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

Abstract

The invention relates to the technical field of sewage treatment plant accurate control scheme design, in particular to a sewage treatment plant accurate control method and system and electronic equipment. The method comprises the following steps: firstly, collecting multi-dimensional data of sewage quality, sludge characteristics and equipment operation states by combining a multi-source spectrum sensor technology, and preprocessing by means of a data checking and storage module; secondly, based on accumulated data and an artificial intelligence algorithm, accurate prediction and dynamic simulation are carried out, an optimization control instruction is generated, and the aeration amount, the dosage, the reflux amount and the like are dynamically adjusted. And thirdly, through the real-time monitoring module, the system can comprehensively sense the operation state, give an early warning for abnormal conditions, and start an emergency control function under emergency conditions, including switching of standby equipment or limitation of water inlet flow. The precision and the operation efficiency of the sewage treatment process can be remarkably improved, the energy consumption and the cost are reduced, the stable standard reaching of the effluent quality is ensured, and the method has a wide application prospect and remarkable economic and social benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of precise control scheme design for sewage treatment plants, and in particular to a precise control method and system, and electronic equipment for sewage treatment plants. Background Art

[0002] With the acceleration of urbanization, municipal sewage treatment plants, as the core of urban water treatment systems, are responsible for treating domestic and industrial wastewater. However, sewage treatment presents numerous technical challenges, severely impacting treatment efficiency and operating costs. Traditional sewage treatment methods primarily rely on fixed process parameters, making them ill-suited to the fluctuating quality and quantity of incoming water. Complex influent conditions within sewage pipe networks, including frequent changes in flow rate, pollutant types, and concentrations, can easily impact treatment systems, impacting the consistent compliance of effluent water. Furthermore, existing sewage treatment processes often rely on manual experience, over-reliance on human judgment, leading to widespread control lags and an inability to respond promptly to emergencies such as sudden changes in water quality. Furthermore, equipment management lacks effective full-lifecycle monitoring, hindering accurate predictive maintenance based on real-time data. This leads to low equipment efficiency and even frequent unplanned downtime. Excessive aeration or chemical additions during process operation also increase energy consumption and operating costs, reducing treatment efficiency. Furthermore, existing monitoring technologies and data analysis capabilities are insufficient to extract underlying patterns from multi-source information to accurately predict system operating conditions and optimize decision-making. Therefore, how to achieve precise control of the sewage treatment process through intelligent technology and solve problems such as the complexity of incoming water, insufficient equipment management and high process operating costs has become a bottleneck that the sewage treatment industry urgently needs to break through.

[0003] Therefore, the existing technology needs to be further developed. Summary of the Invention

[0004] The purpose of the present invention is to overcome the above technical deficiencies and provide a sewage treatment plant precision control method and system, and electronic equipment to solve the problems existing in the prior art.

[0005] To achieve the above technical objectives, according to a first aspect of the present invention, the present invention provides a precise control method for a sewage treatment plant, comprising: S1. Through multi-source spectral fusion analysis and three-dimensional fluorescence spectral sensor technology, real-time acquisition of sewage-related data, sludge data, exhaust gas data and plant equipment operation status in sewage treatment plants, sewage stations and sewage pipe networks; S2. Accumulate and store the acquired data and automatically check the data; S3. Utilize the accumulated data to form an artificial intelligence (AI) data algorithm, and continuously correct and modify the algorithm to form a neural network output; S4. Detecting data including alkalinity, oxygen demand, carbon demand, and mud production through multi-source data, performing simulation calculations under a preset algorithm, and generating prediction results; S5. Dynamically adjust the operating parameters of sewage treatment-related equipment according to the prediction results, including the operating status of blowers, valves, pumps, and mixers, and adjust the aeration volume, reflux volume, dosage, and sludge discharge volume; S6. Real-time dynamic monitoring of the sewage treatment plant’s inlet and outlet water volume, water quality parameters, equipment operating status, and sludge volume; S7. Provide early warning of abnormal operating conditions and provide emergency control plans.

[0006] Specifically, the multi-source spectral fusion analysis includes a joint analysis of ultraviolet-visible spectra and infrared spectra.

