Water affair treatment process optimization control method, system, equipment and medium

Abstract

The invention relates to the technical field of automatic control, and discloses a water affair treatment process optimization control method, system and equipment and a medium. The method comprises the steps of performing state judgment on sensor detection data through a multi-parameter state recognition algorithm to obtain a processing stage switching instruction; modeling a microbial activity change rule according to the switching instruction to obtain a staged prediction model; optimizing the prediction model through a switching model prediction control algorithm to obtain a multi-target control strategy; performing coordinated calculation on the set value according to the control strategy to obtain a distributed control instruction; and performing driving control on the equipment by using the control instruction through the field bus to obtain a coordinated operation state. According to the invention, the control precision and the multi-target coordination optimization capability of the water affair treatment process are improved.

Description

Technical Field

[0001] The present application relates to the field of automatic control technology, and in particular to a water treatment process optimization control method, system, equipment and medium. Background Art

[0002] Existing water treatment process control methods primarily employ traditional PID control or simple automated control systems. These methods monitor a single or limited set of water quality parameters, such as dissolved oxygen and pH, to achieve basic control of process parameters such as aeration and recirculation. These control methods are typically based on empirical parameter settings and fixed control strategies. They maintain basic treatment results under stable operating conditions and have been widely used in sewage treatment plants.

[0003] However, traditional control methods cannot accurately identify the transition timing between different reaction stages such as anaerobic, anoxic, and aerobic in the biochemical treatment process, resulting in a mismatch between the control strategy and the actual process stage; secondly, a single control model is difficult to adapt to the differences in the changing laws of microbial activity in each treatment stage, resulting in insufficient control accuracy and delayed response; thirdly, the existing methods lack a multi-objective coordinated optimization mechanism and cannot simultaneously take into account multiple objectives such as water quality compliance, energy conservation and consumption reduction, and stable operation.

[0004] How to build an intelligent control method that can accurately identify and process stage switching, adaptively adjust the control model, and achieve multi-objective coordinated optimization. This requires that the control system must have the state recognition capability of multi-parameter fusion, be able to establish corresponding prediction models based on the biochemical reaction characteristics of different stages, and realize dynamic switching and coordinated optimization of control strategies through advanced optimization algorithms, thereby overcoming the technical bottleneck of poor control performance of existing technologies under complex working conditions. Summary of the Invention

[0005] This application provides a water treatment process optimization control method, system, device, and medium. By constructing a water treatment optimization method based on multi-parameter state recognition and switching model predictive control, this method addresses the existing technology's inability to accurately identify treatment stage transitions and adaptively adjust control strategies. This method improves the control accuracy and multi-objective coordinated optimization capabilities of the water treatment process.

[0006] In the first aspect, the present application provides a method for optimizing and controlling a water treatment process, which comprises: performing state discrimination processing on the detection data of a dissolved oxygen sensor, an oxidation-reduction potential sensor, and a pH sensor through a multi-parameter state recognition algorithm to obtain a processing stage switching instruction, including: arranging a dissolved oxygen sensor, an oxidation-reduction potential sensor, and an pH sensor in an anaerobic tank, an anoxic tank, and an aerobic tank, respectively, collecting water quality parameters of each treatment unit in real time, and obtaining a multi-parameter detection data sequence; performing a ternary parameter fusion calculation on the multi-parameter detection data sequence, and comparing the dissolved oxygen concentration value with a preset low oxygen threshold and a high oxygen threshold. The dissolved oxygen state level is obtained by comparison and judgment; based on the dissolved oxygen state level, the redox potential value is segmented and identified, and the potential value is interval-matched with the negative potential threshold, the zero potential threshold, and the positive potential threshold to obtain the redox potential state level; according to the redox potential state level, the pH change rate is gradient calculated, and the pH change rate is differentially compared with the preset change rate threshold to obtain a water quality gradient change identifier; the water quality gradient change identifier is time-series verified with the parameter change trend of multiple consecutive sampling periods, and the conversion timing of the anaerobic reaction stage, the anoxic reaction stage, and the aerobic reaction stage is judged and confirmed to obtain a processing stage switching instruction; Optionally, modeling the variation pattern of microbial activity in each biochemical reaction stage according to the processing stage switching instruction to obtain a stage-by-stage prediction model includes: Identifying and calculating the phosphorus release kinetic parameters of the anaerobic reaction stage based on the processing stage switching instruction, correlating the microbial activity index with the phosphorus release rate, and obtaining the dynamic response parameters of the anaerobic stage; Modeling and calculating the denitrification process parameters of the anoxic reaction stage according to the processing stage switching instruction, coupling analysis of the denitrifying bacteria activity and the total nitrogen removal rate, and obtaining the dynamic response parameters of the anoxic stage; The processing stage switching instruction is associated with the nitrification and phosphorus absorption process in the aerobic reaction stage, and the activity changes of nitrifying bacteria and phosphate-accumulating bacteria are synchronously modeled to obtain dynamic response parameters of the aerobic stage; Performing transfer characteristic analysis based on the anaerobic stage dynamic response parameters, the anoxic stage dynamic response parameters, and the aerobic stage dynamic response parameters, converting the input-output response relationship of each stage into a predictive control structure to obtain stage transfer characteristic data; The stage transfer characteristic data are combined into a model according to the temporal relationship of anaerobic-anoxic-aerobic, and the switching conditions and boundary constraints of each stage model are set and processed to obtain a staged prediction model.

[0007] Optionally, the stepwise prediction model is subjected to rolling optimization processing by switching a model predictive control algorithm to obtain a multi-objective control strategy, including: The phased prediction model is input into the switching model prediction controller to set the prediction time domain, and the water quality change trend of multiple control cycles in the future is predicted and calculated to obtain the prediction time domain data; Based on the predicted time domain data, target values ​​of effluent chemical oxygen demand, total nitrogen concentration, and total phosphorus concentration are constrained and set, and the water quality requirements are converted into an optimization objective function to obtain multi-objective constraint conditions; According to the multi-objective constraints, cost weights are allocated to aeration energy consumption, chemical consumption, and sludge production, and a weighted combination of water quality control objectives and energy conservation and consumption reduction objectives is performed to obtain a comprehensive optimization objective function; The comprehensive optimization objective function is solved and calculated by a rolling optimization algorithm, and the optimal control sequence in the control time domain is iteratively searched to obtain a rolling optimization control sequence; Based on the rolling optimization control sequence, the control strategies for the anaerobic stage, the anoxic stage, and the aerobic stage are extracted in sections, and the optimal control parameters of each stage are strategically combined according to the time sequence relationship to obtain a multi-objective control strategy.

[0008] Optionally, the phased prediction model is input into a switching model prediction controller to set a prediction time domain, and a water quality change trend of multiple control cycles in the future is predicted and calculated to obtain prediction time domain data, including: Inputting the dynamic response parameters of the anaerobic stage, the dynamic response parameters of the anoxic stage, and the dynamic response parameters of the aerobic stage in the staged prediction model into the switching model predictive controller, initializing and configuring the model parameters of each stage, and obtaining the initial state data of the controller; Setting time windows for the prediction time domain length and the control time domain length based on the initial state data of the controller, dividing the time range of the prediction control into multiple continuous control cycles, and obtaining time domain window configuration parameters; The initial state value of the water quality state variable at the current moment is set according to the time domain window configuration parameters, and the current values ​​of the dissolved oxygen concentration, redox potential, and pH are used as the prediction starting point to obtain the prediction initial state vector; Recursively calculate the predicted initial state vector and the staged prediction model to gradually predict the changes in water quality parameters in each future control period to obtain a multi-period water quality change trajectory; Based on the multi-period water quality change trajectory, data integration is performed on the change trends of chemical oxygen demand removal rate, total nitrogen removal rate, and total phosphorus removal rate, and the prediction sequences of various water quality indicators are combined and arranged in chronological order to obtain predicted time domain data.

