Intelligent control method and system for gas production and distribution based on DO-ammonia nitrogen collaborative perception

Abstract

The invention discloses an intelligent gas production and distribution control method and system based on DO-ammonia nitrogen collaborative perception, and relates to the field of sewage aeration control. Through a data time sequence alignment technology based on hydraulic retention time and in combination with a cooperative control strategy of activated sludge dynamic feedforward and ammonia nitrogen constraint type fuzzy PID feedback, the aeration control system eliminates space-time dislocation caused by physical transmission lag of a biochemical pool; the self-adaptive capability of automatically adjusting the response sensitivity according to the inlet water ammonia nitrogen load level is realized, so that the accurate real-time matching of the aeration rate and the actual oxygen demand of the biochemical reaction is realized, and the operation energy consumption is obviously reduced while the stable and standard reaching of the outlet water quality is ensured.

Description

Technical Field

[0001] This invention relates to the field of wastewater aeration control, specifically to an intelligent control method and system for gas production and distribution based on DO-ammonia nitrogen synergistic sensing. Background Technology

[0002] Currently, wastewater treatment plants commonly use the activated sludge process as their core treatment technology. The energy consumption of the aeration system typically accounts for more than 50% of the total energy consumption of the entire plant, making it a crucial element for energy conservation and cost reduction. Traditional aeration control often employs a single-loop PID feedback control strategy. This involves using a dissolved oxygen (DO) sensor installed at the aerobic end of the biological treatment tank to collect DO values ​​in real time, comparing them with a set target value (e.g., 2.0 mg / L), and then using a PID controller with fixed parameters to adjust the blower frequency or air valve opening to eliminate DO deviation. Some advanced control systems introduce the influent flow rate as a feedforward variable, adjusting the aeration rate proportionally to changes in the influent flow rate in advance, attempting to compensate for load changes before they occur.

[0003] However, existing aeration control technologies have significant shortcomings. First, the biological reactor is a system with large hysteresis, large inertia, and nonlinear characteristics. The water flow from the influent end to the aerobic end requires several hours of hydraulic retention time. Existing technologies often ignore this physical hysteresis and directly use the current influent flow rate to control the aeration at the current moment. This leads to a severe misalignment between input disturbances and output responses in time and space, causing control actions to always be ahead of or behind the actual biological needs. Second, traditional single DO feedback control is essentially a "post-event correction" mechanism. Relying solely on DO deviation adjustment lacks awareness of pollutant concentrations (especially ammonia nitrogen load). When the influent ammonia nitrogen concentration fluctuates drastically, the fixed-parameter PID controller cannot distinguish between DO decreases caused by load shocks and DO changes caused by equipment fluctuations. This easily leads to slow response at high loads, resulting in ammonia nitrogen exceeding the standard, or over-adjustment at low loads, leading to energy waste and equipment vibration. Summary of the Invention

[0004] This invention proposes an intelligent control method and system for gas production and distribution based on DO-ammonia nitrogen synergistic sensing. It aims to solve the problems of low aeration control accuracy, weak resistance to shock loads, and high energy consumption caused by neglecting hydraulic transmission lag in existing sewage treatment aeration control methods, as well as the lack of adaptive capability to ammonia nitrogen load changes.

[0005] The intelligent control method for gas production and distribution based on DO-ammonia nitrogen synergistic sensing includes the following steps: S1. Based on the monitoring instruments at the biological treatment tank, collect the real-time dissolved oxygen concentration of the aerobic tank, the real-time ammonia nitrogen concentration at the inlet, and the real-time inlet flow rate. Then, perform noise reduction and time-series alignment processing on the collected multi-source data to generate standardized current operating status data. Specifically, this step addresses the spatiotemporal misalignment caused by long-distance flow in the biological treatment tank by constructing a dynamic data queue based on physical flow velocity. There is a significant physical distance between the inlet of the biological treatment tank and the outlet of the aerobic tank, requiring several hours of hydraulic retention time for wastewater to flow through this distance. The system uses real-time flow data to calculate the current flow velocity and determine the backtracking step size. It accurately extracts the original ammonia nitrogen load data of the "clump of water" that caused the current change in dissolved oxygen in the aerobic tank from the historical data buffer when it entered the system several hours ago. This historical load data is logically bound to the current dissolved oxygen feedback data to construct a standardized state dataset with strict physical causal relationships. Compared with existing technologies, traditional control logic usually ignores the hydraulic transmission time and directly uses the influent ammonia nitrogen at the current moment to control the aeration rate at the current moment. This results in the aeration response always being ahead of the actual demand on the time axis. This phase error causes the blower to aerate prematurely before the pollutants reach the aerobic tank, resulting in energy waste. When the pollutants actually arrive, the dissolved oxygen drops due to the adjustment lag. This step completely eliminates the control error caused by the transmission delay through physical timing alignment.

[0006] S2. Based on the current operating status data and combined with the activated sludge reaction kinetics, calculate the theoretical oxygen demand required to remove ammonia nitrogen load at the current moment, and convert the theoretical oxygen demand into a feedforward aeration control signal; Specifically, this step establishes a direct mapping channel from pollutant load to air supply within the controller using chemometric principles to achieve immediate response to shock loads. The algorithm, based on the time-aligned total mass load of influent ammonia nitrogen, accurately calculates the absolute mass of oxygen required to oxidize this load using the nitrification mechanism. Combining this with the oxygen transfer efficiency curve of the on-site aerator and water temperature and pressure parameters, it reverse-engineers the biochemical oxygen demand (BOD) into the physical air volumetric flow rate required by the blower, directly generating the basic frequency command to drive the frequency converter. Compared to existing technologies, traditional PID control is essentially a reactive correction mechanism. The controller can only sense and increase aeration after the dissolved oxygen concentration has been consumed by pollutants and has substantially decreased. This lag makes it easy for effluent indicators to exceed standards when facing high ammonia nitrogen shocks. The feedforward mechanism used in this step can adjust the blower output in advance based on the changing trend of the influent load before dissolved oxygen fluctuates.

