An intelligent aeration optimization control method and system for sewage treatment plants

Abstract

The present invention provides an intelligent aeration optimization control method and system for a sewage treatment plant, relating to the technical field of sewage treatment. The method includes: collecting data on water quality, water volume, and aeration volume in real time; analyzing and processing the data on water quality, water volume, and aeration volume to form a data set of the sewage treatment process, including the change rate of water quality indicators, the fluctuation of water volume, and aeration efficiency information; establishing and training a dynamic mathematical model in the sewage treatment process according to the data set of the sewage treatment process to reflect the relationship between water quality, water volume, and aeration volume. The present invention integrates real-time monitoring, model prediction, and intelligent adjustment technologies, effectively optimizes the aeration control strategy, improves the sewage treatment efficiency, ensures stable water quality, and promotes energy conservation and environmental protection.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and particularly to an intelligent aeration optimization control method and system for sewage treatment plants. Background Art

[0002] Traditional aeration control technologies often rely on manual experience for adjustment and lack precise control means, which results in the difficulty of accurately matching the aeration volume with the actual demand and is prone to the situations of insufficient aeration or over-aeration.

[0003] In the way of manually controlling the start and stop of the blower and adjusting the air volume by adjusting the valve, the operator often adjusts the aeration volume according to experience or a fixed schedule. However, the actual water quality and treatment load are dynamically changing, and the fixed aeration strategy is difficult to meet the actual demand.

[0004] For example, when the sewage load suddenly increases, if the aeration volume is not adjusted in time, it may lead to a decrease in the dissolved oxygen concentration, affect the activity of microorganisms, and further reduce the treatment efficiency.

[0005] In addition, due to the lack of intelligent adjustment means, traditional aeration control technologies often result in high energy consumption. Aeration equipment such as blowers consumes a large amount of electric energy during operation, and the traditional control method is difficult to optimize the energy consumption.

[0006] For example, the blower may operate at a constant speed regardless of how the actual demand changes. This "one-size-fits-all" operation mode causes the blower to still operate at a high speed when the sewage load is low, resulting in energy consumption waste. For example, during nighttime or holidays, the sewage load is usually low, but the blower still operates according to the peak load during the day, resulting in unnecessary energy consumption. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide an intelligent aeration optimization control method and system for sewage treatment plants, which realizes the automation, intelligence, and high efficiency of the sewage treatment process.

[0008] To solve the above technical problem, the technical solution of the present invention is as follows:

[0009] In the first aspect, an intelligent aeration optimization control method for sewage treatment plants, the method includes:

[0010] Collecting water quality, water volume, and aeration volume data in real time;

[0011] Analyzing and processing the water quality, water volume, and aeration volume data to form a sewage treatment process data set, including the change rate of water quality indicators, the fluctuation of water volume, and aeration efficiency information;

[0012] Based on the sewage treatment process data set, establish and train a dynamic mathematical model in the sewage treatment process to reflect the relationship between water quality, water volume and aeration volume;

[0013] Use the dynamic mathematical model to predict the water quality change trend in the future for a period of time to obtain the predicted values of future water quality indicators, the estimated situation of water volume change and the aeration demand adjustment suggestions;

[0014] According to the dynamic mathematical model and real-time water quality and water volume data, adopt the fuzzy control algorithm to automatically adjust the aeration volume and aeration time parameters to obtain the final aeration control strategy;

[0015] Through the real-time monitoring device, conduct real-time monitoring on the adjusted sewage treatment process, and use the butterfly optimization algorithm to conduct intelligent comparison and analysis on the monitoring data, the predicted values of the dynamic mathematical model and the final aeration control strategy; when an abnormal situation occurs, immediately issue an alarm and take corresponding measures to form a feedback closed loop.

[0016] Furthermore, analyze and process the water quality, water volume and aeration volume data to obtain the processed data, including the change rate of water quality indicators, the fluctuation of water volume and the aeration efficiency information, including:

[0017] According to the preset time interval, use the sensor to collect the original data of water quality, water volume and aeration volume in real time;

[0018] According to the original water quality data, calculate the change rate of each water quality indicator through the difference algorithm to reflect the change trend of water quality over time; analyze the fluctuation of influent and effluent water volume, and calculate the standard deviation and variance statistics to measure the stability of water volume; according to the operating parameters of the aerator, including operating power, aeration time and sewage treatment effect, calculate the aeration efficiency index to evaluate the aeration efficiency;

[0019] Integrate the change rate of water quality indicators, the fluctuation of water volume and the aeration efficiency information to form a sewage treatment process data set.

[0020] Furthermore, based on the sewage treatment process data set, establish and train a dynamic mathematical model in the sewage treatment process to reflect the relationship between water quality, water volume and aeration volume, including:

[0021] Extract the characteristic variables reflecting the relationship between water quality, water volume and aeration volume from the original data, including the change rate of water quality indicators, the fluctuation of water volume, and the aeration efficiency;

[0022] Take the time series model as the dynamic mathematical model, and determine the input variables, output variables, parameters and structure of the dynamic mathematical model;

[0023] Divide the characteristic variables into a training set and a validation set, and use the training set to train the dynamic mathematical model. By adjusting the parameters and structure of the dynamic mathematical model, make the dynamic mathematical model reflect the relationship between water quality, water volume, and aeration volume.

[0024] Furthermore, use the dynamic mathematical model to predict the water quality change trend in the future for a period of time, so as to obtain the predicted values of future water quality indicators, the estimated situation of water volume change, and the adjustment suggestions for aeration demand, including:

[0025] Collect the current water quality and water volume related data, and input the current data into the dynamic mathematical model to predict the water quality change trend in the future for a period of time, and obtain the predicted values of future water quality indicators;

[0026] The dynamic mathematical model analyzes the time series characteristics of the water volume data according to the historical water volume data and the current water volume change trend, estimates the water volume change situation in the future for a period of time, and obtains the estimated result of future water volume;

[0027] According to the predicted values of water quality indicators and the estimated results of water volume change, analyze the requirements of the sewage treatment process to obtain the adjustment suggestions for aeration demand.

[0028] Furthermore, according to the dynamic mathematical model and the real-time water quality and water volume data, adopt the fuzzy control algorithm to automatically adjust the aeration volume and aeration time parameters to achieve the control and optimization of the aeration process, including:

[0029] Input the real-time water quality and water volume data collected in real time, the predicted values of future water quality indicators output by the dynamic mathematical model, and the estimated results of future water volume into the fuzzy control algorithm. Through the membership function, convert the real-time water quality and water volume data and the predicted values into fuzzy linguistic variables to form the fuzzified input data;

[0030] According to the preset fuzzy rules, the fuzzy control algorithm infers the fuzzified input data to obtain the fuzzified output data, that is, the adjustment suggestions for aeration volume and aeration time;

[0031] Convert the fuzzified output data into numerical values, that is, the actual adjustment values of aeration volume and aeration time, and automatically adjust the aeration equipment in the sewage treatment plant according to the actual adjustment values of aeration volume and aeration time to obtain the final aeration control strategy.

