Aoa condition control system based on online process twin simulation

Abstract

The present application relates to the technical field of automatic control of sewage biochemical treatment and digital twinning of industrial processes, in particular to an AOA working condition control system based on online process twinning simulation, which comprises a physical quantity acquisition interface module, a twinning deduction and parameter reconstruction engine, a feedforward optimization control module and a model self-adaptive calibration module; environmental input, actuator operation and fluid process data are collected, the output power time partial derivative and the second state quantity attenuation slope are extracted to construct a joint feature matrix, the feedforward compensation margin is mapped, and the future first state quantity prediction sequence is generated by discrete solution; the optimal frequency setting value sequence is solved and issued based on the residual vector and the total energy consumption function; the model self-calibration is triggered according to the error between the actual first state quantity and the prediction sequence, so as to realize the advance response to load impact, energy consumption collaborative constraint and model drift closed-loop correction.

Description

Technical Field

[0001] This invention relates to the field of automatic control for wastewater biochemical treatment and digital twin technology for industrial processes, specifically to an AOA (Automatic Operational Analysis) control system based on online process twin simulation. Background Technology

[0002] In existing wastewater biological treatment systems, the AOA biological treatment section, which is formed by arranging anaerobic tanks, anoxic tanks, and aerobic tanks in sequence, is quite common. The system usually includes an influent water quality and flow detection unit, a dissolved oxygen and oxidation-reduction potential monitoring unit, as well as actuators such as blowers and return pumps. The blowers and return pumps adjust the oxygen supply intensity and return flow in the tank through preset control logic to maintain the effluent water quality in compliance with standards. Most existing control methods rely on feedback adjustment based on dissolved oxygen at the end of the aerobic tank, effluent indicators, or fixed empirical parameters. Under conditions such as fluctuating influent load, temperature changes, industrial mixing, or rainstorm impact, short-term correction is usually achieved by increasing the blower frequency or adjusting the return flow. Moreover, sensor data and actuator operation data from different sources are mostly collected separately by the field controller and then used for control. However, relying solely on terminal state quantities for delayed feedback makes it difficult to reflect the transmission process of front-end load shocks to the interior of the biochemical system in a timely manner. Furthermore, increasing the aeration rate alone can easily lead to increased energy consumption and damage to the anaerobic environment in the anoxic zone. In addition, after long-term operation, deviations may occur between the model parameters and the on-site microbial community status, resulting in inconsistencies between the control results and the actual operating conditions. Consequently, the AOA process suffers from problems such as untimely response, insufficient collaborative control capabilities, and poor operational stability under complex disturbances. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides an AOA (Automatic Operational Analysis) control system based on online process twin simulation. Specifically, the technical solution of this invention includes: The physical quantity acquisition interface module connects to the sensors and actuators of the multi-stage fluid reaction system to acquire environmental input data including influent load, influent flow rate, temperature and water quality physicochemical change indicators, actuator operation data including real-time current, frequency, torque and power, and fluid process data including first and second state quantities. The twin simulation and parametric reconstruction engine extracts the time partial derivative of the power and the decay slope of the second state quantity to construct a joint feature matrix, which is mapped to a feedforward compensation margin through a preset twin simulation model. Using the first and second state quantities as initial conditions, the mechanism differential equation is solved discretely according to a preset time step to generate a prediction sequence of the first state quantity within the future time window. The feedforward optimization control module calculates the residual vector between the predicted sequence and the preset first state quantity safety threshold, calls the quadratic programming solver, and aims to minimize the quadratic polynomial formed by the weighted sum of squared residuals and the total energy consumption function representing the power integral. With the feedforward compensation margin and the maximum operating frequency as constraints, it solves the optimal frequency setpoint sequence and converts it into control commands. The model adaptive calibration module obtains the actual first state quantity after the instruction is executed and calculates its absolute error with the predicted sequence. When the number of consecutive errors exceeding the limit reaches the preset judgment number, the self-calibration instruction is triggered to correct the dynamic mechanism coefficients of the mechanism differential equation; otherwise, the current parameters are maintained.

[0004] Optionally, the multi-stage fluid reaction system is an AOA biochemical treatment system; the first state quantity is the dissolved oxygen concentration in the aerobic tank; the second state quantity is the oxidation-reduction potential at the front end of the aerobic tank; the actuator includes a blower frequency converter and a nitrification liquid return pump; and the kinetic mechanism coefficient is the microbial yield coefficient.

[0005] Optionally, the feedforward compensation margin characterizes the maximum power output increment limit of the influent load within a future time window under the condition of maintaining a preset anaerobic environment constraint.

[0006] Optionally, the twin inference and parameter reconstruction engine calls a preset fourth-order Runge-Kutta algorithm as a solver, performs discretization solution operation on the mechanism differential equation with a preset time step, and outputs the prediction sequence at discrete time nodes.

[0007] Optionally, when the self-calibration instruction is triggered, the model adaptive calibration module extracts the actual first state variables within a preset historical time window to form a sliding window dataset; the model adaptive calibration module calls the least squares algorithm to perform nonlinear fitting correction operation on the dynamic mechanism coefficients based on the sliding window dataset.

[0008] Optionally, the system further includes an abnormal circuit breaker defense module, which monitors abrupt changes in water quality physicochemical indicators in the environmental input data; configured as follows: If the water quality physicochemical mutation index is greater than the preset biochemical survival boundary threshold, the twin simulation model is determined to be ineffective, the control command of the feedforward optimization control module is blocked, and the maximum power command and water inlet cut-off command are output. If the water quality physicochemical mutation index is less than or equal to the preset biochemical survival boundary threshold, the control authority of the feedforward optimization control module is maintained.

[0009] Optionally, the optimal frequency setpoint sequence forms an S-shaped acceleration / deceleration transition curve in its time-series distribution; The feedforward optimization control module uses the preset ratio of the system volume of the multi-stage fluid reaction system to the influent flow rate as the hydraulic residence time. Before the fluid, represented by the influent load, reaches the mid-spatial position of the multi-stage fluid reaction system based on the hydraulic residence time, it outputs an initial incremental command according to the S-shaped acceleration / deceleration transition curve, using the spatial margin to compensate for the time lag of the reaction.

[0010] Optionally, the physical quantity acquisition interface module includes a heterogeneous protocol adaptation unit; the heterogeneous protocol adaptation unit reads the environmental input data through the analog input channel and reads the actuator operation data through the industrial bus protocol; the heterogeneous protocol adaptation unit performs timestamp alignment and data format standardization processing on the acquired data.

[0011] Optionally, the feedforward optimization control module limits the rate of change of frequency between adjacent time nodes of the optimal frequency setpoint sequence to within a preset acceleration threshold based on a preset fluid shear force model, thereby limiting the operating acceleration of the actuator through the preset acceleration threshold.

