Accurate aeration control method and system for sewage treatment plant, and electronic equipment

Abstract

The invention relates to the technical field of control scheme design for precise aeration of a sewage treatment plant, in particular to a control method and system for precise aeration of a sewage treatment plant and electronic equipment. The control method comprises the following steps: acquiring real-time operation data of a sewage treatment process through the data acquisition device, constructing an accurate aeration mathematical model and a mechanism model, performing data mining and analysis by using the control module, calculating the optimal aeration rate, and dynamically adjusting the operation state of the aeration equipment. The system has real-time data processing and control functions. According to the system, the aeration energy consumption is reduced, the effluent quality is guaranteed to reach the standard, the system has the characteristics of intelligence, automation, energy conservation and consumption reduction, through deep integration of multidisciplinary technologies, the intelligent level of aeration control of a sewage treatment plant is remarkably improved, an economic, efficient and reliable solution is provided for the industry, and the system is worthy of popularization and application. The method has wide application prospects and remarkable economic benefits.

Description

Technical Field

[0001] This invention relates to the field of control scheme design technology for precision aeration in wastewater treatment plants, specifically to a control method and system for precision aeration in wastewater treatment plants, and electronic equipment. Background Technology

[0002] Wastewater treatment is a crucial component of modern urban management, and its core component—aeration—accounts for over 65% of the total energy consumption of wastewater treatment plants. Therefore, achieving precise aeration and reducing energy consumption has become a focal point of industry attention. Traditional aeration control methods primarily focus on dissolved oxygen (DO) as the core control objective, adjusting the aeration equipment's operation by setting a fixed DO concentration threshold and utilizing PID control algorithms. However, this method suffers from the following significant problems:

[0003] Ignoring the coupling of multiple factors: Traditional methods only focus on DO concentration and fail to comprehensively consider the dynamic coupling relationship between multiple variables such as influent water quality, sludge concentration, and aeration flow rate, resulting in low aeration efficiency.

[0004] Serious energy waste: Extensive aeration control methods usually come at the cost of excessive aeration to ensure stable DO concentration, but this will result in redundant aeration volume and increase energy consumption.

[0005] Unstable effluent quality: Traditional methods are difficult to adjust the aeration rate in real time when operating conditions change (such as fluctuations in influent quality or changes in sludge concentration), resulting in effluent quality that does not meet standards.

[0006] Poor model adaptability: Most existing aeration models are based on linear assumptions, which cannot cope with the nonlinear and large lag characteristics in the wastewater treatment process, resulting in low model prediction accuracy and poor control effect.

[0007] Therefore, the existing technology still needs further development. Summary of the Invention

[0008] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide a precise aeration control method, system, and electronic equipment for wastewater treatment plants to solve the problems existing in the prior art.

[0009] To achieve the above-mentioned technical objectives, according to a first aspect of the present invention, the present invention provides a method for controlling precise aeration in a wastewater treatment plant, comprising:

[0010] S100. Acquire real-time operational data of the wastewater treatment process through a data acquisition device;

[0011] S200. The control module is used to mine and analyze the operating data, and a precise aeration mathematical model is constructed based on historical data. A precise aeration mechanism model is also established in combination with the sewage treatment process flow.

[0012] S300. Based on the mathematical model and the mechanism model, the optimal aeration rate is calculated through algorithm iteration; according to the calculated optimal aeration rate, the operating status of the aeration equipment is dynamically adjusted through the automatic control system.

[0013] S400 monitors effluent water quality and operational data in real time, and dynamically adjusts model parameters to adapt to changes in operating conditions.

[0014] Specifically, the real-time operational data includes at least one of the following:

[0015] Influent water quality parameters, dissolved oxygen concentration, aeration rate, sludge concentration, and effluent water quality parameters.

[0016] Specifically, the control module includes a data mining module, a model orchestration module, and an instruction issuance module. The data mining module is used to analyze historical and real-time data, the model orchestration module is used to generate and optimize the aeration model, and the instruction issuance module is used to issue the calculated optimal aeration rate to the edge control system.

[0017] Specifically, the construction of a precise aeration mathematical model based on historical data includes:

[0018] Key features were extracted from the operational data, including the dynamic change rate of DO concentration, the fluctuation range of aeration volume, and the cumulative trend of sludge concentration.

[0019] A mathematical model was constructed using a combination of multiple linear regression and time series analysis.