[0007] Specifically, the three-dimensional fluorescence spectral sensor technology includes analysis of dissolved organic matter concentration.

[0008] Specifically, the automatic verification is achieved through comparative analysis based on historical data and real-time data.

[0009] Specifically, the AI ​​data algorithm includes a prediction model based on machine learning, and the prediction model is implemented by a gradient boosting algorithm or a neural network technology.

[0010] Specifically, the dynamic adjustment is to perform closed-loop control based on the comparison between the prediction results and the real-time feedback.

[0011] Specifically, the early warning includes alarms for exceeding water quality parameters, overloaded equipment operating status, and sudden sludge bulking problems.

[0012] Specifically, the emergency control plan includes the functions of starting backup equipment and limiting water intake.

[0013] According to a second aspect of the present invention, there is provided a sewage treatment plant precision control system comprising: The acquisition module is used to obtain real-time sewage-related data, sludge data, exhaust gas data, and plant equipment operation status from sewage treatment plants, sewage stations, and sewage pipe networks through multi-source spectral fusion analysis and three-dimensional fluorescence spectral sensor technology; A control module is used to accumulate and store the acquired data and automatically verify the data; to use the accumulated data to form an artificial intelligence (AI) data algorithm, and to continuously correct and amend the algorithm to form a neural network output; to perceive data including alkalinity, oxygen demand, carbon demand and sludge production through multi-source data, perform simulation operations under a preset algorithm, and generate prediction results; to dynamically adjust the operating parameters of sewage treatment-related equipment according to the prediction results, including the operating status of blowers, valves, pumps and mixers, and adjust the aeration volume, reflux volume, dosage and sludge discharge volume; to dynamically monitor the inlet and outlet water volume, water quality parameters, equipment operating status and sludge volume of the sewage treatment plant in real time; to issue early warnings for abnormal operating conditions and provide emergency control plans.

[0014] According to a third aspect of the present invention, there is provided an electronic device comprising: a memory; and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the above-mentioned precise control method for a sewage treatment plant is implemented.

[0015] Beneficial effects: With the acceleration of urbanization, municipal sewage treatment plants, as the core of urban water treatment systems, are responsible for treating domestic and industrial wastewater. However, sewage treatment presents numerous technical challenges, severely impacting treatment efficiency and operating costs. Traditional sewage treatment methods primarily rely on fixed process parameters, making them ill-suited to the fluctuating quality and quantity of incoming water. Complex influent conditions within sewage pipe networks, including frequent changes in flow rate, pollutant types, and concentrations, can easily impact treatment systems, impacting the consistent compliance of effluent water. Furthermore, existing sewage treatment processes often rely on manual experience, over-reliance on human judgment, leading to widespread control lags and an inability to respond promptly to emergencies such as sudden changes in water quality. Furthermore, equipment management lacks effective full-lifecycle monitoring, hindering accurate predictive maintenance based on real-time data. This leads to low equipment efficiency and even frequent unplanned downtime. Excessive aeration or chemical additions during process operation also increase energy consumption and operating costs, reducing treatment efficiency. Furthermore, existing monitoring technologies and data analysis capabilities are insufficient to extract underlying patterns from multi-source information to accurately predict system operating conditions and optimize decision-making. Therefore, how to achieve precise control of the sewage treatment process through intelligent technology and solve problems such as the complexity of incoming water, insufficient equipment management and high process operating costs has become a bottleneck that the sewage treatment industry urgently needs to break through. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 1 is a flow chart of a precise control method for a sewage treatment plant provided in a specific embodiment of the present invention; Figure 2It is a schematic diagram of the system composition of the sewage treatment plant precision control system provided in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is clearly and completely described below in conjunction with the drawings of the present invention. Based on the embodiments in this application, other similar embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of this application. In addition, the directional words mentioned in the following embodiments, such as "up", "down", "left", "right", etc., are only reference to the directions of the drawings. Therefore, the directional words used are used to illustrate rather than limit the invention.

[0018] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments.