[0012] Optionally, the system is deployed in a hierarchical architecture that includes edge computing nodes and field controllers; The physical quantity acquisition interface module resides in the field controller and performs real-time physical quantity acquisition operations; The twin inference and parameter reconstruction engine, the feedforward optimization control module, and the model adaptive calibration module reside on the edge computing node and perform concurrent computing and model evolution operations.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. This system extracts the time partial derivative of power and the decay slope of the second state variable through twin inference and parametric reconstruction engine to construct a joint feature matrix. Using the first and second state variables as initial conditions, it discretizes the mechanism differential equation to generate a predicted sequence of the first state variable within the future time window. At the same time, it combines the ratio of system volume to influent flow rate as the hydraulic residence time. Before the fluid reaches the middle spatial position of the multi-stage fluid reaction system, it outputs the initial increment command in advance according to the S-shaped acceleration and deceleration transition curve. This mechanism effectively compensates for the time lag of the reaction by utilizing spatial margin, and solves the problem that traditional methods that rely solely on the terminal state variable cannot cope with the front-end load impact. 2. The feedforward optimization control module uses the feedforward compensation margin and maximum operating frequency as constraints to ensure that when absorbing the influent load within the future time window and increasing power output, the preset anaerobic environment constraints are strictly maintained. This avoids the drawback of simply increasing the aeration rate in traditional control, which can easily disrupt the anaerobic environment in the anoxic zone. At the same time, the system solves the optimal frequency setpoint sequence with the joint objective of minimizing the sum of squared residuals and the total energy consumption function representing the power integral. This avoids the actuator from operating in a high-energy-consumption state for a long time, achieving a balance between process stability and reducing the value of the energy integral function. 3. The model adaptive calibration module obtains the actual first state variables after command execution and calculates their absolute error with the predicted sequence. When the number of consecutive errors exceeding the limit reaches the preset judgment number, the system extracts the actual first state variables within the preset historical time window to form a sliding window dataset, and calls the least squares algorithm to perform nonlinear fitting correction on the dynamic mechanism coefficients. This mechanism enables the model to dynamically adapt to changes in field conditions and states, solving the problem of control inaccuracy caused by deviations between model parameters and actual conditions after long-term operation. 4. This system monitors the water quality physicochemical mutation indicators in the environmental input data through the abnormal circuit breaker defense module. When the indicator exceeds the preset biochemical survival boundary threshold, the twin simulation model is promptly determined to be faulty, the feedforward control command is blocked, and the maximum power command and water inlet cut-off command are output, forming physical isolation and safety protection under extreme conditions. At the same time, the system combines the fluid shear force model to limit the rate of change of frequency of adjacent time nodes of the optimal frequency setpoint sequence within the preset acceleration threshold, which limits the operating acceleration of the actuator and avoids physical damage to the biochemical system caused by the rapid acceleration of the equipment. Attached Figure Description

[0014] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0016] like Figure 1 As shown, the AOA operating condition control system based on online process twin simulation includes a physical quantity acquisition interface module, a twin inference and parameter reconstruction engine, a feedforward optimization control module, and a model adaptive calibration module. The physical quantity acquisition interface module connects to the sensors and actuators of the multi-stage fluid reaction system to acquire environmental input data including influent load, influent flow rate, temperature and water quality physicochemical change indicators, actuator operation data including real-time current, frequency, torque and power, and fluid process data including first and second state quantities; The twin simulation and parametric reconstruction engine extracts the time partial derivative of power and the decay slope of the second state variable to construct a joint feature matrix, which is then mapped to a feedforward compensation margin through a pre-set twin simulation model. Using the first and second state variables as initial conditions, the mechanism differential equation is solved discretely according to a preset time step to generate a prediction sequence of the first state variable within the future time window. The feedforward optimization control module calculates the residual vector between the predicted sequence and the preset first state variable safety threshold, calls the quadratic programming solver, and aims to minimize the quadratic polynomial formed by the weighted sum of squared residuals and the total energy consumption function representing the power integral. With the feedforward compensation margin and the maximum operating frequency as constraints, it solves the optimal frequency setpoint sequence and converts it into control commands. The model adaptive calibration module obtains the actual first state quantity after the instruction is executed and calculates its absolute error with the predicted sequence. When the number of consecutive errors exceeding the limit reaches the preset judgment number, the dynamic mechanism coefficients of the self-calibration instruction correction mechanism differential equation are triggered; otherwise, the current parameters are maintained.

