An aeration dynamic balance digital control method and system for coping with flow state distortion

Abstract

The application discloses an aeration dynamic balance digital control method and system for coping with flow state distortion, relates to the technical field of sewage treatment, and comprises the following steps: constructing a sewage treatment aerobic tank multi-point dissolved oxygen and flow state synchronous monitoring system, continuously collecting spatial distribution data of dissolved oxygen, water flow velocity and density in the tank, and generating a flow state distribution graph reflecting the flow state distribution characteristics in the tank; based on the flow state distribution graph, the collected water flow velocity gradient and dissolved oxygen difference are subjected to collaborative feature extraction, and an abnormal area with flow state distortion is identified. Through multi-point monitoring and dynamic weighted control, the application realizes real-time identification of the flow state of the aerobic tank and accurate regulation of the air quantity, maintains the balance of the dissolved oxygen distribution, improves the oxygen transfer efficiency and the operation stability; by introducing a fluid correction factor and a time sequence prediction model, a control closed loop with self-learning and energy-saving optimization capabilities is established, and the intelligence and economy of the aeration process are improved.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, specifically to a digital control method and system for dynamic equilibrium of aeration in response to flow distortion. Background Technology

[0002] Dynamic equilibrium digital control of aeration in response to flow distortion refers to the process in biological reaction tanks or aeration tanks where abnormalities such as turbulence, short-circuiting, or dead zones occur due to factors like uneven influent flow rate, density flow, and dissolved oxygen distribution. This is achieved by establishing a multi-point real-time monitoring system for dissolved oxygen and fluid dynamics, and using digital control algorithms to adaptively adjust aeration volume, bubble distribution, and blower frequency to restore flow field equilibrium and oxygen transfer efficiency. The core of this approach lies in combining the dynamic coupling characteristics of the flow field and dissolved oxygen field to identify fluid velocity gradients, density stratification, and dissolved oxygen differences in real time. Through a feedback control model, precise air distribution is achieved, maintaining optimal oxygen supply and flow velocity matching in different areas. This avoids localized over-aeration or hypoxia, thereby maintaining the metabolic activity of the microbial community and the overall treatment stability of the reaction tank, achieving a dual balance between optimal energy consumption and biochemical reaction efficiency.

[0003] Existing technologies have the following shortcomings: During the operation of aerobic tanks in wastewater treatment, frequent fluctuations in influent water quality, quantity, and density often lead to flow distortions within the tank, such as short-circuiting, density flow, and abnormal local dissolved oxygen distribution. These flow anomalies cause traditional single-point dissolved oxygen-based control strategies to fail, resulting in increased aeration energy consumption, localized over-aeration or hypoxia, fluctuations in microbial activity, and unstable effluent quality. Existing control systems generally lack the ability to identify and respond to dynamic changes in flow patterns, making it difficult to achieve accurate oxygen distribution and automatic correction when sensor data is abnormal or flow patterns are unbalanced. Especially under conditions such as influent switching, flow field disturbances, or sensor malfunctions, the system still relies on local single-point data for control, ultimately leading to delays, misjudgments, and energy waste in aeration regulation, making it difficult to meet the stable operation requirements under complex conditions.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a digital control method and system for dynamic equilibrium of aeration in response to flow distortion, so as to solve the problems in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a digital control method for dynamic equilibrium of aeration to cope with flow distortion, comprising the following steps:

[0007] Step 1: Dissolved oxygen probes, flow velocity sensing units, and density measurement components are installed at the inlet, middle, and outlet of the aerobic wastewater treatment tank to construct a multi-point simultaneous monitoring system for dissolved oxygen and flow regime. This system continuously collects spatial distribution data of dissolved oxygen, water flow velocity, and density within the tank and generates a flow regime distribution map reflecting the flow regime distribution characteristics within the tank through high-frequency synchronous sampling.

[0008] Step 2: Based on the flow distribution map, perform collaborative feature extraction on the collected water flow velocity gradient and dissolved oxygen difference to identify abnormal regions with flow distortion, and generate a flow state mapping table containing short-flow regions and density flow boundaries at the control end to determine the flow state characteristics of each region.

[0009] Step 3: Perform dynamic aeration balance control according to the flow state mapping table, reduce the weight of dissolved oxygen data corresponding to abnormal areas, and adaptively adjust the air output of the aeration system based on the multi-point weighted average calculation results, so that the dissolved oxygen distribution in the pool gradually tends to a spatially balanced state.

[0010] Step 4: During the dynamic aeration balance control process, the fluid correction factor is calculated using the synchronously monitored water flow velocity and density data to compensate for the deviation of dissolved oxygen measurement values, so as to correct the measurement error caused by local turbulence, bubble interference or sensor drift, thereby ensuring the accuracy and reliability of the control feedback benchmark.

[0011] Step 5: Upload the multi-point dissolved oxygen distribution results after deviation compensation to the digital water platform. Combine historical operation data, influent characteristics and energy consumption records to construct an aeration demand prediction model based on time series data, and carry out adaptive pre-adjustment control for future aeration adjustments.

[0012] Step six: Relying on the long-term data accumulation of the digital water platform, the dynamic aeration balance control logic is periodically optimized. Based on historical flow distortion characteristics and energy consumption trends, the fluid correction factor weights and control parameters are updated to form a closed loop of dynamic balance digital control for aeration with self-learning and self-evolution capabilities.

[0013] Preferably, the step of generating a flow state mapping table containing short-flow regions and density flow boundaries at the control end includes:

[0014] After obtaining the complete flow pattern distribution map, the water flow velocity data and dissolved oxygen concentration data were spatially stratified. The data of the pool inlet, middle and outlet regions in the same time slice were divided according to the physical structure, and the water flow velocity gradient and dissolved oxygen concentration difference between adjacent measuring points were calculated.

[0015] Based on the flow pattern distribution map, the direction of change of water flow velocity gradient and dissolved oxygen concentration is compared. When the flow velocity changes abruptly and the dissolved oxygen does not change synchronously, it is identified as a short flow zone. When the vertical density difference and dissolved oxygen stratification coexist, it is identified as a density flow boundary.

[0016] The identified short-flow zones and density flow boundaries are labeled with the spatial coordinates of the aerobic pool to generate a flow state mapping table that records the direction of water flow velocity gradient, dissolved oxygen concentration deviation, and density difference in each zone, which can be used for subsequent dynamic aeration balance control.

[0017] Preferably, the flow state mapping table is dynamically updated according to the time series after it is generated, and the newly collected water flow velocity gradient, dissolved oxygen concentration difference and density difference data are superimposed on the corresponding spatial location to form a time-series mapping structure that can reflect the change trajectory of the short flow zone and density flow boundary, which is used to guide the continuous optimization of subsequent aeration volume allocation.

[0018] Preferably, the step of performing dynamic aeration balance control based on the flow state mapping table includes:

[0019] The regional information in the flow state mapping table is imported into the control process to classify the spatial regions of the aerobic tank from the inlet to the outlet, identify the short-flow zone, density flow stratification zone and slow flow zone, and record their dissolved oxygen concentration.

[0020] Based on the regional classification results in the flow state mapping table, the dissolved oxygen data of each region are weighted and integrated, weights are assigned, and the weighted average dissolved oxygen value is calculated. The results are used as the target reference for aeration adjustment.

[0021] Based on the deviation between the weighted average dissolved oxygen value and the set target range, the air output of the aeration equipment is dynamically adjusted, increasing the air supply to the short flow zone and low dissolved oxygen zone, and reducing the air supply to the density flow stratification zone and over-aeration zone.

[0022] Spatial feedback correction is performed on the gas volume regulation results, and the flow state mapping table is updated according to the changes in dissolved oxygen distribution, so as to continuously optimize the gas volume distribution and maintain the stable operation of the system.

[0023] Preferably, during the spatial feedback correction of the gas volume adjustment results, the dissolved oxygen change trend before and after the gas volume adjustment is compared based on the dissolved oxygen monitoring data at multiple points. When the dissolved oxygen concentration in the short-flow zone or density flow stratification zone tends to stabilize, the abnormality marker of the zone is updated to a stable state, and the new dissolved oxygen data is written into the flow state mapping table to achieve continuous optimization and dynamic updating of the gas volume allocation strategy.

[0024] Preferably, the step of calculating a fluid correction factor using synchronously monitored water flow velocity and density data to compensate for deviations in dissolved oxygen measurements includes:

[0025] The flow velocity measurement unit and density measurement component at the inlet, middle and outlet of the aerobic tank synchronously collect the changes in water flow velocity magnitude, direction and density gradient, and generate a raw data sequence containing time stamps, spatial location and change trend.

[0026] The fluid correction factor is calculated based on continuous data of water flow velocity and density, and then spatially distributed to each dissolved oxygen monitoring point according to the intensity of velocity change and the difference in density gradient.

[0027] The calculated and allocated fluid correction factor is dynamically superimposed and adjusted with the real-time dissolved oxygen measurement value to compensate for the deviation of the measurement results at each monitoring point. The corrected dissolved oxygen data is then fed back to the aeration control process to ensure the accuracy and reliability of the control feedback benchmark.

