made significant progress, but the traditional operation and management of sewage treatment plants still faces many challenges.

[0003] Monitoring and control methods: Traditional sewage treatment plants mainly rely on manual monitoring and operation. This method has the problem of data accuracy and timeliness being difficult to guarantee, and manual operation is inefficient and difficult to cope with complex and changeable sewage treatment situations. With the continuous development of technologies such as the Internet of Things and sensors, real-time monitoring and control of the sewage treatment process has become possible.

[0004] Data analysis and decision support: In terms of data processing, traditional sewage treatment plants lack efficient data analysis tools and methods, making it difficult to mine valuable information from massive data to support management decisions. The introduction of technologies such as big data and artificial intelligence makes it possible to conduct in-depth mining and analysis of sewage treatment process data, providing strong support for optimizing operational strategies.

[0005] Remote monitoring and management: Traditional sewage treatment plant management methods make it difficult to achieve remote monitoring and management, which limits management efficiency and response speed. The rapid development of the Internet and mobile communication technology has made it possible to remotely monitor and manage sewage treatment plants. Managers can understand the operating status of sewage treatment plants anytime and anywhere and deal with abnormal situations in a timely manner.

[0006] Although the existing intelligent sewage treatment plant operation and management technology has made significant progress in some aspects, there are still some drawbacks and limitations, which are mainly reflected in the following aspects:

[0007] Limitations of sensor technology: Although sensor technology has been widely used in sewage treatment, the stability and reliability of sensors are still a key issue. Sensor failure or inaccurate data may lead to false alarms or missed alarms, affecting the stable operation of sewage treatment plants.

[0008] Accuracy of data analysis algorithms: Data analysis algorithms are one of the core elements of intelligent sewage treatment plant operation and management systems, but the accuracy of the algorithms directly affects the performance and effectiveness of the system. At present, some data analysis algorithms still have large errors and need to be continuously optimized and improved.

[0009] High system construction cost: The construction of an intelligent sewage treatment plant operation and management system requires a large amount of capital investment, including hardware equipment purchase, software development, system integration, etc. This may be a large burden for some small sewage treatment plants, limiting the popularization and application of intelligent technology.

[0010] Data security issues: The intelligent management system involves a large amount of sensitive data, such as the operation data and process parameters of the sewage treatment plant. Data security issues cannot be ignored. Once the data is leaked or tampered with, it may have a serious impact on the stable operation of the sewage treatment plant and environmental protection.

[0011] In summary, although the existing intelligent sewage treatment plant operation and management technology has made significant progress in some aspects, there are still some drawbacks and limitations. In order to overcome these drawbacks, it is necessary to continuously optimize and improve relevant technologies, improve the stability and reliability of the system, reduce construction costs, and strengthen data security protection. Summary of the invention

[0012] The present invention aims to provide an intelligent sewage treatment plant operation and management system to solve the problems existing in the current operation and management of sewage treatment plants, such as backward monitoring and control means, insufficient data analysis and decision-making support, and limited remote monitoring and management. By integrating advanced technologies such as the Internet of Things, big data, and artificial intelligence, real-time monitoring, precise control, intelligent analysis, and optimized scheduling of the sewage treatment process can be achieved, thereby improving the efficiency and quality of sewage treatment, reducing operating costs, and ensuring data security and system stability.

[0013] To achieve the above object, the present invention adopts the following technical solutions:

[0014] An intelligent sewage treatment plant operation and management system, the system comprising:

[0015] a) Data acquisition module, which uses the IoT sensor network to collect water quality parameters, flow data, equipment status data and energy consumption data in the sewage treatment process in real time, and integrates these data into the cloud data center;

[0016] b) Intelligent analysis and prediction module, which uses big data analysis technology and machine learning algorithms to clean, integrate and extract features from the collected data, predict water quality trends by building a time series prediction model, identify abnormal conditions through cluster analysis, predict equipment failures through classification algorithms, and generate early warning signals;

[0017] c) optimizing the scheduling control module, adjusting the sewage treatment process parameters using a multi-objective optimization algorithm according to the output of the intelligent analysis and prediction module;

[0018] d) Remote monitoring and maintenance module: By building a remote monitoring platform, the equipment status is monitored in real time, deep learning models are used for remote fault diagnosis, equipment maintenance suggestions are provided, and the operation and maintenance process is simplified;

[0019] e) Energy efficiency management and reporting module, which automatically generates operation reports based on historical data and real-time data, including energy consumption analysis reports, processing efficiency evaluation reports, and cost-benefit analysis reports. It uses indicators to evaluate operational performance and provide decision support for managers.

[0020] Furthermore, the time series prediction model in the intelligent analysis and prediction module specifically adopts a long short-term memory network model, and its model structure includes an input layer, multi-layer LSTM units, a fully connected layer and an output layer, which is used to predict the changing trend of water quality parameters.

[0021] Furthermore, the multi-objective optimization algorithm in the optimization scheduling control module adopts a non-dominated sorting genetic algorithm II with an elite strategy, and its objective function includes:

[0022] Minimize energy consumption:

[0023]

[0024] Among them, P i (x) is the power of device i under decision variable x, t i is the running time of device i.

[0025] Maximize processing efficiency:

[0026]

[0027] Outlet water quality meets the standards:

[0028]

[0029] in, is the actual value of the j-th water quality parameter under the decision variable x, is the standard value of the jth water quality parameter, m is the number of types of water quality parameters, It is the maximum value among all water quality parameter standard values.

[0030] Furthermore, the deep learning model in the remote monitoring and maintenance module uses a convolutional neural network for remote fault diagnosis, and its model structure includes:

[0031] Input layer: receives the original image or time series data of device status data;

[0032] Convolution layer: extract feature maps through multiple convolution kernels, each convolution kernel corresponds to a feature;

[0033] Pooling layer: Use maximum pooling or average pooling to reduce the dimension of the feature map and retain important features, including key water quality index features, equipment operation efficiency features, treatment process stability features, and energy efficiency and cost features;

[0034] Fully connected layer: Flattens the output of the pooling layer and connects it to the fully connected layer for feature fusion and classification decision;

[0035] Output layer: Use the softmax function to output the probability distribution of fault diagnosis.