[0009] Optionally, the coordinated calculation and processing of the aeration volume setting value, the reflux ratio setting value, and the stirring intensity setting value according to the multi-objective control strategy to obtain a distributed control instruction includes: Separating the anaerobic stage control parameters, the anoxic stage control parameters, and the aerobic stage control parameters in the multi-objective control strategy into stage parameters, classifying and extracting the optimal control parameters of each stage, and obtaining a stage-by-stage control parameter set; The aeration volume setting value of the aerobic tank is calculated based on the staged control parameter set, and the oxygen demand of the nitrification reaction and the oxygen demand of the phosphorus accumulating bacteria are superimposed to obtain the optimized aeration volume setting value; According to the staged control parameter set, a coupling calculation is performed on the reflow ratio setting value in the anoxic tank and the reflow ratio setting value outside the aerobic tank, and the denitrification efficiency requirement is balanced with the sludge concentration control requirement to obtain an optimized reflow ratio setting value; Correlating the staged control parameter set with the stirring intensity of the anaerobic tank and the anoxic tank, respectively optimizing the phosphorus release mixing intensity and the denitrification mixing intensity to obtain an optimized stirring intensity setting value; Based on the optimized aeration volume setting value, the optimized reflux ratio setting value, and the optimized stirring intensity setting value, control instructions are encapsulated, and each control parameter is grouped and configured according to the execution device address to obtain a distributed control instruction.

[0010] Optionally, the distributed control instructions are used to drive and control the blower, the reflux pump, and the agitator through a fieldbus communication protocol to obtain a coordinated operating state of the water treatment equipment, including: The distributed control instructions are classified and parsed according to the blower control instructions, the reflux pump control instructions, and the agitator control instructions, and the control parameters of each device are converted into data formats to obtain a device-specific control data packet; Based on the device-specific control data packet, the data frame structure of the field bus communication protocol is encapsulated and processed, and the control instruction data and the device address information are assembled in the protocol format to obtain a bus communication data frame; Sending a drive signal to the frequency converter of the aerobic pool blower according to the bus communication data frame, converting the optimized aeration volume setting value into a blower speed control signal, and obtaining the blower operation control state; The bus communication data frame is connected to the frequency conversion controllers of the internal reflux pump and the external reflux pump to perform pump speed adjustment control on the optimized reflux ratio setting value to obtain the reflux pump operation control state; Based on the bus communication data frame, instructions are transmitted to the motor controllers of the anaerobic tank and anoxic tank agitators, the optimized stirring intensity setting value is converted into a motor power control signal, and synchronized with the blower operation control status and the reflux pump operation control status to obtain the coordinated operation status of the water treatment equipment.

[0011] In a second aspect, the present application provides a water treatment process optimization control system, the water treatment process optimization control system comprising: The discrimination module is used to perform state discrimination processing on the detection data of the dissolved oxygen sensor, the redox potential sensor, and the pH sensor through a multi-parameter state recognition algorithm to obtain a processing stage switching instruction, including: arranging a dissolved oxygen sensor, a redox potential sensor, and a pH sensor in the anaerobic tank, the anoxic tank, and the aerobic tank respectively, collecting the water quality parameters of each processing unit in real time, and obtaining a multi-parameter detection data sequence; performing a ternary parameter fusion calculation on the multi-parameter detection data sequence, comparing the dissolved oxygen concentration value with the preset low oxygen threshold and high oxygen threshold to obtain a dissolved oxygen state level; based on the The dissolved oxygen state level identifies the redox potential value in segments, and performs interval matching of the potential value with the negative potential threshold, the zero potential threshold, and the positive potential threshold to obtain the redox potential state level; the pH change rate is gradient calculated according to the redox potential state level, and the pH change rate is differentially compared with the preset change rate threshold to obtain a water quality gradient change indicator; the water quality gradient change indicator is time-series verified with the parameter change trend of multiple consecutive sampling cycles, and the conversion timing of the anaerobic reaction stage, the anoxic reaction stage, and the aerobic reaction stage is judged and confirmed to obtain a processing stage switching instruction; A modeling module is used to model the change pattern of microbial activity in each biochemical reaction stage according to the processing stage switching instruction to obtain a stage-by-stage prediction model; An optimization module is used to perform rolling optimization processing on the staged prediction model by switching the model predictive control algorithm to obtain a multi-objective control strategy; A control module, configured to coordinate and calculate an aeration volume setting value, a reflux ratio setting value, and a stirring intensity setting value according to the multi-objective control strategy to obtain a distributed control instruction; The driving module is used to drive and control the blower, reflux pump and agitator through the field bus communication protocol using the distributed control instructions to obtain the coordinated operation status of the water treatment equipment.

[0012] In a third aspect, a water treatment process optimization control device is provided, comprising: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the water treatment process optimization control device executes the above-mentioned water treatment process optimization control method.

[0013] In a fourth aspect, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium, and when the computer-readable storage medium is run on a computer, the computer executes the above-mentioned water treatment process optimization control method.

[0014] In the technical solution provided by this application, the detection data of the dissolved oxygen sensor, redox potential sensor, and pH sensor are processed for state discrimination through a multi-parameter state recognition algorithm, which can accurately identify the transition timing of different biochemical reaction stages such as anaerobic, anoxic, and aerobic in the water treatment process, avoiding the limitations of traditional methods that rely on a single parameter or time node for stage judgment. This method ensures the accuracy and timeliness of stage switching instructions through ternary parameter fusion calculation and gradient change identification. Based on the processing stage switching instructions, the law of microbial activity changes in each biochemical reaction stage is modeled and processed, and a staged prediction model for the characteristics of different reaction stages is established, overcoming the problem that the traditional unified model cannot adapt to the differentiated control requirements of each stage. By switching the model predictive control algorithm for rolling optimization processing, the predictive control is combined with multi-objective optimization to achieve a coordinated balance of multiple objectives such as water quality compliance and energy saving and consumption reduction, solving the problem of poor overall performance caused by single-objective control in the existing technology. The aeration volume set value, reflux ratio set value, and stirring intensity set value are coordinated and calculated, and the distributed control architecture ensures the synchronous and coordinated operation of each execution device, avoiding the risk of single point failure that may arise from centralized control.

[0015] In particular, the switching model predictive control algorithm fully considers the stage characteristics of the biochemical reaction process and the changes in microbial activity. By establishing specialized prediction models for the phosphorus release dynamics in the anaerobic stage, the denitrification process in the anoxic stage, and the nitrification and phosphorus absorption process in the aerobic stage, the control algorithm can accurately match the process requirements of different treatment stages. The multi-parameter state recognition algorithm is optimized for the correlation and timing characteristics of key parameters such as dissolved oxygen, redox potential, and pH value in the water treatment process. The accuracy and robustness of stage recognition are ensured through ternary parameter fusion and gradient change detection. The generation and execution mechanism of distributed control instructions fully considers the dynamic response characteristics and coordination requirements of equipment such as blowers, reflux pumps, and agitators, and realizes real-time transmission of control instructions and synchronous monitoring of equipment status through the fieldbus communication protocol. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A schematic diagram of an embodiment of a water treatment process optimization control method according to an embodiment of the present invention; Figure 2 A schematic diagram of an embodiment of a water treatment process optimization control system according to an embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of the water treatment process optimization control device in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The embodiments of the present application provide a water treatment process optimization control method, system, equipment and medium. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, an optimization control method for a water treatment process includes: Step S101: performing state discrimination processing on the detection data of the dissolved oxygen sensor, the redox potential sensor, and the pH sensor using a multi-parameter state recognition algorithm to obtain a processing stage switching instruction; Step S102: Modeling the variation pattern of microbial activity in each biochemical reaction stage according to the processing stage switching instruction to obtain a stage-by-stage prediction model; Step S103: performing rolling optimization processing on the staged prediction model by switching the model predictive control algorithm to obtain a multi-objective control strategy; Step S104: Coordinate and calculate the aeration volume setting value, the reflux ratio setting value, and the stirring intensity setting value according to the multi-objective control strategy to obtain a distributed control instruction; Step S105: The distributed control instructions are used to drive and control the blower, the reflux pump, and the agitator through the field bus communication protocol to obtain the coordinated operation status of the water treatment equipment.

[0020] It is understandable that the execution subject of the present application can be a water treatment process optimization control device, or a terminal or a server, which is not limited here. The embodiment of the present application is described by taking the server as the execution subject as an example.