[0028] Preferably, the fluid correction factor is dynamically updated during spatial allocation based on the intensity of flow rate change, density gradient difference, and flow state of the area at each monitoring point, and the compensation range is adjusted in real time when the aeration rate or temperature changes, so that the compensated dissolved oxygen data continuously reflects the oxygen transfer state of different areas of the aerobic tank.

[0029] Preferably, the steps of constructing an aeration demand prediction model based on time-series data and adaptively pre-regulating aeration adjustments include:

[0030] After completing the dissolved oxygen deviation compensation, the dissolved oxygen concentration, flow rate, density and fluid correction factor of each monitoring point in the aerobic tank are classified and organized, and uploaded to the digital water platform with time as the main index to form a continuous dissolved oxygen spatial distribution dataset.

[0031] By utilizing the historical operational information stored in the platform, the uploaded multi-point dissolved oxygen distribution results are fused and matched with historical aeration volume, influent flow rate, influent water quality and energy consumption records to generate a multidimensional dataset containing the relationship between time, flow rate, dissolved oxygen concentration and energy consumption.

[0032] An aeration demand prediction model is constructed based on the fused multidimensional dataset. Based on influent fluctuations, changes in oxygen transfer efficiency, and energy consumption trends, the model predicts changes in oxygen demand over future periods, thereby achieving adaptive pre-regulation control of aeration volume.

[0033] Preferably, the steps for periodically optimizing the dynamic aeration balance control logic based on the long-term data accumulation of the digital water management platform include:

[0034] During long-term operation, the digital water platform continuously receives multi-dimensional operational data from the inlet, middle and outlet of the aerobic tank, including flow velocity distribution, water density, dissolved oxygen concentration, aeration volume, blower frequency, influent flow rate, influent water quality and energy consumption records, and marks them with timestamps and spatial coordinates to form a long-term dataset covering different operating conditions.

[0035] Based on long-term datasets, the flow distortion characteristics and energy consumption variation patterns during historical operation phases are analyzed. The occurrence frequency of short-circuit events, the duration of density stratification, and the trend of oxygen transfer efficiency are extracted, and the fluid correction factor weights and gas volume control parameters of each monitoring area are updated accordingly.

[0036] The updated fluid correction factor weights and gas volume control parameters are applied to the next operating cycle. By comparing the flow distribution, dissolved oxygen uniformity and energy consumption changes before and after optimization, self-learning and self-evolution are achieved, so that the control logic is continuously optimized and the aeration dynamic equilibrium control closed loop is maintained.

[0037] A dynamic equilibrium digital control system for aeration in response to flow distortion includes a multi-point monitoring and acquisition module, a flow characteristic identification module, a dynamic aeration regulation module, a dissolved oxygen deviation compensation module, an aeration prediction and control module, and a control logic optimization module.

[0038] The multi-point monitoring and acquisition module constructs a multi-point simultaneous monitoring system for dissolved oxygen and flow pattern in the aerobic tank of wastewater treatment. It continuously collects spatial distribution data of dissolved oxygen, water flow velocity and density in the tank, and generates a flow pattern distribution map reflecting the flow pattern distribution characteristics in the tank.

[0039] The flow pattern feature recognition module performs collaborative feature extraction on the collected water flow velocity gradient and dissolved oxygen difference based on the flow pattern distribution map, identifies abnormal areas with flow pattern distortion, and generates a flow pattern state mapping table containing short flow regions and density flow boundaries at the control end;

[0040] The dynamic aeration control module performs dynamic aeration balance control according to the flow state mapping table, reduces the weight of dissolved oxygen data corresponding to abnormal areas, and adaptively adjusts the air output of the aeration system based on the multi-point weighted average calculation results.

[0041] The dissolved oxygen deviation compensation module calculates the fluid correction factor using synchronously monitored water flow velocity and density data during dynamic aeration balance control, and performs deviation compensation on the dissolved oxygen measurement value to correct the measurement error caused by local turbulence, bubble interference or sensor drift.

[0042] The aeration prediction and control module uploads the multi-point dissolved oxygen distribution results after deviation compensation to the digital water platform. Combining historical operating data, influent characteristics and energy consumption records, it constructs an aeration demand prediction model based on time-series data to perform adaptive pre-adjustment control for future aeration adjustments.

[0043] The control logic optimization module, relying on the long-term data accumulation of the digital water platform, periodically optimizes the dynamic aeration balance control logic and updates the fluid correction factor weights and control parameters based on historical flow distortion characteristics and energy consumption trends.

[0044] In the above technical solution, the technical effects and advantages of the present invention include:

[0045] This invention establishes a synchronous monitoring system for dissolved oxygen, water flow velocity, and density at multiple points, enabling real-time identification and spatially balanced control of flow regime changes within the aerobic tank. This allows the aeration process to automatically adjust the air output according to the oxygen demand of different areas, thus maintaining the stability of dissolved oxygen distribution even under flow field disturbances or load fluctuations. The method achieves more precise air distribution through dynamic correction and weighted average control of dissolved oxygen data weights in abnormal areas, preventing localized over-aeration or hypoxia, and effectively improving oxygen transfer efficiency and the overall operational stability of the reactor.

[0046] This invention introduces a fluid correction factor and a time-series prediction model into dynamic aeration balance control, enabling the control logic to possess adaptive and self-evolving capabilities. It can optimize parameters based on long-term operating data and energy consumption trends, thereby achieving forward-looking and energy-saving air volume regulation under complex operating conditions. This control closed loop can continuously correct deviations and self-optimize, ensuring the system maintains a balance between oxygen supply and energy consumption under different seasons and load conditions, significantly improving the intelligence level of aeration control. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0048] Figure 1 A flowchart of a method for dynamic equilibrium digital control of aeration to cope with flow distortion provided by the present invention;

[0049] Figure 2 This is a flowchart illustrating how the present invention generates a flow state mapping table containing short-flow regions and density flow boundaries at the control end.

[0050] Figure 3 This is a flowchart illustrating the dynamic aeration balance control performed according to the flow state mapping table in this invention.

[0051] Figure 4 This is a schematic diagram of a digital control system for dynamic equilibrium of aeration in response to flow distortion, provided by the present invention. Detailed Implementation

[0052] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0053] This invention provides, for example Figures 1 to 3 The method for dynamic equilibrium digital control of aeration to address flow regime distortion includes the following steps:

[0054] Step 1: Dissolved oxygen probes, flow velocity sensing units, and density measurement components are installed at the inlet, middle, and outlet of the aerobic wastewater treatment tank to construct a multi-point simultaneous monitoring system for dissolved oxygen and flow regime. This system continuously collects spatial distribution data of dissolved oxygen, water flow velocity, and density within the tank and generates a flow regime distribution map reflecting the flow regime distribution characteristics within the tank through high-frequency synchronous sampling.

[0055] A flow pattern distribution map reflecting the flow pattern distribution characteristics within the pool is generated through high-frequency synchronous sampling. The specific steps are as follows:

[0056] In the layout of the aerobic tank for wastewater treatment, monitoring points are sequentially set at the inlet, middle, and outlet of the tank according to the main flow direction. Each location is equipped with a dissolved oxygen probe, a flow velocity sensor, and a density measurement component. The dissolved oxygen probe is an electrochemical probe with automatic temperature compensation and an antifouling coating to ensure stable operation even in wastewater environments containing suspended particles and air bubbles. The flow velocity sensor is a Doppler ultrasonic sensor capable of measuring three-dimensional flow velocity components. The sensor probe is embedded in the inner wall of the main flow channel near the middle water layer, allowing it to sense both the main flow velocity and detect local eddies caused by aeration or backflow. The density measurement component uses the principle of micro-pressure difference or ultrasonic time difference propagation. By setting two measuring points vertically, it measures the density difference of water at different depths in real time. Each measuring point is connected to the tank structure via a fixed bracket made of corrosion-resistant stainless steel. The probe insertion depth is set at one-third, one-half, and two-thirds of the tank's water depth to cover the vertically varying layers of the water body. All probes undergo cable protection and signal shielding after installation to prevent air bubbles and high-frequency electrical interference from affecting data transmission stability during aeration. This layout ensures that the entire aerobic tank, from the main water flow path to the effluent, the lateral diffusion zone, and potential dead zones, can be monitored and covered, thus forming a comprehensive multi-point monitoring foundation.

[0057] After the sensor equipment was deployed, the dissolved oxygen probe, flow velocity sensing unit, and density measurement component were configured for synchronous data acquisition. The sampling signal of each sensing unit was controlled by a unified time trigger source, ensuring that all measuring points acquired data at the same time. The sampling frequency was set from once per second to five times per second, and could be adjusted appropriately according to the volume and hydraulic retention time of the aerobic tank to capture short-term response characteristics of the flow pattern during aeration on / off, fluctuations in influent flow rate, or changes in the return flow ratio. The acquired data included instantaneous dissolved oxygen concentration, instantaneous flow velocity magnitude and direction, instantaneous water density, and temperature information at the time of acquisition. All acquired data was synchronously recorded in the form of timestamps and stored in real time in the data storage terminal. To avoid abnormal readings caused by external electromagnetic interference or bubble aggregation, the sensor surface was periodically cleaned of adhering substances using a microbubble flushing structure during the acquisition process to maintain sampling stability. This high-frequency synchronous acquisition method ensures that monitoring data from different spatial locations correspond completely in the time dimension, thus accurately reflecting the true flow characteristics of the aerobic tank under instantaneous conditions.

