Multi-sensor fusion micro-nano aeration tail water treatment process monitoring method and system

Abstract

The application belongs to the technical field of process monitoring and control, and particularly relates to a multi-sensor fusion micro-nano aeration tail water treatment process monitoring method and system. The method comprises the following steps: obtaining water flow rate data, pollutant concentration distribution data and jet angle data of a tail water treatment pool, and fusing to obtain tail water state data; establishing a micro-nano bubble dynamics model based on the tail water state data and the jet angle data and determining bubble trajectory distribution information; calculating a gas-liquid contact efficiency index; when the gas-liquid contact efficiency index is lower than a contact efficiency threshold value, determining a target jet angle sequence based on a genetic algorithm; updating to obtain target bubble distribution information; and outputting a jet angle control configuration according to water body disturbance intensity data after the target jet angle sequence is executed, aeration energy consumption, the target bubble distribution information and the re-acquired tail water state data. The application can improve the effective contact degree of bubbles and pollutants and reduce invalid aeration energy consumption.

Description

Technical Field

[0001] This application belongs to the field of process monitoring and control technology, specifically a method and system for monitoring micro-nano aeration tailwater treatment process using multi-sensor fusion. Background Technology

[0002] In advanced effluent treatment, micro- and nano-aeration can improve gas-liquid mass transfer conditions in the water and provide a more stable oxygen transfer environment for pollutant degradation. With stricter emission standards and increased energy consumption constraints, the industry is no longer satisfied with simply increasing aeration volume, but is paying more attention to the spatial distribution and residence time of bubbles within the tank, as well as their actual contact with pollutant-rich areas. Existing monitoring, control, and data acquisition systems can typically collect operational data such as water quality, flow rate, and equipment power, and can start, stop, or adjust the intensity of aeration equipment. However, most control strategies still rely on fixed spray angles, empirical thresholds, or single water quality indicators, making it difficult to reflect the dynamic relationship between changes in water flow velocity, pollutant concentration distribution, and spray angle.

[0003] In actual operation, micro- and nano-bubbles may concentrate in localized areas or rise rapidly before fully contacting pollutants, resulting in insufficient disturbance in the pollutant-rich area, low gas-liquid contact efficiency, and increased ineffective aeration energy consumption. Existing methods also lack a closed-loop mechanism that unifies bubble trajectory modeling, contact efficiency quantification, injection angle adjustment, and energy consumption feedback, making it difficult to formulate executable injection angle control configurations in a timely manner based on changes in the effluent state. Summary of the Invention

[0004] To address the above issues, this application provides a multi-sensor fusion method and system for monitoring the micro-nano aeration tailwater treatment process, which at least solves the problem of how to dynamically adjust the micro-nano aeration spray angle according to changes in tailwater state, so as to improve gas-liquid contact efficiency and reduce ineffective aeration energy consumption.

[0005] To achieve the above objectives, the technical solution adopted in this application is as follows: In a first aspect, this application provides a multi-sensor fusion method for monitoring micro-nano aeration effluent treatment processes, the method comprising: Acquire water flow velocity data, pollutant concentration distribution data, and jet angle data of the effluent treatment tank, and fuse the water flow velocity data and pollutant concentration distribution data to obtain effluent status data. A dynamic model of micro-nano bubbles was established based on tailwater state data and jet angle data, and bubble trajectory distribution information was determined. The gas-liquid contact efficiency index is calculated based on bubble trajectory distribution information and pollutant concentration distribution data. When the gas-liquid contact efficiency index is lower than the contact efficiency threshold, the target injection angle sequence is determined based on a genetic algorithm; The target bubble distribution information is obtained by updating the bubble trajectory distribution information according to the target injection angle sequence; The jet angle control configuration is output based on the water disturbance intensity data, aeration energy consumption, target bubble distribution information, and reacquired tailwater state data after executing the target jet angle sequence.

[0006] In one possible implementation, effluent status data is obtained by fusing water flow velocity data and pollutant concentration distribution data, including: dividing the effluent treatment tank into multiple spatial grids according to the tank area; aligning the water flow velocity data and pollutant concentration distribution data in time; determining the flow velocity value and pollutant concentration value corresponding to each spatial grid; and generating effluent status data based on the flow velocity value and pollutant concentration value corresponding to each spatial grid.