[0032] Furthermore, through the real-time monitoring device, conduct real-time monitoring on the adjusted sewage treatment process, and use the butterfly optimization algorithm to conduct intelligent comparison and analysis on the monitoring data, the predicted values of the dynamic mathematical model, and the final aeration control strategy; when abnormal situations occur, including water quality exceeding the standard and equipment failure, immediately issue an alarm and take corresponding measures to form a feedback closed loop, including:

[0033] Compare the real-time monitoring data with the predicted values of the dynamic mathematical model, and analyze the differences, including comparing the actual water quality with the predicted water quality, and the actual water volume with the predicted water volume;

[0034] Use the butterfly optimization algorithm to intelligently analyze the differences among the monitoring data, predicted values, and control strategies, and determine the final solution by simulating the foraging behavior of butterflies;

[0035] According to the final solution, automatically adjust the parameters of the dynamic mathematical model, and continuously monitor the abnormal conditions during the sewage treatment process through the real-time monitoring device, including exceeding the water quality standard and equipment failure; when an abnormal condition is detected, an alarm is issued, and corresponding measures are automatically taken according to the type and severity of the abnormal condition, including adjusting the aeration volume, shutting down the faulty equipment, and starting the standby equipment, so as to form a feedback closed loop.

[0036] Furthermore, use the butterfly optimization algorithm to intelligently analyze the differences among the monitoring data, predicted values, and control strategies, and determine the final solution by simulating the foraging behavior of butterflies, including:

[0037] Initialize the butterfly population, each butterfly represents a control strategy adjustment plan, and calculate the fitness score of each butterfly according to the differences among the monitoring data, predicted values, and control strategies;

[0038] Simulate the foraging behavior of butterflies, including local search and global search, and continuously update the positions and fitness scores of the butterfly population through iteration;

[0039] When the iteration reaches the preset number of iterations, determine the final solution from the butterfly population according to the fitness scores of individuals.

[0040] Furthermore, the calculation formula for the fitness score of each butterfly is:

[0041] ;

[0042] Among them, represents the fitness score of each butterfly; represents the total number of monitoring points; represents the index of the monitoring point; represents the actual value of the th monitoring point; represents the predicted value of the th monitoring point; represents the difference weight; represents the total cost; represents the cost weight; represents the total number of state indicators; represents the index of the state indicator; represents the The actual value of a status indicator; Indicating the benchmark value of the status indicator; Indicating the status deviation weight.

[0043] In a second aspect, an intelligent aeration optimization control system for a sewage treatment plant includes:

[0044] A data acquisition module for real-time collecting water quality, water volume, and aeration volume data through sensors at the sewage treatment site;

[0045] A data analysis module for analyzing and processing the collected water quality, water volume, and aeration volume data to form a data set of the sewage treatment process;

[0046] A model establishment module for establishing and training a dynamic mathematical model in the sewage treatment process according to the data set of the sewage treatment process to reflect the relationship between water quality, water volume, and aeration volume;

[0047] A prediction and optimization module for using the dynamic mathematical model to predict the water quality change trend in the future for a period of time, obtaining the predicted values of future water quality indicators, the estimated situation of water volume change, and suggestions for adjusting aeration requirements;

[0048] A control strategy generation module for automatically adjusting the aeration volume and aeration time parameters by using a fuzzy control algorithm according to the dynamic mathematical model and real-time water quality and water volume data to generate a final aeration control strategy;

[0049] A real-time monitoring and feedback module for real-time monitoring the adjusted sewage treatment process through a real-time monitoring device, and intelligently comparing and analyzing the monitoring data with the predicted values of the dynamic mathematical model and the final aeration control strategy by using the butterfly optimization algorithm; when abnormal situations occur, including water quality exceeding the standard and equipment failures, alarms are issued and corresponding measures are taken to form a feedback closed loop.

[0050] In a third aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described above is implemented.

[0051] The above solution of the present invention has at least the following beneficial effects:

[0052] By collecting and analyzing water quality, water volume, and aeration volume data in real time, the real-time state of the sewage treatment process can be grasped more accurately. Using a dynamic mathematical model to predict future water quality change trends makes the aeration control more forward-looking and scientific, effectively improving the sewage treatment efficiency and the water quality compliance rate. By automatically adjusting the aeration volume and aeration time parameters through a fuzzy control algorithm, the aeration process can be precisely controlled according to actual needs, avoiding over-aeration or under-aeration, thereby saving energy and reducing operating costs.

[0053] Establishing and training a dynamic mathematical model can reflect the complex relationship between water quality, water volume, and aeration volume, enhancing the adaptability and stability of the control system. The application of real-time monitoring devices and the butterfly optimization algorithm can detect and handle abnormal situations in a timely manner, such as exceeding water quality standards and equipment failures, ensuring the continuity and reliability of the sewage treatment process. This method integrates various technologies such as data collection, analysis and processing, model prediction, and intelligent control, realizing the intelligent management of the sewage treatment process.

[0054] By intelligently comparing and analyzing the monitoring data with the model prediction values and control strategies, the aeration control strategy can be continuously optimized, improving the intelligent level of sewage treatment. Optimizing the aeration control strategy can reduce energy consumption and pollutant emissions, which is beneficial to environmental protection and sustainable development. Improving the sewage treatment efficiency and the water quality compliance rate helps to meet the increasingly strict environmental protection regulations. Brief Description of the Drawings

[0055] Figure 1 is a schematic flow chart of an intelligent aeration optimization control method for a sewage treatment plant provided by an embodiment of the present invention.

[0056] Figure 2 is a schematic diagram of an intelligent aeration optimization control system for a sewage treatment plant provided by an embodiment of the present invention. Detailed Embodiment

[0057] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0058] As Figure 1 shown, an embodiment of the present invention proposes an intelligent aeration optimization control method for a sewage treatment plant, and the method includes the following steps:

[0059] Step 1, collect water quality, water volume, and aeration volume data in real time;

[0060] Step 2: Analyze and process the water quality, water volume, and aeration volume data to form a sewage treatment process data set, including the change rate of water quality indicators, the fluctuation of water volume, and aeration efficiency information;

[0061] Step 3: Establish and train a dynamic mathematical model in the sewage treatment process based on the sewage treatment process data set to reflect the relationship between water quality, water volume, and aeration volume;

[0062] Step 4: Use the dynamic mathematical model to predict the water quality change trend in the next period of time to obtain the predicted values of future water quality indicators, the estimated situation of water volume change, and the aeration demand adjustment suggestions;

[0063] Step 5: According to the dynamic mathematical model and real-time water quality and water volume data, adopt a fuzzy control algorithm to automatically adjust the aeration volume and aeration time parameters to obtain the final aeration control strategy;

[0064] Step 6: Through a real-time monitoring device, conduct real-time monitoring on the adjusted sewage treatment process, and use the butterfly optimization algorithm to conduct intelligent comparison and analysis on the monitoring data, the predicted values of the dynamic mathematical model, and the final aeration control strategy; when an abnormal situation occurs, immediately issue an alarm and take corresponding measures to form a feedback closed loop.