[0058] After simultaneously collecting dissolved oxygen, flow velocity, and density data from multiple locations, spatial correlation processing was performed on this data. First, based on the installation coordinates of each sensor within the tank, the data collected at the same time were rearranged according to location, forming a three-dimensional spatial information set including the inlet, middle, and outlet at each moment. Then, based on the directional component provided by the flow velocity sensing unit, the flow direction at different locations was compared to determine the continuity of the main flow line and whether there was any deviation. Next, combined with dissolved oxygen concentration data, the dissolved oxygen gradient changes between different points were analyzed, calculating the trend of oxygen concentration increase or decrease sequentially from the inlet to the outlet. By combining this with density data, it was possible to identify whether density stratification occurred due to temperature or dissolved substance concentration. A significant density difference between the middle and bottom indicates vertical stratification within the tank, which may lead to insufficient dissolved oxygen transfer from the lower layer. By comparing the relationship between flow velocity direction and dissolved oxygen concentration changes at each measuring point, it was possible to determine whether short-circuiting occurred in local areas, i.e., situations where the flow velocity is significantly higher than in adjacent areas but the dissolved oxygen concentration does not increase accordingly. The data is not compressed or filtered during processing to maintain the temporal continuity and spatial integrity of the original data, so that it can be used to generate continuous flow structure information later.

[0059] Finally, the processed data is dynamically stitched and spatially mapped in chronological order to generate a flow pattern distribution map reflecting the overall flow characteristics within the pool. During generation, time is used as the horizontal axis and the spatial location of the aerobic pool as the vertical axis. The flow velocity, density, and dissolved oxygen data at each moment are visualized and recombined to reflect the water flow path, oxygen distribution changes, and density stratification trends. By stitching together continuous time segments, a flow pattern evolution map is formed that dynamically displays the water movement state within the pool. This map shows the flow field distribution in the aerobic pool at different operating stages, such as the velocity enhancement region when the influent flow increases, the bubble diffusion range when the aeration intensity increases, and the water layer separation boundary caused by density changes. In the data display, flow velocity is represented by color gradients, dissolved oxygen concentration is expressed as contour lines, and density changes are represented by vertical stratified strips, so that the entire map not only shows the flow direction but also the relationship between dissolved oxygen transfer and density changes. By comparing the flow patterns at different time points, the dynamic changes in the flow structure after aeration adjustments or changes in influent conditions can be observed, providing a direct reference for identifying short-flow channels, local stagnation zones, or areas with uneven gas distribution. The resulting flow pattern distribution map provides complete data input and spatial basis for subsequent flow anomaly identification and dynamic aeration balance control.

[0060] Through the above implementation steps, the entire process from physical deployment to data acquisition and spatial reconstruction can be realized, enabling comprehensive perception of the flow state, dissolved oxygen distribution, and density changes within the aerobic tank in both time and space dimensions. The entire implementation process ensures high accuracy and high synchronicity of the monitoring data, allowing the generated flow pattern distribution map to accurately reflect the flow pattern changes caused by influent variations, aeration disturbances, or temperature stratification during wastewater treatment.

[0061] Step 2: Based on the flow distribution map, perform collaborative feature extraction on the collected water flow velocity gradient and dissolved oxygen difference to identify abnormal regions with flow distortion, and generate a flow state mapping table containing short-flow regions and density flow boundaries at the control end to determine the flow state characteristics of each region.

[0062] The flow state mapping table containing short-flow regions and density flow boundaries is generated at the control end. The specific steps are as follows:

[0063] After obtaining a complete flow distribution map, the flow velocity and dissolved oxygen concentration data from multiple points within the map were spatially stratified. Data from different measuring points within the same time slice were divided according to the physical structure of the aerobic tank. Each time slice included the flow velocity direction, velocity magnitude, and corresponding dissolved oxygen concentration values ​​for the inlet, middle, and outlet regions of the tank. This stratification method establishes a two-dimensional data correlation between time and space, clearly demonstrating the flow differences between different regions at the same time. Based on this, continuous analysis was performed on the flow velocity data, converting the velocity difference between adjacent measuring points into a spatial distribution of the flow velocity gradient to reflect the acceleration, deceleration, and directional changes of the flow. Simultaneously, the spatial comparison of dissolved oxygen concentration differences at each measuring point was performed to obtain the trend of dissolved oxygen distribution changes. By comparing the flow velocity gradient and the direction of dissolved oxygen concentration changes within the same time slice, it is possible to identify whether the oxygen transfer process is consistent with the fluid motion process. When the flow velocity increases but the dissolved oxygen concentration does not rise synchronously, it indicates a decrease in oxygen transfer efficiency in that area, suggesting obstructed gas-liquid contact, which may be due to short-circuiting or localized disturbances causing abnormal flow patterns. Conversely, when the flow velocity is low but the dissolved oxygen concentration is excessively high, it indicates localized over-aeration or bubble retention, and the water body may be in a slow-moving dead zone. This process allows us to correlate the flow change trends in each monitoring area with the dissolved oxygen distribution characteristics, providing a data foundation for the next step of anomaly area identification.

[0064] After spatially comparing the differences in water velocity gradient and dissolved oxygen concentration, the results were used to identify areas with flow regime distortions within the tank. Specifically, in the flow regime distribution map, when the water velocity gradient abruptly changes in a certain area without a corresponding change in dissolved oxygen concentration, short-flow phenomenon is identified in that area. This means that some water flows downstream without sufficient mixing, resulting in insufficient oxygen transfer. For areas exhibiting vertical dissolved oxygen concentration stratification and differences in density measurements, these are identified as stratification boundaries formed by density flow. The dissolved oxygen renewal rate of the lower fluid in these areas is slower, making them prone to anoxic conditions. During the identification process, continuous comparison of flow regime distribution maps across different time slices allows tracking the formation, expansion, and dissipation of these abnormal areas, thereby determining whether they are temporary disturbances or long-term structural distortions. For example, when a short-flow area persists in the same spatial location across multiple time slices, it indicates a structural flow field anomaly in that area, possibly caused by uneven aeration or interference from the return flow direction. Conversely, when short-flow characteristics appear briefly and disappear with fluctuations in the inflow, it indicates a transient disturbance in the region. This time-series analysis enables the identification results to not only possess spatial accuracy but also the dynamic ability to reflect changes in flow patterns.

[0065] It should be noted that:

[0066] A "sudden change" is defined as the variation in the flow velocity gradient between adjacent monitoring points exceeding a set threshold within a unit of time. Specifically, if the rate of change of the instantaneous flow velocity difference between adjacent monitoring points exceeds 30% of the previous average value within three consecutive sampling periods, or if the rate of change of the flow velocity gradient exceeds 0.15–0.25 m / s... 2 This indicates that the flow regime in the region is changing rapidly, forming a distinct velocity discontinuity zone, which can be identified as a region of abrupt change in water velocity gradient.

[0067] After identifying the flow distortion regions, the results are combined with the spatial structure information of the aerobic tank to generate a flow state mapping table containing short-flow zones and density flow boundaries. This mapping table, based on the spatial coordinates of the aerobic tank, labels and classifies the state characteristics of each monitoring area. For locations identified as short-flow zones, the mapping table records the corresponding flow velocity gradient direction, dissolved oxygen concentration deviation, and formation time to reflect their uneven flow characteristics. For areas with density flow stratification, the mapping table records the density difference between the upper and lower layers, the vertical dissolved oxygen concentration difference, and the corresponding spatial layer thickness to reflect the range and intensity of water stratification. For areas maintaining normal flow, the mapping table records the velocity and dissolved oxygen matching status as a reference. In this way, the mapping table can comprehensively describe the flow state of different areas within the aerobic tank, using time as the index and space as the coordinates. When performing dynamic aeration equalization adjustments, the control system can assign different control priorities and weights to different areas based on the state information recorded in this mapping table, thereby achieving precise adjustment based on flow distribution. As the operation progresses, newly collected data will be continuously added to and updated in the mapping table, enabling the mapping table to reflect the historical trajectory of flow pattern changes, thereby providing a basis for long-term control strategy optimization.

[0068] Through the implementation of the above steps, the water flow movement, dissolved oxygen distribution characteristics, and density changes within the aerobic tank can be fully identified and recorded in both spatial and temporal dimensions. The entire process begins with spatial stratification analysis of the flow pattern distribution map, gradually extracting the synergistic characteristics of the water flow velocity gradient and dissolved oxygen differences. Then, through temporal continuity comparison, abnormal areas are identified, ultimately forming a flow pattern state mapping table with spatial coordinates and temporal sequence information. This provides data support and a basis for judgment in the subsequent precise air volume allocation and oxygen supply regulation during the aeration balance control stage. This process, from monitoring data to spatial identification and then to state mapping, enables the control system to gain a comprehensive perception of the internal flow structure of the aerobic tank, achieving real-time location and classification of short-circuit, density flow, and localized dissolved oxygen anomalies, thus laying the foundation for dynamic balance control operation.

[0069] Step 3: Perform dynamic aeration balance control according to the flow state mapping table, reduce the weight of dissolved oxygen data corresponding to abnormal areas, and adaptively adjust the air output of the aeration system based on the multi-point weighted average calculation results, so that the dissolved oxygen distribution in the pool gradually tends to a spatially balanced state.