[0007] In one possible implementation, a micro / nano bubble dynamics model is established based on tailwater state data and jet angle data, and bubble trajectory distribution information is determined. This includes: determining the movement path, bubble coverage area, and bubble residence time of micro / nano bubbles in each spatial grid based on the jet angle data and the flow velocity value corresponding to each spatial grid; and generating bubble trajectory distribution information based on the movement path, bubble coverage area, and bubble residence time.

[0008] In one possible implementation, the gas-liquid contact efficiency index is calculated based on bubble trajectory distribution information and pollutant concentration distribution data, including: determining the pollutant concentration weight corresponding to each spatial grid based on the pollutant concentration distribution data; determining the bubble coverage probability corresponding to each spatial grid based on the bubble trajectory distribution information; and calculating the gas-liquid contact efficiency index based on the pollutant concentration weight, the bubble coverage probability, and the bubble residence time corresponding to each spatial grid.

[0009] In one possible implementation, the target injection angle sequence is determined based on a genetic algorithm, including: constructing multiple candidate injection angle sequences and using each candidate injection angle sequence as an individual in the genetic algorithm population; inputting each candidate injection angle sequence into a micro / nano bubble dynamics model to obtain the candidate bubble trajectory distribution information corresponding to each candidate injection angle sequence; calculating the candidate gas-liquid contact efficiency index corresponding to each candidate injection angle sequence based on the candidate bubble trajectory distribution information corresponding to each candidate injection angle sequence; and determining the target injection angle sequence from the multiple candidate injection angle sequences based on the candidate gas-liquid contact efficiency index.

[0010] In one possible implementation, the candidate injection angle sequence is generated according to the angle adjustment constraint, which includes the injection angle range, the single angle adjustment amplitude, and the angle adjustment period; the target injection angle sequence is the candidate injection angle sequence that satisfies the angle adjustment constraint and has the highest corresponding candidate gas-liquid contact efficiency index among multiple candidate injection angle sequences.

[0011] In one possible implementation, the target bubble distribution information is obtained by updating the bubble trajectory distribution information based on the target injection angle sequence, including: inputting the target injection angle sequence into a micro / nano bubble dynamics model to obtain the target bubble trajectory distribution information; and determining the target bubble coverage area and target bubble residence time distribution based on the target bubble trajectory distribution information to obtain the target bubble distribution information.

[0012] In one possible implementation, the water disturbance intensity data is determined based on the change in water flow velocity after executing the target spray angle sequence; the aeration energy consumption is determined based on the gas flow rate, operating power, and operating time of the aeration equipment when executing the target spray angle sequence.

[0013] In one possible implementation, the output injection angle control configuration includes: determining an effective aeration area based on target bubble distribution information and pollutant concentration distribution data, wherein the effective aeration area is the overlapping area of ​​the bubble coverage area represented by the target bubble distribution information and the pollutant distribution area represented by the pollutant concentration distribution data; and when the reacquired tailwater state data meets the feedback update conditions, outputting the injection angle control configuration based on the effective aeration area, water body disturbance intensity data, and aeration energy consumption, wherein the feedback update conditions include the water body flow velocity change exceeding the flow velocity change threshold, or the pollutant concentration change exceeding the concentration change threshold.

[0014] Secondly, this application provides a multi-sensor fusion-based monitoring system for micro-nano aerated wastewater treatment processes, used to implement a multi-sensor fusion-based monitoring method for micro-nano aerated wastewater treatment processes. The system includes: The data acquisition module is used to acquire water flow velocity data, pollutant concentration distribution data, and spray angle data of the effluent treatment tank, and to fuse the water flow velocity data and pollutant concentration distribution data to obtain effluent status data. The model building module is used to establish a micro-nano bubble dynamics model based on tailwater state data and jet angle data, and to determine bubble trajectory distribution information. The index calculation module is used to calculate the gas-liquid contact efficiency index based on bubble trajectory distribution information and pollutant concentration distribution data. An angle determination module is used to determine the target injection angle sequence based on a genetic algorithm when the gas-liquid contact efficiency index is lower than the contact efficiency threshold. The distribution update module is used to update the bubble trajectory distribution information according to the target injection angle sequence to obtain the target bubble distribution information; The configuration output module is used to output the jet angle control configuration based on the water disturbance intensity data, aeration energy consumption, target bubble distribution information, and reacquired tailwater state data after executing the target jet angle sequence.