[0017] This embodiment provides an AOA operating condition control mechanism based on online process twin simulation. Specifically, the mechanism is deployed in the AOA biochemical section of a municipal wastewater treatment plant with a daily treatment capacity of 50,000 tons to cope with operating condition disturbances caused by the combined effects of rainfall shock loads, upstream industrial mixing, and diurnal water volume fluctuations. In this main scenario, the anaerobic tank, anoxic tank, and aerobic tank are arranged sequentially along the plug flow direction. Blowers and return pumps serve as the main regulating actuators, and the system continuously maintains the effluent ammonia nitrogen and total nitrogen to meet the standards, while suppressing the problems of excessive aeration energy consumption and the destruction of the anoxic zone by dissolved oxygen. The following is a detailed description: The physical quantity acquisition interface module collects environmental input data, actuator operation data, and fluid process data from the field sensor network; the environmental input data reflects the state of external loads entering the biochemical system. For example, the influent flow rate reflects the amount of water entering the tank per unit time, the temperature reflects the thermal environment in which the microorganisms are metabolically active, and the physicochemical mutation indicators reflect whether there is strong acid, strong alkali, heavy metal, or other abnormal influent that is sufficient to destroy the activity of the bacterial community. The actuator operation data reflects the current power output status of the system for oxygen supply or recirculation. The output power and torque can be used to identify the actual work state of the blower when overcoming pipeline resistance and liquid level changes. The first and second state quantities in the fluid process data together reflect whether the biochemical state at the reaction front in the pool is developing towards imbalance. Based on this, the twin simulation and parametric reconstruction engine monitors fluid process data and extracts features in real time. It does not rely on the feedback of water quality indicators at the end of the system. Instead, it extracts joint features from the absolute value of the time partial derivative of the output power and the trend of the second state variable. Specifically, the joint feature matrix is ​​configured in the data structure as a structured two-dimensional array with time series nodes as rows and the calculated time partial derivative of the output power and the decay slope of the second state variable as columns. In a physical sense, the time partial derivative of the blower's output power can reflect whether the oxygen supply driving force suddenly increases per unit time; the decay slope of the second state quantity can reflect how the electron acceptor environment at the front of the pool is changing; the combination of the two can distinguish the difference between short-term mechanical fluctuations and load shocks that have been transmitted to the front of the biochemical reaction. To facilitate understanding, a concrete example can be provided: Suppose data is continuously collected at three time points during a certain period, and the actual output power values ​​at these three time points are denoted as follows: , , The actual values ​​of the second state variables at the corresponding time points are denoted as follows: , , ; If output power arrive The increase in the second state quantity is greater than the first preset threshold, and the second state quantity... arrive If the rate of change of the downward trend is greater than the second preset threshold, then the next row in the joint feature matrix will reflect the state of rising oxygen demand, increased load on the actuator, and rapid consumption of the front-end redox environment. The twin model maps this state to a feedforward compensation margin within the first preset range, for example, by interpolation addressing through a preset multidimensional regular surface. The preset multidimensional regular surface is a data mapping model pre-constructed by a surface fitting algorithm, which is based on the data of the correspondence between three parameters—influent load, output power time partial derivative, and second state quantity decay slope—and the maximum allowable frequency increase of the blower that does not damage the anoxic environment of the hypoxic zone under historical stable operating conditions extracted offline. The dual deterioration trend reflected in the matrix is ​​converted into a restrictive power output safety margin parameter, indicating that the upper limit of the actuator's output needs to be limited according to the feedforward compensation margin. If the increase in output power is equal to the first preset threshold or the rate of change of the downward trend of the second state quantity is equal to the second preset threshold, and the aforementioned condition of being greater than is not met, or if it is between the preset lower limit of fluctuation and the first preset threshold, the system maintains the current compensation margin assessment state; conversely, if the rate of change of power is lower than or equal to the preset lower limit of fluctuation and the fluctuation amplitude of the second state quantity is less than or equal to the preset range, then the corresponding feedforward compensation margin is within the second preset range, indicating that the system still has a certain amount of advance adjustment space. After completing the parameter reconstruction, the twin simulation and parameter reconstruction engine uses the first and second state variables acquired in real time as initial boundary conditions to perform discretization solution on the mechanism differential equations in the process twin simulation model, and outputs the predicted sequence of the first state variables within the future time window. Among them, the mechanism differential equations are constructed based on the material balance theory of the International Water Association activated sludge model, which specifically includes the convection diffusion equation of dissolved oxygen in the aerobic tank, the gas-liquid two-phase oxygen mass transfer equation, and the aerobic respiration metabolic kinetic equations of nitrifying bacteria and heterotrophic bacteria. The predicted sequence represents an engineering prediction of the oxygen supply and demand balance trend in the aerobic zone over a period of time. If the predicted value continues to reach the preset safety threshold warning range, it indicates that after the load shock has been delayed by hydraulic transport and microbial metabolism, there may be insufficient oxygen supply in the future. If the predicted value is greater than the preset safety upper limit, it means that there is a risk of over-aeration, which may subsequently damage the anoxic zone environment through backflow. If the predicted value is within the normal range below the warning range, the feedforward optimization control module will not trigger incremental control commands and will maintain the basic feedback operation state. After receiving the prediction sequence, the feedforward optimization control module calculates the residual vector between the predicted value and the safety threshold, and calls the quadratic programming solver to generate the optimal frequency setpoint sequence. The engineering implications of this solution process are: it is necessary to control the risk of hypoxia within a safe range in the future, and to avoid the blower from operating in a high-energy-consumption state for a long time due to short-term pressure difference. Since the residual vector represents the deviation in the dimension of dissolved oxygen concentration, while the total energy consumption function represents the dimension of energy integral, direct calculation will lead to an imbalance in weight allocation or divergence in the solution process due to the inconsistency in the dimensions and orders of magnitude of different physical quantities. Therefore, when constructing the joint objective, the system pre-determines the two types of variables by performing dimensionless division on the two types of variables based on the system's preset baseline allowable concentration deviation and nominal energy consumption constant. Based on the real-time priority of ensuring process compliance and reducing electromechanical energy consumption on site, dynamic weighting factors are allocated, and scalar superposition is implemented to form a standard quadratic objective function. Specifically, the total energy consumption function is like the discrete-time integral of the real-time output power of the blower within a future time window; the quadratic objective function is specifically expressed as: a quadratic polynomial weighted sum of the squares of the predicted dissolved oxygen residual vector and the dimensionless total energy consumption function, according to the dynamic weighting factors allocated by the system in real time. To facilitate understanding, a concrete deduction example can be provided: Suppose that the predicted values ​​of the first state variables generated at four discrete future time points are respectively... , , , The corresponding set security threshold value is If the predicted values ​​D3 and D4 are lower than the safety threshold T and the difference is greater than the preset limit, while the predicted values... and If the system is still within the safe zone, the solver will not directly output the highest frequency at the current moment. Instead, it will output a sequence of progressively increasing frequency setpoints, containing four discrete frequency setpoints. , , , Meanwhile, it ensures that each discrete frequency setpoint does not exceed the feedforward compensation margin and the equipment's allowable upper limit; this sequence is converted into control commands and issued to the actuator for implementation; The model adaptive calibration module is used to suppress model drift after long-term operation. Its basic logic is as follows: after the control command is executed, the actual first state quantity is re-acquired and compared with the value at the corresponding time in the previous prediction sequence. If the actual value deviates from the predicted value, it means that the mechanism parameters in the model that reflect the microbial reactivity, oxygen transfer efficiency, or mixing status in the pool no longer fit the field conditions. At this time, the system triggers the self-calibration command to correct the kinetic mechanism coefficients. If the error is within the allowable range, the current parameters are maintained to avoid model output oscillation or divergence caused by frequent parameter adjustments. In terms of fault-tolerant control, if environmental input data is missing, timestamps are abnormal, or the sensor range is significantly exceeded within a certain acquisition cycle, the data for that cycle will not directly participate in parameter reconstruction. Instead, the system will call the most recent valid data or enter a conservative operating mode to prevent erroneous data from driving erroneous control. If the operating data returned by the actuator indicates that the inverter has reached the upper limit frequency but the first state quantity continues to deteriorate, the system will maintain the alarm state and retain the conditions for subsequent abnormal defense modules to take over. If the model error occasionally exceeds the threshold but does not reach the number of consecutive judgments, it will only be recorded as a short-term disturbance and will not immediately trigger parameter correction. During the evening hours following a summer rainstorm, the influent flow rate of the wastewater treatment plant increased briefly, and the influent organic load rose, while the conventional dissolved oxygen sensor at the end of the aerobic tank did not show any obvious abnormalities. At this time, the system identified the oxygen supply pressure for the next 2 to 4 hours in advance by combining the trends of power changes and process state changes at the front end of the aerobic tank. The twin model generated a prediction sequence of the first state variable that might decline within the future window. Based on this, the feedforward optimization control module issued a blower frequency command to smoothly increase the frequency and simultaneously constrained the action amplitude of the relevant actuators. After the control was executed for a period of time, the prediction results were checked back with the actual on-site detection values, and the kinetic mechanism coefficients were updated if necessary. The purpose of this step is to transform AOA process control from end-lag feedback to predictive feedforward control based on physical quantity reconstruction and mechanism deduction, so as to achieve early response to load shocks, coordinated constraints on energy consumption, and closed-loop correction of model drift. In this embodiment, the multi-stage fluid reaction system is an AOA biochemical treatment system; the first state quantity is the dissolved oxygen concentration in the aerobic tank; the second state quantity is the oxidation-reduction potential at the front end of the aerobic tank; the actuators include a blower frequency converter and a nitrification liquid return pump; and the kinetic mechanism coefficient is the microbial yield coefficient.