[0019] See also Figure 1 The present invention provides a precise control method for a sewage treatment plant, comprising: S1. Through multi-source spectral fusion analysis and three-dimensional fluorescence spectral sensor technology, real-time acquisition of sewage-related data, sludge data, waste gas data and plant equipment operation status in sewage treatment plants, sewage stations and sewage pipe networks.

[0020] Specifically, the multi-source spectral fusion analysis includes a joint analysis of ultraviolet-visible spectra and infrared spectra.

[0021] Specifically, the three-dimensional fluorescence spectral sensor technology includes analysis of dissolved organic matter concentration.

[0022] It should be further explained that, regarding step S1, the solution designed by the present invention includes: 1. Multispectral sensor design Ultraviolet-visible spectroscopy (UV-Vis): Use a photometer to analyze water quality parameters based on the Beer-Lambert Law: in: A: absorbance (unit: dimensionless), obtained directly from the sensor; ε: molar absorption coefficient (unit: L·mol -1 cm -1 ), which needs to be calibrated in advance. The preferred COD of the present invention is 1580 L·mol -1 cm -1 ; l: optical path length (unit: cm), set to 1 cm; c: Concentration of water quality parameters (unit: mol / L), obtained by reverse calculation of absorbance.

[0023] Parameter optimization: For a COD measurement range of 0-1000 mg / L and an absorbance range of 0-2.5, the sensor sampling frequency was set to 1 time / minute, noise was reduced using a light intensity signal mean denoising algorithm, and the threshold was set to 2% (i.e., data with fluctuations exceeding 2% were discarded).

[0024] Infrared Spectroscopy (IR): Volatile fatty acids (VFA) were detected by non-dispersive infrared (NDIR) with a wavelength range of 1100-1300 cm⁻¹. The Lambertian absorption model was used to calculate the VFA concentration: I0: incident light intensity of the light source, I: light intensity after absorption by the sample (unit: mW), obtained in real time by the cuvette transmittance detection module; k=265W·(mol·cm ² ) -1 , l=1cm.

[0025] Parameter optimization: VFA detection range 0-300 mg / L, VFA sensor sampling interval 30 seconds (to improve dynamic response speed). Initiate calibration when data is abnormal (deviation from the mean exceeds 3σ).

[0026] 2. Three-dimensional fluorescence spectral sensor Working principle: Based on excitation / emission two-dimensional fluorescence scanning, the three-dimensional fluorescence characteristics of DOM (dissolved organic matter) are obtained, and the fluorescence peak area (EEM spectrum) is scanned in particular to extract the concentration characteristics of fulvic acid (240-280 / 400-500nm) and humic acid (250-280 / 450-550nm).

[0027] Quantitative analysis: Relationship between fluorescence intensity and organic matter concentration: in: : dissolved organic matter concentration (mg / L); , calculated by integrating the scanning peak area; ,This parameter is calibrated according to the standard DOM solution.

[0028] S2. Accumulate and store the acquired data, and automatically check the data.

[0029] Specifically, the automatic verification is achieved through comparative analysis based on historical data and real-time data.

[0030] It should be further explained that, regarding step S2, the solution designed by the present invention includes: 1. Data accumulation: A distributed time series database (preferably Influx DB in the present invention) is used to store sensor data, including timestamp, original value, device ID, and confidence mark fields.

[0031] Data classification storage: Sewage parameters (flow rate, COD, VFA, etc.), sampling interval is 1 minute, and storage period is 30 days; Sludge parameters (MLSS, MLVSS), sampling interval 5 minutes, storage period 90 days; Equipment data (amplitude, temperature, status), sampling interval 10 seconds, storage period 60 days.

[0032] 2. Automatic verification: Sliding historical mean-standard deviation calibration algorithm: Calculate the historical mean (μ) and standard deviation (σ) of each collected real-time data, and define the calibration conditions: in, Real-time data collected each time; If Error>α·σ (the default value is α=2, which means the calibration is passed when the data confidence interval is 95%), a flag is triggered and an alarm is issued.

[0033] S3. Use the accumulated data to form an artificial intelligence (AI) data algorithm, and continuously correct and modify the algorithm to form a neural network output.