[0070] Dynamic aeration balance control is performed based on the flow state mapping table. The specific steps are as follows:

[0071] After generating the flow state mapping table, all area information from the table is imported into the control process. The flow state mapping table includes the spatial coordinates of each monitoring point in the aerobic tank, the direction of the water flow velocity gradient, the dissolved oxygen concentration difference, density distribution characteristics, and anomaly area markers. Based on the mapping table, the spatial regions of the tank from inlet to outlet are classified to determine which regions belong to the flow stability zone and which are identified as short-flow zones, density flow stratification zones, or sluggish flow zones. In practice, the dissolved oxygen concentration change trend of each region is first analyzed. When a region is found to have a persistently low dissolved oxygen concentration and a flow velocity higher than neighboring regions, it is marked as a short-flow zone; when the density difference between the upper and lower layers is large and vertical stratification of dissolved oxygen distribution occurs, the region is marked as a density flow stratification zone; when the flow velocity is consistently low and the dissolved oxygen concentration is consistently high, the region is marked as a local over-aeration or bubble retention zone. After classification, the anomaly type, spatial location, and corresponding dissolved oxygen characteristic value of each region are recorded in the mapping table, providing a usable data foundation for subsequent gas volume adjustment stages. At this stage, by reading the abnormal area information in the mapping table, the gas volume regulation logic can specifically identify the spatial range that needs to be adjusted, rather than uniformly processing the entire pool.

[0072] After inputting data for abnormal areas, the dissolved oxygen data from each area are weighted and integrated to form an average value representing the overall oxygen supply status of the tank. The weighting process is based on the area classification results in the mapping table, assigning different data weights to different areas. The stable flow zone is given a higher weight, and its dissolved oxygen data accounts for a major proportion in the weighted average calculation, reflecting the overall operating status of the aerobic tank. The data weights of the short-flow zone and density flow stratification zone are correspondingly reduced due to deviations or lags in dissolved oxygen measurements, to minimize the interference of local anomalies on the overall judgment. To ensure that the weighted result reflects the true impact of each area on oxygen transfer, the weights are adjusted based on the spatial volume ratio of the aerobic tank, the density of aeration head arrangements, and the water depth distribution, so that the weighted result reflects both the operating status of the main areas and the changing trends of local areas. During this process, dissolved oxygen data from each monitoring point is read synchronously and integrated according to the assigned weights. The final weighted average dissolved oxygen value represents the overall oxygen supply level of the tank at the current moment, and this value will serve as the target reference input for air volume adjustment.

[0073] It should be noted that:

[0074] To ensure that the weighted results reflect both the overall operational status of the main aerobic tank area and the changing trends of local areas, the weights of each monitoring point are differentiated based on the geometric characteristics and aeration structure distribution of the aerobic tank during implementation. Specifically, the basic weights can be determined according to the spatial volume ratio of each area, the distribution of the number of aeration heads, and the differences in water depth, and then dynamically fine-tuned based on real-time operational characteristics.

[0075] For example, in an aerobic tank with a total volume of 3000 cubic meters, assuming the tank is divided into three monitoring zones: the inlet zone (40% of the total volume), the middle zone (45%), and the outlet zone (15%), with 4, 6, and 2 dissolved oxygen probes installed respectively. Based on the volume ratio and aeration head density, the initial weights can be set as follows: inlet zone 0.4, middle zone 0.45, and outlet zone 0.15. During actual operation, if the dissolved oxygen concentration in the middle zone fluctuates significantly, for example, dropping from 3.2 mg / L to 2.5 mg / L in a short period while other zones show smaller changes, the weight of the middle zone is temporarily increased to 0.5 during weighted calculation, while the weights of the inlet and outlet zones are appropriately reduced to 0.35 and 0.15 respectively, to increase the sensitivity of the weighted result to dynamic changes in the middle zone. If the weighted average dissolved oxygen changes from 2.9 mg / L to 2.7 mg / L, the control system can determine that the overall oxygen supply in the tank is low based on the new weighted result and promptly increase the aeration rate.

[0076] When determining the weights of dissolved oxygen data for each region, three parameters are comprehensively considered: the spatial volume ratio of the aerobic tank, the density of aeration head distribution, and the water depth distribution. The weighting factors of their contribution to oxygen transfer are used as the basis for adjustment. Specifically, firstly, a basic weight is set based on the volume ratio of each region to ensure that the overall oxygen supply calculation conforms to the spatial ratio. Secondly, the density of aeration head distribution is used as a correction coefficient for oxygen transfer capacity. When a region has a large number of aeration heads or a concentrated distribution, its oxygen supply capacity per unit volume is stronger, and the corresponding weight is increased proportionally to the density; conversely, it is decreased. Finally, a diffusion correction coefficient is set based on the water depth distribution. Considering the characteristics of greater oxygen transfer resistance and longer reaction time in deep water areas, the weight of deep water areas is appropriately increased to reflect their delayed oxygen consumption impact. These three parameters together constitute the comprehensive weight adjustment model: weight Wi = α × Vi + β × Di + γ × Hi, where Vi is the volume ratio, Di is the aeration head density ratio, Hi is the water depth correction coefficient, and α, β, and γ are proportional factors determined based on operational experience or historical data. The comprehensive weights calculated by this model can dynamically reflect the true contribution of each region to oxygen transfer, making the weighted results not only consistent with the structural characteristics of the pool, but also possessing the rationality and adaptability of the control logic.

[0077] By combining spatial proportions with real-time changes in dynamic weighting, the weighted results can reflect the overall oxygen supply level in the main areas while maintaining sufficient responsiveness to sudden changes in local areas. This balances global representativeness with local sensitivity, enabling precise feedback control of aeration status.

[0078] Subsequently, based on the generated weighted average dissolved oxygen value, the gas output of the aeration equipment is dynamically adjusted. During the gas volume adjustment process, the gas supply and distribution ratio are gradually adjusted by comparing the deviation between the weighted average dissolved oxygen value and the set target concentration range. When the weighted average dissolved oxygen value is lower than the preset lower limit, the air supply of the blower is gradually increased, and gas is preferentially distributed to aeration heads identified as low dissolved oxygen or short-flow areas in the mapping table, so that these areas can receive additional oxygen support; when the weighted average dissolved oxygen value is higher than the preset upper limit, the overall aeration volume is appropriately reduced, and the gas output of density flow stratification zones and over-aeration zones is preferentially reduced to avoid excessive gas retention and increased energy consumption. The gas volume adjustment involves not only changes in the overall output intensity, but also spatial differential control of aeration distribution. For areas with stable flow in the pool, the original aeration intensity is maintained; for areas with low flow velocity or insufficient oxygen transfer efficiency, the aeration duration is appropriately increased; for areas with obvious density stratification, the mixing of upper and lower water layers is promoted by extending the bubble residence time or adjusting the aeration head outlet pressure, so as to make the dissolved oxygen distribution more balanced. This targeted gas volume regulation method can not only improve the overall stability of dissolved oxygen distribution, but also achieve precise intervention in abnormal areas at the local level, thereby gradually improving the flow balance of the entire aerobic tank.

[0079] After dynamically adjusting the gas output, spatial feedback correction is performed on the adjustment results to achieve continuous equilibrium in dissolved oxygen distribution. This feedback process is based on dissolved oxygen monitoring data from multiple locations, comparing the trends of dissolved oxygen changes before and after gas output adjustment, and analyzing the response of gas output changes to oxygen concentration in different regions. When the dissolved oxygen concentration in a certain region gradually recovers and stabilizes after an increase in gas output, it indicates that the oxygen supply recovery effect in that region is good, and the adjustment intensity can be appropriately reduced in the next cycle. When the dissolved oxygen in a certain region remains high after a decrease in gas output, it indicates that there is bubble retention or insufficient mixing in that region, and a lower aeration intensity needs to be maintained in subsequent cycles until the gas diffusion is uniform. Through this continuous feedback correction, the oxygen concentration changes in each region are always kept within the dynamic equilibrium range. As the adjustment process continues, regions that were previously identified as abnormal gradually recover to a stable state, the abnormality markers in the flow state mapping table are updated, and new dissolved oxygen data is written into the table, thus reflecting the latest flow field distribution and oxygen supply status. This feedback process not only enables continuous optimization of gas distribution, but also gives the control logic the ability to adapt and adjust over time. It can automatically reconstruct the gas distribution strategy when the quality, quantity, or temperature of the incoming water changes, thus maintaining the long-term stable operation of the system.

[0080] Regarding the determination of "tending towards stability":

[0081] When the dissolved oxygen concentration in a certain area gradually decreases over multiple consecutive sampling cycles (e.g., 5-10 consecutive sampling cycles, corresponding to approximately 30 seconds to 5 minutes) after an increase in aeration volume, and the rate of change of dissolved oxygen concentration between adjacent sampling points is lower than a set threshold (e.g., the change amplitude does not exceed ±0.1 mg / L or the relative change rate is less than 3%), and the dissolved oxygen concentration curve changes from an upward trend to a flattening trend or remains within the set target range, the dissolved oxygen concentration in that area is considered to be "tending to stabilize." This indicates that the oxygen transfer process in that area has returned to equilibrium, and the local dissolved oxygen supply and demand relationship has reached a dynamic balance, suggesting that the aeration adjustment effect is good.