[0015] Compared with existing technologies, the advantages and beneficial effects of this application are as follows: By acquiring water flow velocity data, pollutant concentration distribution data, and jet angle data, and fusing the water flow velocity data and pollutant concentration distribution data into tailwater state data, a unified expression of water flow state and pollutant spatial distribution is achieved, providing a stable input for subsequent bubble motion analysis.

[0016] By establishing a micro-nano bubble dynamics model based on tailwater state data and injection angle data, the bubble motion path, bubble coverage area, and bubble residence time were determined, enabling the quantification of the influence of injection angle on bubble distribution.

[0017] By calculating the gas-liquid contact efficiency index based on bubble trajectory distribution information and pollutant concentration distribution data, the effective contact degree between bubbles and pollutant-rich areas can be judged, avoiding the need to evaluate the treatment process solely based on aeration intensity.

[0018] By determining the target injection angle sequence based on a genetic algorithm when the gas-liquid contact efficiency index is lower than the contact efficiency threshold, the injection angle has been transformed from empirical setting to active search and adjustment.

[0019] By updating the bubble trajectory distribution information based on the target injection angle sequence and obtaining the target bubble distribution information, the angle adjustment results were re-validated at the model level.

[0020] By combining water disturbance intensity data, aeration energy consumption, and reacquired tailwater state data to output the jet angle control configuration, a feedback loop between treatment effect and energy consumption constraints is achieved. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the process of this application; Figure 2 This is a block diagram of the module composition of the system in this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solution, the present application will be described in detail below with reference to the embodiments. The description in this section is only exemplary and explanatory, and should not be used to limit the scope of protection of the present application in any way.

[0023] Multi-sensor fusion is a fundamental data organization method in process monitoring and control. Its core lies in correlating operational data obtained from different detection objects, sampling frequencies, and spatial locations according to a unified time and spatial reference, enabling a continuous and calculable state representation that is difficult for a single sensor to fully reflect. In the micro-nano aerated wastewater treatment process, data such as water flow velocity, pollutant concentration, aeration jet angle, and equipment operating energy consumption reflect water movement, pollutant distribution, bubble entry direction, and equipment operating status, respectively. If these data are collected and stored independently by the monitoring and control and data acquisition systems, it is difficult to support a unified judgment on the spatial distribution of bubbles, gas-liquid contact efficiency, and the relationship between jet angle adjustment. Therefore, this application introduces multi-sensor data fusion into the monitoring of the micro-nano aerated wastewater treatment process. By correlating water flow velocity data, pollutant concentration distribution data, and jet angle data, wastewater state data that can participate in bubble motion modeling and contact efficiency calculation is formed. Based on this, a closed-loop monitoring and control process is established for jet angle adjustment, bubble distribution updating, and energy consumption feedback.

[0024] like Figure 1 As shown, a multi-sensor fusion method for monitoring micro-nano aeration effluent treatment processes is described, the method comprising: Acquire water flow velocity data, pollutant concentration distribution data, and jet angle data of the effluent treatment tank, and fuse the water flow velocity data and pollutant concentration distribution data to obtain effluent status data. The effluent treatment tank is equipped with flow velocity detection units, water quality detection units, and angle detection units. The flow velocity detection units collect water flow velocity data from different areas of the tank, the water quality detection units collect pollutant concentration distribution data from different areas of the tank, and the angle detection units record the spray angle data of the aeration nozzles during the current operating cycle. When the treatment equipment receives the above data, it configures the sampling time, sampling location, and data source markers for each set of data, and maps the water flow velocity data and pollutant concentration distribution data to the same spatial reference of the tank. Through this processing method, the effluent state data can represent the current water flow state and pollutant distribution state in the effluent treatment tank. The spray angle data establishes a correlation with the effluent state data under the same operating cycle and serves as the execution state input for the subsequent micro / nano bubble dynamics model.