[0020] The input features of the multiple linear regression model are DO concentration, aeration rate, and sludge concentration. The output is the optimized value of aeration rate. The weight parameters are optimized using the least squares method, and the specific formula is as follows:

[0021]

[0022] Among them, w1, w2, ..., w n is the feature weight, and b is the bias term, which is obtained through training with historical data.

[0023] Specifically, the precise aeration mechanism model combines mathematical modeling of the wastewater treatment plant process with consideration of aeration nonlinearity and large hysteresis characteristics to ensure that the effluent quality meets standards even under complex operating conditions.

[0024] Specifically, the dynamic adjustment of the aeration equipment's operating status includes adjusting the frequency of the aeration fan or the valve opening to control the aeration volume.

[0025] Specifically, based on the nonlinear characteristics of the aeration process, a dynamic relationship between aeration rate and DO concentration is established:

[0026]

[0027] Among them, C DO Q represents the DO concentration. a ir represents the aeration rate, S represents the sludge concentration, and k1, k2, and k3 are dynamic parameters measured in the experiment.

[0028] Specifically, the dynamically corrected model parameters include online updates of the weight parameters of the mathematical model and the mechanistic model.

[0029] According to a second aspect of the present invention, a control system for precise aeration in a wastewater treatment plant is provided, comprising:

[0030] Data acquisition device, used to acquire real-time operational data of the wastewater treatment process;

[0031] The control module is used to mine and analyze the operating data, construct a precise aeration mathematical model based on historical data, and establish a precise aeration mechanism model in conjunction with the wastewater treatment process flow; it is used to calculate the optimal aeration rate through algorithm iteration based on the mathematical model and mechanism model; it is used to dynamically adjust the operating status of the aeration equipment through the automatic control system according to the calculated optimal aeration rate; and it is used to monitor the effluent water quality and operating data in real time and dynamically correct the model parameters to adapt to changes in operating conditions.

[0032] According to a third aspect of the present invention, an electronic device is provided, comprising: a memory; and a processor, wherein the memory stores computer-readable instructions, which, when executed by the processor, implement the above-described control method for precise aeration in a wastewater treatment plant.

[0033] Beneficial effects:

[0034] This invention proposes a precise aeration control method, system, and electronic equipment for wastewater treatment plants. By combining big data analysis, artificial intelligence modeling, and a cloud-edge collaborative architecture, it significantly improves the accuracy and efficiency of aeration control, and has the following beneficial effects:

[0035] 1. By leveraging cloud-based big data analysis and artificial intelligence modeling, the aeration rate is dynamically optimized, significantly reducing aeration energy consumption while ensuring effluent quality meets standards. Tests show that compared to traditional methods, this invention can save an average of 30% on aeration volume, resulting in annual energy savings of up to millions of yuan.

[0036] 2. This invention balances energy consumption optimization and water quality compliance in its design. By monitoring key parameters such as DO concentration, influent water quality, and sludge concentration in real time, it dynamically adjusts the aeration strategy to ensure that the effluent water quality consistently meets industry standards, thus solving the problem of sacrificing one aspect for another in traditional methods.

[0037] 3. This invention combines the dynamic nonlinear characteristics of the aeration process to construct a precise aeration mechanism model with multivariate coupling, and improves the model's adaptability through online learning and dynamic parameter correction. Test results show that this invention can maintain efficient control even under complex operating conditions such as fluctuations in influent water quality and changes in sludge concentration, demonstrating significant technical advantages.

[0038] 4. Through a cloud-edge collaborative architecture, this invention supports horizontal expansion and vertical iterative optimization across multiple wastewater treatment plants, providing flexible scalability. For example, after applying this invention, a water utilities group successfully replicated its precision aeration model to multiple water plants, significantly improving the group's overall management level.

[0039] 5. This invention automates and intelligentizes the aeration process, reducing reliance on manual intervention. Users can achieve efficient management through a minimally staffed mode. Taking a water plant as an example, the implementation of this invention has saved nearly 500,000 yuan in labor costs annually.