[0065] In the embodiment of the present invention, through the sensors at the sewage treatment site, real-time data collection ensures that the system can accurately and timely obtain the key information in the sewage treatment process. Through in-depth analysis and processing of the original data, more valuable information can be extracted, such as the change trend of water quality indicators, the fluctuation law of water volume, and the level of aeration efficiency, providing rich data support for the establishment and optimization of the model.

[0066] The establishment of the dynamic mathematical model enables the system to more accurately describe the complex relationships in the sewage treatment process, providing a powerful tool for prediction and optimization. Through training, the model can continuously learn and adapt to the changes in the sewage treatment process, improving the accuracy of prediction and the adaptability of the control strategy. The realization of the prediction function enables the system to perceive the changes in the sewage treatment process in advance, providing decision-making support for operators. Through prediction, the system can timely adjust the aeration control strategy to cope with future water quality changes and water volume fluctuations, ensuring the stable operation of the sewage treatment process.

[0067] The application of the fuzzy control algorithm enables the system to dynamically adjust the aeration volume and aeration time according to real-time data and water quality changes, achieving optimized control of the sewage treatment process. This adaptive control method improves the control accuracy and response speed of the system, reducing energy consumption and treatment costs. Through real-time monitoring devices, the adjusted sewage treatment process is monitored in real time, and the butterfly optimization algorithm is used to intelligently compare and analyze the monitored data with the predicted values of the dynamic mathematical model and the final aeration control strategy; when abnormal situations occur, including exceeding water quality standards and equipment failures, an alarm is immediately issued and corresponding measures are taken to form a feedback closed loop.

[0068] The realization of real-time monitoring and intelligent comparison and analysis functions can promptly detect and handle abnormal situations in the sewage treatment process. Through the butterfly optimization algorithm, the system can deeply analyze the monitored data, find the deviations from the predicted values and control strategies, and provide adjustment suggestions for the operators. At the same time, the establishment of an alarm and emergency handling mechanism ensures that the system can respond quickly when abnormal situations occur, guaranteeing the stable operation of the sewage treatment process.

[0069] In a preferred embodiment of the present invention, step 1, the real-time acquisition of water quality, water volume, and aeration volume data may include:

[0070] In the embodiment of the present invention, water quality, water volume, and aeration volume data are real-time acquired through sensors at the sewage treatment site. Specifically, according to the characteristics and requirements of the sewage treatment process, appropriate water quality sensors (such as dissolved oxygen sensors, pH sensors, turbidity sensors, etc.), water volume sensors (such as flow meters), and aeration volume sensors (such as gas flow meters) are selected to ensure that the selected sensors have characteristics such as high precision, good stability, and strong anti-interference ability to meet the requirements of real-time data acquisition. Determine the installation positions of the sensors at the sewage treatment site to ensure that the sensors can accurately measure the relevant data of water quality, water volume, and aeration volume, and install and debug the sensors according to the installation instructions of the sensors and the requirements of the sewage treatment process.

[0071] Build a data acquisition system, including a data collector, data transmission lines, and data processing software, etc., to ensure that the data acquisition system can communicate stably and reliably with the sensors, realizing the real-time transmission and processing of data. The sensors acquire water quality, water volume, and aeration volume data in real time according to the preset sampling frequency. The acquired data includes the real-time values of water quality indicators (such as dissolved oxygen concentration, pH value, turbidity, etc.), the real-time flow of water volume, and the real-time value of aeration volume, etc. The acquired data is transmitted to the data collector through data transmission lines (such as cables, optical fibers, etc.).

[0072] In a preferred embodiment of the present invention, in step 2, the water quality, water volume, and aeration volume data are analyzed and processed to obtain processed data, including the change rate of water quality indicators, the fluctuation of water volume, and aeration efficiency information, which may include:

[0073] Step 221, at preset time intervals, use sensors to collect the original data of water quality, water volume, and aeration volume in real time;

[0074] Step 222, according to the original water quality data, calculate the change rate of each water quality indicator through the difference algorithm to reflect the change trend of water quality over time; analyze the fluctuation of the influent and effluent water volumes, and calculate standard deviation and variance statistics to measure the stability of water volume; according to the operating parameters of the aerator, including operating power, aeration time, and sewage treatment effect, calculate the aeration efficiency index to evaluate the aeration efficiency;

[0075] Step 223, integrate the change rate of water quality indicators, the fluctuation of water volume, and aeration efficiency information to form a data set of the sewage treatment process.

[0076] In the embodiment of the present invention, a fixed time interval, such as every minute, every five minutes, or every ten minutes, etc., is set in the data acquisition system as the data acquisition frequency of the sensor. The sensor automatically triggers a data acquisition instruction according to the preset time interval and obtains the original data of water quality (such as dissolved oxygen, pH value, turbidity, etc.), water volume (such as influent flow rate, effluent flow rate), and aeration volume (such as aerator operating power, aeration time) in real time. The collected original data is transmitted to the data collector through a data transmission line and stored in a specified database or file.

[0077] Step 222, extract the original water quality data from the database, arrange it in chronological order, and for each water quality indicator (such as dissolved oxygen, pH value, turbidity, etc.), use the difference algorithm to calculate the difference between two adjacent time points, that is, the change rate ; where is the change rate at time ; is the change rate at time . Store the calculated change rate in a new database field, extract the original data of the influent and effluent water volumes from the database, and use statistical software to calculate the standard deviation and variance of the influent and effluent water volumes to measure the stability of water volume, and store the calculation results in a specified database field.

[0078] Extract the original data of the operating parameters of the aerator (such as operating power, aeration time) and the sewage treatment effect (such as BOD / COD removal rate) from the database. According to the definition and calculation formula of aeration efficiency (such as ), calculate the aeration efficiency index; where is the aeration efficiency; is the sewage removal rate; is the operating power of the aerator; is the aeration time, and store the calculation result into the specified database field.

[0079] Step 223: Extract the calculation results of the water quality index change rate, water volume fluctuation, and aeration efficiency index from the database, integrate these data in chronological order to form a sewage treatment process data set containing multiple time points. Store the integrated data set into the specified database table.

[0080] Suppose a sewage treatment plant decides to implement a data analysis system to optimize its treatment process. This system can collect and analyze water quality, water volume, and aeration volume data in real time to provide key treatment efficiency indicators:

[0081] Set the time interval to 5 minutes in the data acquisition system. The sensor is automatically triggered according to this time interval to collect the original data of water quality indicators (such as dissolved oxygen, pH value, turbidity), water volume (inlet flow, outlet flow), and aeration volume (aerator operating power, aeration time) in real time. The collected data is transmitted to the data collector through the data transmission line and stored in the specified database.

[0082] Extract the original water quality data from the database, arrange it in chronological order. For each water quality indicator (such as dissolved oxygen), use the difference algorithm to calculate the difference between two adjacent time points (such as 10:00 and 10:05), that is, the change rate. For example, the change rate of dissolved oxygen at 10:05 = dissolved oxygen(10:05) - dissolved oxygen(10:00), and store the calculated change rate into the new database field. Extract the original data of the influent volume and effluent volume from the database. Use statistical software to calculate the standard deviation and variance of the influent volume and effluent volume to measure the stability of the water volume. For example, calculate the standard deviation and variance of the influent flow to understand the fluctuation of the influent volume. Store the calculation result into the specified database field. Extract the operating parameters of the aerator (such as the operating power is 10 kW and the aeration time is 2 hours) and the sewage treatment effect (such as the BOD removal rate is 80%) from the database. According to the definition and calculation formula of the aeration efficiency, calculate the aeration efficiency index. For example, the aeration efficiency = = 0.04 (or 4%, depending on the unit), and store the calculation result into the specified database field.