[0025] The effluent status data is obtained by integrating water flow velocity data and pollutant concentration distribution data, including: dividing the effluent treatment tank into multiple spatial grids according to the tank area; aligning the water flow velocity data and pollutant concentration distribution data in time; determining the flow velocity value and pollutant concentration value corresponding to each spatial grid; and generating effluent status data based on the flow velocity value and pollutant concentration value corresponding to each spatial grid.

[0026] In one embodiment, the data fusion process incorporates spatial discretization of the tank and sampling timing calibration to ensure that data from different sensors correspond within the same spatial location and time window. The treatment equipment divides the effluent treatment tank into multiple spatial grids, each corresponding to a specific water area within the tank. The size of the spatial grids is determined based on the tank length, width, water depth, flow velocity detection unit density, water quality detection unit density, and the influence range of the aeration nozzles. For rectangular tanks, equidistant divisions can be made along the length, width, and depth directions. For tanks with guide vanes, partitions, or locally bent boundaries, the spatial grids at the boundaries can be modified according to the actual flowable area, ensuring that each spatial grid corresponds to an effective area capable of participating in water exchange.

[0027] The processing equipment configures a spatial location identifier for each spatial grid, which records the grid's extent within the effluent treatment tank. After the flow velocity and water quality detection units upload sampling results, the processing equipment assigns these results to the corresponding spatial grids based on the sampling location. When multiple flow velocity sampling results exist for a single spatial grid within the same time window, the processing equipment determines the weight of each sampling result based on the distance between the sampling location and the grid center, and generates the corresponding flow velocity value for that grid. Similarly, when multiple pollutant concentration sampling results exist for a single spatial grid within the same time window, the processing equipment generates the corresponding pollutant concentration value for that grid in the same manner. In the event of transient outliers, the processing equipment removes or reduces their weight based on the changing trends of adjacent time windows and adjacent spatial grids, preventing single-point anomalies from altering the effluent status data.

[0028] The time alignment process is performed in units of preset sampling windows, which are determined based on the aeration nozzle adjustment cycle, the frequency of water flow velocity changes, and the water quality detection response time. When the sampling frequency of water flow velocity data is higher than that of pollutant concentration distribution data, the treatment equipment statistically analyzes multiple flow velocity sampling results within the same sampling window to obtain a flow velocity value that matches the pollutant concentration sampling time. When the sampling frequency of pollutant concentration distribution data is higher than that of water flow velocity data, the treatment equipment statistically analyzes multiple pollutant concentration sampling results within the same sampling window to obtain a pollutant concentration value that matches the water flow velocity sampling time. The statistical method can use the average value; in cases of significant influent disturbance or large fluctuations in detection data, the median value method can be used.

[0029] When a spatial grid within the current sampling window lacks valid sampling results, the processing device completes the data based on the sampling results of adjacent spatial grids and the sampling results of the previous sampling window for that spatial grid, and configures a completion marker for the completion result. The completion marker is used to prompt subsequent models to reduce the data reliability when reading data from that spatial grid. If multiple consecutive sampling windows for the same spatial grid lack valid sampling results, the processing device marks that spatial grid as a region to be verified and retains this marker in the tailwater state data. The generated tailwater state data uses spatial grids as the basic unit, with each data unit including a spatial location identifier, sampling time, flow velocity value, pollutant concentration value, and data source marker. Through this data structure, subsequent processing can directly read the water flow state and pollutant distribution state according to the spatial grid, and incorporate them into the bubble motion analysis process along with the injection angle data within the same operating cycle.

[0030] A dynamic model of micro-nano bubbles was established based on tailwater state data and jet angle data, and bubble trajectory distribution information was determined. The treatment equipment reads the spatial grid, flow velocity, and pollutant concentration values ​​from the effluent status data, and also reads the injection angle data within the same operating cycle. The injection angle data is used to determine the initial movement direction of the micro-nano bubbles when they leave the aeration nozzle, and the flow velocity value corresponding to the spatial grid is used to determine the direction in which the water flow drives the bubble movement. Using the spatial grid as the calculation unit, the treatment equipment progressively updates the movement position of the micro-nano bubbles within the effluent treatment tank, recording the spatial grids the bubbles pass through, their residence time within the spatial grids, and the area covered by the bubbles. These records are compiled into bubble trajectory distribution information and used as input data for subsequent gas-liquid contact efficiency calculations.