[0021] Specifically, a multi-parameter state recognition algorithm is used to accurately identify different biochemical reaction stages in water treatment. The algorithm first places dissolved oxygen, redox potential, and pH sensors in anaerobic tanks, anoxic tanks, and aerobic tanks to collect water quality parameter data in real time, forming a multi-parameter detection data sequence. Its core technology, the ternary parameter fusion calculation, is as follows: first, the dissolved oxygen concentration value is compared with the high and low oxygen thresholds, marked as low, medium, and high oxygen states, to obtain the dissolved oxygen state level; based on this, the redox potential value is segmented and matched with the negative, zero, and positive potential threshold intervals to obtain the redox potential state level; then, based on this level, the pH change rate gradient is calculated, and the difference between the current and previous pH values ​​is divided by the time interval to obtain the change rate. This is compared with the preset threshold, and if the threshold is exceeded, a water quality gradient change indicator is generated. In the timing verification phase, this identifier is compared and analyzed with the parameter change trends of multiple consecutive sampling cycles. Within three consecutive sampling cycles, the dissolved oxygen concentration continues to decrease and the redox potential tends to negative values, which is determined to be the anaerobic reaction stage; the dissolved oxygen concentration remains moderate and the redox potential fluctuates around zero, which is determined to be the anoxic reaction stage; the dissolved oxygen concentration increases significantly and the redox potential turns positive, which is determined to be the aerobic reaction stage, and finally a processing stage switching instruction is generated. Based on the treatment stage switching instructions, specialized modeling is performed to determine the changes in microbial activity during each biochemical reaction stage. During the anaerobic reaction stage, the phosphorus release kinetic parameters are identified by analyzing the correlation between microbial activity indicators and phosphorus release rates. Microbial activity indicator data under anaerobic conditions is collected, and changes in phosphorus release rates are monitored. A mathematical model is established through regression analysis to obtain the dynamic response parameters for the anaerobic stage. During the anoxic reaction stage, the denitrification process parameter modeling and calculation focuses on the coupled analysis of denitrifying bacteria activity and total nitrogen removal rate. Real-time monitoring of denitrifying bacteria activity change data is performed, and combined with the total nitrogen concentration decrease rate, a multivariate linear regression algorithm is used to identify quantitative relationships, forming the dynamic response parameters for the anoxic stage. During the aerobic reaction stage, the nitrification and phosphorus absorption process is linked to the treatment stage switching instructions. The activity changes of nitrifying bacteria and phosphate-accumulating bacteria are modeled simultaneously. Water quality data for the aerobic stage is collected, activity changes are monitored, and the relationship with nitrogen and phosphorus removal efficiency is analyzed. A mathematical model is established to obtain the dynamic response parameters for the aerobic stage. Transfer characteristic analysis integrates the dynamic response parameters of the anaerobic, anoxic, and aerobic stages, identifies the mutual influence and response characteristics of each stage, converts the input-output response relationship into a predictive control structure, and sets switching conditions and boundary constraints according to the anaerobic-anoxic-aerobic time sequence combination model to form a complete staged prediction model. The phased prediction model is input into the switching model predictive controller to set the prediction time domain. The switching model predictive control algorithm can handle multimodal systems. The dynamic response parameters of the anaerobic, anoxic, and aerobic phases are input into the controller for initialization. Time windows for the prediction and control time domains are set, and the prediction control time range is divided into multiple continuous control cycles. The prediction time domain data is generated through recursive calculation. Starting from the current dissolved oxygen concentration, redox potential, and pH, the phased prediction model is used to derive the changes in water quality parameters in each future control cycle, resulting in a multi-cycle water quality change trajectory. The multi-objective constraint setting converts the target values ​​of effluent chemical oxygen demand, total nitrogen concentration, and total phosphorus concentration into an optimization objective function. Each indicator must meet emission requirements. Cost weighting assigns weights to aeration energy consumption, chemical consumption, and sludge production. These three factors are respectively related to blower power, flocculant and disinfectant dosage, and subsequent treatment costs. Water quality control and energy conservation and consumption reduction goals are weighted and combined into a comprehensive optimization objective function. The rolling optimization algorithm iteratively searches for the optimal control sequence in the control time domain, recalculates the optimal solution in each control cycle, dynamically adjusts the control strategy based on real-time water quality, extracts the optimal control parameters for each stage in segments, and combines them into a multi-objective control strategy by time. Based on the multi-objective control strategy, parameters are separated into stages, and control parameters for the anaerobic, anoxic, and aerobic stages are categorized and extracted to form a set of staged control parameters. The aeration rate setpoint calculation for the aerobic tank takes into account both the oxygen demand for nitrification and the oxygen demand for phosphorus uptake by phosphate-accumulating bacteria. The former is calculated based on ammonia nitrogen concentration and nitrification efficiency, while the latter is determined based on phosphorus removal and the oxygen consumption coefficient of phosphate-accumulating bacteria. The two components are combined to obtain the optimized aeration rate setpoint. The coupled calculation of the reflow ratio setpoint involves the internal reflow ratio in the anoxic tank and the external reflow ratio in the aerobic tank. The internal reflow ratio affects denitrification efficiency, while the external reflow ratio affects sludge concentration control. The optimized reflow ratio setpoint is calculated by balancing these two requirements. The associated calculation of the agitation intensity setpoint is performed for the anaerobic and anoxic tanks, respectively, based on the mixing requirements for phosphorus release and denitrification, to obtain the optimized agitation intensity setpoint. Finally, the optimized aeration rate, reflow ratio, and agitation intensity setpoints are grouped and configured according to the execution device address. The control instructions for the blower, reflow pump, and agitator each contain the corresponding setpoints, forming a distributed control instruction. Distributed control commands are categorized and parsed by device type. Blower control commands, reflux pump control commands, and agitator control commands correspond to different device control parameters. After data format conversion, device-specific control data packets are generated. The fieldbus communication protocol, a commonly used communication standard in industrial automation, combines control command data with device address information in a protocol format, creating a data frame structure that complies with the fieldbus communication standard. The blower drive is controlled via a frequency converter (VFD). The optimized aeration volume setpoint is converted into a corresponding fan speed control signal. The VFD adjusts the blower's operating frequency based on the speed signal, achieving precise aeration volume control. The VFD controller for the reflux pump receives the optimized reflux ratio setpoint and converts it into a corresponding pump speed control signal, controlling the reflux volume by adjusting the pump's operating speed. The agitator motor controller receives the optimized agitation intensity setpoint and converts it into a motor power control signal, regulating the agitator's speed and power output. Synchronous coordination between devices is achieved through the fieldbus network. The operating status information of the blower, reflux pump, and agitator is transmitted to the control center in real time. The control center coordinates and dispatches according to the operating status of each device. When a device malfunctions, the operating parameters of other devices can be adjusted in time to ensure the stable operation of the entire water treatment process.

[0022] In a specific embodiment, the process of executing step S101 may specifically include the following steps: Dissolved oxygen sensors, redox potential sensors, and pH sensors are placed in the anaerobic tank, anoxic tank, and aerobic tank respectively to collect water quality parameters of each treatment unit in real time and obtain multi-parameter detection data sequences; Performing a ternary parameter fusion calculation on the multi-parameter detection data sequence, comparing the dissolved oxygen concentration value with the preset hypoxia threshold and hyperoxia threshold to obtain the dissolved oxygen state level; Based on the dissolved oxygen state level, the redox potential value is segmented and matched with the negative potential threshold, the zero potential threshold, and the positive potential threshold to obtain the redox potential state level; Performing a gradient calculation on the pH change rate according to the redox potential state level, and comparing the pH change rate with a preset change rate threshold to obtain a water quality gradient change indicator; The water quality gradient change mark and the parameter change trend of multiple consecutive sampling periods are time-series verified, the switching timing of the anaerobic reaction stage, the anoxic reaction stage and the aerobic reaction stage is judged and confirmed, and the processing stage switching instruction is obtained.

[0023] Specifically, dissolved oxygen sensors, redox potential sensors, and pH sensors are installed in three different biochemical reaction areas: the anaerobic, anoxic, and aerobic tanks. Each sensor monitors the corresponding water quality parameter in real time using electrochemical principles. The dissolved oxygen sensor uses electrochemical membrane electrode technology to determine the dissolved oxygen concentration in the water by measuring the intensity of the oxygen reduction current on the electrode surface. The output analog signal is converted to a digital signal via an analog-to-digital converter. The redox potential sensor uses a platinum electrode as an indicator electrode and a reference electrode as a reference point. The potential difference between the two electrodes reflects the redox properties of the water. The potential difference directly reflects the tendency of the water to gain or lose electrons. The pH sensor, based on the ion selectivity of a glass electrode, determines the acidity or alkalinity of the water by measuring the potential change caused by hydrogen ion concentration. The voltage signal output by the sensor is logarithmically related to the pH value. A data acquisition controller synchronously collects the signals from each sensor at a preset sampling frequency, chronologically arranging the data from the nine sensors in the three tanks to form a multi-parameter detection data sequence containing information such as timestamp, sensor number, value, and unit.