[0040] In summary, this invention significantly improves the intelligence level of aeration control in wastewater treatment plants through the deep integration of multidisciplinary technologies, providing the industry with an economical, efficient, and reliable solution with broad application prospects and significant economic benefits. Attached Figure Description

[0041] Figure 1 This is a schematic flowchart of the precise aeration control method for wastewater treatment plants provided in a specific embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the system composition of the precision aeration control system for a wastewater treatment plant provided in a specific embodiment of the present invention. Detailed Implementation

[0043] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments in this application, other similar embodiments obtained by those skilled in the art without creative effort should all fall within the scope of protection of this application. Furthermore, directional terms mentioned in the following embodiments, such as "up," "down," "left," and "right," are only for reference to the directions in the accompanying drawings; therefore, the directional terms used are for illustrative purposes and not for limiting the invention.

[0044] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments.

[0045] Please see Figure 1 This invention provides a method for controlling precise aeration in a wastewater treatment plant, comprising:

[0046] S100. Acquire real-time operational data of the wastewater treatment process through a data acquisition device.

[0047] Specifically, the real-time operational data includes at least one of the following:

[0048] Influent water quality parameters, dissolved oxygen concentration, aeration rate, sludge concentration, and effluent water quality parameters.

[0049] It should be noted that the data acquisition device is used to obtain real-time operational data of the wastewater treatment process, including influent water quality parameters, dissolved oxygen concentration, aeration rate, sludge concentration, and effluent water quality parameters.

[0050] Sensor deployment: Sensors are deployed at key nodes in the wastewater treatment plant, such as the inlet, aeration tank, sedimentation tank, and outlet.

[0051] Influent water quality parameters, such as chemical oxygen demand (COD), biochemical oxygen demand (BOD), and suspended solids (SS), are measured using a multi-parameter water quality sensor with a sampling frequency of once per minute.

[0052] Dissolved oxygen concentration (DO): A fluorescence DO sensor is used, with a measurement accuracy of ≤±0.2mg / L. The sensor is installed at multiple points in the aeration tank, and the sampling frequency is 1 time / 30 seconds.

[0053] Aeration rate: Measured by a flow meter installed at the outlet of the aeration blower, with an accuracy of ±2% and a sampling frequency of 1 time / 10 seconds.

[0054] Sludge concentration (MLSS): An optical turbidity sensor is used, with a measurement range of 0-10 g / L and an accuracy of ±0.1 g / L, installed inside the aeration tank.

[0055] Effluent water quality parameters include COD, BOD, ammonia nitrogen (NH3-N), and total phosphorus (TP), which are measured using an online water quality analyzer at a sampling frequency of once per hour.

[0056] Data preprocessing: The sensor data is cleaned, denoised, and interpolated. After removing outliers, the data is smoothed using a sliding window averaging method with a sliding window size of 5.

[0057] S200. The control module is used to mine and analyze the operating data, and a precise aeration mathematical model is constructed based on historical data. A precise aeration mechanism model is also established in combination with the sewage treatment process.

[0058] Specifically, the control module includes a data mining module, a model orchestration module, and an instruction issuance module. The data mining module is used to analyze historical and real-time data, the model orchestration module is used to generate and optimize the aeration model, and the instruction issuance module is used to issue the calculated optimal aeration rate to the edge control system.

[0059] Specifically, the construction of a precise aeration mathematical model based on historical data includes:

[0060] Key features were extracted from the operational data, including the dynamic change rate of DO concentration, the fluctuation range of aeration volume, and the cumulative trend of sludge concentration.

[0061] A mathematical model was constructed using a combination of multiple linear regression and time series analysis.

[0062] The input features of the multiple linear regression model are DO concentration, aeration rate, and sludge concentration. The output is the optimized value of aeration rate. The weight parameters are optimized using the least squares method, and the specific formula is as follows:

[0063]

[0064] Among them, w1, w2, ..., w n Here, b represents the feature weights, and b represents the bias term, which are obtained through training with historical data.

[0065] The time series analysis model was used to predict the dynamic changes in DO concentration. The difference order p=1, the autoregression order q=1, and the stationary time series analysis window size was 60 minutes.

[0066] Specifically, the precise aeration mechanism model combines mathematical modeling of the wastewater treatment plant process with consideration of aeration nonlinearity and large hysteresis characteristics to ensure that the effluent quality meets standards even under complex operating conditions.

[0067] Specifically, the method for constructing the precise aeration mechanism model includes:

[0068] Model orchestration module: Combines wastewater treatment process flow to construct a precise aeration mechanism model.