[0018] This embodiment provides a parameter implementation mechanism for an AOA biochemical treatment system. Specifically, in the aforementioned mainline scenario of a municipal wastewater treatment plant, the multi-stage fluid reaction system clearly corresponds to the AOA biochemical section formed by the series connection of an anaerobic tank, an anoxic tank, and an aerobic tank. The first state quantity is selected as the dissolved oxygen concentration of the aerobic tank, and the second state quantity is selected as the oxidation-reduction potential at the front end of the aerobic tank. The actuator specifically adopts a blower frequency converter and a nitrification liquid return pump, and the kinetic mechanism coefficient is selected as the microbial yield coefficient. The details are as follows: In the AOA system, the dissolved oxygen concentration in the aerobic tank directly determines whether nitrifying bacteria have sufficient electron acceptor conditions, making it one of the key process quantities characterizing the sustainability of the aerobic reaction. Correspondingly, the redox potential at the front end of the aerobic tank is not physically mapped to the dissolved oxygen concentration, but rather its response time precedes the combined state of the end-of-aerobic tank detection indicators, the front-end reaction intensity, and the electron acceptor consumption rate. When this potential shows a continuous decaying trend, it often indicates an increase in upstream load or accelerated consumption of biodegradable organic matter at the front end. This change usually precedes a significant decrease in dissolved oxygen at the end of the aerobic tank. Therefore, using both as initial boundary conditions and state observation quantities is more consistent with the transmission law in the AOA process where front-end parameters respond earlier and end-of-aerobic parameters exhibit spatiotemporal lag. The blower frequency converter is responsible for oxygen supply regulation, while the nitrification liquid return pump is responsible for nitrate return and hydraulic distribution regulation. The above actuators need to be jointly controlled because increasing the aeration rate alone cannot ensure the stable treatment of the AOA process. If the blower rate is increased alone without considering the return path, excess dissolved oxygen in the aerobic section may flow into the anoxic tank with the liquid phase return, thereby weakening the denitrification environment. Conversely, if only the return flow rate is changed but the oxygen supply is insufficient, the nitrification reaction in the aerobic tank cannot be maintained. Therefore, the two actuators together constitute an oxygen supply-return coordinated control unit. The microbial yield coefficient is used to characterize the combined tendency of microorganisms to convert substrate into biomass and reactivity. This parameter is not a fixed constant, but is affected by factors such as seasonal temperature, sludge age, and the degree of microbial community recovery after toxic shock. Using it as a kinetic mechanism coefficient that needs to be corrected is beneficial to keep the model close to the actual microbial community state, so as to avoid the error caused by using fixed factory calibration parameters for a long time. In terms of fault-tolerant control, if the dissolved oxygen sensor in the aerobic tank experiences a reading lag due to sludge biofilm formation, the system can temporarily maintain short-term projections based on the most recent effective value combined with the front-end oxidation-reduction potential trend and issue a maintenance prompt. If the oxidation-reduction potential sensor experiences a short-term drift but the dissolved oxygen and actuator operating status are stable, the control weight of this anomaly will not be amplified to avoid single-point measurement errors dominating global control. If either the blower or the return pump is in a maintenance bypass state, the system can implement conservative control on the actuators still online, but at the same time tighten the prediction threshold to reduce the risk of over-reliance on a single actuator. During continuous operation of the same wastewater treatment plant during the rainy season, the low nighttime temperature reduces the metabolic rate of microorganisms, while the mixing of industrial pre-treated effluent from upstream causes the oxidation-reduction potential at the front end of the aerobic tank to decline prematurely. Based on this, the system judges that the concentration of electron acceptors at the front end is close to the preset lower limit, and, combined with the real-time value of dissolved oxygen in the aerobic tank, adjusts the blower frequency in advance, while appropriately coordinating the operation rhythm of the nitrification liquid return pump. After several cycles of operation, if it is found that the actual dissolved oxygen recovery rate is inconsistent with the model prediction, the microbial yield coefficient is further corrected to match the bacterial activity during the low-temperature season. The purpose of this step is to map the abstract control parameters one-to-one with the real pool, real sensors and real actuators in the AOA process, so as to achieve a direct connection between the theoretical control parameters and the actual control system. In this embodiment, the feedforward compensation margin characterizes the maximum power output increment limit of the multi-stage fluid reaction system in absorbing the influent load within a future time window under the condition of maintaining a preset anaerobic environment constraint.

[0019] This embodiment provides a constraint definition mechanism for feedforward compensation margin. Specifically, in the aforementioned main scenario, directly increasing the aeration rate based solely on the single objective of potential anoxic conditions in the future aerobic tank can easily improve local dissolved oxygen in a short period of time, but at the same time destroy the anaerobic environment in the anoxic tank. Therefore, it is necessary to set a feedforward compensation margin to limit the maximum power output margin that can be increased in advance without destroying the reaction boundary of the preceding tank. The following is a detailed explanation: Feedforward compensation margin is not simply a safety factor, but rather the maximum additional power space that the system can release in the future to absorb the increase in influent load. The so-called maximum power space is mainly reflected in the range that the blower frequency can be adjusted upward, and the corresponding reflux adjustment margin. Its limitations come from two directions: first, whether the aerobic section itself can effectively utilize the increased oxygen, and second, whether the increased oxygen will diffuse to the anoxic zone through the mixed liquor and reflux path, thereby raising the oxidation level in the anoxic zone. Only when both of these conditions are met simultaneously is the increased power effective and safe. To facilitate understanding, a concrete example of logical deduction can be provided: Suppose the inflow load trend of the system in the next three discrete time periods is as follows: , , ,and Higher than the former two; if the joint feature matrix indicates that the front-end redox environment is close to the anoxic boundary, then although there is a risk of anoxic conditions in the aerobic tank in the future, the feedforward compensation margin will not give a result of a significant increase in aeration, but will only allow a segmented, reversible power increment that meets the first preset range; conversely, if the relevant state parameters of the anoxic zone are still within the preset safe range, but the oxygen demand of the aerobic section is increasing, then the feedforward compensation margin can be relaxed accordingly; that is, this parameter is not used to drive the equipment to operate at a higher load, but to limit the equipment to a range that can cope with future load shocks without destroying the overall process biochemical boundary; In terms of control actions, the feedforward optimization control module embeds this parameter as a hard constraint into the frequency setpoint sequence solution process; in this way, even if the prediction sequence shows that the first state quantity will drop in a certain period of the future, the oxygen supply can only be gradually increased within the allowable range of the compensation margin, and cannot be radically adjusted at the expense of the hypoxic environment. In terms of fault-tolerant control, if the critical state parameters of the anoxic zone fail to return in time, making it impossible to reliably confirm the boundary of the anoxic environment, the system will automatically tighten the feedforward compensation margin and limit the power output increment according to a conservative strategy. If the predicted fluctuation of the influent load parameter is lower than the on-site noise level, the system can assume that there is no actual impact sufficient to trigger feedforward compensation, and at this time, only the conventional feedback is maintained. If the compensation margin is close to zero for a certain period of time, it indicates that the current process has no control margin to safely increase the power output in advance. Although the system can continue to issue warnings, it will not output incremental commands that exceed the boundary. On the morning of the second day after continuous rainfall at the wastewater treatment plant, the influent load began to shift from being dominated by domestic sewage to being mixed with high-organic-matter water carried by the initial rainwater runoff. The system identified an increase in oxygen supply pressure in the aerobic section from the changes in the front-end status, but at the same time determined that the relevant indicators of the anaerobic environment in the anoxic zone were close to the destruction boundary. Therefore, the feedforward compensation margin was compressed to a small range. Subsequently, the control module only allowed the blower to increase its frequency in advance according to a small change rate limit value, and simultaneously limited the backflow coupling effect, thereby avoiding the adverse effect of achieving the dissolved oxygen concentration in the aerobic tank while destroying the anaerobic environment in the anoxic tank. The purpose of this mechanism is to explicitly introduce the inter-pool coupling constraints in the AOA process into the feedforward control, so as to maintain the stability of the denitrification boundary in the anoxic zone when responding to load shocks in advance. In this embodiment, the twin inference and parametric reconstruction engine calls the preset fourth-order Runge-Kutta algorithm as the solver, performs discretization solution operation on the mechanism differential equation with a preset time step, and outputs the prediction sequence at discrete time nodes.