[0036] Furthermore, the energy consumption analysis report in the energy efficiency management and reporting module uses energy efficiency ratio and unit processing cost as evaluation indicators, and the calculation formulas are:

[0037] Energy efficiency ratio:

[0038]

[0039] Unit processing cost:

[0040]

[0041] Among them, the total cost includes energy cost, chemical cost and maintenance cost.

[0042] Furthermore, the system also includes an adaptive learning module, which dynamically adjusts the model parameters and algorithm strategies in the intelligent analysis and prediction module and optimizes the scheduling and control module according to the actual operation data and feedback of the system, so as to improve the adaptive ability and prediction accuracy of the system.

[0043] The present invention proposes an intelligent sewage treatment plant operation and management system, which has the following beneficial effects:

[0044] 1. Improve operational efficiency: Through real-time monitoring, data analysis and automatic control systems, the intelligent management system can optimize the operating process, reduce unnecessary energy and drug consumption, and thus improve the efficiency of the entire treatment process;

[0045] 2. Reduce operating costs: Intelligent management reduces the need for manpower and reduces the error rate in traditional work such as manual inspection and data entry. At the same time, through continuous monitoring of equipment performance, preventive maintenance can be carried out to reduce the occurrence of failures and further reduce costs;

[0046] 3. Enhanced decision support: Based on the collected big data, the smart platform can provide scientific data analysis to help management make more accurate and timely decisions, such as adjusting treatment process parameters according to changes in water quality;

[0047] 3. Ensure effluent quality: By precisely controlling key process parameters such as dosage and aeration volume, the intelligent system helps maintain stable effluent quality and ensures compliance with environmental standards;

[0048] 4. Improve emergency response capabilities: When abnormal situations occur, such as sudden changes in water quality or equipment failure, the intelligent system can quickly issue an alarm and take corresponding measures to prevent the situation from deteriorating and improve the ability to respond to emergencies;

[0049] 5. Enhanced transparency and traceability: All operation records and data can be stored and tracked, which enhances the transparency of the operation process, facilitates the review of regulatory agencies, and facilitates internal audits and continuous improvements;

[0050] 6. Promote sustainable development: Through energy saving and consumption reduction and efficient operation, smart sewage treatment plants promote the recycling of water resources and are an important part of achieving sustainable development goals.

[0051] In summary, a more advanced intelligent sewage treatment plant operation and management system will significantly improve the operational efficiency of sewage treatment plants, reduce operating costs, enhance decision support, ensure effluent quality, improve emergency response capabilities, enhance transparency and traceability, and promote sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a system structure diagram of an intelligent sewage treatment plant operation and management system of the present invention;

[0053] Figure 2 This is an implementation flow chart of an intelligent sewage treatment plant operation and management system of the present invention. DETAILED DESCRIPTION

[0054] Technical solution content

[0055] The present invention proposes an intelligent sewage treatment plant operation and management system, which integrates Internet of Things technology, big data analysis, machine learning and deep learning algorithms, and aims to realize intelligent monitoring, optimized scheduling and energy efficiency management of the sewage treatment process.

[0056] 1. System Modules and Implementation Methods

[0057] 1. Data acquisition module

[0058] The data acquisition module collects water quality parameters (such as pH value, dissolved oxygen DO, chemical oxygen demand COD, etc.), flow data, equipment status data (such as motor current, vibration, temperature, etc.) and energy consumption data (such as electricity consumption) in real time through the Internet of Things sensor network deployed in various key links of the sewage treatment plant. These data are transmitted to the cloud data center wirelessly or wiredly, providing a basis for subsequent intelligent analysis.

[0059] 1.1 Specific implementation of this module:

[0060] 1.1.1 Sensor deployment:

[0061] Water quality sensors are deployed at key links such as the water inlet, reaction tank, sedimentation tank, and outlet of the sewage treatment plant to monitor water quality parameters in real time, such as pH, dissolved oxygen (DO), chemical oxygen demand (COD), biological oxygen demand (BOD), suspended solids (SS), ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP);

[0062] Deploy vibration sensors, temperature sensors, and current sensors on key equipment such as water pumps, fans, and agitators to monitor the operating status and energy consumption data of the equipment in real time;

[0063] Flow meters are deployed at flow measurement points to monitor sewage flow in real time.

[0064] 1.1.2 Data Transmission:

[0065] The data collected by the sensor is transmitted to the data collector or gateway via wired or wireless methods. Wired methods include RS485, Ethernet, etc., and wireless methods include Wi-Fi, LoRa, NB-IoT, etc.

[0066] The data collector or gateway transmits the received data to the cloud data center through a dedicated network or the Internet.

[0067] 1.1.3 Data Storage:

[0068] The cloud data center receives and stores data from data collectors or gateways to ensure data integrity and security.

[0069] Data storage uses distributed database or cloud storage technology to support the storage and efficient query of large-scale data.

[0070] 1.2 Specific data types

[0071] The types of data collected by the data collection module include but are not limited to the following:

[0072] 1.2.1 Water quality parameter data:

[0073] pH value: reflects the acidity and alkalinity of sewage;

[0074] Dissolved oxygen (DO): reflects the oxygen content in sewage and is crucial to the biological treatment process;

[0075] Chemical oxygen demand (COD): reflects the content of organic matter in sewage and is an important indicator for measuring the degree of sewage pollution;

[0076] Biological oxygen demand (BOD): reflects the content of biodegradable organic matter in sewage;

[0077] Suspended solids (SS): reflects the content of suspended particles in sewage;

[0078] Ammonia nitrogen (NH3-N): reflects the content of ammonia nitrogen in sewage and has an impact on the biological treatment process;

[0079] Total nitrogen (TN): reflects the total content of nitrogen in sewage;

[0080] Total phosphorus (TP): reflects the total content of phosphorus in sewage.