[0082] Regarding the determination of "high value state":

[0083] When the dissolved oxygen concentration in a certain area remains above the system's set upper limit threshold (e.g., above the target dissolved oxygen concentration by more than 3.0 mg / L or exceeding the set upper limit by 10%) for multiple consecutive sampling cycles after a reduction in gas volume, and the dissolved oxygen curve does not show a significant downward trend or the rate of decline is lower than [the threshold value is missing from the original text], When the concentration reaches a certain level, the area is considered to be in a "high-value state." This state usually reflects bubble retention or insufficient local mixing, indicating that oxygen has not been effectively consumed or diffused. In subsequent control cycles, a lower aeration intensity should be maintained until the concentration returns to the normal range.

[0084] Through the above steps, the entire process from flow state identification to dissolved oxygen weighted processing, from dynamic gas volume adjustment to spatial feedback correction, is realized. This allows aeration control to move beyond single-point dissolved oxygen feedback and make comprehensive decisions based on the overall spatial state of the entire tank. Each step is implemented around the coupling characteristics of flow distribution and dissolved oxygen, enabling the control process to balance energy consumption optimization and oxygen supply balance, thereby maintaining the reaction efficiency and operational stability of the aerobic tank under different operating conditions.

[0085] Step 4: During the dynamic aeration balance control process, the fluid correction factor is calculated using the synchronously monitored water flow velocity and density data to compensate for the deviation of dissolved oxygen measurement values, so as to correct the measurement error caused by local turbulence, bubble interference or sensor drift, thereby ensuring the accuracy and reliability of the control feedback benchmark.

[0086] The fluid correction factor is calculated using synchronously monitored water flow velocity and density data to compensate for deviations in dissolved oxygen measurements. The specific steps are as follows:

[0087] During dynamic aeration equalization control, velocity measurement units and density measurement components deployed at the inlet, middle, and outlet of the aerobic tank synchronously collect data on the flow state and density changes of the water at different spatial locations. The velocity measurement units measure the magnitude, direction, and instantaneous fluctuation amplitude of the water flow in real time, with a sampling frequency consistent with the dissolved oxygen probe to ensure time-matching accuracy. Each acquisition includes horizontal and vertical velocity components and their corresponding rate of change, reflecting the disturbance of the water body under the influence of rising aeration bubbles. The density measurement components simultaneously acquire water density and temperature data in the vertical direction, with measurement points distributed at one-third, one-half, and two-thirds of the water depth, recording density gradient changes at different depths in real time. When there are large temperature or dissolved concentration differences, the density measurement components can capture the formation and dissipation of stratification. During continuous acquisition, water flow velocity and density data are recorded synchronously, forming a raw data sequence containing time stamps, spatial locations, instantaneous velocity, density values, and trends. To ensure data integrity, each set of data is time-series aligned immediately after acquisition, ensuring that data from different measuring points correspond at the same sampling time. This high-frequency, synchronous dynamic acquisition method comprehensively reflects the coupling state of water flow velocity and density within the pool, providing accurate basic data for generating fluid correction factors.

[0088] After acquiring continuous data on water flow velocity and density, this data is comprehensively analyzed to generate a fluid correction factor, which is then precisely allocated spatially. The fluid correction factor is generated based on the intensity of velocity changes, density gradient differences, and their temporal fluctuation trends. First, at each monitoring point, the velocity fluctuation range within the same time period is compared with the average density gradient. When velocity changes frequently and density changes significantly, it indicates strong fluid disturbance and unstable bubble movement in the area, resulting in significant interference with the dissolved oxygen sensor signal; in this case, a higher fluid correction factor is generated. Conversely, when the velocity is stable and the density distribution is uniform, it indicates stable water movement and uniform bubble diffusion; in this case, a lower fluid correction factor is generated. Subsequently, based on the spatial location of the monitoring points, the calculated fluid correction factor is allocated to the corresponding dissolved oxygen probes according to the region. Higher fluid correction factor values ​​are assigned to short-flow regions with higher flow velocities and mid-flow regions with higher aeration intensities to offset instantaneous dissolved oxygen fluctuations caused by turbulence. Medium fluid correction factor values ​​are assigned to density-stratified regions with slower flow velocities to correct for deviations in the dissolved oxygen probe caused by bubble obstruction or sensing hysteresis. Lower fluid correction factor values ​​are assigned to the outlet region with uniform flow patterns to maintain data stability. The spatial allocation results of the fluid correction factors are stored in tabular form, with each dissolved oxygen detection point corresponding to a real-time updated correction value that is continuously adjusted according to changes in flow patterns. This allocation method allows for accurate correction of dissolved oxygen data at each measurement point based on the degree of fluid disturbance in its region, thereby reducing local measurement errors caused by flow field inhomogeneity or stratification.

[0089] It should be noted that:

[0090] Regarding the determination of frequent changes in flow velocity and significant changes in density:

[0091] When the difference in water flow velocity between adjacent moments within a continuous monitoring period (e.g., 5 to 10 sampling periods) exceeds 0.1 m / s, and the rate of velocity change exceeds 30% of the previous average, the flow velocity in that area is considered to be frequently changing. Simultaneously, if the density difference between the upper and lower water layers exceeds 0.03 g / cm³ within the same time period, and the density gradient shows a continuously increasing trend, it indicates significant density changes in that area. When one or both of these conditions are met, or both are satisfied, it can be concluded that the fluid disturbance in that area is strong and the bubble movement is unstable, requiring a higher fluid correction factor for signal compensation.

[0092] Regarding the criteria for determining stable flow velocity and uniform density distribution:

[0093] When the velocity difference between adjacent sampling points is less than 0.03 m / s and the velocity change rate remains within 10% during a continuous monitoring period, the flow velocity in that area is considered to be in a stable state. Simultaneously, if the vertical density difference is less than 0.01 g / cm³ and the density gradient change does not exceed 5% of the previous average, it indicates that the water density distribution is uniform and no significant stratification or disturbance has occurred. Under these conditions, the flow field is in a stable operating state, the dissolved oxygen sensing signal fluctuates little, and a lower fluid correction factor value can be used to maintain sensitivity and response speed.

[0094] Regarding the specific range of values ​​for the higher and lower values:

[0095] To ensure appropriate and repeatable correction, the fluid correction factor is limited to a range of 0 to 1. For regions with frequent flow rate changes and significant density fluctuations, a higher correction factor is used, typically in the range of 0.6 to 0.8, to enhance smoothing compensation and counteract signal fluctuations caused by turbulence. For regions with stable flow rates and uniform density, a lower correction factor is used, typically in the range of 0.1 to 0.3, to maintain sensor response sensitivity. For intermediate states (such as regions with slight disturbances or weak stratification), a moderate value (approximately 0.3 to 0.6) is used to achieve a comprehensive balance between stability and responsiveness.

[0096] After obtaining the fluid correction factor and completing spatial allocation, the real-time dissolved oxygen measurements at each monitoring point are compensated for deviations, and the correction results are fed back into the aeration control process. The compensation process is achieved by dynamically superimposing and adjusting the fluid correction factor with the current dissolved oxygen measurement. For probes in high-turbulence regions, the frequent passage of bubbles across the sensor head surface causes periodic fluctuations in dissolved oxygen readings. In this case, the fluid correction factor is used as a compensation ratio to smooth the readings and reduce errors caused by sudden peaks. For probes in density stratification regions, dissolved oxygen response typically exhibits lag. The fluid correction factor compensates for this time delay based on the current density difference, making the measurement results more consistent with the actual oxygen transfer state. For probes in stable flow regions, the compensation effect is smaller, only performing minor corrections when the sensor experiences slight drift. The compensated dissolved oxygen data is returned to the control logic in real time as a feedback benchmark for air volume adjustment and aeration balance control. During the compensation process, the fluid correction factor is not a fixed value but dynamically adjusted with time and operating conditions. When aeration volume increases, flow velocity rises, or temperature difference widens, the fluid correction factor is automatically updated, and the compensation amplitude increases accordingly. When the flow field stabilizes, the fluid correction factor gradually decreases, allowing the measured data to return to the original response. The corrected dissolved oxygen data, after continuous acquisition and updating over a period of time, gradually forms a set of dynamically stable oxygen concentration curves. These curves accurately reflect the oxygen transfer status in different areas of the aerobic tank and are used to guide subsequent air volume allocation and energy consumption optimization. This real-time compensation mechanism effectively offsets measurement deviations caused by flow disturbances, bubble obstruction, and sensor drift, ensuring that aeration control decisions are based on accurate and reliable data under any operating condition.

[0097] Regarding how the fluid correction factor is used as a compensation ratio for smoothing:

[0098] The fluid correction factor participates in the correction of dissolved oxygen measurements as a proportionality coefficient during implementation. Its calculation is based on real-time data of velocity fluctuations and density gradient differences. In highly turbulent regions, when the velocity change rate is large or the density gradient is significant, the correction factor ranges from 0.2 to 0.6. This is used to proportionally smooth the original dissolved oxygen signal, i.e., reducing deviation by multiplying the correction factor by (measured value change amplitude). This proportional smoothing method effectively reduces instantaneous signal fluctuations caused by bubble obstruction or sudden velocity changes, resulting in more stable measurement results.