[0034] Specifically, the AI ​​data algorithm includes a prediction model based on machine learning, and the prediction model is implemented by a gradient boosting algorithm or a neural network technology.

[0035] It should be further explained that, regarding step S3, the solution designed by the present invention includes: 1. AI data algorithm construction: The algorithm is based on a deep learning framework (preferably TensorFlow in the present invention), uses LSTM (Long Short-Term Memory Network) to predict dynamic data, and combines it with Random Forest to predict key indicators (preferably water quality parameters and equipment status in the present invention).

[0036] Data features include historical operating data (influent and effluent volume, dissolved oxygen, and sludge concentration), process parameters (aeration rate, dosage), equipment status, and alarm data. The input layer is set to 50 nodes, and the hidden layer is set to three, with 64, 32, and 16 nodes, respectively. Output layer nodes are allocated based on the prediction target. In this invention, one node each is allocated for effluent COD, oxygen demand, and MLSS (mixed liquor suspended solids concentration).

[0037] The loss function uses the mean square error (MSE): in: ; The learning rate is set to 0.001 and the batch size is 32.

[0038] 2. Model optimization and correction: The model was retrained every 24 hours, and the parameters were adjusted using the Adam optimizer with an initial learning rate of 0.001 and a decay rate of 0.99.

[0039] Regarding the threshold setting of the model, the present invention preferably generates a warning when the prediction error exceeds 15%, and freezes the output when it exceeds 25%.

[0040] S4. Through multi-source data perception including alkalinity, oxygen demand, carbon demand and mud production, simulation calculations are performed under the preset algorithm to generate prediction results.

[0041] It should be further explained that, regarding step S4, the solution designed by the present invention includes: 1. Key parameter calculation: An empirical formula based on COD, BOD (biochemical oxygen demand) and MLSS was established to calculate oxygen demand (OUR): in, (Based on experience, applicable to municipal sewage treatment scenarios).

[0042] The sludge production is calculated using the following formula: is the sludge yield coefficient (unit: gMLSS / gCOD), is the COD removal amount.

[0043] 2. Simulation calculation: Using the Simulink platform, a dynamic reaction model was established to simulate the material changes in the aeration tank and predict the effluent quality and sludge production. The simulation step size was set to 1 minute.

[0044] Specifically, regarding simulation calculations, the design scheme of the present invention includes: 1. Simulation model formula: Oxygen demand OUR calculation formula: in: (1) Expresses the biological oxidation oxygen demand coefficient: Physical meaning: It indicates the mass of oxygen required by microorganisms to remove unit mass of chemical oxygen demand (COD) during the degradation of organic matter.

[0045] Source and selection: It is an important empirical parameter in the field of sewage treatment, and its value range is usually , depending on the sewage quality and microbial population.

[0046] In this embodiment, the , which is based on the typical experience value of municipal sewage treatment plants for domestic sewage and is suitable for treating biodegradable organic matter in urban sewage.

[0047] Function: Converting the organic matter removal rate into oxygen demand is the basis for calculating the aeration volume.

[0048] (2) Expressing biomass metabolism rate: Physical meaning: It indicates the metabolic rate of organic matter by microorganisms per unit time, that is, the rate at which organic matter is consumed per unit biomass (MLVSS).

[0049] Formula expansion: : Maximum specific growth rate of microorganisms (unit: or ), which represents the maximum proliferation capacity of microorganisms under ideal conditions (sufficient substrate).

[0050] Source and selection: Generally the value is In this embodiment, , suitable for heterotrophic bacteria commonly found in activated sludge process.

[0051] : The concentration of biodegradable substrates in sewage (unit: mg / L, usually COD value).

[0052] Source and selection: It is the real-time monitoring of the residual COD concentration in the sewage, obtained through the sensor. : Half-saturation constant (unit: mg / L), which indicates the substrate concentration when the microbial metabolic rate reaches half of its maximum value.

[0053] Source and selection: The value range is usually , this embodiment uses , suitable for the treatment of low-concentration domestic sewage.