[0024] The ternary parameter fusion calculation is the core of the multi-parameter state recognition algorithm. First, the dissolved oxygen concentration values ​​are extracted from the multi-parameter test data and compared with the preset hypoxia threshold (0.5 mg / L) and hyperoxia threshold (2 mg / L). The hypoxia threshold represents the critical condition for an anaerobic environment, while the hyperoxia threshold represents the triggering condition for an aerobic environment. The comparison process uses conditional branching logic. When the dissolved oxygen concentration is below the hypoxia threshold, it is marked as level 1, indicating severe hypoxia; between the two levels, it is marked as level 2, indicating moderate oxygen levels; and above the hyperoxia threshold, it is marked as level 3, indicating sufficient oxygen levels. The state levels are stored digitally to facilitate subsequent logical operations and conditional judgments. The three-level classification clearly distinguishes different oxygen environments, reflecting the survival status and metabolic intensity of microorganisms.

[0025] Segmented identification processing divides the redox potential into intervals based on the dissolved oxygen status level. The potential value range is generally between -400 mV and +400 mV, and the threshold is dynamically adjusted according to the dissolved oxygen status. In the first level, the negative potential threshold is -200 mV, the zero potential is zero, and the positive potential is +100 mV, taking into account the negative potential of anaerobic environments. In the second level, the negative potential threshold is -100 mV, the zero potential remains zero, and the positive potential is +200 mV. Interval matching is achieved through numerical comparison, and the potential values ​​are divided into four levels: reducing environment, weakly reducing environment, weakly oxidizing environment, and oxidizing environment.

[0026] The gradient calculation process quantifies the rate of change in pH based on the redox potential state. The rate of change is calculated by dividing the difference between the current pH value and the previous pH value by the time interval. The preset rate of change threshold is dynamically set based on the potential state: 0.02 pH units per minute in a reducing environment and 0.03 pH units per minute in an oxidizing environment. The difference is compared to determine whether the rate of change exceeds the threshold. If the threshold is exceeded, a water quality gradient change flag is generated and stored as a Boolean type.

[0027] Timing verification ensures the accuracy of phase switching judgments. A sliding window mechanism compares and analyzes parameter change trends over five consecutive sampling periods. The window contains information on changes in dissolved oxygen concentration, redox potential, and pH value, with the change trends determined through linear fitting. The anaerobic phase requires that dissolved oxygen, redox potential, and pH values ​​all show a decreasing or stable trend; the anoxic phase requires that dissolved oxygen levels remain stable at low levels, the redox potential fluctuates to zero, and the pH value remains relatively stable; the aerobic phase requires that dissolved oxygen levels rise, the redox potential rises, and the pH value rises. Multiple conditional combination logic judgments generate phase switching instructions, including phase identification, switch timestamp, confidence level, and other information.

[0028] In a specific embodiment, the process of executing step S102 may specifically include the following steps: Identifying and calculating the phosphorus release kinetic parameters of the anaerobic reaction stage based on the processing stage switching instruction, correlating the microbial activity index with the phosphorus release rate, and obtaining the dynamic response parameters of the anaerobic stage; Modeling and calculating the denitrification process parameters of the anoxic reaction stage according to the processing stage switching instruction, coupling analysis of the denitrifying bacteria activity and the total nitrogen removal rate, and obtaining the dynamic response parameters of the anoxic stage; The processing stage switching instruction is associated with the nitrification and phosphorus absorption process in the aerobic reaction stage, and the activity changes of nitrifying bacteria and phosphate-accumulating bacteria are synchronously modeled to obtain dynamic response parameters of the aerobic stage; Performing transfer characteristic analysis based on the anaerobic stage dynamic response parameters, the anoxic stage dynamic response parameters, and the aerobic stage dynamic response parameters, converting the input-output response relationship of each stage into a predictive control structure to obtain stage transfer characteristic data; The stage transfer characteristic data are combined into a model according to the temporal relationship of anaerobic-anoxic-aerobic, and the switching conditions and boundary constraints of each stage model are set and processed to obtain a staged prediction model.

[0029] Specifically, to model the variations in microbial activity during each biochemical reaction stage, it is first necessary to collect a variety of data related to the process phase transitions. This data includes the reaction phase transition instructions, dissolved oxygen concentration, pH, redox potential, ammonia nitrogen concentration, total nitrogen concentration, and total phosphorus concentration. During the data collection phase, a sensor network collects water quality parameters for each process phase in real time and transmits this data to a central processing system via a fieldbus communication protocol. The collected data is stored in a database and undergoes preliminary cleaning and preprocessing to remove noise and outliers, ensuring data accuracy and reliability. During the data cleaning process, appropriate thresholds are set to exclude data points outside the normal range, ensuring the validity of subsequent analysis. After data preparation is complete, a multi-parameter state recognition algorithm is applied to conduct an in-depth analysis of the relationship between the process phase transition instructions and water quality parameters. By establishing a multivariate regression model with the process phase transition instructions as the independent variable and the water quality parameters as the dependent variable, the variations in microbial activity during the different reaction phases are identified. Specifically, based on historical data, linear and nonlinear regression algorithms are used to fit the water quality parameters for each phase to generate their respective mathematical models. These models can reflect the relationship between microbial activity and water quality parameters under different operating conditions.

[0030] Based on this foundation, machine learning algorithms, such as support vector machines, decision trees, or neural networks, are further utilized to model microbial activity. By training on historical data, the algorithm can identify underlying patterns in microbial activity changes and predict the activity level under specific conditions. By inputting treatment stage switching instructions and corresponding water quality parameters into the trained model, the algorithm outputs predicted microbial activity values ​​for each reaction stage. This process not only improves the model's accuracy but also enhances its adaptability, ensuring its effectiveness across different treatment stages and operating conditions. The modeling process also requires consideration of the temporal relationship between microbial activity and water quality parameters. Time series analysis can capture the dynamic characteristics of microbial activity changes. Using time series analysis methods such as the autoregressive moving average model, modeling microbial activity effectively analyzes its changing trends and predicts future activity levels. This step is crucial for achieving dynamic control, as changes in water quality parameters often have a lag. Timely predictions provide a basis for adjusting control strategies. After the model is established, it is compared with actual monitoring data, and the model's predictive performance is evaluated through methods such as cross-validation to ensure its reliability and accuracy. By comparing the differences between the actual monitored microbial activity and the model prediction values, the model parameters are further optimized and the model fit is improved.

[0031] In a specific embodiment, the process of executing step S103 may specifically include the following steps: The phased prediction model is input into the switching model prediction controller to set the prediction time domain, and the water quality change trend of multiple control cycles in the future is predicted and calculated to obtain the prediction time domain data; Based on the predicted time domain data, target values ​​of effluent chemical oxygen demand, total nitrogen concentration, and total phosphorus concentration are constrained and set, and the water quality requirements are converted into an optimization objective function to obtain multi-objective constraint conditions; According to the multi-objective constraints, cost weights are allocated to aeration energy consumption, chemical consumption, and sludge production, and a weighted combination of water quality control objectives and energy conservation and consumption reduction objectives is performed to obtain a comprehensive optimization objective function; The comprehensive optimization objective function is solved and calculated by a rolling optimization algorithm, and the optimal control sequence in the control time domain is iteratively searched to obtain a rolling optimization control sequence; Based on the rolling optimization control sequence, the control strategies for the anaerobic stage, the anoxic stage, and the aerobic stage are extracted in sections, and the optimal control parameters of each stage are strategically combined according to the time sequence relationship to obtain a multi-objective control strategy.

[0032] Specifically, it is started when it is determined that the current stage is the anaerobic reaction stage based on the processing stage switching instruction. Phosphorus release kinetics refers to the biochemical process in which polyphosphate bacteria release phosphate stored in cells under anaerobic conditions, and its rate is directly affected by the activity state of the microorganisms. The identification calculation first collects the biomass density data of polyphosphate bacteria in the anaerobic tank, and uses the microscope counting method to count the number of polyphosphate bacteria cells in a unit volume of sludge. At the same time, the respiration rate of polyphosphate bacteria is measured as an activity indicator. The respiration rate is measured under standard conditions using an oxygen consumption rate meter. The phosphorus release rate is calculated by continuously monitoring the change in orthophosphate concentration in the anaerobic tank. The phosphorus concentration at the current moment is subtracted from the phosphorus concentration at the previous moment and divided by the time interval to obtain the release rate value. The correlation analysis uses the linear regression method, with the polyphosphate bacteria activity index as the independent variable and the phosphorus release rate as the dependent variable. The regression coefficient is calculated by the least squares method to establish a quantitative relationship model between microbial activity and phosphorus release rate. During the data processing process, it is necessary to eliminate the interference of environmental factors such as temperature and pH value. Through multivariate regression analysis, environmental variables are included in the model as covariates to obtain the corrected dynamic response parameters of the anaerobic stage, which include values ​​such as the basic phosphorus release rate constant, activity influence coefficient, and environmental correction factor.