[0081] 1.2.2 Traffic data:

[0082] Sewage flow: reflects the amount of sewage treated by the sewage treatment plant.

[0083] 1.2.3 Equipment status data:

[0084] Motor current: reflects the energy consumption and operating status of the equipment;

[0085] Vibration data: reflects the vibration condition of the equipment and can be used to predict equipment failure;

[0086] Temperature data: reflects the temperature of the device and can be used to monitor whether the device is overheating.

[0087] 1.2.4 Energy consumption data:

[0088] Electricity consumption: reflects the energy consumption of the equipment and can be used to calculate energy efficiency ratio and unit processing cost.

[0089] In summary, through the above-mentioned specific implementation methods and descriptions of specific data types, the data acquisition module can collect various types of data in the sewage treatment process in real time and accurately, providing a solid foundation for subsequent intelligent analysis.

[0090] 2. Intelligent analysis and prediction module

[0091] The intelligent analysis and prediction module uses the long short-term memory network (LSTM) model to perform time series prediction on the collected data. The structure of the LSTM model includes an input layer, multi-layer LSTM units, a fully connected layer, and an output layer; specifically, the input layer receives time series data, such as historical water quality parameters; the LSTM unit captures long-term dependencies in the data through mechanisms such as forget gates, input gates, candidate cell states, and output gates; the fully connected layer converts the output of the LSTM unit into a prediction result, such as the trend of water quality parameters in the future.

[0092] 2.1 LSTM model construction

[0093] 2.1.1 Data Preprocessing

[0094] The collected historical water quality parameter data (such as COD, NH3-N, TP, etc.) need to be preprocessed, including data cleaning, normalization or standardization, to improve the prediction accuracy and stability of the model;

[0095] Data cleaning mainly involves removing outliers, filling missing values, etc., to ensure the integrity and accuracy of the data;

[0096] Normalization or standardization is the process of scaling data to a specific range or distribution to eliminate dimensional differences between different features.

[0097] 2.1.2 Model structure design

[0098] Input layer: receives preprocessed time series data, such as historical water quality parameter values;

[0099] Multi-layer LSTM units: Capture long-term dependencies in the data through mechanisms such as forget gates, input gates, candidate cell states, and output gates. The number and number of LSTM units can be adjusted according to actual needs to improve the predictive power of the model.

[0100] Fully connected layer: converts the output of the LSTM unit into a prediction result. The number of neurons in the fully connected layer can be determined according to the prediction target (such as the water quality parameter value in the future);

[0101] Output layer: Output prediction results, that is, the changing trend of water quality parameters in the future.

[0102] 2.1.3 Model Training

[0103] The preprocessed time series data is divided into a training set and a test set. The training set is used to train the LSTM model, and the test set is used to evaluate the prediction performance of the model.

[0104] Select appropriate loss functions and optimization algorithms. Common loss functions include mean square error (MSE) and mean absolute error (MAE). Optimization algorithms include Adam and SGD.

[0105] Set appropriate training parameters, such as learning rate, batch size, number of iterations, etc., and perform model training;

[0106] During the training process, you can evaluate the training effect of the model by observing the changes in the loss function and make necessary adjustments.

[0107] 2.1.4 Model Evaluation and Optimization

[0108] Use the test set to evaluate the trained LSTM model and calculate the error between the predicted value and the actual value, such as MAPE, RMSE, etc.

[0109] Optimize the model based on the evaluation results, such as adjusting the number and layers of LSTM units, changing the loss function and optimization algorithm, and adjusting training parameters;

[0110] After multiple iterations of training and optimization, the final LSTM model is obtained.

[0111] 2.2 Forecast Implementation

[0112] 2.2.1 Data Input

[0113] Input the real-time collected time series data into the trained LSTM model.

[0114] 2.2.2 Prediction Output

[0115] The LSTM model makes predictions based on the input data and outputs the changing trends of water quality parameters in the future.

[0116] 2.2.3 Results analysis and application

[0117] Analyze the prediction results to determine whether the changing trend of water quality parameters is in line with expectations;

[0118] According to the prediction results, the operating parameters of the sewage treatment plant can be adjusted or corresponding measures can be taken to optimize the sewage treatment process and improve the effluent quality.

[0119] In summary, the intelligent analysis and prediction module uses the LSTM model to predict the collected time series data, which can accurately judge the future trend of water quality parameter changes; through reasonable model structure design, training and optimization, as well as correct prediction implementation steps, this module can provide strong support for the operation and management of sewage treatment plants.

[0120] 3. Optimize the scheduling control module

[0121] The optimization scheduling control module uses the non-dominated sorting genetic algorithm II (NSGA-II) with an elite strategy for multi-objective optimization. The objective functions include minimizing energy consumption, maximizing treatment efficiency, and meeting the effluent quality standards. Specifically, energy consumption minimization is achieved by calculating the product of equipment power and operating time; treatment efficiency is measured by the ratio of treated water volume to total treatment time; and effluent quality is evaluated by calculating the deviation between the actual water quality parameters and the standard values.

[0122] The NSGA-II algorithm iteratively optimizes decision variables (such as aeration volume, mixed liquor reflux ratio, etc.) through genetic operations (such as selection, crossover, and mutation) until the preset stopping conditions (such as the number of iterations, convergence, etc.) are reached.

[0123] Specifically,

[0124] 3.1 Objective function definition

[0125] 3.1.1 Minimization of energy consumption:

[0126] Objective function:

[0127]

[0128] Among them, P i (x) is the power of device i under decision variable x, t i is the running time of device i.

[0129] Reduce overall energy consumption by optimizing equipment operating parameters and time.

[0130] 3.1.2 Maximize processing efficiency:

[0131] Objective function:

[0132]

[0133] Improve processing efficiency by increasing equipment processing capacity and optimizing processing procedures.

[0134] 3.1.3 The effluent quality meets the standards:

[0135]

[0136] in, is the actual value of the j-th water quality parameter under the decision variable x, is the standard value of the jth water quality parameter, m is the number of types of water quality parameters, It is the maximum value among all water quality parameter standard values;

[0137] By adjusting the treatment process and parameters, the effluent quality is ensured to meet the standards.