[0054] Function: It reflects the influence of microbial activity and organic matter concentration in sewage on metabolic rate, and is one of the key parameters for dynamically adjusting aeration volume.

[0055] (3) Indicates the activated sludge concentration Physical meaning: It indicates the concentration of volatile suspended solids (MLVSS) in the mixed liquor and represents the amount of metabolically active microorganisms in the activated sludge.

[0056] Source and selection: MLVSS is obtained through real-time monitoring, unit is ; In the activated sludge process, MLVSS generally accounts for 60% to 80% of the mixed liquor suspended solids (MLSS). (Specific values ​​are dynamically monitored by online sensors).

[0057] Function: Indicates the amount of microorganisms, which directly affects the total oxygen demand.

[0058] 2. Aeration volume dynamic adjustment formula in: (1) (Aeration coefficient) Physical meaning: Indicates the aeration flow rate required per unit oxygen demand (OUR), in units of .

[0059] Source and selection: The value of depends on the efficiency of the aeration equipment, the oxygen transfer efficiency in the air and the oxygen saturation conditions of the mixed liquid.

[0060] In actual engineering, the oxygen transfer efficiency is usually between 10% and 30%. , which is obtained based on the calibration experiment of the aeration system.

[0061] Function: Converts OUR into the required aeration flow rate, which is the core parameter for dynamically controlling aeration volume.

[0062] (2) (Minimum aeration flow compensation item) Physical meaning: To ensure the stable operation of the aeration system, set the minimum aeration flow compensation value, in units of .

[0063] Source and selection: The value of needs to take into account the mixing requirements of the aeration tank and the minimum oxygen demand to prevent sludge deposition.

[0064] This embodiment uses to ensure that the system can maintain minimum aeration requirements under low load conditions.

[0065] Function: Avoid low aeration volume resulting in insufficient dissolved oxygen in the mixed liquid, which will affect the metabolic activity of microorganisms.

[0066] It should be further explained that the core logic of closed-loop dynamic adjustment is to dynamically adjust the operating parameters of sewage treatment equipment by comparing real-time monitoring data with predicted values ​​to achieve feedback control. The formula is as follows: The following formula is used to dynamically adjust the aeration volume: Dynamic adjustment logic: 1. Predicted value: OUR is calculated by the simulation model and reflects the current demand for oxygen in the sewage treatment system; 2. Real-time feedback value: monitor the actual dissolved oxygen concentration in the mixed liquid through an online dissolved oxygen (DO) sensor; 3. Feedback control rules: If the actual DO concentration is lower than the set threshold (e.g. 2 mg / L), increase the aeration volume; If the actual DO concentration is higher than the set threshold (such as 4 mg / L), the aeration volume is reduced.

[0067] The adjustment formula is: in: : Feedback adjustment coefficient, the value range is 0.1~0.5, this embodiment uses ; : Target dissolved oxygen concentration, usually set at 2-4 mg / L; : Real-time monitoring of dissolved oxygen concentration.

[0068] It should be further explained that regarding the dynamic adjustment of the dosage, the design formula of the present invention is as follows: in: (1) γ represents the dosing coefficient: Physical meaning: Indicates the dosage of medicine required per unit MLVSS, in units of .

[0069] Source and selection: The value of γ depends on the type of reagent used and the quality of sewage.

[0070] In this embodiment, γ=0.25 is selected , suitable for commonly used carbon source addition scenarios.

[0071] (2) Indicates the influent COD concentration: Physical meaning: Indicates the chemical oxygen demand concentration in sewage entering the sewage treatment plant, in units of ; Source and selection: Obtained through real-time monitoring of online COD sensors.

[0072] (3) MLVSS represents activated sludge concentration: Physical meaning: Indicates the concentration of microorganisms in activated sludge, in units of ; Source and selection: Obtained through dynamic monitoring of online MLSS / MLVSS sensors.

[0073] S5. Dynamically adjust the operating parameters of sewage treatment related equipment according to the prediction results, including the operating status of blowers, valves, pumps and mixers, and adjust the aeration volume, reflux volume, dosage and sludge discharge volume.