[0069] Mechanism Model: Based on the nonlinear characteristics of the aeration process, a dynamic relationship between aeration rate and DO concentration is established:

[0070]

[0071] Among them, C DO Q represents the DO concentration. a ir represents the aeration rate, S represents the sludge concentration, and k1, k2, and k3 are dynamic parameters measured experimentally, with values ​​of k1 = 0.05 mg / L / min, k2 = 0.02 min, and k3, respectively. -1 k3 = 0.01 mg / L / min. The rationale for choosing these thresholds is based on experimental data analysis to ensure the applicability of the model under aeration nonlinearity and large hysteresis conditions.

[0072] Command issuance module: issues optimized model parameters and control commands to edge control devices. The communication protocol uses MQTT, with a data transmission frequency of once per minute to ensure real-time performance and low latency.

[0073] S300. Based on the mathematical model and the mechanism model, the optimal aeration rate is calculated through algorithm iteration; according to the calculated optimal aeration rate, the operating status of the aeration equipment is dynamically adjusted through the automatic control system.

[0074] Furthermore, the control module dynamically adjusts the operating status of the aeration equipment based on the calculated optimal aeration rate.

[0075] Control algorithm: A fuzzy PID control algorithm is adopted, which dynamically adjusts the frequency of the aeration fan by combining the deviation between the optimal aeration rate and the real-time DO concentration.

[0076] Input variables: the deviation e between the actual DO concentration and the target value (set to 2 mg / L) and the rate of change of deviation Δe.

[0077] Fuzzy rules:

[0078] If e>0 and Δe>0, then increase the aeration rate;

[0079] If e < 0 and Δe < 0, then reduce the aeration rate.

[0080] Control output: The frequency of the aeration blower is adjusted in 1Hz increments, with an adjustment range of 25-100Hz.

[0081] Equipment linkage: The aeration blower and valves are linked for control. When the aeration volume increases or decreases, the opening of the air inlet valve is adjusted synchronously to avoid over-aeration or under-aeration.

[0082] S400 monitors effluent water quality and operational data in real time, and dynamically adjusts model parameters to adapt to changes in operating conditions.

[0083] Specifically, the dynamic adjustment of the aeration equipment's operating status includes adjusting the frequency of the aeration fan or the valve opening to control the aeration volume.

[0084] Specifically, the real-time monitoring of effluent water quality and operational data is achieved through online sensors.

[0085] Specifically, the dynamically corrected model parameters include online updates of the weight parameters of the mathematical model and the mechanistic model.

[0086] Specifically, the method for dynamically adjusting model parameters to adapt to changes in operating conditions and continuously optimizing the model through online learning includes:

[0087] Online learning algorithm: The recursive least squares (RLS) method is used to dynamically update the weight parameters of the mathematical model.

[0088] Iteration formula:

[0089] Θ(k)=Θ(k-1)+P(k)·φ(k)·(y(k)-φ T (k)·Θ(k-1))

[0090] Where Θ(k) is the weighting parameter, P(k) is the gain matrix, φ(k) is the input feature vector, and y(k) is the actual measured DO concentration. The update frequency is 1 time / 5 minutes.

[0091] Dynamic threshold setting: The threshold of the target DO concentration is automatically adjusted according to changes in operating conditions, with a fluctuation range set at 1.8-2.2 mg / L, ensuring that the effluent water quality meets the standards while reducing energy consumption.

[0092] Furthermore, this invention also achieves remote distribution and horizontal scaling of model parameters through the design of a cloud-edge architecture, supporting the full lifecycle management of multiple wastewater treatment plants.

[0093] Cloud server: Deploys a Hadoop distributed computing platform to store and process massive amounts of running data; uses the TensorFlow framework to build machine learning models, with the model training dataset consisting of one year of historical data and 1024 training samples per batch.

[0094] Edge control equipment: Deploy industrial-grade edge computing gateways to support data acquisition, local model inference, and device control.

[0095] Horizontal scaling: Enables rapid replication and deployment of models and control systems through containerization technologies (such as Docker), reducing deployment time to within 30 minutes.

[0096] Vertical iteration: The model parameters are updated every quarter, and incremental training is carried out based on the latest data to ensure the model's adaptability to changes in working conditions.

[0097] 6. System Integration and Verification

[0098] Scenario verification: A three-month on-site test was conducted at a wastewater treatment plant of a water group, recording key indicators such as aeration volume, DO concentration, and energy consumption.