[0007] S3. Based on the set target dissolved oxygen value and the current operating condition data, define the dissolved oxygen deviation, and use the dissolved oxygen deviation and the ammonia nitrogen concentration in the operating condition data as two-dimensional input variables to perform fuzzy logic deduction to obtain the PID parameter correction amount. Based on the PID parameter correction amount, update the PID controller parameters online and output feedback aeration control signal. Specifically, this step addresses the problem of single PID parameters being unable to adapt to the significant day-night load fluctuations in wastewater treatment plants by introducing operating condition sensing logic. The system defines the influent ammonia nitrogen concentration as a key indicator for assessing the process risk level. Under low ammonia nitrogen concentration conditions at night or with low flow rates, fuzzy logic determines that the system is in a safe zone and automatically lowers the proportional gain of the PID parameters to suppress frequent acceleration and deceleration of the blower, preventing equipment wear and energy waste. Under high ammonia nitrogen concentration shock conditions, the logic determines that the system is in a high-risk zone and automatically increases the proportional and integral gains, forcing the system to respond as quickly as possible to even minor deviations in dissolved oxygen, ensuring sufficient substrate for nitrification. Compared to existing technologies, traditional constant-parameter PID controllers often cause valve oscillations due to excessive gain at low loads, while insufficient gain leads to slow response at high loads. The online parameter self-tuning mechanism implemented in this step makes the controller's control logic more precise.

[0008] S4. The feedforward aeration control signal and the feedback aeration control signal are weighted and superimposed, the equipment safety limit is processed, and the gas distribution execution command is generated and sent to the aeration actuator. Specifically, this step numerically superimposes the feedforward "coarse adjustment" signal based on load calculation and the feedback "fine adjustment" signal based on error correction. The synthesized total command can simultaneously reflect the forward-looking demand at the inlet and the corrective demand at the outlet. Subsequently, this command must be verified by the equipment protection logic, and the system will forcibly limit the output value within the safe range formed by the lower limit of the blower surge frequency and the upper limit of the rated power. Compared with existing technologies, traditional control schemes often lack effective integration of feedforward and feedback, and the control algorithm is separated from the equipment protection logic. This can easily lead to accidents where the frequency command calculated by the algorithm is lower than the blower surge point, causing equipment damage, or higher than the motor rated current, causing tripping. This step deeply integrates process control requirements with equipment mechanical characteristics, ensuring process compliance while preventing hardware failures caused by algorithm output exceeding limits.

[0009] Furthermore, a gas production and distribution intelligent control system based on DO-ammonia nitrogen synergistic sensing is proposed. This system is implemented based on the aforementioned intelligent gas production and distribution control method based on DO-ammonia nitrogen synergistic sensing, and includes: The operating condition data acquisition module is used to collect real-time dissolved oxygen concentration in the aerobic tank, real-time ammonia nitrogen concentration at the inlet, and real-time inlet flow rate from the monitoring instruments at the biological treatment tank site. It also performs noise reduction and time-series alignment processing on the collected multi-source data to generate standardized current operating condition data. The feedforward signal generation module is used to calculate the theoretical oxygen demand required to remove ammonia nitrogen load at the current moment based on the current operating status data and the reaction kinetics of activated sludge, and convert the theoretical oxygen demand into a feedforward aeration control signal. The feedback signal generation module is used to define the dissolved oxygen deviation based on the set target dissolved oxygen value and the current operating condition data, and to perform fuzzy logic deduction based on the dissolved oxygen deviation and the ammonia nitrogen concentration in the operating condition data as two-dimensional input variables to obtain the PID parameter correction amount. Based on the PID parameter correction amount, the PID controller is updated online, and a feedback aeration control signal is output. The gas distribution execution module is used to weight and superimpose the feedforward aeration control signal and the feedback aeration control signal, perform equipment safety limiting processing, generate gas distribution execution instructions, and send them to the aeration actuator.

[0010] Specifically, the implementation principle and process of this system are as follows: The operating data acquisition module first reads real-time electrical signals from the dissolved oxygen sensor, ammonia nitrogen online analyzer, and influent flow meter in parallel through the controller's analog input channel or fieldbus interface. The processor converts the acquired raw electrical signals into standard engineering physical values ​​and temporarily stores them in the instantaneous data register. The module internally runs a moving average filtering program to remove abrupt noise points in the signal. Subsequently, the system allocates a first-in-first-out circular data buffer in the random access memory and writes the real-time flow and ammonia nitrogen data into the buffer in timestamp order. The module calculates the dynamic hydraulic lag time based on the real-time flow value and the preset effective volume constant of the biological treatment tank, and converts this time into the number of buffers. Based on the storage address offset, the pointer backtracks in the buffer and reads the corresponding historical influent ammonia nitrogen and flow rate records. Finally, the extracted historical influent data and the currently collected dissolved oxygen data are assembled into a standardized operating condition status data packet with time-series synchronization characteristics and transmitted to the feedforward signal generation module. The feedforward signal generation module calls the influent flow rate and ammonia nitrogen concentration values ​​from the standardized operating condition status data packet and performs stoichiometric calculations in the arithmetic unit. The program first calculates the product of the flow rate and the difference in ammonia nitrogen concentration to obtain the absolute mass load of ammonia nitrogen to be removed at the current moment. Based on the nitrification chemical ratio, this load is converted into the theoretical biochemical oxygen demand (BOD). The module then overlays the endogenous oxygen demand of heterotrophic bacteria based on flow rate estimation. Next, it calls the oxygen transfer efficiency data table and environmental correction coefficient stored in non-volatile memory, performing a division operation to convert the biochemical oxygen demand into the physical air volumetric flow rate under standard conditions. Finally, the module consults a preset blower flow-frequency characteristic mapping table or a regulating valve flow-opening characteristic curve, using a linear interpolation algorithm to convert the air flow rate value into the corresponding inverter operating frequency value or valve opening percentage value. The feedback signal generation module first calculates the deviation between the current dissolved oxygen measurement value and the set target value in the comparison register, and then compares this deviation with the current influent ammonia nitrogen concentration. The degree data is input to the fuzzification processing unit. The program maps the two precise values ​​into a fuzzy level vector according to the predefined membership function. The fuzzy inference engine searches for the matching control strategy in the expert rule matrix based on the vector, and obtains the real-time correction increment values ​​of the proportional, integral and derivative parameters of the PID controller through the centroid method for defuzzification calculation. The module performs algebraic addition operation on these increment values ​​with the reference PID parameters to generate the actual PID parameter set for the current calculation cycle. Then the module calls the standard PID calculation subroutine and uses the updated parameter set to perform proportional amplification, integral accumulation and derivative prediction calculation on the dissolved oxygen deviation, and outputs a dimensionless feedback regulation value.The gas distribution execution module first reads the basic gas volume command value output by the feedforward signal generation module and the corrected gas volume command value output by the feedback signal generation module. It then performs a weighted summation operation in the arithmetic logic unit to obtain the theoretical total control command. Subsequently, the module initiates a safety limiting logic program, comparing this theoretical command value with the minimum surge frequency and maximum rated power frequency of the blower stored in the configuration register. If the calculated value is lower than the lower limit, it is forcibly assigned the lower limit value; if the calculated value is higher than the upper limit, it is forcibly assigned the upper limit value. Finally, the module writes the verified valid control command into the digital-to-analog converter or fieldbus communication transmission buffer, converting it into a current signal or communication message to directly drive the blower inverter or air regulating valve in the field to perform actions.