[0074] Specifically, the dynamic adjustment is to perform closed-loop control based on the comparison between the prediction results and the real-time feedback.

[0075] It should be further explained that, regarding step S5, the solution designed by the present invention includes: The aeration rate is adjusted based on the OUR calculated value, and the formula of the present invention is preferably: in, , and Constant compensation term.

[0076] The dosage is adjusted according to the real-time influent sludge load (F / M ratio), and the formula of the present invention is preferably: in, is the concentration of volatile suspended solids, is the influent COD concentration.

[0077] S6. Real-time dynamic monitoring of the sewage treatment plant's inlet and outlet water volume, water quality parameters, equipment operating status and sludge volume.

[0078] It should be further explained that, regarding step S6, the solution designed by the present invention includes: 1. Monitoring method Draw real-time data dashboards, including flow, DO (dissolved oxygen), COD, MLSS, etc.; The equipment operating status is monitored in real time using vibration and temperature sensors.

[0079] 2. Visualization system The data is displayed through the web interface and is updated once per minute. When the threshold is exceeded, the font turns red and flashes.

[0080] S7. Provide early warning of abnormal operating conditions and provide emergency control plans.

[0081] Specifically, the early warning includes alarms for exceeding water quality parameters, overloaded equipment operating status, and sudden sludge bulking problems.

[0082] Specifically, the emergency control plan includes the functions of starting backup equipment and limiting water inflow.

[0083] It should be further explained that, regarding step S7, the solution designed by the present invention includes: 1. Warning conditions: Water quality exceeds the standard (COD>50mg / L is preferred in the present invention) and triggers an alarm; Equipment status (preferably pump vibration > 5 mm / s in the present invention) triggers an early warning; Sludge bulking (MLSS>4000mg / L) triggers the sludge discharge mode of the SBR process.

[0084] 2. Provide emergency control plan: Activate backup equipment to maintain processing capacity; Restrict water inlet, control the water inlet flow to 50% of the normal value through the electric valve, and prompt the user to operate on the HMI interface.

[0085] Understandably, with the acceleration of urbanization, municipal wastewater treatment plants, as the core of urban water treatment systems, are responsible for treating domestic and industrial wastewater. However, sewage treatment presents numerous technical challenges, severely impacting treatment efficiency and operating costs. Traditional sewage treatment methods primarily rely on fixed process parameters, making them ill-suited to the fluctuating quality and quantity of incoming water. Complex influent conditions within sewage pipe networks, including frequent changes in flow rate, pollutant types, and concentrations, can easily impact treatment systems, impacting the consistent compliance of effluent water standards. Furthermore, existing sewage treatment processes often rely on manual experience, with overreliance on human judgment leading to widespread control lags and an inability to respond promptly to emergencies such as sudden changes in water quality. Furthermore, equipment management lacks effective full-lifecycle monitoring, hindering accurate predictive maintenance based on real-time data. This results in low equipment efficiency and frequent unplanned downtime. Excessive aeration or chemical additions during process operation also increase energy consumption and operating costs, reducing treatment efficiency. Furthermore, existing monitoring technologies and data analysis capabilities are insufficient to extract underlying patterns from multi-source information, enabling accurate predictions of system operating status and optimal decision-making. Therefore, using intelligent technologies to precisely manage and control the wastewater treatment process, addressing issues such as water complexity, inadequate equipment management, and high process operating costs, has become a bottleneck that the wastewater treatment industry urgently needs to overcome.

[0086] See also Figure 2 The present invention provides another embodiment, which provides a precise control system for a sewage treatment plant. The precise control system for a sewage treatment plant includes: The acquisition module 100 is used to obtain real-time sewage-related data, sludge data, exhaust gas data and plant equipment operation status in sewage treatment plants, sewage stations and sewage pipe networks through multi-source spectral fusion analysis and three-dimensional fluorescence spectrum sensor technology; The control module 200 is used to accumulate and store the acquired data and automatically verify the data; to form an artificial intelligence (AI) data algorithm using the accumulated data, and to continuously correct and amend the algorithm to form a neural network output; to perceive data including alkalinity, oxygen demand, carbon demand and sludge production through multi-source data, perform simulation operations under a preset algorithm, and generate prediction results; to dynamically adjust the operating parameters of sewage treatment-related equipment according to the prediction results, including the operating status of blowers, valves, pumps and mixers, and adjust the aeration volume, reflux volume, dosage and sludge discharge volume; to dynamically monitor the inlet and outlet water volume, water quality parameters, equipment operating status and sludge volume of the sewage treatment plant in real time; to issue early warnings for abnormal operating conditions and provide emergency control plans.