[0138] 3.2 NSGA-II algorithm implementation process

[0139] 3.2.1 Initialize the population:

[0140] A set of initial solutions is randomly generated, each solution contains a set of values ​​of decision variables (such as aeration volume, mixed liquor reflux ratio, etc.).

[0141] 3.2.2 Evaluation of fitness:

[0142] Calculate the fitness value of each solution according to the objective function;

[0143] The fitness value is used to evaluate the quality of the solution, including energy consumption, treatment efficiency and effluent water quality.

[0144] 3.2.3 Non-dominated sorting:

[0145] The non-dominated sorting algorithm is used to classify the solutions in the population according to Pareto superiority and inferiority, and multiple non-dominated hierarchies are obtained.

[0146] 3.2.4 Calculate the congestion:

[0147] Calculate the crowding degree for each non-dominated level solution to maintain the diversity of the population;

[0148] The crowding degree reflects the density of solutions in the solution space and can be calculated by the variable space distance of the solution or the objective function value space distance.

[0149] 3.2.5 Select operation:

[0150] According to the non-dominated sorting and crowding calculation, the parent solution for generating the next generation population is selected;

[0151] Methods such as tournament selection or roulette selection are often used.

[0152] 3.2.6 Genetic Operations:

[0153] Generate offspring solutions through crossover and mutation operations;

[0154] The crossover operation exchanges and combines certain features of the two parent solutions;

[0155] The mutation operation randomly changes the characteristics of a solution.

[0156] 3.2.7 Update population:

[0157] Combine the parent solution and the child solution to update the population;

[0158] Delete some solutions to keep the population size constant.

[0159] 3.2.8 Repeated iteration:

[0160] Repeat steps 2 to 7 until the preset stopping condition (such as number of iterations, convergence, etc.) is reached.

[0161] 3.2.9 Output results:

[0162] Select the best solution as the result of the optimal dispatch control;

[0163] Analyze the characteristics and performance of the optimal solution, including energy consumption, treatment efficiency and effluent quality.

[0164] In summary, the optimization scheduling control module uses the NSGA-II algorithm for multi-objective optimization. By defining objective functions such as minimizing energy consumption, maximizing treatment efficiency, and meeting effluent quality standards, and using genetic operations to iteratively optimize decision variables, the optimal scheduling of sewage treatment plant operating parameters is achieved; the implementation process of this module includes initializing the population, evaluating fitness, non-dominated sorting, calculating congestion, selecting operations, genetic operations, updating the population, and repeated iterations. By incorporating specific calculation formulas, the module can accurately evaluate the pros and cons of each solution and find the optimal solution to achieve the optimization goal.

[0165] 4. Remote monitoring and maintenance module

[0166] The remote monitoring and maintenance module uses convolutional neural network (CNN) for remote fault diagnosis. The structure of the CNN model includes input layer, convolution layer, pooling layer, fully connected layer and output layer; the input layer receives the original image or time series data of the equipment status data; the convolution layer extracts the feature map through multiple convolution kernels; the pooling layer reduces the dimension of the feature map; the fully connected layer performs feature fusion and classification decision; the output layer outputs the probability distribution of fault diagnosis.

[0167] 4.1 Module Overview

[0168] The remote monitoring and maintenance module is designed to perform remote fault diagnosis on equipment status data through the CNN model. This module uses the powerful feature extraction and classification capabilities of CNN to accurately identify equipment faults.

[0169] 4.2 CNN Model Structure

[0170] Input layer: receives raw images or time series data of device status data, which can be real-time data from sensors, monitoring equipment, etc., or historical data;

[0171] Convolutional layer: Convolution operation is performed on the input data through multiple convolution kernels (also called filters) to extract feature maps; the convolutional layer can learn local features in the data, which are crucial for subsequent fault diagnosis;

[0172] Pooling layer: downsamples the feature map output by the convolutional layer to reduce the dimension of the feature map while retaining important features, including:

[0173] Key water quality index characteristics: such as chemical oxygen demand (COD), biological oxygen demand (BOD), ammonia nitrogen concentration, etc. These indicators directly reflect the pollution degree and treatment effect of sewage and are important bases for optimizing treatment strategies;

[0174] Equipment operating efficiency characteristics: including operating parameters of key equipment such as pumps, aerators, and agitators (such as current, speed, energy consumption, etc.). These characteristics help identify signs of equipment failure and optimize scheduling to ensure efficient operation of the treatment process;

[0175] Process stability characteristics: By monitoring the flow, liquid level, pressure and other parameters during the process, the stability and continuity of the entire process can be evaluated, and potential unstable factors can be discovered and adjusted in a timely manner;

[0176] Energy efficiency and cost characteristics: including the total energy consumption during the treatment process, unit treatment cost, etc. These characteristics are crucial for evaluating operational efficiency and formulating energy-saving and consumption-reduction strategies;

[0177] The pooling layer helps reduce the amount of computation and improves the generalization ability of the model;

[0178] Fully connected layer: Flattens the feature map output by the pooling layer, and performs feature fusion and classification decisions through multiple fully connected layers.

[0179] Feature fusion specifically includes:

[0180] Water quality feature fusion:

[0181] The water quality parameters (such as COD, BOD, ammonia nitrogen concentration, pH value, etc.) at different monitoring points and time points are integrated to fully reflect the overall water quality of the sewage;

[0182] Integrate the water quality change characteristics of different treatment stages (such as pretreatment, biochemical treatment, deep treatment, etc.) to evaluate the treatment effect and optimize the treatment strategy;

[0183] Equipment operation feature fusion:

[0184] Integrate the operating parameters of different devices (such as current, speed, energy consumption, vibration, etc.) to monitor the operating status and performance of the equipment;

[0185] Integrate equipment fault warning information to detect potential faults in advance and take corresponding maintenance measures;

[0186] Process stability feature fusion:

[0187] Integrate the flow, liquid level, pressure and other parameters in the treatment process to evaluate the stability and continuity of the entire treatment process;

[0188] Integrate abnormal event information (such as equipment failure, water quality exceeding standards, etc.) to quickly respond and adjust processing strategies;

[0189] Energy efficiency and cost characteristics fusion:

[0190] Integrate data such as total energy consumption and unit processing cost during the treatment process to evaluate operational efficiency and energy-saving and consumption-reduction effects;

[0191] Integrate historical data and real-time data to develop more reasonable energy management strategies;

[0192] The fully connected layer maps the extracted features to different fault types;

[0193] Output layer: Outputs the probability distribution of fault diagnosis. The output layer usually uses a softmax function to convert the output of the fully connected layer into a probability distribution, indicating the possibility of each fault type.