[0083] Extract the calculation results of the water quality index change rate, water volume fluctuation, and aeration efficiency index from the database. Integrate these data in chronological order to form a sewage treatment process data set containing multiple time points. For example, create a data set for each day, including the water quality change rate, water volume fluctuation, and aeration efficiency index every 5 minutes within that day, and store the integrated data set in the specified database table.

[0084] By automatically collecting, processing, and analyzing data on water quality, water volume, and aeration volume, manual intervention can be reduced and data processing efficiency can be improved. By calculating the water quality index change rate, water volume fluctuation, and aeration efficiency index, the machine can accurately reflect the real-time state and historical change trend of the sewage treatment process, providing decision-making support for operators. Based on the processed data set, abnormal situations in the sewage treatment process, such as excessive water quality, large water volume fluctuations, or low aeration efficiency, can be detected in a timely manner, and corresponding measures can be taken for adjustment and optimization, thereby improving the sewage treatment effect. Through automated data collection and analysis, the frequency and cost of manual inspections can be reduced, and the operation and maintenance costs can be lowered. At the same time, the optimized sewage treatment process can also reduce energy consumption and chemical agent usage, further reducing the operation costs.

[0085] In a preferred embodiment of the present invention, step 3, establishing and training a dynamic mathematical model in the sewage treatment process according to the sewage treatment process data set to reflect the relationship between water quality, water volume, and aeration volume, may include:

[0086] Step 331, extract characteristic variables reflecting the relationship between water quality, water volume, and aeration volume from the original data, including the change rate of water quality indicators, the fluctuation of water volume, and aeration efficiency;

[0087] Step 332, use the time series model as the dynamic mathematical model, and determine the input variables and output variables of the dynamic mathematical model, the parameters and structure of the dynamic mathematical model;

[0088] Step 333, divide the characteristic variables into a training set and a validation set, and use the training set to train the dynamic mathematical model. By adjusting the parameters and structure of the dynamic mathematical model, the dynamic mathematical model can reflect the relationship between water quality, water volume, and aeration volume.

[0089] In an embodiment of the present invention, raw data is extracted from the sewage treatment process data set, including the change rates of water quality indicators (such as dissolved oxygen, pH value, turbidity, etc.), the fluctuation conditions of water volume (such as standard deviation, variance, etc.), and aeration efficiency. The raw data is cleaned and standardized to remove outliers and missing values to ensure the accuracy and consistency of the data. According to the characteristics and requirements of the sewage treatment process, characteristic variables reflecting the relationship among water quality, water volume, and aeration volume are extracted from the preprocessed data. These characteristic variables may include the change rate sequence of water quality indicators, the statistical quantities of water volume fluctuation conditions, and the index values of aeration efficiency, etc.

[0090] Step 332, according to the dynamic characteristics and data characteristics of the sewage treatment process, select a time series model as the dynamic mathematical model, including the ARIMA model. Determine the input variables and output variables of the dynamic mathematical model. The input variables may include the change rates of water quality indicators, the fluctuation conditions of water volume, etc., while the output variable is the aeration volume. According to the number and characteristics of the input variables, determine the parameters and structure of the dynamic mathematical model, which includes the order of the model, the selection of lag terms, the number of layers and nodes of the neural network, etc.

[0091] Step 333, divide the extracted characteristic variables into a training set and a validation set. The training set is used to train the dynamic mathematical model, while the validation set is used to evaluate the performance and generalization ability of the model. Use the training set to train the dynamic mathematical model. During the training process, by adjusting the parameters and structure of the model, the model can accurately reflect the relationship among water quality, water volume, and aeration volume. Optimization algorithms such as gradient descent and backpropagation can be used to update the parameters of the model during the training process to minimize the loss function. Use the validation set to validate the trained dynamic mathematical model and evaluate the performance and generalization ability of the model. If the performance of the model does not meet the requirements, the performance of the model can be improved by adjusting the parameters and structure of the model and increasing the amount of training data.

[0092] By establishing and training a dynamic mathematical model, the machine can predict and adjust the aeration volume in real time, making the sewage treatment process more efficient and stable. The dynamic mathematical model can reduce the frequency and cost of manual intervention and lower the operation and maintenance costs. At the same time, the optimized sewage treatment process can also reduce energy consumption and chemical agent usage, further reducing the operation costs. The dynamic mathematical model can accurately reflect the relationship among water quality, water volume, and aeration volume, thereby optimizing the sewage treatment process and improving the treatment effect. This helps to reduce pollutant emissions and protect water resources.

[0093] In a preferred embodiment of the present invention, step 4 above, using the dynamic mathematical model to predict the water quality change trend in a future period of time to obtain the predicted values of future water quality indicators, the estimated situation of water volume change, and the aeration demand adjustment suggestions, may include:

[0094] Step 441: Collect the current water quality and quantity related data, and input the current data into the dynamic mathematical model to predict the water quality change trend within a future period of time, and obtain the predicted values of water quality indicators within a future period of time.

[0095] Step 442: The dynamic mathematical model analyzes the time series characteristics of the water quantity data according to the historical water quantity data and the current water quantity change trend, estimates the water quantity change situation within a future period of time, and obtains the estimated result of the future water quantity.

[0096] Step 443: Analyze the requirements of the sewage treatment process based on the predicted values of water quality indicators and the estimated results of water quantity changes, and obtain the adjustment suggestions for aeration demand.

[0097] In the embodiment of the present invention, the current water quality data is collected in real time from sensors, including key indicators such as dissolved oxygen, pH value, turbidity, etc. The collected data is standardized or normalized to better meet the input requirements of the dynamic mathematical model. Time series analysis is performed on the data to extract features such as trends and periodicity. A suitable dynamic mathematical model, such as ARIMA, is selected for predicting the water quality change trend. The model parameters are trained according to the historical water quality data to optimize the model performance. The preprocessed current water quality data is input into the dynamic mathematical model, and the prediction time range is set, such as the next 24 hours, 48 hours, etc., and the model is run to obtain the predicted values of water quality indicators within a future period of time. The prediction results are stored in the database for subsequent analysis and use.

[0098] Step 442: Collect the historical water quantity data and the current water quantity change trend from sensors. Perform time series analysis on the water quantity data to identify features such as trends, periodicity, seasonality, etc. According to the historical water quantity data, calculate statistical quantities such as mean, standard deviation, autocorrelation coefficient, etc. A suitable dynamic mathematical model, such as SARIMA, is selected for estimating the water quantity change. According to the historical water quantity data and the current change trend, the model parameters are trained to optimize the model performance. The current water quantity change trend is input into the dynamic mathematical model, and the estimation time range is set, such as the next week, month, etc., and the model is run to obtain the estimated result of the future water quantity within a future period of time.