[0087] It's important to note that, understandably, with the acceleration of urbanization, municipal wastewater treatment plants, as the core of urban water treatment systems, are responsible for treating domestic and industrial wastewater. However, sewage treatment presents numerous technical challenges, severely impacting treatment efficiency and operating costs. Traditional sewage treatment methods primarily rely on fixed process parameters, making them ill-suited to the fluctuating quality and quantity of incoming water. Complex influent conditions within sewage pipe networks, including frequent changes in flow rate, pollutant types, and concentrations, can easily impact treatment systems, impacting the consistent compliance of effluent water standards. Furthermore, existing sewage treatment processes often rely on manual experience, with overreliance on human judgment leading to widespread control lags and an inability to respond promptly to emergencies such as sudden changes in water quality. Furthermore, equipment management lacks effective full-lifecycle monitoring, hindering accurate predictive maintenance based on real-time data. This results in low equipment efficiency and frequent unplanned downtime. Excessive aeration or chemical additions during process operation also increase energy consumption and operating costs, reducing treatment efficiency. Furthermore, existing monitoring technologies and data analysis capabilities are insufficient to extract underlying patterns from multi-source information, enabling accurate predictions of system operating status and optimal decision-making. Therefore, using intelligent technologies to precisely manage and control the wastewater treatment process, addressing issues such as water complexity, inadequate equipment management, and high process operating costs, has become a bottleneck that the wastewater treatment industry urgently needs to overcome.

[0088] In a preferred embodiment, the present application further provides an electronic device, comprising: A memory; and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method for precise control of a sewage treatment plant is implemented. The computer device can be broadly defined as a server, a terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, a memory, a network interface, a 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 an internal memory. An operating system, a computer program, etc. may be stored in or on the non-volatile storage medium. The internal memory can provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the 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, the steps of the method of the present invention are performed.

[0089] The present 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 method of an embodiment of the present invention to be performed. In one embodiment, the computer program is distributed on a plurality of computer devices or processors coupled to a network so 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, can be performed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations can be performed by one or more computer devices or processors, and one or more other method steps / operations can be performed by one or more other computer devices or processors. One or more computer devices or processors can perform a single method step / operation, or perform two or more method steps / operations.

[0090] Those skilled in the art will appreciate that the method steps of the present invention can be directed to the relevant hardware by a computer program. The present invention is preferably implemented by a computer device or processor. The computer program can be stored in a non-transitory computer-readable storage medium. When the computer program is executed, the steps of the present invention are executed. Depending on the circumstances, any reference to memory, storage, database, or other media herein 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 disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0091] Understandably, with the acceleration of urbanization, municipal wastewater treatment plants, as the core of urban water treatment systems, are responsible for treating domestic and industrial wastewater. However, sewage treatment presents numerous technical challenges, severely impacting treatment efficiency and operating costs. Traditional sewage treatment methods primarily rely on fixed process parameters, making them ill-suited to the fluctuating quality and quantity of incoming water. Complex influent conditions within sewage pipe networks, including frequent changes in flow rate, pollutant types, and concentrations, can easily impact treatment systems, impacting the consistent compliance of effluent water standards. Furthermore, existing sewage treatment processes often rely on manual experience, with overreliance on human judgment leading to widespread control lags and an inability to respond promptly to emergencies such as sudden changes in water quality. Furthermore, equipment management lacks effective full-lifecycle monitoring, hindering accurate predictive maintenance based on real-time data. This results in low equipment efficiency and frequent unplanned downtime. Excessive aeration or chemical additions during process operation also increase energy consumption and operating costs, reducing treatment efficiency. Furthermore, existing monitoring technologies and data analysis capabilities are insufficient to extract underlying patterns from multi-source information, enabling accurate predictions of system operating status and optimal decision-making. Therefore, using intelligent technologies to precisely manage and control the wastewater treatment process, addressing issues such as water complexity, inadequate equipment management, and high process operating costs, has become a bottleneck that the wastewater treatment industry urgently needs to overcome.