[0020] This embodiment provides a discretization solution mechanism for twin models. Specifically, in the aforementioned main scenario, if the future pool reaction is extrapolated based solely on the state of the current sampling point, the asymptotic nature of biochemical reactions and hydraulic transport can easily be ignored. Therefore, a pre-set fourth-order Runge-Kutta algorithm is used to discretize and solve the mechanism differential equation in order to obtain the predicted sequence at future discrete time nodes. The process is described below: The reaction process in the AOA system is not instantaneous, but unfolds gradually in a continuous chain of water inlet transport, in-pool mixing, microbial uptake, oxygen transfer, and reflux coupling. The purpose of using discretization is to reassess the direction of state change at each preset time step, so that the prediction results retain the continuity of the mechanism and are easy to connect with the on-site control cycle. The technical advantage of the fourth-order Runge-Kutta algorithm is that, compared with the overly coarse one-step extrapolation method, it can more stably describe the trend of state change in a short period of time and reduce the prediction deviation caused by excessively large step size. To facilitate understanding, a concrete example of deduction can be provided: Let the current time be denoted as . The system needs to predict four discrete nodes in the future, denoted as _____. , , , The first state quantity at time step; the twin model does not directly take the current time step as the first state quantity. Instead of replicating the state to subsequent discrete nodes, the state is updated segment by segment by comprehensively considering the state transitions brought about by oxygen input, microbial oxygen consumption, temperature correction, and reflux in each short period of time. The corresponding discrete nodes obtained in this way , , , First state quantity prediction value , , , This will present a trajectory that better reflects the physical changes within the pool; if the predicted values ​​at the first two discrete nodes... and If the decrease is less than the first threshold, then the predicted values ​​of the next two discrete nodes... and If the decrease is greater than the second threshold, the feedforward optimization control module can identify that the deviation event of the predicted first state quantity being lower than the safety threshold has a time and space lag, but has already propagated along the hydraulic path to the middle and later sections, thereby implementing early control; The preset time step can be set according to the on-site sampling frequency and control cycle to match the sampling cycle of discrete nodes with that of programmable logic controller and the solution cycle of edge nodes. If the time step exceeds the preset upper limit, it will be impossible to characterize the transient process, and if it is lower than the preset lower limit, it will increase the computational load and amplify the measurement noise. Therefore, its setting should take into account both process time delay and on-site computing power conditions. In terms of fault-tolerant control, if missing values ​​occur in multiple consecutive sampling cycles, resulting in incomplete initial boundaries for discrete solution, the current simulation cycle will not output aggressive control results, but will instead revert to conservative operation; if the model gives a predicted value that significantly exceeds the physical boundaries of the process at a certain discrete node, such as the predicted first state variable showing a sudden change in amplitude that exceeds the physical constraint range, the system will mark the node as untrustworthy and trigger a model consistency check, instead of directly using the abnormal node for instruction solution; Two hours before the evening peak water inflow at the wastewater treatment plant, the edge computing nodes performed discrete simulations of the operating conditions for the next four hours according to a preset step size. The prediction results showed that the aerobic tank was still basically stable in the first hour, but significant oxygen supply pressure would appear at several discrete nodes in the subsequent hours. Since the discrete sequence had refined the risk propagation path to multiple time points, the feedforward optimization control module was able to output a smooth adjustment action with limited rate of change before the risk actually reached the middle and later stages of the aerobic tank. The purpose of this mechanism is to enable the twin model to output executable prediction sequences with time resolution, thereby achieving reliable docking between the prediction results and the on-site control cycle. In this embodiment, when the self-calibration instruction is triggered, the model adaptive calibration module extracts the actual first state variables within a preset historical time window to form a sliding window dataset; the model adaptive calibration module calls the least squares algorithm to perform nonlinear fitting correction operations on the dynamic mechanism coefficients based on the sliding window dataset.

[0021] This embodiment provides a model adaptive calibration mechanism. Specifically, in the aforementioned main scenario, even if the initial twin model is derived from empirical calibration under stable operating conditions, the model may still gradually deviate from the actual situation due to seasonal changes, changes in sludge age, and changes in microbial community structure. Therefore, after detecting that the prediction error exceeds the limit continuously, the system extracts the actual first state variables within the historical time window to form a sliding window dataset, and uses the least squares method to perform nonlinear fitting correction on the kinetic mechanism coefficients. The reasons for using a sliding window instead of full historical data are as follows: the AOA process has obvious stages; data beyond the preset historical time window may correspond to different water temperatures, different sludge properties and different influent structures. If mixed, it is easy to introduce outdated historical states into the current model; the sliding window retains the real response trajectory that is closest to the current bacterial community respiration capacity in a recent period of time, so it is more suitable as a basis for recalibration. The role of the least squares method here is to optimize the model parameters so that the kinetic mechanism coefficients approach the physical characteristics under real-time operating conditions. That is, to find a set of mechanism coefficients that are closer to the current operating conditions, so that the state change trend of the model output is closer to the actual process on site. The necessity of nonlinear fitting correction is that the relationship between microbial yield and dissolved oxygen change is not a simple linear relationship, and there is a coupling effect between temperature, load and sludge activity. By correcting through the sliding window dataset, the model can reflect the true substrate utilization capacity and yield level of the microbial community in the current period. For ease of understanding, a concrete derivation example can be provided: Suppose that the sliding window contains the actual first state variables at six consecutive time points. to And the corresponding historical prediction values ​​for the six time points. to ;like The sequence remains below If the sequence indicates that the model's predicted value is higher than the actual recovery capacity on-site, the fitted and corrected microbial yield coefficient will be adjusted towards a more conservative direction; if sequences are generally higher than If the sequence is incorrect, it indicates that the model's predicted value is lower than the actual bacterial community activity, and the parameters can be adjusted back appropriately; after the adjustment is completed, the new parameters will enter the next round of twin simulation cycle; In terms of fault tolerance control, if there is a distortion segment within the sliding window caused by long-term equipment maintenance, sensor distortion, or abnormal water inflow, then that segment will not be included in the fitting set; if the amount of available data is insufficient to support stable correction, then the system will maintain the existing parameters and postpone calibration; if the new parameters after fitting exceed the allowable range of process experience, then they will not be directly replaced, but the system will use amplitude limiting correction to avoid pushing the model into an unreasonable range by a single abnormal data. After the wastewater treatment plant entered winter, the pool temperature continued to drop. The system obtained the actual first state quantity after the command was executed, calculated the absolute error between it and the predicted sequence, and when the error exceeded the preset threshold and exceeded the limit in multiple consecutive control cycles, the system extracted the actual first state quantity in the most recent 24 hours to form a sliding window and corrected the microbial yield coefficient. After the update, the new twin model more closely predicted the oxygen demand under low temperature conditions, thereby reducing the cumulative error and actual lag deviation between the predicted sequence and the actual physical quantity. The purpose of this mechanism is to enable the twin model to dynamically calibrate with seasonal and microbial community evolution, thereby achieving predictive stability under long-term operating conditions. In this embodiment, the system also includes an abnormal circuit breaker defense module, which monitors abrupt changes in water quality physicochemical indicators in the environmental input data; configured as follows: If the water quality physicochemical mutation index exceeds the preset biochemical survival boundary threshold, the twin simulation model is deemed to have failed, the control commands of the feedforward optimization control module are blocked, and the maximum power command and water inlet cut-off command are output. If the water quality physicochemical mutation index is less than or equal to the preset biochemical survival boundary threshold, the control authority of the feedforward optimization control module is maintained.