[0099] Step 443: Combine the predicted values of water quality indicators and the estimated results of water quantity changes, analyze the requirements of the sewage treatment process, and consider factors such as the operating parameters, treatment effect, and energy consumption of the aerator. According to the change trends of water quality and water quantity, evaluate the aeration demand of the sewage treatment process, set the target value or threshold of aeration efficiency to judge whether the aeration demand needs to be adjusted, and generate the adjustment suggestions for aeration demand according to the demand evaluation results. The suggestions include adjusting the operating power, aeration time, and operating strategy of the aerator.

[0100] Suppose a sewage treatment plant is using a dynamic mathematical model to predict water quality trends, estimate water volume changes, and generate suggestions for adjusting aeration requirements:

[0101] Assume that the current water quality data shows that the dissolved oxygen concentration is 6 mg / L, the pH value is 7.2, and the turbidity is 10 NTU.

[0102] Based on historical water quality data, the dynamic mathematical model predicts that the dissolved oxygen concentration will drop to 5 mg / L within the next 24 hours, the pH value will remain around 7.2, and the turbidity will rise to 15 NTU. Historical water volume data shows that the influent flow rate peaks from 8 am to 10 am every day, and there is also a small peak from 4 pm to 6 pm.

[0103] According to the current water volume change trend, the dynamic mathematical model estimates that the influent flow rate will increase by 10% overall within the next week. Combining the predicted values of water quality indicators and the estimated results of water volume changes, analyze the aeration requirements in the sewage treatment process. Considering the decrease in dissolved oxygen concentration and the increase in turbidity, it is recommended to increase the operating power and aeration time of the aerator to improve the treatment effect.

[0104] The specific suggestions are: adjust the operating power of the aerator from 10 kW to 12 kW, and extend the aeration time from 2 hours to 2.5 hours.

[0105] By predicting water quality trends and water volume changes through a dynamic mathematical model, the parameters of the sewage treatment process can be adjusted in a timely manner to improve treatment efficiency. Adjusting the operating power and aeration time of the aerator according to actual needs can avoid energy consumption and cost expenditures. Generating suggestions for adjusting aeration requirements provides support for the decision-making of the sewage treatment plant, helps optimize the treatment process and improve water quality. By real-time monitoring and predicting changes in water quality, water volume, and aeration requirements, problems can be discovered and solved in a timely manner, improving the stability and reliability of the system.

[0106] In a preferred embodiment of the present invention, in step 5 above, according to the dynamic mathematical model and real-time water quality and water volume data, using a fuzzy control algorithm to automatically adjust the aeration volume and aeration time parameters to achieve the control and optimization of the aeration process, may include:

[0107] Step 551, input the real-time water quality and water volume data collected in real time, and the predicted values of future water quality indicators and the estimated results of future water volume output by the dynamic mathematical model into the fuzzy control algorithm. Through the membership function, convert the real-time water quality and water volume data and the predicted values into fuzzy language variables to form fuzzy input data;

[0108] Step 552, according to the preset fuzzy rules, the fuzzy control algorithm infers the fuzzy input data to obtain fuzzy output data, that is, suggestions for adjusting the aeration volume and aeration time;

[0109] Step 553: Convert the fuzzified output data into numerical values, i.e., the actual adjustment values of the aeration volume and aeration time, and automatically adjust the aeration equipment in the sewage treatment plant according to the actual adjustment values of the aeration volume and aeration time to obtain the final aeration control strategy.

[0110] In the embodiment of the present invention, water quality (such as dissolved oxygen, pH value, turbidity, etc.) and water volume (such as influent flow rate, effluent flow rate) data are collected in real time. The predicted values of future water quality indicators and the estimated results of future water volume are obtained from the dynamic mathematical model, and these data are integrated together as the input of the fuzzy control algorithm. According to the characteristics of the water quality and water volume data, appropriate membership functions are defined. For example, for the dissolved oxygen concentration, three fuzzy linguistic variables, namely "low", "medium", and "high", can be defined and corresponding membership functions are assigned to them. Similar membership functions can also be defined for the predicted values and estimated results to reflect their fuzziness.

[0111] Using the defined membership functions, convert the real-time water quality and water volume data and the predicted values and estimated results into fuzzy linguistic variables.

[0112] For example, if the real-time dissolved oxygen concentration is 5 mg / L, and its membership function is defined as "low" for 0 - 6 mg / L, "medium" for 6 - 8 mg / L, and "high" for 8 - 12 mg / L, then this concentration can be fuzzified as "low" or "medium" (specifically depending on the shape and definition of the membership function).

[0113] Step 552: Define a set of fuzzy rules based on the experience and knowledge of the sewage treatment process. For example, "If the dissolved oxygen concentration is low and the influent flow rate is high, then increase the aeration volume". The fuzzy rules are expressed in the form of "If... then...", where the "If" part is the condition and the "then" part is the conclusion. Use the fuzzy rules to reason about the fuzzified input data. For each fuzzy rule, calculate its satisfaction degree (i.e., the matching degree between the condition part and the input data). According to the satisfaction degree and combined with the conclusion of the rule, obtain the fuzzified output data (i.e., the adjustment suggestions for the aeration volume and aeration time).

[0114] Step 553: Use a defuzzification method (such as the weighted average method) to convert the fuzzified output data into numerical values. For example, if the fuzzified output data is "increase the aeration volume", then after defuzzification, a specific increase amount (such as 10% or 20%) may be obtained. Automatically adjust the aeration equipment in the sewage treatment plant according to the actual adjustment values of the aeration volume and aeration time obtained after defuzzification. For example, increase or decrease the operating power of the aerator, extend or shorten the aeration time, etc. Take the adjusted aeration volume and aeration time as part of the aeration control strategy. Continuously monitor the water quality and water volume data, as well as the prediction results of the dynamic mathematical model, in order to update the aeration control strategy in a timely manner.

[0115] Suppose a sewage treatment plant is using a fuzzy control algorithm to adjust the aeration volume:

[0116] The real-time dissolved oxygen concentration is 5 mg / L, and the influent flow rate is 1000 m³ / h. The dynamic mathematical model predicts that the future dissolved oxygen concentration will drop to 4 mg / L and the influent flow rate will increase to 1200 m³ / h. These data are fuzzified into "low dissolved oxygen concentration" and "high influent flow rate" using membership functions. Define the fuzzy rule: "If the dissolved oxygen concentration is low and the influent flow rate is high, then increase the aeration volume by 20%". Use this rule to reason about the fuzzified input data and obtain a conclusion with a high degree of satisfaction. After defuzzification, a suggestion to increase the aeration volume by 20% is obtained. According to this suggestion, the operating power of the aerator is automatically adjusted to increase it by 20%, forming a new aeration control strategy.