[0194] 4.3 Specific implementation process

[0195] 4.3.1 Data collection and preprocessing

[0196] Collect raw images or time series data of equipment status data;

[0197] Preprocess the data, including filtering, noise reduction, normalization and other operations, to improve data quality and model training efficiency;

[0198] The data is divided into training set, validation set and test set for model training, validation and testing.

[0199] 4.3.2 Model construction

[0200] According to the CNN model structure, use deep learning frameworks (such as TensorFlow, PyTorch, etc.) to build models;

[0201] Set the model's hyperparameters, such as convolution kernel size, number, pooling method, number of fully connected layers, etc.

[0202] 4.3.3 Model Training

[0203] Use the training set data to train the CNN model;

[0204] During the training process, the weight and bias parameters of the model are optimized through the back-propagation algorithm;

[0205] Use validation set data to validate the model and adjust hyperparameters to optimize model performance.

[0206] 4.3.4 Model Evaluation

[0207] Use the test set data to evaluate the trained model;

[0208] Calculate the model's accuracy, recall, F1 score and other indicators to evaluate the model's performance;

[0209] If the model performance does not meet the requirements, return to the model building and training stage for adjustment.

[0210] 4.3.5 Remote monitoring and maintenance

[0211] Deploy the trained CNN model to the remote monitoring and maintenance module;

[0212] Receive equipment status data in real time and use CNN models for fault diagnosis;

[0213] The diagnostic results are output to the user, providing maintenance suggestions or remote guidance.

[0214] 4.3.6 Continuous Optimization and Improvement

[0215] Continuously optimize and improve the CNN model based on feedback and data changes in actual applications;

[0216] Collect new data for model updating and retraining;

[0217] Introduce new techniques and methods to improve the performance and accuracy of the model.

[0218] Through the above steps, the remote monitoring and maintenance module can realize remote monitoring and fault diagnosis of equipment status, and improve equipment reliability and maintenance efficiency.

[0219] 5. Energy efficiency management and reporting module

[0220] The energy efficiency management and reporting module automatically generates operation reports based on historical data and real-time data. The report content includes energy consumption analysis report, processing efficiency evaluation report, cost-benefit analysis report, etc. The energy consumption analysis report uses energy efficiency ratio (EER) as an evaluation indicator and unit processing cost (UTC) as another evaluation indicator.

[0221] 5.1 Module Overview

[0222] The energy efficiency management and reporting module automatically generates a variety of operational reports, including energy consumption analysis reports, processing efficiency evaluation reports, cost-benefit analysis reports, etc., by collecting and analyzing data. These reports help enterprises or organizations identify problems in energy use, optimize operational strategies, and improve energy efficiency and cost-effectiveness.

[0223] 5.2 Implementation process of energy consumption analysis report

[0224] 5.2.1 Data Collection

[0225] Collect historical and real-time energy consumption data of enterprises or institutions;

[0226] The data should include total energy consumption (such as electricity, water, gas, etc.) and related data such as treated water volume.

[0227] 5.2.2 Data Preprocessing

[0228] Clean and organize the collected data to ensure its accuracy and completeness;

[0229] Convert data to a format suitable for energy efficiency ratio (EER) calculation.

[0230] 5.2.3 Energy Efficiency Ratio (EER) Calculation

[0231] According to the formula energy efficiency ratio:

[0232]

[0233] 5.2.4 Report Generation

[0234] Integrate the calculated energy efficiency ratio results into the energy consumption analysis report;

[0235] The report should include information such as historical trends of energy efficiency ratios, current levels, and comparisons with other companies or institutions;

[0236] Based on the energy efficiency ratio results, put forward suggestions and measures for energy saving and consumption reduction.

[0237] 5.3 Implementation process of unit processing cost report

[0238] 5.3.1 Data Collection

[0239] Collect historical and real-time cost data of enterprises or institutions;

[0240] The data should include total costs (such as labor costs, material costs, equipment maintenance costs, etc.) and relevant data such as the amount of water treated.

[0241] 5.3.2 Data Preprocessing

[0242] Clean and organize the collected data to ensure its accuracy and completeness;

[0243] Convert the data format to make it suitable for calculation of unit processing cost (UTC).

[0244] 5.3.3 Calculation of Unit Processing Cost (UTC)

[0245] Unit processing cost:

[0246]

[0247] Among them, the total cost includes energy cost, drug cost and maintenance cost;

[0248] This formula represents the cost per cubic meter of water treated.

[0249] 5.3.4 Report Generation

[0250] Integrate the calculated unit treatment cost results into the cost-benefit analysis report;

[0251] The report should include information such as historical trends in unit processing costs, current levels, and comparisons with other businesses or institutions;

[0252] Based on the unit processing cost results, put forward suggestions and measures to reduce costs and improve efficiency.

[0253] 5.4 Implementation process of other reports

[0254] In addition to the energy consumption analysis report and unit treatment cost report, the energy efficiency management and reporting module can also generate other types of reports such as treatment efficiency evaluation report and cost-effectiveness analysis report. The implementation process of these reports is similar to the above process, but needs to be adjusted according to the specific evaluation indicators and calculation methods.

[0255] 5.5 Continuous Optimization and Improvement

[0256] 5.5.1 Data Quality Monitoring

[0257] Regularly conduct quality checks on the collected data to ensure its accuracy and completeness;

[0258] Monitor and evaluate data quality to identify and resolve data issues in a timely manner.