[0099] Regarding the "Explanation of the Compensation Principle in High Turbulence Regions": In high turbulence regions, pressure fluctuations on the probe surface caused by bubbles and water flow shear often generate periodic high-frequency noise in the sensing signal. Compensation is achieved by performing a moving average or weighted average calculation on the real-time acquired signal, with the calculation window typically set to 3–5 sampling periods. The correction factor controls the smoothing coefficient, ensuring that the output value retains the dissolved oxygen variation trend after removing high-frequency noise, thus achieving signal stabilization. This processing method does not alter the measurement baseline; it is only used to suppress transient errors caused by turbulent disturbances.

[0100] Explanation of time delay compensation for density stratification zones: In density stratification zones, due to impeded mixing between the upper and lower water layers, dissolved oxygen changes exhibit a response lag. At this time, the system automatically determines the time delay compensation amount based on the density gradient difference Δρ, with the delay time typically ranging from 2 to 10 seconds. Delay compensation is achieved by overlaying the measured values ​​of this time period to the next moment in the calculation, allowing the data to more accurately reflect the diffusion rate of dissolved oxygen in the stratified state. In this way, the correction result is consistent with the actual oxygen transfer process, avoiding misjudgments of insufficient dissolved oxygen.

[0101] Regarding the update and response rules of the fluid correction factor: The fluid correction factor is updated based on the real-time changes in monitored flow velocity and density. When the flow velocity increases or the temperature difference causes an increase in the density gradient, the system automatically increases the correction factor value; when the flow field tends to stabilize or the temperature difference decreases, the correction factor decreases proportionally, so that the compensation intensity is dynamically adjusted according to the operating conditions. Its update cycle is synchronized with the data sampling cycle, typically 1 to 2 seconds. Through this periodic automatic update, the system always maintains a compensation accuracy that matches the current flow field state.

[0102] Regarding the compensation value limits and calculation boundaries: To prevent over-correction from causing measurement distortion, the fluid correction factor is limited to a range of 0 to 1; where 0 indicates no compensation and 1 indicates full-amplitude smoothing. It is typically automatically adjusted to the range of 0.1 to 0.6 based on the tank's operating conditions. A higher value is used when the flow field is severely disturbed to enhance smoothness; a lower value is used when the flow is stable to maintain sensitivity. This range has been determined through extensive experimental verification, effectively eliminating the effects of turbulence while ensuring the accurate and reliable trend of dissolved oxygen changes, providing a stable data foundation for aeration control.

[0103] Through the implementation of the above-mentioned continuous steps, the real-time acquisition of water flow velocity and density data, the generation and distribution of fluid correction factors, and the deviation compensation of dissolved oxygen data form a complete dynamic correction process, ensuring that dissolved oxygen measurements remain stable and consistent under different flow conditions. The entire process centers on physical measurement parameters, linking fluid disturbances with measurement responses to ensure that the feedback signal accurately reflects the actual oxygen transfer state during the operation of the aerobic tank. This maintains the stability and reliability of aeration balance control and ensures a dynamic match between energy utilization and oxygen supply.

[0104] Step 5: Upload the multi-point dissolved oxygen distribution results after deviation compensation to the digital water platform. Combine historical operation data, influent characteristics and energy consumption records to construct an aeration demand prediction model based on time series data, so as to realize adaptive pre-adjustment control for future aeration regulation.

[0105] A predictive model for aeration demand based on time-series data is constructed for adaptive pre-adjustment control of future aeration adjustments. The specific steps are as follows:

[0106] After dissolved oxygen deviation compensation was completed, the data from each monitoring point in the aerobic tank were categorized, organized, and uploaded to the digital water management platform. The data for each monitoring point included its spatial location number, sampling time, corrected dissolved oxygen concentration, corresponding flow velocity and density information at the time of acquisition, current fluid correction factor, and temperature and bubble distribution at that point. During the upload process, all monitoring point data were sorted by time as the primary index, enabling the platform to reconstruct the dynamic distribution changes of dissolved oxygen within the tank in chronological order. Each uploaded data set contained continuous readings over multiple time periods, with sampling intervals maintained between seconds and minutes, ensuring the platform could obtain a continuous curve of dissolved oxygen changes. Before uploading, each data set was preprocessed to remove outliers caused by bubble obstruction or short-term fluctuations, and recorded in structured data format, including monitoring point coordinates, time markers, and dissolved oxygen distribution values, enabling the platform to accurately identify the oxygen distribution status at different locations. The uploaded data was automatically categorized into three types on the platform: inlet area data, middle area data, and outlet area data, for subsequent spatiotemporal comparison of dissolved oxygen distribution by region. By using this high-frequency, multi-location, and continuous uploading method, a dynamically changing spatial image of oxygen concentration can be generated on the platform, providing a direct input basis for subsequent predictions.

[0107] After uploading, the system utilizes historical operational information stored on the platform to fuse and compare the currently uploaded multi-point dissolved oxygen data. Historical data includes aeration volume distribution records for each time period, blower operating frequency, influent flow rate, influent water quality parameters (including chemical oxygen demand, ammonia nitrogen concentration, suspended solids content, and temperature), effluent water quality, and corresponding energy consumption records. During the fusion process, the currently uploaded real-time dissolved oxygen distribution curve is matched with historical data under similar operating conditions. Matching dimensions include time period, influent characteristics, aeration volume, and bubble diffusion status. For example, when the influent flow rate is at its peak and the temperature is low, the system will prioritize using historical data from similar conditions to identify oxygen distribution patterns. By overlaying historical and real-time data, the similarity between the current dissolved oxygen distribution trend and historical trends can be observed, thus determining whether the current operation tends towards insufficient oxygen supply or high energy consumption. During the fusion process, energy-related operational segments from the past are also extracted, such as gas output and corresponding energy consumption data under the same dissolved oxygen distribution, to determine the optimal range for matching gas volume and oxygen concentration. Through this data fusion approach, the platform can generate a multidimensional dataset containing time, flow rate, dissolved oxygen concentration, energy consumption, and effluent quality, providing fundamental parameters for building an aeration demand prediction model. This stage combines dissolved oxygen data with the process operation history, establishing not only temporal continuity but also dynamic correspondences between data points, thus providing a basis for predicting future trends.

[0108] After data fusion, a time-series-based aeration demand prediction model is constructed, and this model is used to implement adaptive pre-regulation control. The aeration demand prediction model is built upon the fused multidimensional dataset, comprehensively considering factors such as influent fluctuations, changes in oxygen transfer efficiency, and energy consumption response. The model uses time as the main thread, correlating the changing trend of dissolved oxygen distribution with changes in aeration energy consumption to identify leading signals for aeration volume adjustments. For example, when historical data shows that under a certain influent combination, an increase in influent ammonia nitrogen concentration is usually accompanied by a decrease in dissolved oxygen concentration and an increase in energy consumption, the model can capture this pattern and issue a pre-increase signal for aeration when the current influent water quality is detected to be similar. In actual operation, when the platform receives new influent parameters and flow regime monitoring results, the prediction model calculates the oxygen demand trend for future time periods (e.g., the next 10 minutes to 1 hour) based on historical patterns. If the model predicts a potential decrease in dissolved oxygen concentration, it increases aeration intensity and appropriately increases the influent air volume in advance. Conversely, if it predicts excessively high oxygen concentration in subsequent periods, it reduces aeration volume or decreases aeration pressure in certain areas to prevent over-aeration. Simultaneously, the model incorporates historical energy consumption trends to limit the adjustment range of air volume, ensuring that energy consumption remains within a reasonable range while meeting oxygen requirements. As operational data continues to be input, the predictive model continuously updates its time weights, incorporating features from new data to maintain accurate predictions under different seasons and influent loads. With continuous model iteration, the platform develops a proactive adjustment logic for future operating conditions, enabling the aeration system to adjust before load changes occur, achieving true adaptive pre-regulation.

[0109] Through the above steps, the entire process of uploading, merging, and predicting compensated multi-point dissolved oxygen distribution data is realized, transforming the aeration control process from passive response to proactive prediction. This implementation not only establishes an aeration demand prediction model through the integration of time-series data but also makes future oxygen supply adjustments predictable by correlating historical operating patterns with real-time monitoring. Therefore, the system can proactively allocate air volume and regulate oxygen supply under complex conditions such as influent fluctuations, energy consumption changes, and seasonal temperature differences, maintaining efficient and stable operation of the aeration process and providing a reliable dynamic control basis for continuous energy saving in the aerobic tank and stable effluent quality.

[0110] Step 6: Based on the long-term data accumulation of the digital water platform, the dynamic aeration balance control logic is periodically optimized. The fluid correction factor weight and control parameters are updated based on historical flow distortion characteristics and energy consumption trends to form a closed loop of dynamic balance digital control for aeration with self-learning and self-evolution capabilities.