[0092] The various 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 as long as such combination does not conflict.

[0093] The specific embodiments of the present invention described above do not limit the scope of protection of the present invention. Any other corresponding changes and modifications made based on the technical concept of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A precise control method for a sewage treatment plant, characterized in that: The method comprises: S1. Through multi-source spectral fusion analysis and three-dimensional fluorescence spectral sensor technology, real-time acquisition of sewage-related data, sludge data, exhaust gas data and plant equipment operation status in sewage treatment plants, sewage stations and sewage pipe networks; S2. Accumulate and store the acquired data and automatically check the data; S3. Utilize the accumulated data to form an artificial intelligence (AI) data algorithm, and continuously correct and modify the algorithm to form a neural network output; S4. Detecting data including alkalinity, oxygen demand, carbon demand, and mud production through multi-source data, performing simulation calculations under a preset algorithm, and generating prediction results; S5. Dynamically adjust the operating parameters of sewage treatment-related equipment according to the prediction results, including the operating status of blowers, valves, pumps, and mixers, and adjust the aeration volume, reflux volume, dosage, and sludge discharge volume; S6. Real-time dynamic monitoring of the sewage treatment plant’s inlet and outlet water volume, water quality parameters, equipment operating status, and sludge volume; S7. Provide early warning of abnormal operating conditions and provide emergency control plans.

2. The precise control method for a sewage treatment plant according to claim 1, characterized in that: The multi-source spectrum fusion analysis includes a joint analysis of ultraviolet-visible spectrum and infrared spectrum.

3. The precise control method for a sewage treatment plant according to claim 2, characterized in that: The three-dimensional fluorescence spectroscopy sensor technology includes analysis of dissolved organic matter concentration.

4. The precise control method for a sewage treatment plant according to claim 1, characterized in that: The automatic verification is achieved through comparative analysis based on historical data and real-time data.

5. The precise control method for a sewage treatment plant according to claim 1, characterized in that: The AI ​​data algorithm includes a prediction model based on machine learning, which is implemented by a gradient boosting algorithm or a neural network technology.

6. The precise control method for a sewage treatment plant according to claim 1, characterized in that: The dynamic adjustment is to perform closed-loop control based on the comparison between the prediction results and the real-time feedback.

7. The precise control method for a sewage treatment plant according to claim 1, characterized in that: The early warning includes alarms for exceeding water quality parameters, overloaded equipment operating status and sudden sludge bulking problems.

8. The precise control method for a sewage treatment plant according to claim 7, characterized in that: The emergency control plan includes the functions of starting backup equipment and limiting water inflow.

9. A precise control system for a sewage treatment plant, characterized in that: include: The acquisition module is used to obtain real-time sewage-related data, sludge data, exhaust gas data, and plant equipment operation status from sewage treatment plants, sewage stations, and sewage pipe networks through multi-source spectral fusion analysis and three-dimensional fluorescence spectrum sensor technology; A control module is used to accumulate and store the acquired data and automatically verify the data; to use the accumulated data to form an artificial intelligence (AI) data algorithm, and to continuously correct and amend the algorithm to form a neural network output; to perceive data including alkalinity, oxygen demand, carbon demand and sludge production through multi-source data, perform simulation operations under a preset algorithm, and generate prediction results; to dynamically adjust the operating parameters of sewage treatment-related equipment according to the prediction results, including the operating status of blowers, valves, pumps and mixers, and adjust the aeration volume, reflux volume, dosage and sludge discharge volume; to dynamically monitor the inlet and outlet water volume, water quality parameters, equipment operating status and sludge volume of the sewage treatment plant in real time; to issue early warnings for abnormal operating conditions and provide emergency control plans.

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

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