[0099] Test results: Aeration volume was reduced by an average of 30%, dissolved oxygen concentration was optimized by 22%, and energy consumption was reduced by 25%.

[0100] Data security: AES-256 symmetric encryption algorithm is used to encrypt data transmission, and digital signatures are used to verify data integrity, ensuring data security and reliability during the expansion of multiple water plants.

[0101] The reasons for setting the preferred values ​​in this invention are as follows:

[0102] Weight parameters (e.g., k1, k2, k3): determined based on experimental data analysis to ensure the applicability of the model under aeration nonlinearity and large hysteresis conditions.

[0103] Target DO concentration threshold (1.8-2.2 mg / L): Meets industry effluent water quality standards while avoiding energy waste caused by excessive aeration.

[0104] Model update frequency (1 time / 5 minutes): Achieving a balance between real-time performance and computational efficiency to ensure timely dynamic correction.

[0105] Extended technologies (such as containerization): Reduce deployment time and adapt to the management needs of multiple water plants.

[0106] It is understood that this invention proposes a control method, system, and electronic equipment for precise aeration in wastewater treatment plants. By combining big data analysis, artificial intelligence modeling, and a cloud-edge collaborative architecture, it significantly improves the accuracy and efficiency of aeration control, and has the following beneficial effects:

[0107] 1. By leveraging cloud-based big data analysis and artificial intelligence modeling, the aeration rate is dynamically optimized, significantly reducing aeration energy consumption while ensuring effluent quality meets standards. Tests show that compared to traditional methods, this invention can save an average of 30% on aeration volume, resulting in annual energy savings of up to millions of yuan.

[0108] 2. This invention balances energy consumption optimization and water quality compliance in its design. By monitoring key parameters such as DO concentration, influent water quality, and sludge concentration in real time, it dynamically adjusts the aeration strategy to ensure that the effluent water quality consistently meets industry standards, thus solving the problem of sacrificing one aspect for another in traditional methods.

[0109] 3. This invention combines the dynamic nonlinear characteristics of the aeration process to construct a precise aeration mechanism model with multivariate coupling, and improves the model's adaptability through online learning and dynamic parameter correction. Test results show that this invention can maintain efficient control even under complex operating conditions such as fluctuations in influent water quality and changes in sludge concentration, demonstrating significant technical advantages.

[0110] 4. Through a cloud-edge collaborative architecture, this invention supports horizontal expansion and vertical iterative optimization across multiple wastewater treatment plants, providing flexible scalability. For example, after applying this invention, a water utilities group successfully replicated its precision aeration model to multiple water plants, significantly improving the group's overall management level.

[0111] 5. This invention automates and intelligentizes the aeration process, reducing reliance on manual intervention. Users can achieve efficient management through a minimally staffed mode. Taking a water plant as an example, the implementation of this invention has saved nearly 500,000 yuan in labor costs annually.

[0112] In summary, this invention significantly improves the intelligence level of aeration control in wastewater treatment plants through the deep integration of multidisciplinary technologies, providing the industry with an economical, efficient, and reliable solution with broad application prospects and significant economic benefits.

[0113] Please see Figure 2 The present invention provides another embodiment, which provides a control system for precise aeration in a wastewater treatment plant. The control system for precise aeration in a wastewater treatment plant includes:

[0114] The data acquisition device 100 is used to acquire real-time operational data of the wastewater treatment process.

[0115] The control module 200 is used to mine and analyze the operating data, construct a precise aeration mathematical model based on historical data, and establish a precise aeration mechanism model in conjunction with the wastewater treatment process; it is used to calculate the optimal aeration rate through algorithm iteration based on the mathematical model and mechanism model; it is used to dynamically adjust the operating status of the aeration equipment through the automatic control system according to the calculated optimal aeration rate; and it is used to monitor the effluent water quality and operating data in real time and dynamically correct the model parameters to adapt to changes in operating conditions.

[0116] It should be noted that this invention proposes a precise aeration control method, system, and electronic equipment for wastewater treatment plants. By combining big data analysis, artificial intelligence modeling, and a cloud-edge collaborative architecture, it significantly improves the accuracy and efficiency of aeration control, and has the following beneficial effects:

[0117] 1. By leveraging cloud-based big data analysis and artificial intelligence modeling, the aeration rate is dynamically optimized, significantly reducing aeration energy consumption while ensuring effluent quality meets standards. Tests show that compared to traditional methods, this invention can save an average of 30% on aeration volume, resulting in annual energy savings of up to millions of yuan.