[0011] The beneficial effects of the invention are: This invention constructs standardized current operating status data that accurately reflects the causal relationship of biochemical reactions by denoising and time-series alignment of multi-source data based on the hydraulic retention time of the biological treatment tank. Combined with a two-factor feedforward compensation model based on activated sludge reaction kinetics and a fuzzy adaptive PID feedback adjustment strategy incorporating ammonia nitrogen concentration as a constraint, the control system can completely eliminate the spatiotemporal misalignment caused by physical transmission lag in the biological treatment tank. It can both predict the theoretical oxygen demand in advance based on the fluctuation trend of influent ammonia nitrogen load for rapid feedforward coarse adjustment and intelligently change the controller's response sensitivity based on real-time ammonia nitrogen concentration levels for precise feedback correction. This achieves precise dynamic matching of blower aeration volume with the actual oxygen demand of the biological system throughout all time periods. While effectively suppressing ammonia nitrogen shock loads and ensuring stable effluent quality, it fundamentally eliminates energy waste caused by adjustment lag or over-adjustment. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the intelligent control method for gas production and distribution based on DO-ammonia nitrogen synergistic sensing proposed in Embodiment 1 of the present invention. Detailed Implementation

[0013] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the invention, and not all of them. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0015] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention. It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0016] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or machine that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or machine. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or machine that includes said element.

[0017] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0018] Example 1 Among them, such as Figure 1 The intelligent control method for gas production and distribution based on DO-ammonia nitrogen synergistic sensing includes the following steps: S1. Based on the monitoring instruments at the biological treatment tank, collect the real-time dissolved oxygen concentration of the aerobic tank, the real-time ammonia nitrogen concentration at the inlet, and the real-time inlet flow rate. Then, perform noise reduction and time-series alignment processing on the collected multi-source data to generate standardized current operating status data. S2. Based on the current operating status data and combined with the activated sludge reaction kinetics, calculate the theoretical oxygen demand required to remove ammonia nitrogen load at the current moment, and convert the theoretical oxygen demand into a feedforward aeration control signal; S3. Based on the set target dissolved oxygen value and the current operating condition data, define the dissolved oxygen deviation, and use the dissolved oxygen deviation and the ammonia nitrogen concentration in the operating condition data as two-dimensional input variables to perform fuzzy logic deduction to obtain the PID parameter correction amount. Based on the PID parameter correction amount, update the PID controller parameters online and output feedback aeration control signal. S4. The feedforward aeration control signal and the feedback aeration control signal are weighted and superimposed, the equipment safety limit is processed, and the gas distribution execution command is generated and sent to the aeration actuator.

[0019] Furthermore, step S1 specifically includes the following sub-steps: S101. Read the raw dissolved oxygen data at the end of the aerobic tank using a dissolved oxygen sensor, read the raw ammonia nitrogen data at the inlet of the anoxic tank using a water quality analyzer, and read the raw flow data of the inlet pipe using a flow meter. S102. Obtain the preset effective volume constant of the biochemical tank, and divide the effective volume constant of the biochemical tank by the original flow rate data to calculate the instantaneous hydraulic retention time series that changes with time; S103. Based on the instantaneous hydraulic residence time sequence, establish a time lag mapping relationship between the influent moment and the effluent response moment. The mapping relationship is used to define the time delay of the fluid at the influent end reaching the end of the aerobic tank. S104. Based on the time lag mapping relationship, shift the raw ammonia nitrogen data and raw flow data backward on the time axis so that the timestamps of the raw ammonia nitrogen data and raw flow data correspond to the timestamps of the raw dissolved oxygen data; generate time-synchronized standardized current operating condition data.

[0020] Furthermore, in step S103, the time delay is specifically the actual time length taken for the water to flow from the inlet sampling point (i.e., the ammonia nitrogen monitoring location, for example, located at the inlet of the anoxic or anaerobic tank) to the end sampling point of the aerobic tank (i.e., the dissolved oxygen monitoring location).

[0021] Specifically, step S1 mainly addresses the problem of data spatiotemporal misalignment caused by large lags and nonlinear characteristics in wastewater treatment biochemical systems. In actual engineering, the dissolved oxygen (DO) state at the end of the aerobic tank is not determined by the current influent ammonia nitrogen load, but by the wastewater load that entered the biochemical tank several hours earlier. In terms of process, three dimensions of data are first collected in parallel: the DO value reflecting the current aeration effect (i.e., the dissolved oxygen at the end of the aerobic tank), the influent ammonia nitrogen value reflecting input disturbances, and the influent flow rate value. Further, the algorithm enters the crucial time alignment stage. The system treats the biochemical tank as a plug-flow or completely mixed reactor, dynamically calculating the hydraulic retention time (HRT) using real-time flow data. Since the influent flow rate fluctuates, the HRT also changes dynamically. The algorithm constructs a dynamic queue or circular buffer, timestamps the ammonia nitrogen and flow data at the influent end and stores them in the queue. By calculating the instantaneous HRT, the algorithm backtracks in the historical data queue to find the influent moment that truly corresponds to the DO value at the end of the aerobic tank in terms of physical and biochemical reactions at the current moment. Through spatiotemporal translation technology, the influent data is pushed forward on the time axis, so that the input variable (load) and the output variable (DO response) are aligned in causal logic, thereby constructing a standardized state dataset that can truly reflect the causal relationship of biochemical reactions.

[0022] Furthermore, the specific calculation process for the current operating condition data is as follows: ; ; Among them, the Indicates the real-time inflow rate, the Indicates the instantaneous hydraulic residence time, the Indicates the current sampling time, the express The raw traffic data collected in real time, the This indicates the real-time ammonia nitrogen concentration at the inlet. express The raw ammonia nitrogen data collected at all times, the This indicates the real-time dissolved oxygen concentration. Indicates the current sampling time The raw dissolved oxygen data collected, the This represents the preset effective volume constant of the biochemical pool. This represents the raw traffic data. It should be noted that the... The moment is a time lag mapping relationship.