[0117] Automatically adjusting the aeration volume and aeration time through the fuzzy control algorithm can more effectively remove pollutants in the sewage and improve the treatment efficiency. Adjusting the aeration volume according to the real-time water quality and water volume data and the prediction results can avoid unnecessary energy consumption and cost expenditure. The fuzzy control algorithm can handle uncertainties and ambiguities, making the system more adaptable to the complex and changeable sewage treatment environment. Even when the water quality and water volume data fluctuate greatly, the stability and robustness of the system can be maintained. Automatically adjusting the aeration equipment, reducing manual intervention, and improving the automation level of sewage treatment contribute to the intelligent and refined management of the sewage treatment process.

[0118] In a preferred embodiment of the present invention, in step 6 above, the adjusted sewage treatment process is monitored in real time through a real-time monitoring device, and the butterfly optimization algorithm is used to intelligently compare and analyze the monitored data with the predicted values of the dynamic mathematical model and the final aeration control strategy; when an abnormal situation occurs, including exceeding the water quality standard and equipment failure, an alarm is immediately issued and corresponding measures are taken to form a feedback closed loop, which may include:

[0119] Step 661, compare the real-time monitored data with the predicted values of the dynamic mathematical model, analyze the differences, including comparing the actual water quality with the predicted water quality and the actual water volume with the predicted water volume;

[0120] Step 662, use the butterfly optimization algorithm to intelligently analyze the differences between the monitored data, the predicted values, and the control strategy, and determine the final solution by simulating the foraging behavior of butterflies;

[0121] Step 663: Automatically adjust the parameters of the dynamic mathematical model according to the final solution, and continuously monitor abnormal situations during the sewage treatment process through a real-time monitoring device, including exceeding water quality standards and equipment failures; when an abnormal situation is detected, an alarm is issued, and corresponding measures are automatically taken according to the type and severity of the abnormal situation, including adjusting the aeration volume, shutting down the faulty equipment, and starting the standby equipment to form a feedback closed loop.

[0122] In an embodiment of the present invention, actual water quality (such as dissolved oxygen, pH value, turbidity, etc.) and water volume data during the sewage treatment process are obtained through a real-time monitoring device, and predicted water quality and water volume data are obtained from the dynamic mathematical model to ensure that the real-time monitoring data is aligned with the predicted values in the time dimension for accurate comparison. Calculate the differences between the actual water quality and the predicted water quality, such as absolute differences, relative differences, etc., calculate the differences between the actual water volume and the predicted water volume, and analyze the reasons for the differences, such as model errors, external interferences, etc.

[0123] Step 662: Set the parameters of the butterfly optimization algorithm, such as population size, number of iterations, sensing mode, etc., initialize the butterfly population, where each butterfly represents a solution, and according to the differences between the monitoring data, predicted values, and control strategies. Define a fitness function to calculate the fitness value of each butterfly, which reflects the quality of its solution.

[0124] Simulate the foraging behavior of butterflies, including global search and local search. In the global search stage, butterflies move in a better direction according to the concentration of the fragrance (i.e., the fitness value). In the local search stage, butterflies conduct a fine search near their current positions. Iteratively update the positions of the butterflies until the preset number of iterations is reached, and determine the final solution according to the fitness value. The final solution contains the intelligent analysis results of the differences between the monitoring data, predicted values, and control strategies.

[0125] Step 663: Automatically adjust the parameters of the dynamic mathematical model according to the final solution to improve the prediction accuracy of the model. Continuously monitor abnormal situations during the sewage treatment process through a real-time monitoring device, such as exceeding water quality standards, equipment failures, etc. Set an abnormal threshold, and when the monitoring data exceeds the threshold, it is determined as an abnormal situation. When an abnormal situation is detected, an alarm is immediately issued to notify relevant personnel or systems. Corresponding measures are automatically taken according to the type and severity of the abnormal situation. For example, adjust the aeration volume to improve water quality; shut down the faulty equipment to prevent the accident from expanding; start the standby equipment to ensure the continuity of the sewage treatment process.

[0126] Suppose a sewage treatment plant is using the butterfly optimization algorithm to conduct intelligent analysis on monitoring data and predicted values and form a feedback closed loop:

[0127] The real-time monitoring device shows that the current dissolved oxygen concentration is 4 mg / L, while the predicted value of the dynamic mathematical model is 5 mg / L. The actual influent flow rate is 900 m³ / h, and the predicted value is 950 m³ / h. Calculate the difference and analyze the reasons. It may be caused by model errors or external interferences. Initialize the butterfly optimization algorithm, set the population size to 50, and the number of iterations to 100. Define the fitness function, consider the differences between the monitoring data, predicted values, and control strategies, perform iterative optimization, simulate the foraging behavior of butterflies, and find the final solution. The final solution indicates that certain parameters of the dynamic mathematical model need to be adjusted to improve the prediction accuracy. Automatically adjust the parameters of the dynamic mathematical model according to the final solution. The real-time monitoring device detects that the dissolved oxygen concentration drops to 3 mg / L, which is lower than the set threshold of 4 mg / L, and determines that the water quality exceeds the standard. Immediately issue an alarm and automatically increase the aeration volume to improve the water quality, and record the abnormal situation and treatment measures.

[0128] Through real-time monitoring and intelligent analysis, anomalies can be promptly detected and addressed, enhancing the stability of the sewage treatment process. Based on the differences between the monitoring data and predicted values, the parameters of the dynamic mathematical model are automatically adjusted to improve the model's prediction accuracy. The intelligent analysis results and anomaly handling measures provide support for the decision-making of the sewage treatment plant, facilitating the optimization of the treatment process and the improvement of water quality. Measures such as automatically adjusting the aeration volume, shutting down faulty equipment, and starting standby equipment reduce manual intervention, increase the automation level of sewage treatment, contribute to the intelligent and refined management of the sewage treatment process, lower labor costs, and improve work efficiency.

[0129] In another preferred embodiment of the present invention, in step 662, the differences between the monitoring data, predicted values, and control strategies are intelligently analyzed using the butterfly optimization algorithm, and the final solution is determined by simulating the foraging behavior of butterflies, which may include:

[0130] Step 6621, initialize the butterfly population. Each butterfly represents a control strategy adjustment plan, and calculate the fitness score of each butterfly based on the differences between the monitoring data, predicted values, and control strategies. The calculation formula for the fitness score of each butterfly is:

[0131] ;

[0132] Wherein, represents the fitness score of each butterfly; represents the total number of monitoring points; represents the index of the monitoring point; represents the actual value of the th monitoring point; represents the predicted value of the th monitoring point; represents the difference weight; Represents the cost required to implement the control strategy adjustment; Represents the total cost; Represents the cost weight; Represents the total number of status indicators; Represents the index of the status indicator; Represents the actual value of the th status indicator; Represents the benchmark value of the th status indicator; Represents the status deviation weight;

[0133] Step 6622, simulate the foraging behavior of the butterfly, including local search and global search, and continuously update the position and fitness score of the butterfly population through iteration;

[0134] Step 6623, when the iteration reaches the preset number of iterations, determine the final solution from the butterfly population according to the fitness score of each individual.

[0135] In the embodiment of the present invention, a population containing multiple butterflies is randomly generated. Each butterfly represents a control strategy adjustment plan, and the position of each butterfly (i.e., the control strategy parameter) is randomly initialized within the allowed range. For each butterfly, according to the differences between the monitoring data, the predicted values, and the control strategies, the fitness score is calculated using the formula .