[0033] Denitrification process parameter modeling and calculations are performed after receiving the command to switch to the anoxic reaction phase. Denitrification is a biological denitrification process in which denitrifying bacteria reduce nitrate and nitrite to nitrogen gas in an anoxic environment. Fluorescence in situ hybridization (FISH) was used to quantitatively measure the population structure and abundance of denitrifying bacteria in the anoxic tank, generating biological indicators of denitrifying bacterial activity. Denitrifying bacterial activity was characterized by measuring the activity levels of nitrate reductase and nitrite reductase spectrophotometrically. The total nitrogen removal rate was calculated by monitoring the difference in total nitrogen concentration between the influent and effluent water of the anoxic tank. A coupled analysis model was used to establish the mathematical relationship between denitrifying bacterial activity and total nitrogen removal rate. Nonlinear regression was used to fit the data, and the model used the Michaelis-Menten equation to describe the relationship between enzyme activity and removal rate. Data processing corrected for influencing factors such as carbon source concentration, temperature, and dissolved oxygen concentration. Parameter sensitivity analysis was performed to determine the weighting coefficients for each factor, thereby forming a dynamic response parameter matrix for the anoxic phase.

[0034] The associated processing of the nitrification and phosphorus absorption process is started after receiving the switching instruction of the aerobic reaction stage. The nitrification process is the process in which ammonia-oxidizing bacteria and nitrite-oxidizing bacteria gradually oxidize ammonia nitrogen into nitrate, and the phosphorus absorption process is the process in which polyphosphate-accumulating bacteria absorb a large amount of phosphate under aerobic conditions and store it as polyphosphate. Synchronous modeling requires monitoring the activity changes of nitrifying bacteria and polyphosphate-accumulating bacteria separately, and the activity change data is collected using a real-time monitoring system. The modeling process takes into account the synergistic effect of the two biochemical processes of nitrification and phosphorus absorption, and uses a coupled differential equation group to describe the growth dynamics of nitrifying bacteria and polyphosphate-accumulating bacteria. The equation group is solved by the numerical integration method to obtain the dynamic response parameters of the activity of nitrifying bacteria and polyphosphate-accumulating bacteria over time in the aerobic stage.

[0035] Transfer characteristic analysis systematically integrates the dynamic response parameters of the anaerobic, anoxic, and aerobic stages. The analysis begins by identifying the input and output variables for each stage. The response relationship is transformed using a state-space approach, converting the dynamic response parameters of each stage into matrix forms of state and output equations. The predictive control structure uses a multi-input, multi-output transfer function matrix representation. The Laplace transform converts the time-domain differential equations into frequency-domain algebraic equations, facilitating controller design and parameter adjustment. The stage transfer characteristic data includes parameters such as the gain matrix, time constant matrix, and coupling coefficient matrix between each stage.

[0036] The model combination connects the stage transfer characteristic data in an orderly manner according to the temporal relationship of anaerobic-anoxic-aerobic, and the interface relationship between the models of each stage is established during the combination process. The switching condition setting is based on the threshold judgment of the water quality parameters. The boundary constraint processing ensures the continuity and stability during the switching of each stage. The continuity conditions of the state variables and the material balance constraints are used to prevent numerical jumps or non-convergence during the switching process. The constraints include mass conservation constraints, energy balance constraints, biological rationality constraints, etc. The constraints are incorporated into the optimization objective function through the Lagrange multiplier method. The staged prediction model uses a piecewise linearization method to deal with the nonlinear characteristics of each stage, and performs linear approximation near each working point to form a piecewise continuous linear model set.

[0037] In a specific embodiment, the step of inputting the staged prediction model into the switching model prediction controller to set the prediction time domain, and the process of predicting and calculating the water quality change trend for multiple control cycles in the future can specifically include the following steps: Inputting the dynamic response parameters of the anaerobic stage, the dynamic response parameters of the anoxic stage, and the dynamic response parameters of the aerobic stage in the staged prediction model into the switching model predictive controller, initializing and configuring the model parameters of each stage, and obtaining the initial state data of the controller; Setting time windows for the prediction time domain length and the control time domain length based on the initial state data of the controller, dividing the time range of the prediction control into multiple continuous control cycles, and obtaining time domain window configuration parameters; The initial state value of the water quality state variable at the current moment is set according to the time domain window configuration parameters, and the current values ​​of the dissolved oxygen concentration, redox potential, and pH are used as the prediction starting point to obtain the prediction initial state vector; Recursively calculate the predicted initial state vector and the staged prediction model to gradually predict the changes in water quality parameters in each future control period to obtain a multi-period water quality change trajectory; Based on the multi-period water quality change trajectory, data integration is performed on the change trends of chemical oxygen demand removal rate, total nitrogen removal rate, and total phosphorus removal rate, and the prediction sequences of various water quality indicators are combined and arranged in chronological order to obtain predicted time domain data.

[0038] Specifically, the switching model predictive controller is an advanced control algorithm specifically designed for multimodal dynamic systems. It automatically selects the appropriate predictive model for control calculations based on the current operating state. During the initialization configuration phase, the dynamic response parameters of the anaerobic, anoxic, and aerobic phases from the phased predictive models are input into the corresponding modules of the controller. Anaerobic phase parameters include values ​​such as the phosphorus release rate constant, the activity coefficient of phosphate-accumulating bacteria, and the organic matter consumption rate. Anoxic phase parameters include values ​​such as the denitrification rate constant, the activity coefficient of denitrifying bacteria, and the carbon-nitrogen ratio influencing factor. Aerobic phase parameters include values ​​such as the nitrification rate constant, the phosphorus uptake rate constant, and the oxygen transfer coefficient. Model parameter initialization configuration is achieved through parameter matrix assignment. The dynamic response parameters for each phase are arranged in a matrix format using the state equation and output equation format, forming a parameter database within the controller. The controller's initial state data includes key information such as the currently active model identifier, parameter matrix dimension information, model switching logic judgment conditions, and initial values ​​of state variables. This data lays the foundation for subsequent predictive calculations and control decisions.

[0039] The time window is calculated based on the time constant and response speed parameters in the controller's initial state data. The prediction time domain length is generally set to three to five times the maximum time constant to ensure prediction accuracy, while the control time domain length is relatively short to reduce the computational burden. When setting the time window, the dominant time constant of the system is first calculated based on the dynamic response parameters of each stage. The anaerobic stage is dominated by the phosphorus release time constant, the anoxic stage is dominated by the denitrification time constant, and the aerobic stage is dominated by the nitrification time constant. The prediction time domain is divided using an equal interval segmentation method, dividing the total prediction time domain into multiple control cycles with a fixed time step size. The division of continuous control cycles must take into account the sampling frequency and actuator response speed limitations. Typically, the control cycle length is an integer multiple of the sampling period and is no less than the actuator's minimum action time. Time domain window configuration parameters include the number of prediction steps, the number of control steps, the sampling time, and the prediction start and end times.

[0040] Setting the initial state value is the starting point for the prediction calculation. Real-time sensor measurements are used to obtain the current water quality state variables, such as dissolved oxygen concentration, redox potential, pH, and other key parameters. The measured data is then preprocessed, including filtering and denoising, outlier detection, and data calibration. Filtering and denoising use a moving average method to eliminate noise. Outlier detection uses a set threshold to remove data that deviates from the normal range. Data calibration corrects for deviations based on the sensor calibration curve. Dissolved oxygen concentration, redox potential, and pH together form the basic vector describing the water quality state. The initial prediction state vector is stored as a column vector, whose dimensionality matches the number of state variables in the state equation.

[0041] Recursive calculation is the core of the predictive control algorithm. It is based on the prediction of the initial state vector and combines it with a staged prediction model to perform step-by-step iterative calculations. Recursive calculation uses numerical integration methods to solve the differential equation system. Common methods include the Euler method and the Runge-Kutta method. The latter is more accurate but has a larger computational workload. In each control cycle, the state value at the next moment is calculated based on the current state and control input. The state transition equation describes the change of state variables over time, and the output equation describes the relationship between the observed quantity and the state variable. The model switching of different biochemical reaction stages must be considered during the prediction process. When the stage switching conditions are encountered, the recursive calculation automatically switches to the next stage model to maintain the continuity of the state variables. The multi-period water quality change trajectory records the water quality parameter values ​​at each prediction moment in the form of a time series, including information such as timestamp, dissolved oxygen concentration, redox potential, and pH prediction value.