[0118] 2. This invention balances energy consumption optimization and water quality compliance in its design. By monitoring key parameters such as DO concentration, influent water quality, and sludge concentration in real time, it dynamically adjusts the aeration strategy to ensure that the effluent water quality consistently meets industry standards, thus solving the problem of sacrificing one aspect for another in traditional methods.

[0119] 3. This invention combines the dynamic nonlinear characteristics of the aeration process to construct a precise aeration mechanism model with multivariate coupling, and improves the model's adaptability through online learning and dynamic parameter correction. Test results show that this invention can maintain efficient control even under complex operating conditions such as fluctuations in influent water quality and changes in sludge concentration, demonstrating significant technical advantages.

[0120] 4. Through a cloud-edge collaborative architecture, this invention supports horizontal expansion and vertical iterative optimization across multiple wastewater treatment plants, providing flexible scalability. For example, after applying this invention, a water utilities group successfully replicated its precision aeration model to multiple water plants, significantly improving the group's overall management level.

[0121] 5. This invention automates and intelligentizes the aeration process, reducing reliance on manual intervention. Users can achieve efficient management through a minimally staffed mode. Taking a water plant as an example, the implementation of this invention has saved nearly 500,000 yuan in labor costs annually.

[0122] In summary, this invention significantly improves the intelligence level of aeration control in wastewater treatment plants through the deep integration of multidisciplinary technologies, providing the industry with an economical, efficient, and reliable solution with broad application prospects and significant economic benefits.

[0123] In a preferred embodiment, this application also provides an electronic device, the electronic device comprising:

[0124] The computer device includes a memory and a processor, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the control method for precise aeration in a wastewater treatment plant. The computer device can be broadly categorized as a server, terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, communication interface, etc., connected via a system bus. The processor of the computer device can be used to provide the necessary computing, processing, and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system, computer programs, etc. The internal memory can provide an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface and communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, it performs the steps of the method of the present invention.

[0125] This invention can be implemented as a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the steps of the methods of embodiments of the invention to be performed. In one embodiment, the computer program is distributed across multiple network-coupled computer devices or processors, such that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be executed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be executed by one or more computer devices or processors, and one or more other method steps / operations may be executed by one or more other computer devices or processors. One or more computer devices or processors may execute a single method step / operation, or execute two or more method steps / operations.

[0126] Those skilled in the art will understand that the method steps of this invention can be performed by a computer program instructing related hardware, such as a computer device or processor, to perform the steps of this invention when executed. Depending on the context, any references herein to memory, storage, databases, or other media may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0127] It is understood that this invention proposes a control method, system, and electronic equipment for precise aeration in wastewater treatment plants. By combining big data analysis, artificial intelligence modeling, and a cloud-edge collaborative architecture, it significantly improves the accuracy and efficiency of aeration control, and has the following beneficial effects:

[0128] 1. By leveraging cloud-based big data analysis and artificial intelligence modeling, the aeration rate is dynamically optimized, significantly reducing aeration energy consumption while ensuring effluent quality meets standards. Tests show that compared to traditional methods, this invention can save an average of 30% on aeration volume, resulting in annual energy savings of up to millions of yuan.

[0129] 2. This invention balances energy consumption optimization and water quality compliance in its design. By monitoring key parameters such as DO concentration, influent water quality, and sludge concentration in real time, it dynamically adjusts the aeration strategy to ensure that the effluent water quality consistently meets industry standards, thus solving the problem of sacrificing one aspect for another in traditional methods.

[0130] 3. This invention combines the dynamic nonlinear characteristics of the aeration process to construct a precise aeration mechanism model with multivariate coupling, and improves the model's adaptability through online learning and dynamic parameter correction. Test results show that this invention can maintain efficient control even under complex operating conditions such as fluctuations in influent water quality and changes in sludge concentration, demonstrating significant technical advantages.

[0131] 4. Through a cloud-edge collaborative architecture, this invention supports horizontal expansion and vertical iterative optimization across multiple wastewater treatment plants, providing flexible scalability. For example, after applying this invention, a water utilities group successfully replicated its precision aeration model to multiple water plants, significantly improving the group's overall management level.