[0023] Specifically, in step S2, a time mapping function based on fluid dynamics is established; first, the instantaneous hydraulic residence time is calculated. This is about real-time traffic. The inverse proportional function means that when the influent flow rate increases, the wastewater flows through the biological treatment tank faster and the lag time becomes shorter; conversely, it becomes longer. Secondly, regarding dissolved oxygen... Take the current time directly. The sampled values, for the influent flow rate and ammonia nitrogen If the current value is not taken, then the value is taken instead. Historical data at any given time. This historical data is obtained through a historical database with a time index. It is calculated each time a control calculation is performed. And find it in the history using an interpolation algorithm (linear interpolation). The precise numerical values ​​corresponding to each moment enable feedforward calculations and fuzzy controllers to perform operations based on input and output data within the same water range.

[0024] For example, the sampling period is set according to the instrument's response time and biochemical reaction rate, and is generally set to 1-5 minutes.

[0025] Furthermore, the size of the historical data buffer involved in this step must be set to be greater than the maximum possible hydraulic residence time (for example, 1.5 times the design HRT) to prevent the corresponding historical data from being unavailable under extremely low flow conditions. This parameter and the sampling period are written into the configuration file during the system initialization phase.

[0026] Furthermore, regarding the preset effective volume constant of the biochemical pool involved in this step... This parameter is determined based on the civil engineering design drawings or as-built measurement data of the wastewater treatment plant. When setting it, the dead zone volume must be removed, and the actual effective flow volume from the anoxic zone to the end of the aerobic zone in the biological treatment tank must be calculated. During system commissioning, hydraulic retention time distribution is typically measured by adding tracers (such as Rhodamine B or lithium salts). The volume parameter is corrected based on the peak time of the tracer effluent curve to ensure that the calculated HRT matches the actual flow pattern. These methods are all standard technical approaches in the field.

[0027] Furthermore, step S2 includes the following sub-steps: S201. Based on the real-time influent flow rate and real-time ammonia nitrogen concentration at the influent end in the standardized current operating condition data, and combined with the preset effluent ammonia nitrogen discharge threshold, calculate the oxygen mass flow rate required to oxidize the current influent ammonia nitrogen load. S202. By introducing oxygen transfer efficiency parameters and environmental correction coefficients, the calculated oxygen mass flow rate is converted into the gas supply flow rate under standard conditions; S203. By using a linear proportional mapping method based on the maximum rated capacity of the aeration equipment, the air supply flow rate is converted into a feedforward aeration control signal of blower frequency or valve opening.

[0028] Specifically, step S2 employs an open-loop feedforward control strategy based on a mechanistic model. It calculates the absolute oxygen mass required to remove current pollutants using time-aligned flow rate and ammonia nitrogen concentration load data. The calculation process includes compensation for the oxygen demand of ammonia nitrogen nitrification and the basic oxygen demand for heterotrophic bacteria removing carbonaceous organic matter. The system introduces oxygen transfer efficiency and an environmental correction coefficient to convert the standard oxygen demand into air volumetric flow rate under on-site conditions. Finally, based on the flow characteristic curve of the blower or regulating valve, the volumetric flow rate is mapped to a frequency or opening command that the actuator can recognize, achieving immediate response to influent load fluctuations and pre-adjustment of the air supply. Furthermore, the feedforward aeration control signal is specifically represented as follows: ; Among them, the This indicates the feedforward aeration control signal, the This represents the dimensional conversion and mapping coefficients from oxygen demand to control signal. Indicates the real-time inflow rate, the This indicates the real-time ammonia nitrogen concentration at the inlet. This indicates the preset ammonia nitrogen emission threshold for effluent. The oxygen demand coefficient for ammonia nitrogen oxidation is represented by the following: The standard oxygen transfer efficiency of the aeration system is represented by the following: The wastewater impurity correction factor is represented by the following. The salinity and pressure correction coefficients are indicated by the following. This indicates the saturated dissolved oxygen concentration in clear water. This indicates the real-time dissolved oxygen concentration. This represents the baseline oxygen demand compensation coefficient for removing carbonaceous organic matter.

[0029] Specifically, the stoichiometric relationship of the nitrification reaction in the activated sludge process is quantified through signal generation logic. The calculation logic first obtains the ammonia nitrogen removal load, i.e., the product of the flow rate and the concentration difference, and multiplies it by the ammonia nitrogen oxidation oxygen demand coefficient to obtain the theoretical oxygen demand for the nitrification reaction. At the same time, an organic matter removal oxygen demand compensation term based on flow rate is added to obtain the total oxygen demand. The denominator is converted into the air supply flow rate under standard conditions by introducing standard oxygen transfer efficiency and environmental correction factors such as wastewater properties, pressure, and temperature. Finally, it is multiplied by the dimension conversion coefficient to output a control signal that matches the hardware interface. In addition, the dimension conversion and mapping coefficient from oxygen demand to control signal are also considered. It should be noted that this system uses a linear conversion gain coefficient, which physically represents the actuator control quantity corresponding to a unit standard air supply flow rate. It is determined by the physical characteristics of the aeration equipment (rated power, rated flow rate) and the controller's output range. The specific logic for determining its value is as follows: Obtain the maximum control command value of the aeration actuator, such as the blower or air regulating valve, under full-scale operation (e.g., the maximum operating frequency of the frequency converter or the 100% full opening of the valve). Simultaneously, obtain the maximum rated air supply flow rate that the equipment can provide under standard conditions. Divide the maximum control command value by the maximum rated air supply flow rate; the quotient is the mapping coefficient. In actual control, by multiplying the calculated theoretical air supply flow rate by this coefficient, a linear mapping from physical air demand to hardware control signals can be achieved.

[0030] Furthermore, the preset effluent ammonia nitrogen discharge threshold is set according to the pollutant discharge standard limits implemented in the area where the wastewater treatment plant is located. A safety margin is reserved when setting the threshold to cope with sudden shocks, and the setting is based on the requirements of the discharge permit issued by the environmental protection department.

[0031] Furthermore, the oxygen demand coefficient for ammonia nitrogen oxidation is set based on the theoretical value of the nitrification reaction chemical equation; the oxygen transfer efficiency and environmental correction coefficient are set based on the technical parameter table provided by the aeration equipment manufacturer and the on-site clean water oxygenation test data; and the baseline oxygen demand compensation coefficient is set based on the correlation analysis of influent chemical oxygen demand and flow rate in historical operating data.