[0042] Data integration converts the original predicted data of multi-cycle water quality change trajectories into treatment effect evaluation indicators. For example, the chemical oxygen demand removal rate is calculated by dividing the difference between the influent and effluent concentrations by the influent concentration. The total nitrogen and total phosphorus removal rates are calculated using the same method. The trend analysis uses a linear fitting method to perform linear regression analysis on the data of each removal rate changing over time. The slope of the regression line reflects the trend of change in the removal rate. A positive slope indicates an upward trend, and a negative slope indicates a downward trend. The prediction sequences of each water quality indicator are arranged in chronological order to form an ordered array. The combination arrangement process aligns the chemical oxygen demand removal rate, total nitrogen removal rate, and total phosphorus removal rate sequences according to the same time index to form a multi-dimensional prediction data matrix. The predicted time domain data is stored in matrix form, with row indexes corresponding to time nodes, column indexes corresponding to different water quality indicators, and matrix elements being the predicted values ​​of the indicators at the corresponding moments.

[0043] In a specific embodiment, the process of executing step S104 may specifically include the following steps: Separating the anaerobic stage control parameters, the anoxic stage control parameters, and the aerobic stage control parameters in the multi-objective control strategy into stage parameters, classifying and extracting the optimal control parameters of each stage, and obtaining a stage-by-stage control parameter set; The aeration volume setting value of the aerobic tank is calculated based on the staged control parameter set, and the oxygen demand of the nitrification reaction and the oxygen demand of the phosphorus accumulating bacteria are superimposed to obtain the optimized aeration volume setting value; According to the staged control parameter set, a coupling calculation is performed on the reflow ratio setting value in the anoxic tank and the reflow ratio setting value outside the aerobic tank, and the denitrification efficiency requirement is balanced with the sludge concentration control requirement to obtain an optimized reflow ratio setting value; Correlating the staged control parameter set with the stirring intensity of the anaerobic tank and the anoxic tank, respectively optimizing the phosphorus release mixing intensity and the denitrification mixing intensity to obtain an optimized stirring intensity setting value; Based on the optimized aeration volume setting value, the optimized reflux ratio setting value, and the optimized stirring intensity setting value, control instructions are encapsulated, and each control parameter is grouped and configured according to the execution device address to obtain a distributed control instruction.

[0044] Specifically, stage-by-stage parameter separation is a data processing process that categorizes and organizes the comprehensive control parameters output by a multi-objective control strategy according to the biochemical reaction stage. The control parameters output by the multi-objective control strategy include the set values ​​for multiple control variables, such as aeration rate, recirculation ratio, and agitation intensity, for different time periods. Parameter separation first divides the control parameter sequence into time periods based on the timing of the stage switching instructions. The anaerobic stage corresponds to the period when the dissolved oxygen concentration is below 0.5 mg / L, the anoxic stage corresponds to the period when the dissolved oxygen concentration is between 0.5 and 2 mg / L, and the aerobic stage corresponds to the period when the dissolved oxygen concentration is above 2 mg / L. The classification extraction process groups the control parameter values ​​within each time period by parameter type. The anaerobic stage control parameters primarily include the agitation intensity setpoint and the inlet flow rate control value; the anoxic stage control parameters include the internal recirculation ratio setpoint and the agitation intensity setpoint; and the aerobic stage control parameters include the aeration rate setpoint and the external recirculation ratio setpoint. The optimal control parameters are determined by comparing the parameter values ​​with the objective function value within each time period. The parameter combination that optimizes the objective function is selected as the optimal control parameter for that stage. The staged control parameter set is stored in a structured data format. Each stage corresponds to a parameter set, which contains information such as parameter name, parameter value, parameter unit, applicable time range, etc.

[0045] The aeration setpoint is calculated based on dissolved oxygen demand parameters and microbial activity parameters during the aerobic phase, encompassing the oxidation of ammonia nitrogen and further oxidation of nitrite. The oxygen demand for nitrification is determined by multiplying the influent ammonia nitrogen concentration by the theoretical oxygen consumption coefficient, which is 4.57 grams of oxygen per gram of ammonia nitrogen. The actual oxygen demand needs to be adjusted based on nitrifying bacteria activity and environmental conditions. The oxygen demand for phosphorus uptake by phosphate-accumulating bacteria is based on the biochemical process of phosphate absorption and polyphosphate synthesis in an aerobic environment, requiring approximately 1.5 grams of oxygen per gram of phosphorus removed. The total oxygen demand is the sum of the oxygen demand for nitrification and phosphorus uptake by phosphate-accumulating bacteria. The actual aeration setpoint is then calculated based on the oxygen transfer efficiency and safety factor. The optimized aeration setpoint is calculated by dividing the total oxygen demand by the oxygen transfer coefficient, which is affected by factors such as aerator type, water depth, and water temperature.

[0046] The coupled calculation of the reflow ratio setpoint involves two parameters: the internal reflow ratio in the anoxic tank and the external reflow ratio in the aerobic tank. The internal reflow ratio influences denitrification efficiency and must be determined based on the target total nitrogen removal rate. The external reflow ratio is used to maintain an appropriate sludge concentration in the biochemical tank. Balanced treatment utilizes a multi-objective optimization approach, using weighting coefficients to adjust the importance of denitrification efficiency and sludge concentration control to find the optimal reflow ratio combination. Optimizing the reflow ratio setpoint is achieved through iterative calculations.

[0047] Agitation intensity correlation calculations optimize the design of anaerobic and anoxic tanks based on their different mixing requirements. The agitation intensity in anaerobic tanks ensures sufficient contact between phosphate-accumulating bacteria and organic matter, avoiding excessive agitation. The agitation intensity in anoxic tanks ensures sufficient contact between denitrifying bacteria, nitrates, and organic matter, maintaining an anoxic environment. The optimization process independently calculates agitation intensity for both anaerobic and anoxic tanks, minimizing energy consumption and maximizing treatment effectiveness. A cost-benefit analysis determines the most economical agitation intensity.

[0048] The control instruction package converts the optimized aeration volume setpoint, reflux ratio setpoint, and stirring intensity setpoint into a control instruction format that the actuator device can recognize. The control instruction contains fields such as the device address, function code, data value, and checksum. It uses standard industrial communication protocols such as Modbus or Profibus and is transmitted to the actuator device via the fieldbus network.

[0049] In a specific embodiment, the process of executing step S105 may specifically include the following steps: The distributed control instructions are classified and parsed according to the blower control instructions, the reflux pump control instructions, and the agitator control instructions, and the control parameters of each device are converted into data formats to obtain a device-specific control data packet; Based on the device-specific control data packet, the data frame structure of the field bus communication protocol is encapsulated and processed, and the control instruction data and the device address information are assembled in the protocol format to obtain a bus communication data frame; Sending a drive signal to the frequency converter of the aerobic pool blower according to the bus communication data frame, converting the optimized aeration volume setting value into a blower speed control signal, and obtaining the blower operation control state; The bus communication data frame is connected to the frequency conversion controllers of the internal reflux pump and the external reflux pump to perform pump speed adjustment control on the optimized reflux ratio setting value to obtain the reflux pump operation control state; Based on the bus communication data frame, instructions are transmitted to the motor controllers of the anaerobic tank and anoxic tank agitators, the optimized stirring intensity setting value is converted into a motor power control signal, and synchronized with the blower operation control status and the reflux pump operation control status to obtain the coordinated operation status of the water treatment equipment.