[0111] Based on the long-term data accumulation of the digital water management platform, the dynamic aeration balance control logic is periodically optimized. The specific steps are as follows:

[0112] During long-term operation, the digital water management platform continuously collects multi-dimensional operational data from the aerobic tank to form a comprehensive operational history archive. The platform continuously receives data from monitoring points at the inlet, middle, and outlet of the aerobic tank. This data includes flow velocity distribution, changes in flow velocity gradient, water density and its stratification depth, dissolved oxygen concentration at different depths, aeration output, blower frequency, air pressure changes, influent flow rate, influent water quality parameters (including chemical oxygen demand, ammonia nitrogen, and suspended solids content), effluent water quality indicators, and ambient temperature and humidity. Each set of data is marked with a timestamp and the spatial coordinates of the monitoring point to ensure accurate mapping to specific operating periods and locations during later analysis. Simultaneously, the platform organizes the data for temporal continuity, recording daily, weekly, and monthly operational status segments to differentiate operational performance under different conditions. For example, during the rainy season, the inflow rate fluctuates significantly, and the platform records the operational status during this period separately to analyze its impact on flow distortion later. During the low-temperature winter operation phase, the density difference increases, and the platform records the changes in water stratification and oxygen transfer efficiency during this phase. Over time, this operational data accumulates, forming a long-term dataset covering different loads and temperature conditions throughout the year, providing a complete historical reference for periodic optimization. During data storage, the platform also automatically marks the time periods in each operational cycle where flow anomalies occur, such as the duration of short-flows, the frequency of density stratification formation, and the rate of energy consumption increase, to facilitate the extraction and analysis of distortion characteristics later.

[0113] After accumulating a sufficient amount of operational data on the platform, a detailed analysis is conducted on the flow distortion characteristics and energy consumption variation patterns reflected in this data to update the fluid correction factor weights and gas volume control parameters. The analysis is based on time series data, correlating historical records of water velocity gradients, density changes, and dissolved oxygen distribution. First, the types and frequencies of flow distortions in different operating cycles are identified, such as the frequency of short-flow zones, the duration of density stratification, and the magnitude of turbulence intensity changes. The system's energy consumption level and oxygen transfer effect are then statistically analyzed when these distortions occur. When frequent occurrences of short-flow zones, large fluctuations in dissolved oxygen distribution, and persistently high energy consumption are observed in a certain stage, it indicates that the fluid correction factor response sensitivity is insufficient in that stage. The fluid correction factor weight for the corresponding region needs to be increased in the next round of optimization to enhance the response to fluid disturbances. When density stratification in a certain stage leads to persistently low oxygen concentrations in the lower layer without improvement in energy consumption, it indicates that the density correction weight in the control logic is too low. The weight of density difference in the fluid correction factor calculation should be appropriately increased. For energy consumption trend analysis, the energy utilization efficiency of the aeration regulation strategy is determined by comparing the rate of change in air volume and the energy consumption ratio of the blower under the same dissolved oxygen target. When historical data shows that the energy consumption growth rate is significantly higher than the oxygen transfer improvement rate, it indicates that the aeration allocation ratio or response range is unreasonable, and the air volume change threshold or dissolved oxygen control upper and lower limits in the control parameters need to be adjusted. After each operating cycle, the platform recalculates the control parameters for the next cycle based on the above analysis results, including the basic weight of the fluid correction factor for each monitoring area, the weighted dissolved oxygen balance target, the air volume regulation response time, and the pressure range. The updated parameters are matched with the current operating status, so that the control logic for the new cycle can better adapt to the actual operating characteristics of the system. Through this optimization method based on historical characteristics, the control logic is finely adjusted in each cycle, thereby achieving refined matching for different flow conditions.

[0114] After parameter updates are completed, the new control logic is automatically applied in the next operating cycle, forming a closed-loop mechanism with self-learning and self-evolution capabilities through continuous data feedback during operation. The self-learning process focuses on the operating results, identifying the effectiveness of the control logic by comparing the flow distribution, dissolved oxygen uniformity, and energy consumption changes before and after optimization. When the system detects a decrease in the frequency of flow distortion, a reduction in the spatial difference of dissolved oxygen, and a stabilization of energy consumption changes in the optimized operating cycle, the platform saves the fluid correction factors and gas volume adjustment parameters for that cycle as new baseline values ​​and continues to use and fine-tune them in subsequent cycles. When the optimized operating effect is not as expected, such as an increase in energy consumption or a greater fluctuation in local oxygen concentration, the platform recalculates the weight ratios between the fluid correction factors and makes targeted adjustments to the problem area, making the control strategy for the next cycle more reasonable. As the operating cycle progresses, the platform continuously absorbs new operating data, comparing the operating results of each cycle with historical results, enabling the control logic to be continuously optimized over time. For example, during hot seasons, the platform automatically adjusts the sensitivity of the fluid correction factor based on the historical relationship between temperature and density difference, making the aeration volume distribution more aligned with oxygen transfer efficiency. During cold seasons, the platform adjusts the air volume response time based on the historical pattern of dissolved oxygen response delay, preventing the system from over-aeration and wasting energy. As this self-learning process continues, the platform can form a dynamic self-evolution mechanism, enabling the aeration control logic to automatically adapt to changes in different seasons, influent loads, and operating conditions without manual intervention.

[0115] Through the implementation of the above steps, the aeration dynamic equilibrium control logic acquires the ability to continuously optimize and self-improve. The entire process, from data accumulation and feature analysis to parameter updates and the formation of a self-learning closed loop, realizes a transformation from experience-based to data-driven. Relying on the long-term data accumulation of the digital water management platform, the system can continuously update the fluid correction factor weights and control parameters, ensuring that the aeration control logic maintains accurate response and energy efficiency balance under different environmental and operating conditions. This forms a digital control closed loop for aeration dynamic equilibrium with self-learning and self-evolution capabilities, providing stable, energy-saving, and efficient operational assurance for the aerobic tank during long-term operation.

[0116] This invention establishes a synchronous monitoring system for dissolved oxygen, water flow velocity, and density at multiple points, enabling real-time identification and spatially balanced control of flow regime changes within the aerobic tank. This allows the aeration process to automatically adjust the air output according to the oxygen demand of different areas, thus maintaining the stability of dissolved oxygen distribution even under flow field disturbances or load fluctuations. The method achieves more precise air distribution through dynamic correction and weighted average control of dissolved oxygen data weights in abnormal areas, preventing localized over-aeration or hypoxia, and effectively improving oxygen transfer efficiency and the overall operational stability of the reactor.

[0117] This invention introduces a fluid correction factor and a time-series prediction model into dynamic aeration balance control, enabling the control logic to possess adaptive and self-evolving capabilities. It can optimize parameters based on long-term operating data and energy consumption trends, thereby achieving forward-looking and energy-saving air volume regulation under complex operating conditions. This control closed loop can continuously correct deviations and self-optimize, ensuring the system maintains a balance between oxygen supply and energy consumption under different seasons and load conditions, significantly improving the intelligence level of aeration control.

[0118] This invention provides, for example Figure 4 The aeration dynamic equilibrium digital control system shown includes a multi-point monitoring and acquisition module, a flow pattern identification module, a dynamic aeration regulation module, a dissolved oxygen deviation compensation module, an aeration prediction and control module, and a control logic optimization module.

[0119] The multi-point monitoring and acquisition module constructs a multi-point simultaneous monitoring system for dissolved oxygen and flow pattern in the aerobic tank of wastewater treatment. It continuously collects spatial distribution data of dissolved oxygen, water flow velocity and density in the tank, and generates a flow pattern distribution map reflecting the flow pattern distribution characteristics in the tank.

[0120] The flow pattern feature recognition module performs collaborative feature extraction on the collected water flow velocity gradient and dissolved oxygen difference based on the flow pattern distribution map, identifies abnormal areas with flow pattern distortion, and generates a flow pattern state mapping table containing short flow regions and density flow boundaries at the control end;

[0121] The dynamic aeration control module performs dynamic aeration balance control according to the flow state mapping table, reduces the weight of dissolved oxygen data corresponding to abnormal areas, and adaptively adjusts the air output of the aeration system based on the multi-point weighted average calculation results.

[0122] The dissolved oxygen deviation compensation module calculates the fluid correction factor using synchronously monitored water flow velocity and density data during dynamic aeration balance control, and performs deviation compensation on the dissolved oxygen measurement value to correct the measurement error caused by local turbulence, bubble interference or sensor drift.

[0123] The aeration prediction and control module uploads the multi-point dissolved oxygen distribution results after deviation compensation to the digital water platform. Combining historical operating data, influent characteristics and energy consumption records, it constructs an aeration demand prediction model based on time-series data to perform adaptive pre-adjustment control for future aeration adjustments.

[0124] The control logic optimization module, relying on the long-term data accumulation of the digital water platform, periodically optimizes the dynamic aeration balance control logic and updates the fluid correction factor weights and control parameters based on historical flow distortion characteristics and energy consumption trends.

[0125] The present invention provides a method for dynamic equilibrium digital control of aeration to cope with flow distortion, which is implemented by the above-mentioned dynamic equilibrium digital control system for aeration to cope with flow distortion. The specific method and process of the dynamic equilibrium digital control system for aeration to cope with flow distortion are detailed in the above-mentioned embodiment of the method for dynamic equilibrium digital control of aeration to cope with flow distortion, and will not be repeated here.