[0136] Collect the actual values and the predicted values from each monitoring point, and organize them into a data list to determine the cost required to implement the control strategy adjustment and the total cost. Collect the actual values and the benchmark values

[0137] of the status indicators (for example, the benchmark value can be a preset standard value or a historical average), and organize them into a data list. Set the difference weight = 0.5 (indicating that the influence degree of the difference on the fitness score is 50%), the cost weight = 0.3 (indicating that the influence degree of the cost on the fitness score is 30%), and the status deviation weight

[0138] Traverse all monitoring points, calculate the absolute difference between the actual value and the predicted value of each monitoring point, and sum them up to obtain the total difference. Divide the total difference by the sum of the actual values of all monitoring points to get the difference ratio. Multiply the difference ratio by the difference weight = 0.5 to obtain the impact of the difference on the fitness score. Divide the cost required to implement the control strategy adjustment by the total cost to get the cost ratio. Multiply the cost ratio by the cost weight = 0.3 to obtain the impact of the cost on the fitness score.

[0139] Traverse all status indicators, calculate the absolute difference between the actual value of each status indicator and the reference value (for example, the reference value is set to 100 or determined based on historical data), and sum them up to obtain the total deviation. Divide the total deviation by the sum of the actual values of all status indicators to get the deviation ratio. Multiply the deviation ratio by the status deviation weight = 0.2 to obtain the impact of the deviation of the status indicator on the fitness score. Subtract the above three parts of the impact from 1 to obtain the fitness score of each butterfly In this way, the fitness score of each butterfly comprehensively considers the difference between the monitoring data and the predicted value, the cost of implementing the control strategy adjustment, and the deviation degree of the status indicator.

[0140] Step 6622, each butterfly conducts a fine search near its current position to try to find a better solution. The range and step size of the local search are dynamically adjusted according to the current position, historical position, and fitness score.

[0141] The butterfly moves in a better direction according to the concentration of the fragrance (i.e., the fitness score). The area with a higher fragrance concentration indicates a better solution quality, and the butterfly will move towards these areas, continuously iterating and updating the positions and fitness scores of the butterfly population until the preset number of iterations is reached.

[0142] Step 6623, when the iteration reaches the preset number of iterations, select the final butterfly from the butterfly population according to the individual's fitness score as the final solution. The final solution contains the intelligent analysis results of the differences between the monitoring data and the predicted value, and the control strategy.

[0143] Suppose a sewage treatment plant is using the butterfly optimization algorithm to conduct intelligent analysis on the differences between the monitoring data and the predicted value, and the control strategy:

[0144] Randomly generate a population of 50 butterflies, and each butterfly represents an aeration volume adjustment plan. The aeration volume adjustment range is randomly initialized between [0, 100%]. For each butterfly, calculate the fitness score using the above formula according to the differences between the monitoring data and the predicted value, and the control strategy. Suppose the difference weight = 0.5, cost weight = 0.3, state deviation weight = 0.2. Perform local search and global search, and continuously iterate to update the positions and fitness scores of the butterfly population. Assume the number of iterations is 100 times. When the iteration reaches 100 times, select the butterfly with the highest fitness score from the butterfly population as the final solution. The final solution shows that when the aeration volume is adjusted to 80%, the difference between the monitoring data, the predicted value, and the control strategy is the smallest.

[0145] By intelligently analyzing the differences between the monitoring data, the predicted value, and the control strategy, find the final control strategy adjustment plan to improve the accuracy of the control strategy. Consider the cost required to implement the control strategy adjustment when calculating the fitness score to reduce the implementation cost. The intelligent analysis results and the final solution provide support for the decision-making of the sewage treatment plant, helping to optimize the treatment process and improve the water quality. Automatically perform difference analysis, foraging behavior simulation, and final solution determination to reduce manual intervention and improve the automation level of sewage treatment.

[0146] As Figure 2 shown, an embodiment of the present invention also provides an intelligent aeration optimization control system for a sewage treatment plant, including:

[0147] A data acquisition module for real-time collecting water quality, water volume, and aeration volume data through sensors at the sewage treatment site;

[0148] A data analysis module for analyzing and processing the collected water quality, water volume, and aeration volume data to form a sewage treatment process data set;

[0149] A model establishment module for establishing and training a dynamic mathematical model in the sewage treatment process according to the sewage treatment process data set to reflect the relationship between water quality, water volume, and aeration volume;

[0150] A prediction and optimization module for using the dynamic mathematical model to predict the water quality change trend in the future for a period of time, obtaining the predicted values of future water quality indicators, the estimated situation of water volume change, and aeration demand adjustment suggestions;

[0151] A control strategy generation module for automatically adjusting the aeration volume and aeration time parameters according to the dynamic mathematical model and real-time water quality and water volume data, and using a fuzzy control algorithm to generate the final aeration control strategy;

[0152] A real-time monitoring and feedback module for real-time monitoring the adjusted sewage treatment process through a real-time monitoring device, and intelligently comparing and analyzing the monitoring data with the predicted values of the dynamic mathematical model and the final aeration control strategy; when abnormal situations occur, including water quality exceeding the standard and equipment failure, issue an alarm and take corresponding measures to form a feedback closed loop.

[0153] It should be noted that this system corresponds to the above-mentioned method, and all implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0154] An embodiment of the present invention further provides a computing device, including: a processor and a memory storing a computer program. When the computer program is run by the processor, it executes the method as described above. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0155] An embodiment of the present invention further provides a computer-readable storage medium storing instructions. When the instructions are run on a computer, the computer is caused to execute the method as described above. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0156] The above is the preferred implementation manner of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle described in the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A sewage treatment plant intelligent aeration optimization control method, characterized in that: The method comprises: Through sensors at the sewage treatment site, water quality, water quantity, and aeration volume data are collected in real time; Analyze and process water quality, water volume, and aeration volume data to form a sewage treatment process data set, including the rate of change of water quality indicators, fluctuations in water volume, and aeration efficiency information; Based on the sewage treatment process data set, a dynamic mathematical model of the sewage treatment process is established and trained to reflect the relationship between water quality, water quantity and aeration volume; Use dynamic mathematical models to predict water quality trends over a period of time in the future, so as to obtain the predicted values ​​of future water quality indicators, the estimated changes in water volume, and the suggestions for adjusting aeration demand; According to the dynamic mathematical model and real-time water quality and water quantity data, the fuzzy control algorithm is used to automatically adjust the aeration volume and aeration time parameters to obtain the final aeration control strategy; The adjusted sewage treatment process is monitored in real time by a real-time monitoring device, and the butterfly optimization algorithm is used to intelligently compare and analyze the monitoring data with the predicted values ​​of the dynamic mathematical model and the final aeration control strategy; when an abnormal situation occurs, including water quality exceeding the standard and equipment failure, an alarm is immediately issued and corresponding measures are taken to form a feedback loop, including: comparing the real-time monitoring data with the predicted values ​​of the dynamic mathematical model and analyzing the differences; using the butterfly optimization algorithm to intelligently analyze the differences between the monitoring data and the predicted values ​​and the control strategy, and determining the final solution by simulating the foraging behavior of butterflies, including: initializing the butterfly population, each butterfly represents a control strategy adjustment plan, and calculating the fitness score of each butterfly based on the difference between the monitoring data and the predicted values ​​and the control strategy. The fitness score of each butterfly is calculated as follows: ； in, represents the fitness score of each butterfly; Indicates the total number of monitoring points; Indicates the index of the monitoring point; Indicates The actual value of each monitoring point; Indicates The predicted value of each monitoring point; represents the difference weight; represents the cost required to implement the control strategy adjustment; represents the total cost; represents the cost weight; Indicates the total number of status indicators; The index representing the status indicator; Indicates The actual value of the status indicator; Indicates The baseline value of each status indicator; Represents the state deviation weight.