[0022] This embodiment provides an abnormal circuit breaker defense mechanism. Specifically, in the aforementioned main scenario, the above twin inference is based on the premise that the microorganisms still work according to the normal metabolic rules. However, under extreme toxic shocks, strong acid and strong alkali intrusions, or abnormal mixing of heavy metals, the microbial community may be rapidly inactivated. At this time, relying on the conventional model inference will lose its physical basis. Therefore, the system is equipped with an abnormal circuit breaker defense module to monitor the physicochemical mutation indicators and stop the feedforward control when necessary. The following is a detailed description: The biochemical survival boundary threshold is used to distinguish between the disturbance that the microbial community can still tolerate and the process boundary where the microbial community may have been largely inactivated. In this embodiment, the biochemical survival boundary threshold is specifically reflected in the extreme deviation range of the influent pH value, or the alarm threshold for exceeding the toxicity equivalent output by a specific heavy metal ion concentration and a comprehensive toxicity tester. When the physicochemical mutation index exceeds this boundary, it means that the activated sludge no longer meets the normal respiratory and metabolic conditions, and the mechanism model previously established based on the oxygen transfer and microbial proliferation laws is no longer reliable. At this time, continuing to output feedforward incremental commands may not only be ineffective, but may also amplify the consequences of the accident due to misjudging the system capacity. Therefore, the circuit breaker defense module directly blocks the control commands of the feedforward optimization control module and switches to the physical safety priority mode. The significance of outputting the maximum power command is to maximize the mixing and oxygenation capacity as much as possible, and reduce local anaerobic and harmful accumulation, while the bacterial community has not been completely inactivated or there is still local reversible activity. The significance of outputting the influent cut-off command is to prevent abnormal influent from continuing to enter the biological treatment section and avoid the toxic load from expanding to the entire process. The combination of the two forms an emergency measure that combines source isolation and protection within the tank. In terms of fault-tolerant control, if the physical and chemical mutation index is near the boundary but has not clearly exceeded the threshold, the system can maintain the feedforward control authority, while increasing the sampling frequency and issuing an early warning; if the physical and chemical mutation index rises abnormally due to the failure of a single sensor, but other auxiliary quantities do not show consistent abnormalities, the system can first perform cross-validation to avoid false circuit breakers; if the circuit breaker has been triggered, the feedforward control authority can only be gradually restored after the abnormal index returns to the allowable range and after a period of confirmation, rather than immediately taking full control. During a nighttime operation of the wastewater treatment plant, an upstream company mistakenly discharged high-concentration acidic wastewater, causing a rapid deterioration in the influent's physicochemical indicators. Upon detecting that these indicators had exceeded the biochemical survival threshold, the system immediately determined that the conventional twin model had failed, blocking the planned smooth aeration increase command and instead executing an emergency action to cut off maximum power and influent flow. Simultaneously, it issued the highest-level alarm to the central control room. Once the abnormal influent was isolated and the conditions within the tank gradually recovered, the system would then determine whether to reactivate the feedforward simulation based on data from the recovery phase. The purpose of this mechanism is to clarify the applicable boundaries of the system and replace model control with physical security defense when the boundaries are exceeded, thereby achieving reliable underlying security protection under extreme conditions. In this embodiment, the optimal frequency setpoint sequence forms an S-shaped acceleration / deceleration transition curve in terms of time distribution; The feedforward optimization control module uses the ratio of the system volume of the multi-stage fluid reaction system to the influent flow rate as the hydraulic residence time. Before the fluid, represented by the influent load, reaches the middle spatial position of the multi-stage fluid reaction system based on the hydraulic residence time, it outputs the initial incremental command according to the S-shaped acceleration and deceleration transition curve, and uses the spatial margin to compensate for the time lag of the reaction.

[0023] This embodiment provides an S-shaped feedforward regulation mechanism based on hydraulic residence time. Specifically, in the aforementioned main scenario, if there is only a prediction result but no action timing arrangement coupled with the spatial position of the pool, the control may still be too late. Therefore, the ratio of system volume to inflow rate is used as the hydraulic residence time to estimate the time when the current round of inflow load reaches the middle section of the pool, and the initial incremental command is output in advance according to the S-shaped acceleration and deceleration transition curve. The details are as follows: The load impact in the AOA tank does not occur instantaneously at all locations, but rather propagates gradually along the plug flow path; the hydraulic residence time reflects the approximate transport time of the liquid from entering the system to passing through the critical reaction zone, and therefore can be used as a spatial reference for when to start feedforward action; if the aeration rate is increased at a gradual gradient before the load reaches the middle section, then when the load actually reaches the most oxygen-sensitive area, the dissolved oxygen environment in the tank has already completed part of the feedforward compensation and rise, and the spatial margin can be used to offset the inherent time lag of the biochemical reaction; The reason for using an S-shaped acceleration / deceleration transition curve instead of a step increase in frequency is that neither the blower nor the fluid in the tank is suitable for sudden changes in speed. The S-shaped curve rises more slowly in the first part, accelerates in the middle part, and then flattens out in the latter part. This can reduce mechanical shock and make the oxygen supply closer to the load rhythm. For ease of understanding, a concrete example can be provided: Assuming that based on the system volume and influent flow rate, the current high-load influent will enter the middle section of the aerobic tank after two control cycles, the frequency sequence can be derived from... , , , Composition, in which Only a slight increase is expected. Continue to rise, Approaching the target level Switch to a maintenance or gradual reduction approach; in this way, when the load truly reaches a critical point, the oxygen supply increase has already completed the main transition in advance, rather than suddenly increasing after the load is reached. In terms of fault-tolerant control, if the influent flow rate data is abnormal, making it impossible to reliably estimate the hydraulic residence time, the system will not use spatial feedforward timing judgment, but will retreat to a conservative adjustment based solely on the current predicted risk; if there are maintenance compartments or changes in the effective volume of the pool, the system should use the corrected available volume to update the residence time estimation; if the first state quantity at the site has already exceeded the upper limit boundary after the feedforward output, the latter part of the S-curve can be flattened or dropped in advance to avoid over-aeration; Before the wastewater treatment plant discharged water from the industrial park in the morning, the system estimated, based on the effective volume of the tank and the influent flow rate, that the high-load mixed liquor would enter the middle section of the aerobic tank for a period of time. Therefore, the control module did not wait for the dissolved oxygen at the end to start to drop before taking action, but instead gave an initial incremental command according to the S-shaped curve to make the blower smoothly increase the frequency. When the high load actually advanced to the middle section, the aerobic tank already had a dissolved oxygen benchmark environment that met the oxygen demand of the current operating conditions, thereby reducing the need for large-scale adjustment of subsequent feedback control. The purpose of this mechanism is to link time prediction with spatial transport, thereby enabling the use of pool space margin to compensate for reaction lag in advance; In this embodiment, the physical quantity acquisition interface module includes a heterogeneous protocol adaptation unit; the heterogeneous protocol adaptation unit reads environmental input data through the analog input channel and reads actuator operation data through the industrial bus protocol; the heterogeneous protocol adaptation unit performs timestamp alignment and data format standardization processing on the acquired data.