[0126] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A digital control method for dynamic equilibrium aeration in response to flow regime distortion, characterized in that, Includes the following steps: Step 1: Construct a multi-point simultaneous monitoring system for dissolved oxygen and flow regime in the aerobic tank of wastewater treatment, continuously collect spatial distribution data of dissolved oxygen, water flow velocity and density in the tank, and generate a flow regime distribution map reflecting the flow regime distribution characteristics in the tank; Step 2: Based on the flow distribution map, perform collaborative feature extraction on the collected water flow velocity gradient and dissolved oxygen difference to identify abnormal regions with flow distortion, and generate a flow state mapping table containing short-flow regions and density flow boundaries at the control end; Step 3: Perform dynamic aeration balance control according to the flow state mapping table, reduce the weight of dissolved oxygen data corresponding to abnormal areas, and adaptively adjust the air output of the aeration system based on the multi-point weighted average calculation results. Step 4: During the dynamic aeration balance control process, the fluid correction factor is calculated using the synchronously monitored water flow velocity and density data to compensate for the deviation of dissolved oxygen measurement values ​​and correct measurement errors caused by local turbulence, bubble interference or sensor drift. Step 5: Upload the multi-point dissolved oxygen distribution results after deviation compensation to the digital water platform. Combine historical operation data, influent characteristics and energy consumption records to construct an aeration demand prediction model based on time series data, and carry out adaptive pre-adjustment control for future aeration adjustments. Step 6: Based on the long-term data accumulation of the digital water platform, the dynamic aeration balance control logic is periodically optimized, and the fluid correction factor weights and control parameters are updated based on historical flow distortion characteristics and energy consumption trends. The steps for generating a flow state mapping table containing short-flow regions and density flow boundaries at the control end include: After obtaining the complete flow distribution map, the water flow velocity data and dissolved oxygen concentration data were spatially stratified. The data of the pool inlet, middle and outlet regions in the same time slice were divided according to the physical structure, and the water flow velocity gradient and dissolved oxygen concentration difference between adjacent measuring points were calculated. Based on the flow pattern distribution map, the direction of change of water flow velocity gradient and dissolved oxygen concentration is compared. When the water flow velocity changes abruptly and the dissolved oxygen does not change synchronously, it is identified as a short flow zone. When the vertical density difference and dissolved oxygen stratification coexist, it is identified as a density flow boundary. The identified short-flow zones and density flow boundaries are labeled with the spatial coordinates of the aerobic pool to generate a flow state mapping table that records the direction of water flow velocity gradient, dissolved oxygen concentration deviation, and density difference in each zone. The steps for performing dynamic aeration balance control based on the flow state mapping table include: The regional information in the flow state mapping table is imported into the control process to classify the spatial regions of the aerobic tank from the inlet to the outlet, identify the short-flow zone, density flow stratification zone and slow flow zone, and record their dissolved oxygen concentration. Based on the regional classification results in the flow state mapping table, the dissolved oxygen data of each region are weighted and integrated, weights are assigned, and the weighted average dissolved oxygen value is calculated. The results are used as the target reference for aeration adjustment. Based on the deviation between the weighted average dissolved oxygen value and the set target range, the air output of the aeration equipment is dynamically adjusted, increasing the air supply to the short flow zone and low dissolved oxygen zone, and reducing the air supply to the density flow stratification zone and over-aeration zone. Spatial feedback correction is performed on the gas volume regulation results, and the flow state mapping table is updated according to the changes in dissolved oxygen distribution; The steps for calculating fluid correction factors and compensating for deviations in dissolved oxygen measurements using synchronously monitored water flow velocity and density data include: The flow velocity measurement unit and density measurement component at the inlet, middle and outlet of the aerobic tank synchronously collect the changes in water flow velocity magnitude, direction and density gradient, and generate a raw data sequence containing time stamps, spatial location and change trend. The fluid correction factor is calculated based on continuous data of water flow velocity and density, and then spatially distributed to each dissolved oxygen monitoring point according to the intensity of velocity change and the difference in density gradient. The calculated and allocated fluid correction factor is dynamically superimposed and adjusted with the real-time dissolved oxygen measurement value to compensate for the deviation of the measurement results at each monitoring point, and the corrected dissolved oxygen data is fed back to the aeration control process. The steps for constructing an aeration demand prediction model based on time-series data and performing adaptive pre-regulation control for future aeration adjustments include: After completing the dissolved oxygen deviation compensation, the dissolved oxygen concentration, flow rate, density and fluid correction factor of each monitoring point in the aerobic tank are classified and organized, and uploaded to the digital water platform with time as the main index to form a continuous dissolved oxygen spatial distribution dataset. By utilizing the historical operational information stored in the platform, the uploaded multi-point dissolved oxygen distribution results are fused and matched with historical aeration volume, influent flow rate, influent water quality and energy consumption records to generate a multidimensional dataset containing the relationship between time, flow rate, dissolved oxygen concentration and energy consumption. An aeration demand prediction model is constructed based on the fused multidimensional dataset, and the oxygen demand changes in the future are predicted according to influent fluctuations, changes in oxygen transfer efficiency and energy consumption trends.

2. The aeration dynamic equilibrium digital control method for addressing flow pattern distortion according to claim 1, characterized in that, After generation, the flow state mapping table is dynamically updated according to the time series, and the newly collected water flow velocity gradient, dissolved oxygen concentration difference and density difference data are superimposed on the corresponding spatial location to form a time-series mapping structure that reflects the change trajectory of the short flow zone and density flow boundary.

3. The aeration dynamic equilibrium digital control method for addressing flow pattern distortion according to claim 1, characterized in that, During the spatial feedback correction of the gas volume regulation results, the dissolved oxygen change trend before and after the gas volume adjustment is compared based on the dissolved oxygen monitoring data at multiple points. When the dissolved oxygen concentration in the short-flow zone or density flow stratification zone tends to stabilize, the abnormality markers in the short-flow zone or density flow stratification zone are updated to a stable state, and the new dissolved oxygen data is written into the flow state mapping table.

4. The aeration dynamic equilibrium digital control method for addressing flow distortion according to claim 1, characterized in that, When spatially distributing fluid correction factors, the dynamic updates are based on the intensity of flow velocity changes, density gradient differences, and flow status of each monitoring point. The compensation range is adjusted in real time when the aeration rate or temperature changes, so that the compensated dissolved oxygen data continuously reflects the oxygen transfer status of different areas of the aerobic tank.

5. The aeration dynamic equilibrium digital control method for addressing flow pattern distortion according to claim 1, characterized in that, Based on the long-term data accumulation of the digital water management platform, the steps for periodically optimizing the dynamic aeration balance control logic include: The digital water management platform continuously receives multi-dimensional operational data from the inlet, middle and outlet of the aerobic tank, and marks it with timestamps and spatial coordinates to form a long-term dataset covering different operating conditions. Based on long-term datasets, the flow distortion characteristics and energy consumption variation patterns during historical operation phases are analyzed. The occurrence frequency of short-circuit, the duration of density stratification, and the trend of oxygen transfer efficiency are extracted, and the fluid correction factor weights and gas volume control parameters of each monitoring area are updated accordingly. The updated fluid correction factor weights and gas volume control parameters are applied to the next operating cycle. By comparing the flow distribution, dissolved oxygen uniformity and energy consumption changes before and after optimization, self-learning and self-evolution are achieved, so that the control logic is continuously optimized and the aeration dynamic equilibrium control closed loop is maintained.

6. A dynamic equilibrium digital control system for aeration in response to flow regime distortion, used to implement the dynamic equilibrium digital control method for aeration in response to flow regime distortion as described in any one of claims 1-5, characterized in that, It includes a multi-point monitoring and acquisition module, a flow pattern recognition module, a dynamic aeration control module, a dissolved oxygen deviation compensation module, an aeration prediction and control module, and a control logic optimization module; The multi-point monitoring and acquisition module constructs a multi-point simultaneous monitoring system for dissolved oxygen and flow pattern in the aerobic tank of wastewater treatment. It continuously collects spatial distribution data of dissolved oxygen, water flow velocity and density in the tank, and generates a flow pattern distribution map reflecting the flow pattern distribution characteristics in the tank. The flow pattern feature recognition module performs collaborative feature extraction on the collected water flow velocity gradient and dissolved oxygen difference based on the flow pattern distribution map, identifies abnormal areas with flow pattern distortion, and generates a flow pattern state mapping table containing short flow regions and density flow boundaries at the control end; The dynamic aeration control module performs dynamic aeration balance control according to the flow state mapping table, reduces the weight of dissolved oxygen data corresponding to abnormal areas, and adaptively adjusts the air output of the aeration system based on the multi-point weighted average calculation results. The dissolved oxygen deviation compensation module calculates the fluid correction factor using synchronously monitored water flow velocity and density data during dynamic aeration balance control, and performs deviation compensation on the dissolved oxygen measurement value to correct the measurement error caused by local turbulence, bubble interference or sensor drift. The aeration prediction and control module uploads the multi-point dissolved oxygen distribution results after deviation compensation to the digital water platform. Combining historical operating data, influent characteristics and energy consumption records, it constructs an aeration demand prediction model based on time-series data to perform adaptive pre-adjustment control for future aeration adjustments. The control logic optimization module, relying on the long-term data accumulation of the digital water platform, periodically optimizes the dynamic aeration balance control logic and updates the fluid correction factor weights and control parameters based on historical flow distortion characteristics and energy consumption trends.

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