[0031] A dynamic model of micro- and nano-bubbles is established based on tailwater state data and jet angle data, and bubble trajectory distribution information is determined. This includes: determining the movement path, bubble coverage area, and bubble residence time of micro- and nano-bubbles in each spatial grid based on the jet angle data and the flow velocity value corresponding to each spatial grid; and generating bubble trajectory distribution information based on the movement path, bubble coverage area, and bubble residence time.

[0032] In one embodiment, the micro / nano bubble dynamics model is further defined as a discrete trajectory model based on a spatial grid, used to convert the injection angle data and the flow velocity values ​​within the spatial grid into statistically significant bubble movement paths, bubble coverage areas, and bubble residence times. The processing device uses the outlet position of the aeration nozzle as the starting point of the bubble trajectory, the direction corresponding to the current injection angle as the initial direction, and advances the bubble position with a fixed calculation step size. The calculation step size is determined based on the spatial grid size, the flow velocity sampling period, and the aeration nozzle angle adjustment period, ensuring that the bubble displacement within one calculation step does not exceed the scale of adjacent spatial grids. If the calculation step size is too large, the bubble may cross the spatial grid without recording the actual area it traverses; if the calculation step size is too small, the computational load increases but the improvement in control accuracy is limited. Therefore, the calculation step size can be set to an integer fraction of the flow velocity sampling period.

[0033] Within each computational step, the processing device determines the direction of water flow based on the velocity value of the spatial grid where the bubble is currently located, determines the initial direction of bubble movement based on the injection angle data, and combines this with the rising trend of micro / nano bubbles in the water to determine the position update result. The bubble position update can be represented by the following expression: in, For calculating time The position of the bubble. This represents the bubble position after a calculation step. To calculate the step size, For the number The water velocity vector corresponding to the spatial grid. This is the initial jet velocity vector of the bubbles, whose direction is determined by the jet angle data and whose size is determined by the outlet flow velocity of the aeration nozzle or the equipment calibration value. This is the upward velocity vector of the micro / nanobubbles in the water. This expression is used to explain that the bubble trajectory update simultaneously considers water flow, jet direction, and bubble upward trend, and the output is the bubble position at the next calculation time.

[0034] The processing equipment forms a motion path based on the continuous bubble positions. When a bubble falls into a spatial grid, that spatial grid is recorded as the region traversed by the bubble; when a bubble appears consecutively within the same spatial grid for multiple calculation steps, the bubble's dwell time in that spatial grid is accumulated. The bubble dwell time can be expressed by the following expression: in, For the number The bubble residence time corresponding to the spatial grid. The bubble position falls into the numbered The number of computational steps for the spatial grid. This expression is used to calculate the step size. It converts discrete trajectory records into dwell time data corresponding to a spatial grid, and the output is fed into the bubble trajectory distribution information.

[0035] The bubble coverage area is determined based on the area the bubble passes through and its residence time. When a bubble enters a spatial grid but its residence time is less than the minimum effective residence time, that spatial grid is marked as a weak coverage area; when the bubble's residence time reaches the minimum effective residence time, that spatial grid is marked as an effective coverage area. The minimum effective residence time is set based on the spatial grid size, the average water flow velocity, and the aeration nozzle adjustment cycle, and is used to exclude trajectory points that briefly pass by but are unlikely to make effective contact. The treatment equipment writes the movement path, effective coverage area, weak coverage area, and bubble residence time into the bubble trajectory distribution information according to the spatial grid number, so that subsequent processes can read the bubble distribution status under the same spatial grid and perform corresponding calculations with the pollutant concentration distribution data.