[0259] 5.5.2 Model Optimization

[0260] Continuously optimize and improve the calculation models and algorithms of energy efficiency management and reporting modules based on feedback and data changes in actual applications;

[0261] Introduce new technologies and methods to improve the accuracy and reliability of the module.

[0262] 5.5.3 User training and support

[0263] Provide users with module usage training and support to help them better understand and use the module's functions;

[0264] Collect user feedback and suggestions to continuously improve and optimize the user experience of the module.

[0265] Through the above implementation process, the energy efficiency management and reporting module can provide enterprises with comprehensive energy efficiency management and cost-benefit analysis services, helping enterprises to better understand energy usage, optimize operational strategies, and improve energy efficiency and cost-effectiveness.

[0266] 6. Adaptive learning module

[0267] The adaptive learning module dynamically adjusts the model parameters and algorithm strategies in the intelligent analysis and prediction module and optimizes the scheduling and control module based on the actual operation data and feedback of the system. Through continuous learning and optimization, the adaptive ability and prediction accuracy of the system are improved.

[0268] 6.1 Data Collection and Preprocessing

[0269] 6.1.1 Data Collection:

[0270] Collect actual operation data from each module of the system, including sensor data, user behavior data, system status data, etc.;

[0271] Collect user feedback data on the system, such as satisfaction surveys, fault reports, etc.

[0272] 6.1.2 Data preprocessing:

[0273] Clean the collected data to remove noise and outliers;

[0274] The data is normalized and standardized to make it suitable for subsequent analysis and learning.

[0275] 6.2 Model training and updating

[0276] 6.2.1 Feature Extraction:

[0277] Key features are extracted from the preprocessed data, which can reflect the operating status of the system and the user's preferences.

[0278] 6.2.2 Model selection and training:

[0279] Choose the appropriate machine learning or deep learning model based on the specific application scenario;

[0280] The model is trained using the extracted feature data to obtain the initial model parameters.

[0281] 6.2.3 Model update:

[0282] As new data is continuously generated, model parameters are regularly updated to improve the model’s prediction accuracy and adaptability;

[0283] Use incremental learning, online learning and other technologies to achieve real-time updating and optimization of the model.

[0284] 6.3 Algorithm Strategy Adjustment

[0285] 6.3.1 Strategy Analysis:

[0286] Analyze the effectiveness of the current algorithm strategy, including prediction accuracy, system stability, user satisfaction and other aspects.

[0287] 6.3.2 Strategy Adjustment:

[0288] According to the analysis results, adjust the parameters of the algorithm strategy or choose a new strategy;

[0289] For example, in the intelligent analysis and prediction module, the parameters of the prediction model can be adjusted to improve the prediction accuracy; in the optimization scheduling control module, the scheduling strategy can be adjusted to optimize system performance.

[0290] 6.4 Feedback and Iteration

[0291] 6.4.1 User feedback collection:

[0292] Collect user feedback on system performance and prediction results;

[0293] Analyze user feedback to understand user needs and expectations.

[0294] 6.4.2 System performance evaluation:

[0295] Use evaluation metrics (such as accuracy, recall, F1 score, etc.) to evaluate system performance;

[0296] Compare system performance with user requirements to identify gaps and areas for improvement.

[0297] 6.4.3 Iterative Optimization:

[0298] Iteratively optimize the adaptive learning module based on user feedback and system performance evaluation results;

[0299] Continuously optimize processes such as data collection, preprocessing, model training, and algorithm strategy adjustment to improve the system's adaptability and prediction accuracy.

[0300] 6.5 Security and Privacy Protection

[0301] 6.5.1 Data Security:

[0302] Ensure the security of collected data during transmission and storage;

[0303] Use encryption technology to protect sensitive data and prevent data leakage.

[0304] 6.5.2 Privacy Protection:

[0305] Comply with relevant laws and regulations and protect user privacy;

[0306] During the data collection and processing process, anonymization and de-identification measures are taken to ensure that user privacy is not leaked.

[0307] 2. System Implementation Effect

[0308] Through the above specific implementation methods, the intelligent sewage treatment plant operation and management system proposed in the present invention can realize intelligent monitoring, optimized scheduling and energy efficiency management of the sewage treatment process; the system can predict the changing trend of water quality parameters, identify abnormal operating conditions, predict equipment failures, optimize scheduling control parameters, provide remote fault diagnosis and maintenance suggestions, and generate operation reports, etc. These functions help to improve the operating efficiency of sewage treatment plants, reduce energy consumption costs, ensure that effluent water quality meets standards, and extend the service life of equipment.

[0309] In summary, this technical solution is used to solve the problems existing in the current operation and management of sewage treatment plants, such as backward monitoring and control means, insufficient data analysis and decision-making support, and limited remote monitoring and management. By constructing time series prediction models, cluster analysis, classification algorithms, NSGA-II algorithms and other advanced technologies, real-time monitoring, precise control, intelligent analysis and optimized scheduling of the sewage treatment process are realized, effectively solving the problems existing in the current operation and management of sewage treatment plants.

[0310] Specifically, the above time series prediction model, cluster analysis, classification algorithm and non-dominated sorting genetic algorithm II (NSGA-II) with elite strategy are applied in it:

[0311] (1) Time series prediction model predicts water quality change trend

[0312] Technical implementation:

[0313] Data collection: First, collect historical water quality monitoring data of the sewage treatment plant, including key water quality indicators such as COD, BOD, ammonia nitrogen, pH value, etc. at different time points;

[0314] Model construction: Use long short-term memory networks (LSTM), ARIMA models or other time series analysis techniques to build water quality prediction models that can capture the trends and periodicity of water quality indicators over time;

[0315] Prediction and optimization: By inputting current and recent water quality data, the model can predict the trend of water quality changes in the future, thereby providing early warning and decision support for the operation of sewage treatment plants.

[0316] Technical effect:

[0317] Realize real-time monitoring and prediction of water quality change trends, and provide a basis for timely adjustment of treatment strategies.