[0032] Furthermore, step S3 includes the following sub-steps: S301. Define dissolved oxygen deviation and real-time ammonia nitrogen concentration at the inlet in the standardized current operating condition data as fuzzy input variables, establish corresponding fuzzy subsets and membership functions respectively, and convert precise numerical values ​​into fuzzy linguistic variables; S302. Based on the preset fuzzy rule base, combined with the corresponding fuzzy subset and membership function, the PID parameter increment is inferred. The centroid method is used to defuzzify the inferred PID parameter increment, and the defuzzified increment value is superimposed on the PID parameter of the previous moment to obtain the proportional coefficient, integral coefficient and derivative coefficient of the current control cycle. S303. Based on the updated PID parameters, combined with the dissolved oxygen deviation and its rate of change, the feedback aeration control signal is calculated.

[0033] Specifically, step S3, as the core feedback regulation mechanism, is responsible for handling nonlinear errors and unknown disturbances. The algorithm first converts the precise dissolved oxygen deviation and ammonia nitrogen concentration values ​​into fuzzy linguistic variables using a membership function. The fuzzy inference engine determines the control strategy corresponding to the current operating condition based on the expert rule base and outputs a fuzzy set of PID parameter adjustment increments. The geometric center of this fuzzy set is calculated using the centroid method to obtain precise parameter correction values. The system then superimposes these correction values ​​onto the baseline PID parameters in real time to achieve online adaptive scheduling of the control gain. Finally, the updated parameters are combined with the error state to calculate the feedback control quantity to eliminate system steady-state error. By using a fuzzy inference system to solve the time-varying parameters and nonlinear gain problems in biochemical reaction processes, the algorithm maps dissolved oxygen deviation and ammonia nitrogen concentration to a high-dimensional fuzzy feature space. It uses an expert experience base to simulate the decision-making thinking of a senior process engineer, dynamically assesses the system's sensitivity to control based on the current pollution load level and system deviation state, and calculates the geometric center of the fuzzy set using the centroid method to output the optimal PID parameter correction increment. This enables the control algorithm gain to be adaptively adjusted in real time as the operating point drifts, ensuring that the controller maintains ideal dynamic response characteristics and steady-state accuracy across the entire operating range.

[0034] Furthermore, the rule base includes adjustment strategies based on ammonia nitrogen concentration as a constraint, as follows: When the ammonia nitrogen concentration belongs to a highly fuzzy set, increase the output increment of the PID proportional and integral parameters; When the ammonia nitrogen concentration belongs to a low fuzzy set, reduce the output increment of the PID proportional parameter.

[0035] Specifically, this strategy introduces the biochemical reaction rate as a constraint variable for adjusting the stiffness of the control system. Under high ammonia nitrogen concentration conditions, due to the high activity and rapid oxygen consumption of nitrifying bacteria, the system exhibits large hysteresis and high sensitivity characteristics. The rule base drives the controller to output a larger proportional and integral gain to improve the closed-loop bandwidth and anti-disturbance capability, ensuring that dissolved oxygen quickly tracks the set value to prevent ammonia nitrogen penetration. Under low ammonia nitrogen concentration conditions, due to the decrease in reaction rate and the easy occurrence of sludge deflocculation, the system enters the low-load sensitive zone. The rule base drives the controller to reduce the proportional gain to increase the damping ratio, smooth the control output, and suppress the oscillations caused by frequent acceleration and deceleration of the blower.

[0036] Furthermore, the specific update process for the PID parameters is as follows: ; Among them, the This represents the updated scaling factor at the current moment. This represents the initial scaling factor reference value, the This represents the increment of the proportionality coefficient derived from the current dissolved oxygen deviation and ammonia nitrogen concentration. This indicates the dissolved oxygen deviation at the current moment. This represents the real-time ammonia nitrogen concentration at the inlet in the standardized current operating condition data at the current moment. The integral coefficient is the one updated at the current moment. This represents the initial integral coefficient reference value, the This represents the increment of the integral coefficient derived from the current dissolved oxygen deviation and ammonia nitrogen concentration. Represents the updated differential coefficients at the current time. This represents the initial differential coefficient reference value, the This represents the increment of the differential coefficient derived from the current dissolved oxygen deviation and ammonia nitrogen concentration.

[0037] Specifically, the parameter update mechanism adopts a variable gain scheduling architecture that superimposes baseline parameters with dynamic increments. The system uses a set of static baseline PID parameters that maintain steady-state operation as its core, and shapes the control law in real time through dynamic correction terms output by the fuzzy controller. This architecture uses ammonia nitrogen concentration as a scheduling variable to correct the controller's pole configuration, enabling the control system to compensate for changes in object characteristics by instantly adjusting the proportional band and integral time constant when faced with model parameter perturbations caused by sudden changes in influent water quality. Furthermore, the initial baseline values ​​for proportional, integral, and derivative coefficients are determined on-site during the system's clean water commissioning phase using the Ziegler-Nichols critical proportional gain method, and fine-tuned based on the actual response curves after activated sludge cultivation.

[0038] Furthermore, the feedback aeration control signal is specifically represented as follows: ; Among them, the This indicates the feedback aeration control signal, the The scaling factor representing the current time, the This indicates the dissolved oxygen deviation at the current moment. The integral coefficient at the current moment is represented by the following. Indicates the current sampling time, the The discrete-time index representing the summation of integrals, the Indicates the first Dissolved oxygen deviation at time, the The sampling period of the control system is indicated by the following. The differential coefficients at the current moment are represented by the following. This indicates the dissolved oxygen deviation at the previous sampling time. The term representing the differential rate of change of dissolved oxygen deviation.

[0039] Specifically, the feedback signal calculation employs a digital incremental PID algorithm. The proportional component directly amplifies the current dissolved oxygen error value to provide immediate reverse adjustment. The integral component continuously accumulates historical error values ​​through an accumulator to output a gradually increasing signal to eliminate steady-state deviation. The derivative component predicts the error trend and provides proactive suppression by calculating the difference between the errors at two consecutive sampling times. The calculation results of the three components are summed within the CPU to generate a dimensionless correction value, which represents the proportion of aeration volume that needs to be increased or decreased based on the theoretical oxygen demand. Furthermore, step S4 includes the following sub-steps: S401. Set the feedforward control weight and feedback control weight, and linearly superimpose the feedforward aeration control signal and the feedback aeration control signal in proportion to obtain the total air distribution command value. S402. Verify whether the total air distribution command value is within the physical operating range of the aeration equipment. If it exceeds the range, take the boundary value and generate the air distribution execution command.