[0024] This embodiment provides a heterogeneous data acquisition and alignment mechanism. Specifically, in the aforementioned main scenario, environmental input data mostly comes from analog instruments, and actuator operation data mostly comes from the inverter and controller internal registers. If data from different sources are misaligned in time or have inconsistent unit formats, subsequent parameter reconstruction will be distorted. Therefore, a heterogeneous protocol adaptation unit is set up to perform acquisition, alignment, and standardization processing. The following is a detailed description: Analog input channels are typically used to receive continuous physical quantities such as flow rate, temperature, and physicochemical properties. These quantities are often fed into the field controller via 4-20mA. The industrial bus protocol is used to read operating data such as current, frequency, torque, and power returned by the blower frequency converter and return pump controller. Although both serve the same control objective, their sampling periods, transmission delays, and data formats are often different. If timestamp alignment is not performed, the current front-end process state and the blower power at the previous moment may be incorrectly concatenated, causing the joint feature matrix to lose its true physical correspondence. The core of timestamp alignment is to merge data from different sampling sources into a unified control time; the core of data format standardization is to convert data with different protocols, units, and encoding methods into a unified data structure so that the twin engine can directly call it; for ease of understanding, a concrete derivation example can be used: Suppose the target time of a certain control cycle is... Analog channel in Nearby return traffic value and temperature value On the bus side Forward and backward return frequency values With power value The adapter unit will be the closest Furthermore, to meet timeliness requirements, multi-source heterogeneous data is encapsulated into standardized data frames based on reference timestamps, for example { , , , First state variable, second state variable}, and uniformly attached This serves as a reference time, ensuring that subsequent feature extractions correspond to the same process section. In terms of fault tolerance control, if a certain protocol read times out, the system marks the channel as missing to avoid processing outdated data as real-time data; if the analog quantity changes but the bus data is stable, the adapter unit can retain the original value and add a quality label, and let the upper-layer module determine whether to participate in this round of simulation; if the timestamp difference exceeds the allowable range, the current data frame will not enter the critical control link, but will only be used for monitoring and recording. At the wastewater treatment plant, flow meters and thermometers are connected to the programmable logic controller via analog inputs, and the blower frequency converter returns real-time power and frequency via the industrial bus. Before the end of each control cycle, the adapter unit aligns these data from different communication paths to the same reference time, forms a standardized data frame, and uploads it to the edge node. In this way, when the system identifies load shocks, it can ensure that changes in influent, changes in the upstream process, and changes in equipment work do indeed correspond to the same process segment. The purpose of this mechanism is to eliminate timing mismatches and format differences caused by heterogeneous acquisition links, thereby achieving input consistency for subsequent twin inference. In this embodiment, the feedforward optimization control module limits the rate of change of frequency between adjacent time nodes of the optimal frequency setpoint sequence to within a preset acceleration threshold based on a preset fluid shear force model, thereby limiting the operating acceleration of the actuator through the preset acceleration threshold.

[0025] This embodiment provides an actuator acceleration limiting mechanism based on fluid shear force constraints. Specifically, in the aforementioned main scenario, if only the rapid recovery of the aerobic tank state is pursued, the blower frequency may jump significantly between adjacent time nodes. Although the oxygen supply is increased in the short term, it will cause a sudden change in the gas-liquid mixing intensity, increasing the physical shearing of the activated sludge flocs. Therefore, a fluid shear force model is introduced to limit the frequency change rate within a preset acceleration threshold. The following is a detailed explanation: The stable structure of activated sludge flocs is of great significance for subsequent settling and sludge-water separation; when the blower accelerates rapidly, the local turbulence and bubble shear in the tank are enhanced, which may destroy the floc structure and deteriorate the settling performance of the secondary sedimentation tank; therefore, although the optimal frequency setpoint sequence comes from the feedforward solution, it still needs to be constrained by the fluid shear force model to keep the frequency change between adjacent time nodes within the range that the equipment and the biological system can withstand. At the underlying transmission logic, this fluid shear force model decomposes the transmission links of the complex, unobservable internal process mechanism into physical quantities: the short-term rate of change of the blower frequency directly determines the transient ramp-up slope of the aeration volume of the underlying pipe network, while the local sudden increase in the amount of air released by the aeration head will induce a drastic change in the micro velocity gradient of the air-water mixing zone. The peak value of this velocity gradient directly manifests as the fluid shear force on the floc particles. Therefore, the model will maintain the stability of the sludge floc structure and prevent the micro velocity gradient from exceeding the critical safe micro velocity gradient that the floc can withstand. Based on the on-site air-water two-phase flow relationship, it will calculate the maximum allowable ramp-up rate of aeration volume in the pool and reduce its dimension to be equivalent to the preset acceleration threshold of the frequency converter drive command side. The specific mapping equivalent process is as follows: based on the preset empirical formula of the micro velocity gradient in the air-water mixing zone, the maximum aeration increase rate when maintaining the critical safe micro velocity gradient is solved in reverse; based on the air volume-frequency aerodynamic characteristic curve of the blower on site, the maximum aeration increase rate is proportionally converted into the maximum allowable frequency rise rate of the frequency converter, thus obtaining the preset acceleration threshold. For ease of understanding, a concrete example of logical deduction can be provided: Suppose that the optimal frequency setpoint sequence obtained from the original solution of the feedforward optimization control module contains the frequency setpoints of adjacent discrete nodes. , , The frequency setpoint F2 of the second node changes at a rate exceeding the preset upper limit compared to the frequency setpoint F1 of the previous node. At this point, the fluid shear force model determines that the acceleration of the actuator corresponding to this jump may cause increased shear within the pool, i.e., it determines that the velocity gradient induced by this reaction exceeds the critical threshold for floc rupture. Therefore, the system lowers the frequency setpoint. Corrected to a frequency setting value between the first node. and the original frequency setting value The transition values ​​between them, and the frequency setting value of the subsequent third node. The corresponding adjustments are also made accordingly; thus, the final sequence sent to the frequency converter is not simply the unsmoothed initial sequence output from the optimizer, but an executable sequence after floc protection constraints. This acceleration threshold reflects both the mechanical safety boundary of the equipment and the tolerance boundary of the biochemical system to sudden changes in flow regime. By limiting the operating acceleration, both oxygen supply control and sludge structure protection can be taken into account, thereby achieving more stable settling performance in addition to energy saving. In terms of fault tolerance control, if the site is in an abnormal defense mode or an extreme oxygen shortage state, the system can appropriately relax the acceleration limit within the safety framework, but still retain the upper limit boundary to prevent the actuator from suffering excessive mechanical impact; if the key input required by the shear force model is temporarily missing, the system will use a preset conservative threshold for amplitude limiting; if the frequency sequence cannot meet the most basic safe oxygen supply requirements after amplitude limiting, the system will prioritize process safety and issue an operation prompt indicating an increased risk of settlement. During a high-load response at the wastewater treatment plant, the feedforward optimization control module initially aimed to rapidly increase the blower frequency between two control nodes to raise the oxygen supply level in the tank ahead of time. However, the fluid shear force model identified that such an increase might cause enhanced turbulence in the tank, so it smoothed out the frequency increase into a gentler transition value. Subsequent operation showed that the oxygen supply in the aerobic tank was still improved, while the sludge volume index in the secondary sedimentation tank remained in a relatively stable range, without the sedimentation deterioration commonly seen under traditional rapid acceleration control. The purpose of this mechanism is to incorporate the often-overlooked process objective of sludge floc protection into the execution-level constraints, thereby achieving a balance between oxygen supply stability and subsequent settling performance. In this embodiment, the system is deployed in a layered architecture that includes edge computing nodes and field controllers; The physical quantity acquisition interface module resides in the field controller and performs real-time physical quantity acquisition operations. The twin inference and parameter reconstruction engine, the feedforward optimization control module, and the model adaptive calibration module reside on the edge computing node, performing concurrent computation and model evolution operations.