[0318] (2) Cluster analysis to identify abnormal operating conditions

[0319] Technical implementation:

[0320] Data preprocessing: Clean and standardize the operating data of the sewage treatment plant to eliminate noise and outliers;

[0321] Clustering algorithm: Use K-means, DBSCAN and other clustering algorithms to perform cluster analysis on the data in the processing process;

[0322] Abnormality identification: By comparing the clustering results with the clustering patterns of normal operating conditions, abnormal conditions that deviate from the normal range can be identified.

[0323] Technical effect:

[0324] Timely discover and identify abnormal conditions in the sewage treatment process to provide support for rapid response and troubleshooting.

[0325] (3) Classification algorithm predicts equipment failure

[0326] Technical implementation:

[0327] Feature extraction: Extract key features from equipment operation data, such as current, vibration, temperature, etc.;

[0328] Model training: Use classification algorithms such as support vector machine (SVM) and random forest, combined with historical fault data, to build equipment fault prediction models;

[0329] Failure prediction: By inputting real-time equipment operation data, the model is able to predict the probability and type of equipment failure.

[0330] Technical effect:

[0331] Achieve accurate prediction of equipment failures and provide guidance for preventive maintenance and troubleshooting.

[0332] (4) NSGA-II adjusts wastewater treatment process parameters

[0333] Technical implementation:

[0334] Problem definition: The optimization problem of wastewater treatment process parameters is defined as a multi-objective optimization problem, including improving treatment efficiency, reducing energy consumption and reducing pollutant emissions;

[0335] Algorithm application: NSGA-II algorithm is used to perform non-dominated sorting and elite strategy selection on candidate process parameter combinations, gradually approaching the optimal solution;

[0336] Parameter adjustment: According to the output results of the algorithm, adjust the process parameters of the sewage treatment plant, such as aeration volume, reagent dosage, mixed liquid return ratio, etc.;

[0337] The relationship between algorithm and parameter adjustment:

[0338] The NSGA-II algorithm uses multi-objective optimization to balance multiple objectives such as processing efficiency, energy consumption, and pollutant emissions to find the optimal combination of process parameters.

[0339] The output results of the algorithm directly guide the adjustment of process parameters of the sewage treatment plant and achieve optimal scheduling of the treatment process.

[0340] Technical effect:

[0341] Realize precise control and optimized scheduling of sewage treatment process, improve treatment efficiency and quality, and reduce operating costs and environmental impact.

[0342] Specific application examples:

[0343] Operation and management of a city's intelligent sewage treatment plant

[0344] 1. System Overview and Architecture

[0345] System Overview:

[0346] A certain city sewage treatment plant has introduced an intelligent operation and management system. The system is based on technologies such as the Internet of Things, cloud computing, big data analysis, and artificial intelligence, and has achieved comprehensive monitoring, intelligent analysis, and optimized scheduling of the sewage treatment process. The system consists of multiple functional modules, including an adaptive learning module, an intelligent analysis and prediction module, an optimized scheduling control module, and an energy efficiency management and reporting module. Each module works together to improve the efficiency and quality of sewage treatment and reduce operating costs.

[0347] System architecture:

[0348] Data collection layer: collect water quality data, equipment status data, etc. in real time through sensors, instruments and other equipment.

[0349] Data transmission layer: Use IoT technology to transmit the collected data to the cloud server.

[0350] Data processing and analysis layer: Clean, preprocess, and extract features of data transmitted to the cloud, and use machine learning algorithms for model training and prediction.

[0351] Decision-making and optimization layer: Automatically adjust the parameters and scheduling plans in the sewage treatment process based on the prediction results and optimization strategies.

[0352] User interaction layer: provides a visual interface to display real-time data, prediction results, optimization strategies, etc., to facilitate user monitoring and management.

[0353] 2. System Function Module

[0354] Adaptive learning modules

[0355] Data collection and preprocessing: Collect historical water quality data, equipment status data, etc., and perform data cleaning and preprocessing to remove noise and outliers.

[0356] Feature extraction: Extract key features from the preprocessed data, such as water quality indicators (COD, BOD, ammonia nitrogen, etc.) and equipment operating status (current, voltage, speed, etc.).

[0357] Model training and updating: Use machine learning algorithms (such as neural networks, support vector machines, etc.) to train water quality prediction models and equipment failure prediction models, and update and optimize the models based on new data.

[0358] Strategy adjustment: Automatically adjust the parameters and strategies in the optimization scheduling control module based on the model prediction results.

[0359] Intelligent analysis and prediction module

[0360] Water quality prediction: The water quality prediction model trained by the adaptive learning module predicts the trend of water quality changes in the future based on current water quality data and historical data.

[0361] Equipment failure warning: Use equipment failure prediction models to predict the probability of equipment failure, perform maintenance in advance, and reduce the failure rate.

[0362] Abnormality detection: Real-time monitoring of water quality data and equipment status data, abnormal data detection and alarm, reminding management personnel to deal with it in time.

[0363] Optimize the scheduling control module

[0364] Optimization of chemical dosing: According to the water quality prediction results and the historical data of chemical dosing, the appropriate chemical dosing amount is automatically calculated to achieve accurate dosing and reduce chemical costs.

[0365] Aeration volume optimization: Automatically adjust aeration volume based on water quality indicators and aeration history data to improve treatment efficiency and quality.

[0366] Equipment scheduling optimization: Automatically adjust equipment scheduling plans based on equipment status data and prediction results to ensure efficient operation of equipment.

[0367] Energy efficiency management and reporting module

[0368] Energy consumption monitoring: Real-time monitoring of energy consumption data during sewage treatment, including electricity, water, etc.

[0369] Energy efficiency analysis: The energy efficiency ratio (EER) is calculated based on the total energy consumption and the amount of water treated to evaluate the energy efficiency level of the wastewater treatment process.

[0370] Cost-benefit analysis: Analyze the cost-effectiveness of the wastewater treatment process by calculating the unit treatment cost (UTC) based on the total cost and the amount of water treated.