2. The intelligent aeration optimization control method for a sewage treatment plant according to claim 1 is characterized in that: Analyze and process water quality, water quantity, and aeration volume data to obtain processed data, including the rate of change of water quality indicators, fluctuations in water volume, and aeration efficiency information, including: At preset time intervals, sensors are used to collect raw data on water quality, water quantity, and aeration volume in real time; Based on the original water quality data, the change rate of each water quality index is calculated through the difference algorithm to reflect the change trend of water quality over time; the fluctuation of water inlet and outlet is analyzed, and the standard deviation and variance statistics are calculated to measure the stability of water volume; based on the operating parameters of the aerator, including operating power, aeration time and sewage treatment effect, the aeration efficiency index is calculated to evaluate the aeration efficiency; The water quality index change rate, water volume fluctuation and aeration efficiency information are integrated to form a sewage treatment process data set.

3. The intelligent aeration optimization control method for a sewage treatment plant according to claim 2 is characterized in that: Based on the sewage treatment process data set, a dynamic mathematical model of the sewage treatment process is established and trained to reflect the relationship between water quality, water quantity and aeration volume, including: Extract characteristic variables reflecting the relationship between water quality, water quantity and aeration volume from the original data, including the change rate of water quality indicators, fluctuation of water quantity and aeration efficiency; Take the time series model as a dynamic mathematical model, and determine the input variables and output variables, parameters and structure of the dynamic mathematical model; The characteristic variables are divided into a training set and a validation set, and the training set is used to train the dynamic mathematical model. By adjusting the parameters and structure of the dynamic mathematical model, the dynamic mathematical model can reflect the relationship between water quality, water quantity and aeration amount.

4. The intelligent aeration optimization control method for a sewage treatment plant according to claim 3 is characterized in that: Use dynamic mathematical models to predict water quality trends over a period of time in the future to obtain the predicted values ​​of future water quality indicators, the estimated changes in water volume, and suggestions for adjusting aeration demand, including: Collect current water quality and water quantity data, and input the current data into a dynamic mathematical model to predict the water quality trend in the future, and obtain the predicted value of water quality indicators in the future; The dynamic mathematical model analyzes the time series characteristics of water volume data based on historical water volume data and current water volume change trends, estimates water volume changes in the future, and obtains the estimated results of future water volume; Based on the predicted values ​​of water quality indicators and the estimated results of water volume changes, the needs of the sewage treatment process are analyzed and suggestions for adjusting the aeration demand are obtained.

5. The intelligent aeration optimization control method for a sewage treatment plant according to claim 4 is characterized in that: According to the dynamic mathematical model and real-time water quality and water quantity data, the fuzzy control algorithm is used to automatically adjust the aeration volume and aeration time parameters to achieve control and optimization of the aeration process, including: The real-time water quality and water quantity data collected in real time and the future water quality index prediction value and future water quantity estimation result output by the dynamic mathematical model are input into the fuzzy control algorithm, and the real-time water quality, water quantity data and prediction value are converted into fuzzy language variables through the membership function to form fuzzy input data; According to the preset fuzzy rules, the fuzzy control algorithm infers the fuzzy input data and obtains the fuzzy output data, i.e., the adjustment suggestions for aeration volume and aeration time; The fuzzy output data is converted into numerical values, namely the actual adjustment values ​​of aeration volume and aeration time, and the aeration equipment in the sewage treatment plant is automatically adjusted according to the actual adjustment values ​​of aeration volume and aeration time to obtain the final aeration control strategy.

6. The intelligent aeration optimization control method for a sewage treatment plant according to claim 5 is characterized in that: Analyze discrepancies, including comparing actual water quality with predicted water quality and actual water quantity with predicted water quantity; The adjusted sewage treatment process is monitored in real time through the real-time monitoring device, and the butterfly optimization algorithm is used to intelligently compare and analyze the monitoring data with the predicted values ​​of the dynamic mathematical model and the final aeration control strategy; When an abnormal situation occurs, including water quality exceeding the standard or equipment failure, an alarm is immediately issued and corresponding measures are taken to form a feedback loop, including: According to the final solution, the parameters of the dynamic mathematical model are automatically adjusted, and the abnormal conditions in the sewage treatment process are continuously monitored through real-time monitoring devices, including water quality exceeding the standard and equipment failure. When an abnormal situation is detected, an alarm is issued, and according to the type and severity of the abnormal situation, corresponding measures are automatically taken, including adjusting the aeration volume, shutting down faulty equipment, and starting backup equipment, so as to form a feedback closed loop.

7. The intelligent aeration optimization control method for a sewage treatment plant according to claim 6 is characterized in that: The butterfly optimization algorithm is used to intelligently analyze the differences between monitoring data and predicted values ​​and control strategies, and the final solution is determined by simulating the foraging behavior of butterflies, including: Simulate the foraging behavior of butterflies, including local search and global search, and continuously update the location and fitness score of the butterfly population through iteration; When the iteration reaches the preset number of iterations, the final solution is determined from the butterfly population based on the fitness scores of the individuals.

8. An intelligent aeration optimization control system for a sewage treatment plant, the system implementing the method according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, used to collect water quality, water quantity and aeration volume data in real time through sensors at the sewage treatment site; The data analysis module is used to analyze and process the collected water quality, water quantity and aeration volume data to form a sewage treatment process data set; Model building module, used to build and train the dynamic mathematical model of the sewage treatment process based on the sewage treatment process data set to reflect the relationship between water quality, water quantity and aeration volume; The prediction and optimization module is used to use dynamic mathematical models to predict the trend of water quality changes in the future, obtain the predicted values ​​of future water quality indicators, the estimated changes in water volume, and the suggestions for adjusting aeration demand; The control strategy generation module is used to automatically adjust the aeration volume and aeration time parameters according to the dynamic mathematical model and real-time water quality and water quantity data, and generate the final aeration control strategy by using the fuzzy control algorithm; The real-time monitoring and feedback module is used to monitor the adjusted sewage treatment process in real time through the real-time monitoring device, and use the butterfly optimization algorithm to intelligently compare and analyze the monitoring data with the predicted values ​​of the dynamic mathematical model and the final aeration control strategy; when abnormal conditions occur, including water quality exceeding the standard and equipment failure, an alarm is issued and corresponding measures are taken to form a feedback closed loop.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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