[0040] Specifically, step S4 uses a linear weighted method to superimpose the feedforward and feedback signals. The feedforward part serves as the baseline control quantity, handling the main load change response, while the feedback part serves as the correction quantity to eliminate residual errors. The synthesized total command needs to be verified by an amplitude limiting circuit. If the command value exceeds the physical operating range of the blower or regulating valve, it is forcibly truncated to the boundary value to prevent equipment damage due to over-frequency operation or surge and sludge settling accidents due to excessively low frequency. Further, the basic air demand calculated by the feedforward algorithm and the corrected air demand calculated by the feedback algorithm are numerically superimposed to obtain the theoretical total air demand command. This command then enters the safety limiting module, where the program compares it with the preset physical boundaries of the equipment. If the command value is lower than the blower's surge frequency or the minimum air volume required to maintain sludge suspension, the output value is forcibly clamped at the lower limit; if the command value is higher than the blower's rated power frequency, it is clamped at the upper limit. Finally, the processed safety command is output to the frequency converter or regulating valve through the I / O module to drive the physical equipment.

[0041] Furthermore, the total gas distribution command value is specifically expressed as follows: ; Among them, the Indicates the total gas distribution command value, the This represents the preset feedforward control weight coefficient, the This represents the preset feedback control weight coefficient, the This indicates the feedforward aeration control signal, the This indicates a feedback aeration control signal.

[0042] Furthermore, the weighting coefficients for feedforward and feedback control are set based on the confidence level assessment of the accuracy of the feedforward model; regarding the physical operating range of the aeration equipment, this parameter is set based on the technical specifications and performance curves provided by the blower and regulating valve equipment manufacturers.

[0043] Example 2 Furthermore, as a preferred embodiment of the above-described embodiment one, a gas production and distribution intelligent control system based on DO-ammonia nitrogen synergistic sensing is proposed. This system is implemented based on the gas production and distribution intelligent control method based on DO-ammonia nitrogen synergistic sensing described in any one of embodiments one, and includes: The operating condition data acquisition module is used to collect real-time dissolved oxygen concentration in the aerobic tank, real-time ammonia nitrogen concentration at the inlet, and real-time inlet flow rate from the monitoring instruments at the biological treatment tank site. It also performs noise reduction and time-series alignment processing on the collected multi-source data to generate standardized current operating condition data. The feedforward signal generation module is used to calculate the theoretical oxygen demand required to remove ammonia nitrogen load at the current moment based on the current operating status data and the reaction kinetics of activated sludge, and convert the theoretical oxygen demand into a feedforward aeration control signal. The feedback signal generation module is used to calculate the dissolved oxygen deviation based on the set target dissolved oxygen value and the current operating condition data. It also uses the dissolved oxygen deviation and the ammonia nitrogen concentration in the operating condition data as two-dimensional input variables to perform fuzzy logic deduction to obtain the PID parameter correction amount. Based on the PID parameter correction amount, it updates the parameters of the PID controller online and outputs a feedback aeration control signal. The gas distribution execution module is used to weight and superimpose the feedforward aeration control signal and the feedback aeration control signal, perform equipment safety limiting processing, generate gas distribution execution instructions, and send them to the aeration actuator.

[0044] Furthermore, the feedforward aeration control signal is specifically represented as follows: ; Among them, the This indicates the feedforward aeration control signal, the This represents the dimensional conversion and mapping coefficients from oxygen demand to control signal. Indicates the real-time inflow rate, the This indicates the real-time ammonia nitrogen concentration at the inlet. This indicates the preset ammonia nitrogen emission threshold for effluent. The oxygen demand coefficient for ammonia nitrogen oxidation is represented by the following: The standard oxygen transfer efficiency of the aeration system is represented by the following: The wastewater impurity correction factor is represented by the following. The salinity and pressure correction coefficients are indicated by the following. This indicates the saturated dissolved oxygen concentration in clear water. This indicates the real-time dissolved oxygen concentration. This represents the baseline oxygen demand compensation coefficient for the removal of carbonaceous organic matter; The feedback aeration control signal is specifically represented as follows: ; Among them, the This indicates the feedback aeration control signal, the The scaling factor representing the current time, the This indicates the dissolved oxygen deviation at the current moment. The integral coefficient at the current moment is represented by the following. Indicates the current sampling time, the The discrete-time index representing the summation of integrals, the Indicates the first Dissolved oxygen deviation at time, the The sampling period of the control system is indicated by the following. The differential coefficients at the current moment are represented by the following. This indicates the dissolved oxygen deviation at the previous sampling time. The term representing the differential rate of change of dissolved oxygen deviation.

[0045] Example 3 Furthermore, this embodiment proposes a specific construction process for the membership function in the above embodiments: The controller first reads the real-time physical values ​​of dissolved oxygen deviation and influent ammonia nitrogen concentration, mapping these two continuously changing analog quantities to a predefined standard discrete domain within the controller. For the dissolved oxygen deviation variable, the system defines the physical domain range as follows: The value is usually taken as And linearly quantize it into a fuzzy domain of integer order. For the influent ammonia nitrogen concentration variable, the system defines the theoretical domain as follows: The value is usually determined based on the upper limit of the process design. And quantify it into a standardized fuzzy domain of discourse. Within the quantified universe of discourse, fuzzy subsets are partitioned. For dissolved oxygen bias, it is divided into 7 linguistic variable levels: negative, large, and negative. Negative Small negative ,zero , Zhengxiao ,middle Zhengda For ammonia nitrogen concentration, it is divided into three levels of linguistic variables: low ,middle ,high Each linguistic variable corresponds to a specific membership function. Adjacent membership functions maintain a certain overlap in the universe of discourse, with the overlap rate set between 0.2 and 0.5.

[0046] Furthermore, considering the real-time requirements of industrial controller operations, the algorithm selects trigonometric and trapezoidal functions with lower computational complexity as the basic mathematical forms of membership, for the boundary variables at both ends of the universe of discourse ( The semi-trapezoidal function (Z-shaped or S-shaped) is used to cover the region tending towards infinity or infinity, ensuring that extreme input values ​​beyond the range still obtain a constant membership output; for variables in the middle of the universe of discourse ( The controller employs either symmetrical or asymmetrical triangular functions. Within each scan cycle, the controller adjusts the input based on the currently quantized input value. Substitute the values ​​into the following analytical formula to calculate the membership values ​​of each fuzzy subset. .