[0036] The gas-liquid contact efficiency index is calculated based on bubble trajectory distribution information and pollutant concentration distribution data. The processing equipment reads bubble trajectory distribution information and pollutant concentration distribution data, and establishes a correspondence between the two according to the spatial grid number. The pollutant concentration distribution data is used to determine the enrichment area of ​​pollutants in the effluent treatment tank, while the bubble trajectory distribution information is used to determine the coverage and residence time of micro / nano bubbles in each spatial grid. The processing equipment determines the pollutant concentration weight, bubble coverage probability, and bubble residence time for each spatial grid, and converts the above data into evaluation data within the same statistical period. The gas-liquid contact efficiency index is used to represent the effective contact degree between micro / nano bubbles and the pollutant enrichment area. After the gas-liquid contact efficiency index is calculated, it is compared with a contact efficiency threshold; the comparison result is used to determine whether to proceed with the injection angle adjustment process.

[0037] The gas-liquid contact efficiency index is calculated based on bubble trajectory distribution information and pollutant concentration distribution data, including: determining the pollutant concentration weight corresponding to each spatial grid based on the pollutant concentration distribution data; determining the bubble coverage probability corresponding to each spatial grid based on the bubble trajectory distribution information; and calculating the gas-liquid contact efficiency index based on the pollutant concentration weight, the bubble coverage probability, and the bubble residence time corresponding to each spatial grid.

[0038] In one embodiment, the calculation process of the gas-liquid contact efficiency index further defines the determination methods for pollutant concentration weights, bubble coverage probability, and bubble residence time, ensuring that the gas-liquid contact efficiency index has a clear data source and repeatable calculation boundaries. The processing device uses a spatial grid as the basic calculation unit, reading the pollutant concentration value corresponding to each spatial grid. A higher pollutant concentration value indicates a more concentrated concentration of pollutants within that spatial grid, and a greater contribution to the overall processing process when micro- and nano-bubbles enter and remain within that grid. To avoid inconsistencies in the index scale due to changes in the absolute value of pollutant concentration across different operating cycles, the processing device converts the pollutant concentration value of each spatial grid into a pollutant concentration weight. The pollutant concentration weight can be determined using the following expression: in, For the number The pollutant concentration weights corresponding to the spatial grid. For the number The pollutant concentration values ​​corresponding to the spatial grid. This represents the total number of spatial grids. For the number The expression represents the pollutant concentration values ​​corresponding to the spatial grid. It converts pollutant concentration distribution data into weighted data, and the output indicates the participation ratio of different spatial grids in the gas-liquid contact efficiency index. If the pollutant concentration values ​​of all spatial grids are below the detection limit within the current statistical period, the treatment equipment can set the pollutant concentration weights to uniform weights, or mark the current statistical period as a low-pollution-load period to avoid calculation anomalies caused by division by zero. The detection limit is determined based on the range, detection accuracy, and target pollutant type of the water quality detection unit.

[0039] The bubble coverage probability is determined based on bubble trajectory distribution information. The processing device records the number of times a bubble trajectory enters each spatial grid within a statistical cycle, and whether that spatial grid is marked as an effective coverage area. For a single operating cycle, the bubble coverage probability corresponding to spatial grids with effective coverage records can be set to one; the bubble coverage probability corresponding to spatial grids with only weak coverage records can be set to a weak coverage value greater than zero and less than one; and the bubble coverage probability corresponding to spatial grids not traversed by bubble trajectories can be set to zero. The weak coverage value is determined based on the ratio between the bubble residence time and the minimum effective residence time. For multiple operating cycles, the bubble coverage probability can be determined based on the proportion of effective bubble coverage times to the total number of times in the statistical cycle. The bubble coverage probability ranges from zero to one, representing the stability of micro / nano bubbles entering and covering the corresponding spatial grid. This parameter comes from bubble trajectory distribution information and does not require the introduction of new detection objects.