[0050] Specifically, device classification parsing is the data parsing process that groups distributed control commands by executing device type. Distributed control commands contain various control parameters and setpoints. Classification parsing first categorizes control commands by device identifier. For example, blower control commands contain parameters such as aeration volume and start / stop; reflux pump control commands contain parameters such as internal / external reflux ratio; and agitator control commands contain parameters such as agitation intensity. Data format conversion converts control parameters from standardized numerical values ​​to device-specific formats. The data formats and ranges of inverters and controllers vary across devices. For blower inverters, which use frequency setting control, the aeration volume setpoint must be converted to a frequency value, taking into account fan and piping characteristics. For reflux pump inverters, which use speed percentage control, the reflux ratio setpoint must be converted to a pump speed percentage value based on pump performance and flow characteristics. For agitator motor controllers, which use power setting control, the agitation intensity setpoint must be converted to a motor power value, taking into account agitator torque and load characteristics. Device-specific control data packets are stored in a structured format, including fields such as device type identifier, parameter type, value, unit, and timestamp. The data frame structure encapsulation process is based on the standard format requirements of fieldbus communication protocols, which are widely used in industrial automation. Common protocols include Modbus, Profibus, and CAN bus. The first step in the encapsulation process is to determine the communication protocol type and its data frame format. For example, the Modbus protocol data frame contains fields such as the device address, function code, data content, and error checking. Each field has a fixed byte length and format requirements. The device address is a unique identifier for a device in a fieldbus network, typically represented by an eight-bit or sixteen-bit value. Control command data must be encoded according to the protocol format. Numerical data is encoded in binary or hexadecimal, while status data is encoded using bit manipulation. The protocol format assembly arranges the device address, function code, control data, and checksum in the specified order to form a complete data frame. The data frame length is dynamically adjusted based on the amount of control data.

[0051] During the blower drive signal transmission process, the control command is transmitted to the blower inverter via the fieldbus network. The blower in the aerobic pool is a key device for providing aeration volume, and its operating status directly affects the biochemical reaction effect. The first step in sending the drive signal is to establish a communication connection with the blower inverter, including a physical connection (implemented via a bus cable) and a logical connection (identity and parameters confirmed through a handshake protocol). Optimizing the aeration volume setpoint requires conversion into a fan speed control signal, a process that takes into account the blower's performance characteristics and operating conditions. The speed control signal is achieved by adjusting the inverter's output frequency. There is a nonlinear relationship between speed and aeration volume. After receiving the signal, the inverter will automatically adjust the output frequency and voltage to drive the blower to reach the target speed.

[0052] The communication connection for the return pump involves an internal return pump and an external return pump. The internal return pump returns the mixed liquor from the aerobic tank to the anoxic tank, while the external return pump returns the sludge from the secondary sedimentation tank to the biochemical tank. The communication connection establishment process is similar, with data exchange between the controller and the variable frequency drive controller achieved via the fieldbus network. The optimized return ratio setpoint requires conversion to a corresponding pump speed setpoint, based on the pump's flow characteristic curve and piping system characteristics. The internal return ratio is controlled by adjusting the internal return pump's speed, while the external return pump must overcome the weight of the sludge and piping resistance.

[0053] Agitator command transmission independently controls the agitation equipment in the anaerobic and anoxic tanks. The motor controller precisely controls the agitation intensity by adjusting the motor's supply voltage and frequency. The command transmission process sends the optimized agitation intensity setpoint to the motor controller via bus communication data frames. This process includes command encoding, data verification, and communication confirmation. The optimized agitation intensity setpoint must be converted into a motor power control signal, taking into account the agitator's mechanical and load characteristics.

[0054] Synchronous coordination is a control mechanism that ensures the coordinated operation of multiple devices. The operating status of the blower, return pump, and agitator must be consistent in both time and function. Coordinated control is achieved by monitoring the operational feedback from each device. If a device experiences an abnormality, the system automatically adjusts the operating parameters of other devices to maintain overall treatment effectiveness. This coordination mechanism ensures the efficient and stable operation of the water treatment system, improving overall treatment efficiency and water quality control.

[0055] The above describes the water treatment process optimization control method in the embodiment of the present application. The following describes the water treatment process optimization control system in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the water treatment process optimization control system includes: The discrimination module 201 is used to perform state discrimination processing on the detection data of the dissolved oxygen sensor, the redox potential sensor, and the pH sensor through a multi-parameter state recognition algorithm to obtain a processing stage switching instruction, including: arranging a dissolved oxygen sensor, a redox potential sensor, and a pH sensor in the anaerobic tank, the anoxic tank, and the aerobic tank, respectively, collecting the water quality parameters of each processing unit in real time to obtain a multi-parameter detection data sequence; performing a ternary parameter fusion calculation on the multi-parameter detection data sequence, comparing the dissolved oxygen concentration value with the preset low oxygen threshold and high oxygen threshold to obtain a dissolved oxygen state level; based on the The dissolved oxygen state level is used to segmentally identify the redox potential value, and the potential value is interval-matched with the negative potential threshold, the zero potential threshold, and the positive potential threshold to obtain the redox potential state level; the pH change rate is gradient-calculated according to the redox potential state level, and the pH change rate is differentially compared with the preset change rate threshold to obtain a water quality gradient change indicator; the water quality gradient change indicator is time-series verified with the parameter change trend of multiple consecutive sampling periods, and the conversion timing of the anaerobic reaction stage, the anoxic reaction stage, and the aerobic reaction stage is judged and confirmed to obtain a processing stage switching instruction; Modeling module 202, for modeling the variation pattern of microbial activity in each biochemical reaction stage according to the processing stage switching instruction to obtain a stage-by-stage prediction model; An optimization module 203 is configured to perform rolling optimization processing on the staged prediction model by switching the model predictive control algorithm to obtain a multi-objective control strategy; The control module 204 is used to coordinate and calculate the aeration volume setting value, the reflux ratio setting value, and the stirring intensity setting value according to the multi-objective control strategy to obtain a distributed control instruction; The driving module 205 is used to drive and control the blower, the reflux pump and the agitator through the field bus communication protocol using the distributed control instructions to obtain the coordinated operation status of the water treatment equipment.

[0056] above Figure 2 The water treatment process optimization control system in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The water treatment process optimization control device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0057] Reference Figure 3 In the embodiment of the present invention, a water treatment process optimization control device is also provided. The water treatment process optimization control device can be a server, and its internal structure can be as follows: Figure 3As shown. The water treatment process optimization control device includes a processor, memory, display screen, input device, network interface and database connected through a system bus. Among them, the computer-designed processor is used to provide computing and control capabilities. The memory of the water treatment process optimization control device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the water treatment process optimization control device is used to store the corresponding data in this embodiment. The network interface of the water treatment process optimization control device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0058] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the water treatment process optimization control device to which the solution of the present invention is applied.

[0059] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the water treatment process optimization control method.

[0060] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0061] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a water treatment process optimization control device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0062] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A water treatment process optimization control method, characterized in that: The method comprises: The detection data of the dissolved oxygen sensor, the redox potential sensor, and the pH sensor are subjected to state discrimination processing by a multi-parameter state recognition algorithm to obtain a processing stage switching instruction, including: arranging dissolved oxygen sensors, redox potential sensors, and pH sensors in the anaerobic tank, the anoxic tank, and the aerobic tank respectively, collecting the water quality parameters of each processing unit in real time, and obtaining a multi-parameter detection data sequence; performing a ternary parameter fusion calculation on the multi-parameter detection data sequence, comparing the dissolved oxygen concentration value with the preset low oxygen threshold and high oxygen threshold to obtain a dissolved oxygen state level; based on the dissolved oxygen The state level identifies the redox potential value in segments, and performs interval matching of the potential value with the negative potential threshold, the zero potential threshold, and the positive potential threshold to obtain the redox potential state level; the pH change rate is gradient calculated according to the redox potential state level, and the pH change rate is differentially compared with the preset change rate threshold to obtain a water quality gradient change identifier; the water quality gradient change identifier is time-series verified with the parameter change trend of multiple consecutive sampling cycles, and the conversion timing of the anaerobic reaction stage, the anoxic reaction stage, and the aerobic reaction stage is judged and confirmed to obtain a processing stage switching instruction; Modeling the variation patterns of microbial activity in each biochemical reaction stage according to the processing stage switching instructions to obtain a stage-by-stage prediction model; The staged prediction model is subjected to rolling optimization processing by switching the model predictive control algorithm to obtain a multi-objective control strategy; Coordinated calculation and processing of the aeration volume setting value, the reflux ratio setting value, and the stirring intensity setting value are performed according to the multi-objective control strategy to obtain distributed control instructions; The distributed control instructions are used to drive and control the blower, the reflux pump, and the agitator through the field bus communication protocol to obtain the coordinated operation status of the water treatment equipment.