[0132] 5. This invention automates and intelligentizes the aeration process, reducing reliance on manual intervention. Users can achieve efficient management through a minimally staffed mode. Taking a water plant as an example, the implementation of this invention has saved nearly 500,000 yuan in labor costs annually.

[0133] In summary, this invention significantly improves the intelligence level of aeration control in wastewater treatment plants through the deep integration of multidisciplinary technologies, providing the industry with an economical, efficient, and reliable solution with broad application prospects and significant economic benefits.

[0134] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contradict each other. The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made according to the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for controlling precise aeration in a wastewater treatment plant, characterized in that, The method includes: S100. Acquire real-time operational data of the wastewater treatment process through a data acquisition device; S200. The control module is used to mine and analyze the operating data, and a precise aeration mathematical model is constructed based on historical data. A precise aeration mechanism model is also established in combination with the sewage treatment process flow. S300. Based on the mathematical model and the mechanism model, the optimal aeration rate is calculated through algorithm iteration; according to the calculated optimal aeration rate, the operating status of the aeration equipment is dynamically adjusted through the automatic control system. S400 monitors effluent water quality and operational data in real time, and dynamically adjusts model parameters to adapt to changes in operating conditions.

2. The method for controlling precise aeration in a wastewater treatment plant according to claim 1, characterized in that, The real-time operational data includes at least one of the following: Influent water quality parameters, dissolved oxygen concentration, aeration rate, sludge concentration, and effluent water quality parameters.

3. The method for controlling precise aeration in a wastewater treatment plant according to claim 1, characterized in that, The control module includes a data mining module, a model orchestration module, and an instruction issuance module. The data mining module is used to analyze historical and real-time data, the model orchestration module is used to generate and optimize the aeration model, and the instruction issuance module is used to issue the calculated optimal aeration rate to the edge control system.

4. The method for controlling precise aeration in a wastewater treatment plant according to claim 3, characterized in that, The precise aeration mathematical model constructed based on historical data includes: Key features were extracted from the operational data, including the dynamic change rate of DO concentration, the fluctuation range of aeration volume, and the cumulative trend of sludge concentration. A mathematical model was constructed using a combination of multiple linear regression and time series analysis. The input features of the multiple linear regression model are DO concentration, aeration rate, and sludge concentration. The output is the optimized value of aeration rate. The weight parameters are optimized using the least squares method, and the specific formula is as follows: Among them, w1, w2, ..., w n is the feature weight, and b is the bias term, which is obtained through training with historical data.

5. The method for controlling precise aeration in a wastewater treatment plant according to claim 1, characterized in that, The precise aeration mechanism model combines mathematical modeling of the wastewater treatment plant process with consideration of aeration nonlinearity and large hysteresis characteristics to ensure that the effluent quality meets standards even under complex operating conditions.

6. The method for controlling precise aeration in a wastewater treatment plant according to claim 1, characterized in that, The dynamic adjustment of the aeration equipment's operating status includes adjusting the frequency of the aeration fan or the valve opening to control the aeration volume.

7. The method for controlling precise aeration in a wastewater treatment plant according to claim 1, characterized in that, The method for constructing the precise aeration mechanism model includes: Based on the nonlinear characteristics of the aeration process, a dynamic relationship between aeration rate and DO concentration is established: Among them, C DO Q represents the DO concentration. a ir represents the aeration rate, S represents the sludge concentration, and k1, k2, and k3 are dynamic parameters measured in the experiment.

8. The method for controlling precise aeration in a wastewater treatment plant according to claim 1, characterized in that, The dynamically corrected model parameters include weight parameters for the mathematical model and the mechanistic model that are updated online.

9. A control system for precise aeration in a wastewater treatment plant, characterized in that, include: Data acquisition device, used to acquire real-time operational data of the wastewater treatment process; The control module is used to mine and analyze the operating data, construct a precise aeration mathematical model based on historical data, and establish a precise aeration mechanism model in combination with the sewage treatment process; it is used to calculate the optimal aeration rate through algorithm iteration based on the mathematical model and mechanism model; and dynamically adjust the operating status of the aeration equipment through the automatic control system according to the calculated optimal aeration rate. It is used to monitor effluent water quality and operational data in real time, and dynamically adjust model parameters to adapt to changes in operating conditions.

10. An electronic device, characterized in that, include: Memory; The processor, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the precise aeration control method for a wastewater treatment plant according to any one of claims 1 to 8.

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