[0047] For example, a triangular membership function is used. The mathematical expression for the linguistic variable describing the intermediate state is: ; Wherein, x represents the quantized value of the current input; a represents the left bottom point of the fuzzy subset, which is the critical value at which the membership degree begins to be greater than 0; b represents the vertex of the fuzzy subset, which is the complete membership value with a membership degree of 1; and c represents the right bottom point of the fuzzy subset, which is the ending value at which the membership degree returns to 0.

[0048] During runtime, the program calculates the membership degree of the input value x to all defined fuzzy subsets in parallel. Due to the local overlap of membership functions, the input value x at any given time will usually activate two adjacent fuzzy subsets simultaneously (for example, simultaneously belonging to "small" membership degree 0.3 and "middle" membership degree 0.7). The controller temporarily stores this set of membership degree vectors in memory as the weight basis for the rule applicability calculation in the subsequent fuzzy inference module, thereby completing the mapping from the precise numerical space to the fuzzy semantic space.

[0049] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for intelligent control of gas production and distribution based on DO-ammonia nitrogen synergistic sensing, characterized in that, Includes the following steps: S1. Based on the monitoring instruments at the biological treatment tank, collect the real-time dissolved oxygen concentration of the aerobic tank, the real-time ammonia nitrogen concentration at the inlet, and the real-time inlet flow rate. Then, perform noise reduction and time-series alignment processing on the collected multi-source data to generate standardized current operating status data. S2. Based on the current operating status data and combined with the activated sludge reaction kinetics, calculate the theoretical oxygen demand required to remove ammonia nitrogen load at the current moment, and convert the theoretical oxygen demand into a feedforward aeration control signal; S3. Based on the set target dissolved oxygen value and the current operating condition data, define the dissolved oxygen deviation, and use the dissolved oxygen deviation and the ammonia nitrogen concentration in the operating condition data as two-dimensional input variables to perform fuzzy logic deduction to obtain the PID parameter correction amount. Based on the PID parameter correction amount, update the PID controller parameters online and output feedback aeration control signal. S4. The feedforward aeration control signal and the feedback aeration control signal are weighted and superimposed, the equipment safety limit is processed, and the gas distribution execution command is generated and sent to the aeration actuator.

2. The intelligent control method for gas production and distribution based on DO-ammonia nitrogen synergistic sensing as described in claim 1, characterized in that, Step S1 specifically includes the following sub-steps: S101. Read the raw dissolved oxygen data at the end of the aerobic tank using a dissolved oxygen sensor, read the raw ammonia nitrogen data at the inlet of the anoxic tank using a water quality analyzer, and read the raw flow data of the inlet pipe using a flow meter. S102. Obtain the preset effective volume constant of the biochemical tank, and divide the effective volume constant of the biochemical tank by the original flow rate data to calculate the instantaneous hydraulic retention time series that changes with time; S103. Based on the instantaneous hydraulic residence time sequence, establish a time lag mapping relationship between the influent moment and the effluent response moment. The mapping relationship is used to define the time delay of the fluid at the influent end reaching the end of the aerobic tank. S104. Based on the time lag mapping relationship, shift the raw ammonia nitrogen data and raw flow data backward on the time axis so that the timestamps of the raw ammonia nitrogen data and raw flow data correspond to the timestamps of the raw dissolved oxygen data; generate time-synchronized standardized current operating condition data.

3. The intelligent control method for gas production and distribution based on DO-ammonia nitrogen synergistic sensing as described in claim 1, characterized in that, The specific calculation process for the current operating condition data is as follows: ； ； Among them, the Indicates the real-time inflow rate, the Indicates the instantaneous hydraulic residence time, the Indicates the current sampling time, the express The raw traffic data collected in real time, the This indicates the real-time ammonia nitrogen concentration at the inlet. express The raw ammonia nitrogen data collected at all times, the This indicates the real-time dissolved oxygen concentration. Indicates the current sampling time The collected raw dissolved oxygen data, the This represents the preset effective volume constant of the biochemical pool. This represents the raw traffic data.

4. The intelligent control method for gas production and distribution based on DO-ammonia nitrogen synergistic sensing as described in claim 1, characterized in that, Step S2 includes the following sub-steps: S201. Based on the real-time influent flow rate and real-time ammonia nitrogen concentration at the influent end in the standardized current operating condition data, and combined with the preset effluent ammonia nitrogen discharge threshold, calculate the oxygen mass flow rate required to oxidize the current influent ammonia nitrogen load. S202. By introducing oxygen transfer efficiency parameters and environmental correction coefficients, the calculated oxygen mass flow rate is converted into the gas supply flow rate under standard conditions; S203. By using a linear proportional mapping method based on the maximum rated capacity of the aeration equipment, the air supply flow rate is converted into a feedforward aeration control signal of blower frequency or valve opening.

5. The intelligent control method for gas production and distribution based on DO-ammonia nitrogen synergistic sensing as described in claim 4, characterized in that, The feedforward aeration control signal is specifically represented as follows: ； Among them, the This indicates the feedforward aeration control signal, the This represents the dimensional conversion and mapping coefficients from oxygen demand to control signal. Indicates the real-time inflow rate, the This indicates the real-time ammonia nitrogen concentration at the inlet. This indicates the preset ammonia nitrogen emission threshold for effluent. The oxygen demand coefficient for ammonia nitrogen oxidation is represented by the following: The standard oxygen transfer efficiency of the aeration system is represented by the following: The wastewater impurity correction factor is represented by the following. The salinity and pressure correction coefficients are indicated by the following. The dissolved oxygen concentration at saturation in the water is indicated by the following: This indicates the real-time dissolved oxygen concentration. This represents the baseline oxygen demand compensation coefficient for removing carbonaceous organic matter.

6. The intelligent control method for gas production and distribution based on DO-ammonia nitrogen synergistic sensing as described in claim 1, characterized in that, Step S3 includes the following sub-steps: S301. Define dissolved oxygen deviation and real-time ammonia nitrogen concentration at the inlet in the standardized current operating condition data as fuzzy input variables, establish corresponding fuzzy subsets and membership functions respectively, and convert precise numerical values ​​into fuzzy linguistic variables; S302. Based on the preset fuzzy rule base, combined with the corresponding fuzzy subset and membership function, the PID parameter increment is inferred. The centroid method is used to defuzzify the inferred PID parameter increment, and the defuzzified increment value is superimposed on the PID parameter of the previous moment to obtain the proportional coefficient, integral coefficient and derivative coefficient of the current control cycle. S303. Based on the updated PID parameters, combined with the dissolved oxygen deviation and its rate of change, the feedback aeration control signal is calculated.