2. The water treatment process optimization control method according to claim 1, characterized in that: The method of modeling the change rules of microbial activity in each biochemical reaction stage according to the processing stage switching instruction to obtain a stage-by-stage prediction model includes: Identifying and calculating the phosphorus release kinetic parameters of the anaerobic reaction stage based on the processing stage switching instruction, correlating the microbial activity index with the phosphorus release rate, and obtaining the dynamic response parameters of the anaerobic stage; Modeling and calculating the denitrification process parameters of the anoxic reaction stage according to the processing stage switching instruction, coupling analysis of the denitrifying bacteria activity and the total nitrogen removal rate, and obtaining the dynamic response parameters of the anoxic stage; The processing stage switching instruction is associated with the nitrification and phosphorus absorption process in the aerobic reaction stage, and the activity changes of nitrifying bacteria and phosphate-accumulating bacteria are synchronously modeled to obtain dynamic response parameters of the aerobic stage; Performing transfer characteristic analysis based on the anaerobic stage dynamic response parameters, the anoxic stage dynamic response parameters, and the aerobic stage dynamic response parameters, converting the input-output response relationship of each stage into a predictive control structure to obtain stage transfer characteristic data; The stage transfer characteristic data are combined into a model according to the temporal relationship of anaerobic-anoxic-aerobic, and the switching conditions and boundary constraints of each stage model are set and processed to obtain a staged prediction model.

3. The water treatment process optimization control method according to claim 1, characterized in that: The stepwise prediction model is subjected to rolling optimization processing by switching the model predictive control algorithm to obtain a multi-objective control strategy, including: The phased prediction model is input into the switching model prediction controller to set the prediction time domain, and the water quality change trend of multiple control cycles in the future is predicted and calculated to obtain the prediction time domain data; Based on the predicted time domain data, target values ​​of effluent chemical oxygen demand, total nitrogen concentration, and total phosphorus concentration are constrained and set, and the water quality requirements are converted into an optimization objective function to obtain multi-objective constraint conditions; According to the multi-objective constraints, cost weights are allocated to aeration energy consumption, chemical consumption, and sludge production, and a weighted combination of water quality control objectives and energy conservation and consumption reduction objectives is performed to obtain a comprehensive optimization objective function; The comprehensive optimization objective function is solved and calculated by a rolling optimization algorithm, and the optimal control sequence in the control time domain is iteratively searched to obtain a rolling optimization control sequence; Based on the rolling optimization control sequence, the control strategies for the anaerobic stage, the anoxic stage and the aerobic stage are extracted in sections, and the optimal control parameters of each stage are strategically combined according to the time sequence relationship to obtain a multi-objective control strategy.

4. The water treatment process optimization control method according to claim 3, characterized in that: The phased prediction model is input into the switching model prediction controller to set the prediction time domain, and the water quality change trend of multiple control cycles in the future is predicted and calculated to obtain the prediction time domain data, including: Inputting the dynamic response parameters of the anaerobic stage, the dynamic response parameters of the anoxic stage, and the dynamic response parameters of the aerobic stage in the staged prediction model into the switching model predictive controller, initializing and configuring the model parameters of each stage, and obtaining the initial state data of the controller; Setting time windows for the prediction time domain length and the control time domain length based on the initial state data of the controller, dividing the time range of the prediction control into multiple continuous control cycles, and obtaining time domain window configuration parameters; The initial state value of the water quality state variable at the current moment is set according to the time domain window configuration parameters, and the current values ​​of the dissolved oxygen concentration, redox potential, and pH are used as the prediction starting point to obtain the prediction initial state vector; Recursively calculate the predicted initial state vector and the staged prediction model to gradually predict the changes in water quality parameters in each future control period to obtain a multi-period water quality change trajectory; Based on the multi-period water quality change trajectory, data integration is performed on the change trends of chemical oxygen demand removal rate, total nitrogen removal rate, and total phosphorus removal rate, and the prediction sequences of various water quality indicators are combined and arranged in chronological order to obtain predicted time domain data.

5. The water treatment process optimization control method according to claim 1, characterized in that: The method of performing coordinated calculation and processing on the aeration volume setting value, the reflux ratio setting value, and the stirring intensity setting value according to the multi-objective control strategy to obtain distributed control instructions includes: Separating the anaerobic stage control parameters, the anoxic stage control parameters, and the aerobic stage control parameters in the multi-objective control strategy into stage parameters, classifying and extracting the optimal control parameters of each stage, and obtaining a stage-by-stage control parameter set; The aeration volume setting value of the aerobic tank is calculated based on the staged control parameter set, and the oxygen demand of the nitrification reaction and the oxygen demand of the phosphorus accumulating bacteria are superimposed to obtain the optimized aeration volume setting value; According to the staged control parameter set, a coupling calculation is performed on the reflow ratio setting value in the anoxic tank and the reflow ratio setting value outside the aerobic tank, and the denitrification efficiency requirement is balanced with the sludge concentration control requirement to obtain an optimized reflow ratio setting value; Correlating the staged control parameter set with the stirring intensity of the anaerobic tank and the anoxic tank, respectively optimizing the phosphorus release mixing intensity and the denitrification mixing intensity to obtain an optimized stirring intensity setting value; Based on the optimized aeration volume setting value, the optimized reflux ratio setting value, and the optimized stirring intensity setting value, control instructions are encapsulated, and each control parameter is grouped and configured according to the execution device address to obtain a distributed control instruction.

6. The water treatment process optimization control method according to claim 1, characterized in that: The distributed control instructions are used to drive and control the blower, the reflux pump, and the agitator through the field bus communication protocol to obtain the coordinated operation status of the water treatment equipment, including: The distributed control instructions are classified and parsed according to the blower control instructions, the reflux pump control instructions, and the agitator control instructions, and the control parameters of each device are converted into data formats to obtain a device-specific control data packet; Based on the device-specific control data packet, the data frame structure of the field bus communication protocol is encapsulated and processed, and the control instruction data and the device address information are assembled in the protocol format to obtain a bus communication data frame; Sending a drive signal to the frequency converter of the aerobic pool blower according to the bus communication data frame, converting the optimized aeration volume setting value into a blower speed control signal, and obtaining the blower operation control state; The bus communication data frame is connected to the frequency conversion controllers of the internal reflux pump and the external reflux pump to perform pump speed adjustment control on the optimized reflux ratio setting value to obtain the reflux pump operation control state; Based on the bus communication data frame, instructions are transmitted to the motor controllers of the anaerobic tank and anoxic tank agitators, the optimized stirring intensity setting value is converted into a motor power control signal, and synchronized with the blower operation control status and the reflux pump operation control status to obtain the coordinated operation status of the water treatment equipment.

7. A water treatment process optimization control system, characterized in that: For implementing the water treatment process optimization control method according to any one of claims 1 to 6, the water treatment process optimization control system comprises: The discrimination module is used to perform state discrimination processing on the detection data of the dissolved oxygen sensor, the redox potential sensor, and the pH sensor through a multi-parameter state recognition algorithm to obtain a processing stage switching instruction, including: arranging a dissolved oxygen sensor, a redox potential sensor, and a pH sensor in the anaerobic tank, the anoxic tank, and the aerobic tank respectively, collecting the water quality parameters of each processing unit in real time, and obtaining a multi-parameter detection data sequence; performing a ternary parameter fusion calculation on the multi-parameter detection data sequence, comparing the dissolved oxygen concentration value with the preset low oxygen threshold and high oxygen threshold to obtain a dissolved oxygen state level; based on the The dissolved oxygen state level identifies the redox potential value in segments, and performs interval matching of the potential value with the negative potential threshold, the zero potential threshold, and the positive potential threshold to obtain the redox potential state level; the pH change rate is gradient calculated according to the redox potential state level, and the pH change rate is differentially compared with the preset change rate threshold to obtain a water quality gradient change indicator; the water quality gradient change indicator is time-series verified with the parameter change trend of multiple consecutive sampling cycles, and the conversion timing of the anaerobic reaction stage, the anoxic reaction stage, and the aerobic reaction stage is judged and confirmed to obtain a processing stage switching instruction; A modeling module is used to model the change pattern of microbial activity in each biochemical reaction stage according to the processing stage switching instruction to obtain a stage-by-stage prediction model; An optimization module is used to perform rolling optimization processing on the staged prediction model by switching the model predictive control algorithm to obtain a multi-objective control strategy; A control module, configured to coordinate and calculate an aeration volume setting value, a reflux ratio setting value, and a stirring intensity setting value according to the multi-objective control strategy to obtain a distributed control instruction; The driving module is used to drive and control the blower, reflux pump and agitator through the field bus communication protocol using the distributed control instructions to obtain the coordinated operation status of the water treatment equipment.

8. A water treatment process optimization control device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the water treatment process optimization control method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processor is enabled to perform the water treatment process optimization control method according to any one of claims 1 to 6.

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