[0371] Report generation: Automatically generate operation reports, including energy consumption analysis reports, processing efficiency evaluation reports, cost-benefit analysis reports, etc., to provide decision support for managers.

[0372] 3. Specific values ​​and output results

[0373] Energy consumption analysis report

[0374] Data collection: In a certain period of time, the total energy consumption is 1000kWh and the water volume is 500m 3 data.

[0375] Energy efficiency ratio calculation: According to the formula EER = total energy consumption / treated water volume, the energy efficiency ratio is calculated as EER = 1000kWh / 500m 3 =2kWh / m 3 .

[0376] Energy efficiency analysis: Compare with industry standards or historical data to evaluate whether the current energy efficiency level meets the standards or has room for improvement.

[0377] Unit Processing Cost Report

[0378] Data collection: In a certain period of time, the total collection cost is 10,000 yuan and the water treatment volume is 500m 3 data.

[0379] Unit treatment cost calculation: According to the formula UTC = total cost / treated water volume, the unit treatment cost UTC = 10,000 yuan / 500m 3 =20 yuan / m 3 .

[0380] Cost-effectiveness analysis: Compare with industry standards or historical data to analyze whether the current cost-effectiveness level meets the standard or has room for optimization.

[0381] Optimization effect

[0382] Optimization effect of reagent addition: Through the cooperation of the intelligent analysis and prediction module and the optimization scheduling control module, the reagent addition amount is accurately controlled, and the reagent cost is reduced by about 10%, which saves 1,000 yuan of reagent costs.

[0383] Aeration volume optimization effect: By adjusting the aeration volume, the treatment efficiency and quality are improved, while energy consumption is reduced. The energy efficiency ratio is increased by about 5%, which saves 50kWh of electricity.

[0384] Equipment scheduling optimization effect: By optimizing the equipment scheduling plan, the efficient operation of the equipment is ensured, the equipment failure rate is reduced, and the overall operational efficiency is improved.

[0385] IV. Conclusion and Outlook

[0386] The application example of the intelligent sewage treatment plant operation management system shows that by integrating technologies such as the Internet of Things, cloud computing, big data analysis and artificial intelligence, comprehensive monitoring, intelligent analysis and optimized scheduling of the sewage treatment process can be achieved. The system can monitor water quality data, equipment status data and energy consumption in real time, automatically adjust treatment strategies and scheduling plans, improve treatment efficiency and quality, and reduce operating costs. At the same time, through the continuous learning and optimization of the adaptive learning module, the system's adaptive ability and prediction accuracy have been continuously improved.

[0387] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. An intelligent sewage treatment plant operation and management system, characterized in that: The system includes: a) Data acquisition module, which uses the IoT sensor network to collect water quality parameters, flow data, equipment status data and energy consumption data in the sewage treatment process in real time, and integrates these data into the cloud data center; b) Intelligent analysis and prediction module, which uses big data analysis technology and machine learning algorithms to clean, integrate and extract features from the collected data, predict water quality trends by building a time series prediction model, identify abnormal conditions through cluster analysis, predict equipment failures through classification algorithms, and generate early warning signals; c) optimizing the scheduling control module, adjusting the sewage treatment process parameters using a multi-objective optimization algorithm according to the output of the intelligent analysis and prediction module; d) Remote monitoring and maintenance module: By building a remote monitoring platform, the equipment status is monitored in real time, deep learning models are used for remote fault diagnosis, equipment maintenance suggestions are provided, and the operation and maintenance process is simplified; e) Energy efficiency management and reporting module, which automatically generates operation reports based on historical data and real-time data, including energy consumption analysis reports, processing efficiency evaluation reports, and cost-benefit analysis reports. It uses indicators to evaluate operational performance and provide decision support for managers.

2. The intelligent sewage treatment plant operation and management system according to claim 1 is characterized by: The time series prediction model in the intelligent analysis and prediction module specifically adopts a long short-term memory network model, and its model structure includes an input layer, multi-layer LSTM units, a fully connected layer and an output layer, which is used to predict the changing trend of water quality parameters.

3. The intelligent sewage treatment plant operation and management system according to claim 1 is characterized by: The multi-objective optimization algorithm in the optimization scheduling control module adopts a non-dominated sorting genetic algorithm II with an elite strategy, and its objective function includes: Minimize energy consumption: Among them, P i (x) is the power of device i under decision variable x, t i is the running time of device i. Maximize processing efficiency: Outlet water quality meets the standards: in, is the actual value of the j-th water quality parameter under the decision variable x, is the standard value of the jth water quality parameter, m is the number of types of water quality parameters, It is the maximum value among all water quality parameter standard values.

4. The intelligent sewage treatment plant operation and management system according to claim 1 is characterized by: The deep learning model in the remote monitoring and maintenance module uses a convolutional neural network for remote fault diagnosis, and its model structure includes: Input layer: receives the original image or time series data of device status data; Convolution layer: extract feature maps through multiple convolution kernels, each convolution kernel corresponds to a feature; Pooling layer: Use maximum pooling or average pooling to reduce the dimension of the feature map and retain important features, including key water quality index features, equipment operation efficiency features, treatment process stability features, and energy efficiency and cost features; Fully connected layer: Flattens the output of the pooling layer and connects it to the fully connected layer for feature fusion and classification decision; Output layer: Use the softmax function to output the probability distribution of fault diagnosis.

5. The intelligent sewage treatment plant operation and management system according to claim 1 is characterized by: The energy consumption analysis report in the energy efficiency management and reporting module uses energy efficiency ratio and unit processing cost as evaluation indicators, and the calculation formulas are: Energy efficiency ratio: Unit processing cost: Among them, the total cost includes energy cost, chemical cost and maintenance cost.

6. An intelligent sewage treatment plant operation and management system according to any one of claims 1 to 5, characterized in that: The system also includes an adaptive learning module, which dynamically adjusts the model parameters and algorithm strategies in the intelligent analysis and prediction module and optimizes the scheduling and control module according to the actual operation data and feedback of the system, so as to improve the adaptive ability and prediction accuracy of the system.

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