[0026] This embodiment provides a layered deployment mechanism. Specifically, in the aforementioned main scenario, if all computing tasks are allocated to the field controller, the stability of real-time acquisition and execution may be affected due to limited computing power. If all tasks are relied on the host computer or remote platform, communication latency will be introduced. Therefore, the system adopts a layered architecture that combines the field controller and edge computing nodes. The following is a breakdown: The field controller is located close to the sensors and actuators, making it suitable for real-time physical quantity acquisition, basic interlocking, and command forwarding functions; the edge computing nodes have higher computing power, making them suitable for computationally intensive tasks such as twin simulation, parameter reconstruction, frequency sequence optimization, and model self-calibration; after the division of labor, the field side ensures stable data acquisition, reliable command issuance, and priority protection of basic physical security, while the edge side ensures efficient model computation, dynamic policy updates, and effective utilization of historical features; This hierarchical architecture is particularly suitable for the control characteristics of AOA systems. On the one hand, field sampling and equipment control require stable and deterministic periodic responses. On the other hand, twin simulation and model evolution need to complete multiple rounds of calculations in a short period of time and interact with historical window data. Placing heavy computation tasks on edge nodes can prevent field controllers from being affected by excessive load, thus ensuring the reliability of input / output scanning and interlocking. To facilitate understanding, a concrete example of logical deduction can be provided: Suppose that within a certain control cycle, the field controller reaches the target time. Data acquisition is completed and uploaded to the edge nodes. The edge nodes concurrently execute three types of tasks: the first task updates the joint features and feedforward compensation margin; the second task generates future prediction sequences; and the third task checks whether the recent error has reached the self-calibration condition. After completion, the edge nodes return the optimal control result for this cycle to the field controller, which then executes and issues the new control. If the edge nodes have not returned new results at a certain time, the field controller continues to maintain the previous safety command or adopts a conservative value to avoid control link interruption. In terms of fault-tolerant control, if the edge node communication is interrupted for a short time, the field controller maintains the local safe operation strategy and will not lose control due to the failure of the upper-level computing node to return results; if the edge node finds that unsynchronized data has accumulated during the recovery period, it can first synchronize the state and then restore normal feedforward control; if the field controller detects that the local interlocking condition is triggered, such as equipment failure or emergency stop, its local safety logic priority is higher than the optimization instructions issued by the edge node. In the actual deployment of this wastewater treatment plant, the programmable logic controller (PLC) is responsible for reading flow rate, temperature, physicochemical indicators, dissolved oxygen, oxidation-reduction potential, and inverter operating parameters, and uploading them to the edge computing gateway via the industrial network. After receiving the data frame with a unified timestamp, the gateway simultaneously runs twin simulation, frequency optimization, and parameter calibration tasks, and then returns the control instructions for the current cycle to the PLC for execution. In this way, under rapid disturbances such as rainstorms, on-site data acquisition and equipment control remain stable, while more complex model evolution can be continuously completed online. The purpose of this mechanism is to achieve long-term stable operation in industrial settings by balancing real-time performance, model complexity, and system maintainability through hierarchical control and computation.

[0027] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An AOA (Automatic Operational Analysis) control system based on online process twin simulation, characterized in that, It includes a physical quantity acquisition interface module, a twin inference and parameter reconstruction engine, a feedforward optimization control module, and a model adaptive calibration module; The physical quantity acquisition interface module connects to the sensors and actuators of the multi-stage fluid reaction system to acquire environmental input data including influent load, influent flow rate, temperature and water quality physicochemical change indicators, actuator operation data including real-time current, frequency, torque and power, and fluid process data including first and second state quantities. The twin simulation and parametric reconstruction engine extracts the time partial derivative of the power and the decay slope of the second state quantity to construct a joint feature matrix, which is mapped to a feedforward compensation margin through a preset twin simulation model. Using the first and second state quantities as initial conditions, the mechanism differential equation is solved discretely according to a preset time step to generate a prediction sequence of the first state quantity within the future time window. The feedforward optimization control module calculates the residual vector between the predicted sequence and the preset first state quantity safety threshold, calls the quadratic programming solver, and aims to minimize the quadratic polynomial formed by the weighted sum of squared residuals and the total energy consumption function representing the power integral. With the feedforward compensation margin and the maximum operating frequency as constraints, it solves the optimal frequency setpoint sequence and converts it into control commands. The model adaptive calibration module obtains the actual first state quantity after the instruction is executed and calculates its absolute error with the predicted sequence. When the number of consecutive errors exceeding the limit reaches the preset judgment number, the self-calibration instruction is triggered to correct the dynamic mechanism coefficients of the mechanism differential equation; otherwise, the current parameters are maintained.

2. The AOA (Automatic Operational Analysis) control system based on online process twin simulation according to claim 1, characterized in that, The multi-stage fluid reaction system is an AOA biochemical treatment system; the first state quantity is the dissolved oxygen concentration in the aerobic tank; the second state quantity is the oxidation-reduction potential at the front end of the aerobic tank; the actuator includes a blower frequency converter and a nitrification liquor return pump. The kinetic mechanism coefficient is the microbial yield coefficient.

3. The AOA (Automatic Operational Analysis) control system based on online process twin simulation according to claim 1, characterized in that, The feedforward compensation margin characterizes the maximum power output increment limit of the influent load within a future time window under the condition of maintaining a preset anaerobic environment constraint.

4. The AOA (Automatic Operational Analysis) control system based on online process twin simulation according to claim 1, characterized in that, The twin inference and parameter reconstruction engine calls a pre-set fourth-order Runge-Kutta algorithm as a solver, performs discretization solution operation on the mechanism differential equation with a preset time step, and outputs the prediction sequence at discrete time nodes.

5. The AOA (Automatic Operational Analysis) control system based on online process twin simulation according to claim 1, characterized in that, When the self-calibration command is triggered, the model adaptive calibration module extracts the actual first state variables within a preset historical time window to form a sliding window dataset; the model adaptive calibration module calls the least squares algorithm to perform nonlinear fitting correction operation on the dynamic mechanism coefficients based on the sliding window dataset.

6. The AOA (Automatic Operational Analysis) control system based on online process twin simulation according to claim 1, characterized in that, The system also includes an abnormal circuit breaker defense module, which monitors abrupt changes in water quality physicochemical indicators in the environmental input data; configured as follows: If the water quality physicochemical mutation index is greater than the preset biochemical survival boundary threshold, the twin simulation model is determined to be ineffective, the control command of the feedforward optimization control module is blocked, and the maximum power command and water inlet cut-off command are output. If the water quality physicochemical mutation index is less than or equal to the preset biochemical survival boundary threshold, the control authority of the feedforward optimization control module is maintained.

7. The AOA (Automatic Operational Analysis) control system based on online process twin simulation according to claim 1, characterized in that, The optimal frequency setpoint sequence forms an S-shaped acceleration / deceleration transition curve in its temporal distribution. The feedforward optimization control module uses the preset ratio of the system volume of the multi-stage fluid reaction system to the influent flow rate as the hydraulic residence time. Before the fluid, represented by the influent load, reaches the mid-spatial position of the multi-stage fluid reaction system based on the hydraulic residence time, it outputs an initial incremental command according to the S-shaped acceleration / deceleration transition curve, using the spatial margin to compensate for the time lag of the reaction.

8. The AOA (Automatic Operational Analysis) control system based on online process twin simulation according to claim 1, characterized in that, The physical quantity acquisition interface module includes a heterogeneous protocol adaptation unit; the heterogeneous protocol adaptation unit reads the environmental input data through the analog input channel and reads the actuator operation data through the industrial bus protocol; the heterogeneous protocol adaptation unit performs timestamp alignment and data format standardization processing on the acquired data.

9. The AOA (Automatic Operational Analysis) control system based on online process twin simulation according to claim 1, characterized in that, The feedforward optimization control module, based on a preset fluid shear force model, limits the rate of change of frequency between adjacent time nodes of the optimal frequency setpoint sequence to within a preset acceleration threshold, thereby limiting the operating acceleration of the actuator through the preset acceleration threshold.

10. The AOA (Automatic Operational Analysis) control system based on online process twin simulation according to claim 1, characterized in that, The system is deployed in a layered architecture that includes edge computing nodes and field controllers; The physical quantity acquisition interface module resides in the field controller and performs real-time physical quantity acquisition operations; The twin inference and parameter reconstruction engine, the feedforward optimization control module, and the model adaptive calibration module reside on the edge computing node and perform concurrent computing and model evolution operations.

Images