7. The intelligent control method for gas production and distribution based on DO-ammonia nitrogen synergistic sensing as described in claim 6, characterized in that, The rule base contains adjustment strategies based on ammonia nitrogen concentration as a constraint. The specific strategies are as follows: When the ammonia nitrogen concentration belongs to a highly fuzzy set, increase the output increment of the PID proportional and integral parameters. When the ammonia nitrogen concentration belongs to a low fuzzy set, reduce the output increment of the PID proportional parameter.

8. The intelligent control method for gas production and distribution based on DO-ammonia nitrogen synergistic sensing as described in claim 6, characterized in that, The specific update process for the PID parameters is as follows: ； Among them, the This represents the updated scaling factor at the current moment. This represents the initial scaling factor reference value, the This represents the increment of the proportionality coefficient derived from the current dissolved oxygen deviation and ammonia nitrogen concentration. This indicates the dissolved oxygen deviation at the current moment. This represents the real-time ammonia nitrogen concentration at the inlet in the standardized current operating condition data at the current moment. The integral coefficient is the one updated at the current moment. This represents the initial integral coefficient reference value, the This represents the increment of the integral coefficient derived based on the current dissolved oxygen deviation and ammonia nitrogen concentration. Represents the updated differential coefficients at the current time. This represents the initial differential coefficient reference value, the This represents the increment of the differential coefficient derived from the current dissolved oxygen deviation and ammonia nitrogen concentration.

9. The intelligent control method for gas production and distribution based on DO-ammonia nitrogen synergistic sensing as described in claim 6, characterized in that, The feedback aeration control signal is specifically represented as follows: ； Among them, the This indicates the feedback aeration control signal, the The scaling factor representing the current time, the This indicates the dissolved oxygen deviation at the current moment. The integral coefficient at the current moment is represented by the following. Indicates the current sampling time, the The discrete-time index representing the summation of integrals, the Indicates the first Dissolved oxygen deviation at time, the The sampling period of the control system is indicated by the following. The differential coefficients at the current moment are represented by the following. This indicates the dissolved oxygen deviation at the previous sampling time. The term representing the differential rate of change of dissolved oxygen deviation.

10. The intelligent control method for gas production and distribution based on DO-ammonia nitrogen synergistic sensing as described in claim 1, characterized in that, Step S4 includes the following sub-steps: S401. Set the feedforward control weight and feedback control weight, and linearly superimpose the feedforward aeration control signal and the feedback aeration control signal in proportion to obtain the total air distribution command value. S402. Verify whether the total gas distribution command value is within the physical operating range of the aeration equipment. If it exceeds the range, take the boundary value and generate the gas distribution execution command.

11. The intelligent control method for gas production and distribution based on DO-ammonia nitrogen synergistic sensing as described in claim 10, characterized in that, The total valve timing command value is specifically expressed as follows: ； Among them, the This represents the total gas distribution command value, the This represents the preset feedforward control weight coefficient, the This represents the preset feedback control weight coefficient, the This indicates the feedforward aeration control signal, the This indicates a feedback aeration control signal.

12. A gas production and distribution intelligent control system based on DO-ammonia nitrogen synergistic sensing, wherein the system is implemented based on the gas production and distribution intelligent control method based on DO-ammonia nitrogen synergistic sensing as described in any one of claims 1-11, characterized in that, include: The operating condition data acquisition module is used to collect real-time dissolved oxygen concentration in the aerobic tank, real-time ammonia nitrogen concentration at the inlet, and real-time inlet flow rate from the monitoring instruments at the biological treatment tank site. It also performs noise reduction and time-series alignment processing on the collected multi-source data to generate standardized current operating condition data. The feedforward signal generation module is used to calculate the theoretical oxygen demand required to remove ammonia nitrogen load at the current moment based on the current operating status data and the reaction kinetics of activated sludge, and convert the theoretical oxygen demand into a feedforward aeration control signal. The feedback signal generation module is used to define the dissolved oxygen deviation based on the set target dissolved oxygen value and the current operating condition data, and to perform fuzzy logic deduction based on the dissolved oxygen deviation and the ammonia nitrogen concentration in the operating condition data as two-dimensional input variables to obtain the PID parameter correction amount. Based on the PID parameter correction amount, the PID controller is updated online, and a feedback aeration control signal is output. The gas distribution execution module is used to weight and superimpose the feedforward aeration control signal and the feedback aeration control signal, perform equipment safety limiting processing, generate gas distribution execution instructions, and send them to the aeration actuator.

13. The intelligent gas production and distribution control system based on DO-ammonia nitrogen synergistic sensing as described in claim 12, characterized in that, The feedforward aeration control signal is specifically represented as follows: ； Among them, the This indicates the feedforward aeration control signal, the This represents the dimensional conversion and mapping coefficients from oxygen demand to control signal. Indicates the real-time inflow rate, the This indicates the real-time ammonia nitrogen concentration at the inlet. This indicates the preset ammonia nitrogen emission threshold for effluent. The oxygen demand coefficient for ammonia nitrogen oxidation is represented by the following: The standard oxygen transfer efficiency of the aeration system is represented by the following: The wastewater impurity correction factor is represented by the following. The salinity and pressure correction coefficients are indicated by the following. The dissolved oxygen concentration at saturation in the water is indicated by the following: This indicates the real-time dissolved oxygen concentration. This represents the baseline oxygen demand compensation coefficient for the removal of carbonaceous organic matter; The feedback aeration control signal is specifically represented as follows: ； Among them, the This indicates the feedback aeration control signal, the The scaling factor representing the current time, the This indicates the dissolved oxygen deviation at the current moment. The integral coefficient at the current moment is represented by the following. Indicates the current sampling time, the The discrete-time index representing the summation of integrals, the Indicates the first Dissolved oxygen deviation at time, the The sampling period of the control system is indicated by the following. The differential coefficients at the current moment are represented by the following. This indicates the dissolved oxygen deviation at the previous sampling time. The term representing the differential rate of change of dissolved oxygen deviation.

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