[0040] The bubble residence time is derived from the spatial grid residence records in the bubble trajectory distribution information. Since insufficient contact between micro / nanobubbles and pollutants occurs when the residence time is too short, further increasing the residence time after reaching the effective contact requirement has limited marginal contribution to the performance indicator. Therefore, the treatment equipment introduces a reference contact time to normalize the residence time. The reference contact time is set based on the spatial grid size, average water velocity, micro / nanobubble rising velocity, and pollutant type, and can be determined from historical stable operating data or process trial operation data. The gas-liquid contact efficiency index can be calculated using the following expression: in, This is an indicator of gas-liquid contact efficiency. For the number The pollutant concentration weights corresponding to the spatial grid. For the number The probability of bubble coverage corresponding to the spatial grid. For the number The bubble residence time corresponding to the spatial grid. For reference contact time, This represents the total number of spatial grids. The inputs to this expression are the pollutant concentration weights, bubble coverage probability, and bubble residence time. The output is the gas-liquid contact efficiency index corresponding to the current statistical period. Through this calculation, bubbles will not receive a high evaluation if they only cover low-pollution areas, nor will they receive a high evaluation if they enter pollutant-rich areas but have insufficient residence time. Only when bubbles stably cover spatial grids with high pollutant concentrations and maintain effective residence will the gas-liquid contact efficiency index increase. The contact efficiency threshold is determined based on the target effluent quality, the gas-liquid contact efficiency index within historical compliance operating cycles, and the allowable adjustment frequency of the aeration equipment. It is used to distinguish whether the current spray angle can meet the gas-liquid contact requirements.

[0041] When the gas-liquid contact efficiency index is lower than the contact efficiency threshold, the target injection angle sequence is determined based on a genetic algorithm; The treatment equipment compares the current gas-liquid contact efficiency index with a contact efficiency threshold. The contact efficiency threshold is set based on the gas-liquid contact efficiency index during historical compliance operating cycles, the target effluent quality, and the allowable adjustment frequency of the aeration equipment. When the current gas-liquid contact efficiency index is lower than the contact efficiency threshold, the treatment equipment determines that the current injection angle cannot fully cover the pollutant-rich area with micro / nano bubbles and initiates an injection angle sequence search process. A genetic algorithm uses candidate injection angle sequences as the search object, with each candidate sequence corresponding to an injection angle variation scheme within a control cycle. The treatment equipment inputs the candidate injection angle sequences into a micro / nano bubble dynamics model to obtain candidate bubble trajectory distribution information, calculates candidate gas-liquid contact efficiency indices based on this information, and determines the target injection angle sequence from the candidate injection angle sequences.

[0042] The method for determining the target injection angle sequence based on a genetic algorithm includes: constructing multiple candidate injection angle sequences and using each candidate injection angle sequence as an individual in the genetic algorithm population; inputting each candidate injection angle sequence into a micro / nano bubble dynamics model to obtain the candidate bubble trajectory distribution information corresponding to each candidate injection angle sequence; calculating the candidate gas-liquid contact efficiency index corresponding to each candidate injection angle sequence based on the candidate bubble trajectory distribution information; and determining the target injection angle sequence from multiple candidate injection angle sequences based on the candidate gas-liquid contact efficiency index.

[0043] In one embodiment, the genetic algorithm further defines the construction, evaluation, and screening methods for candidate injection angle sequences, ensuring that the source of the target injection angle sequence has a repeatable execution path. The processing device constructs candidate injection angle sequences based on the number of aeration nozzles and the number of control cycles. A candidate injection angle sequence corresponding to a single aeration nozzle can consist of angle values ​​from multiple consecutive control cycles; when multiple aeration nozzles participate in aeration, the candidate injection angle sequences can be arranged according to the aeration nozzle number and control cycle number, enabling each individual in the population to fully represent the angle control scheme within one operating cycle. The initial population can be generated based on current injection angle data, historical compliant operating angles, and allowable adjustment ranges, avoiding a completely random population that would prevent a large number of candidate schemes from being executed by the device.

[0044] The processing device uses each candidate injection angle sequence as a population for a genetic algorithm. When each population individual enters the micro / nano bubble dynamics model, the model maintains the same tailwater state data, only replacing the injection angle input to compare the impact of different angle schemes on bubble trajectory distribution. The model outputs candidate bubble trajectory distribution information corresponding to each candidate injection angle sequence, including candidate movement path, candidate bubble coverage area, and candidate bubble residence time. Following the gas-liquid contact efficiency index calculation method from the previous stage, the processing device calculates the candidate gas-liquid contact efficiency index corresponding to each candidate injection angle sequence, combining pollutant concentration weights, candidate bubble coverage probability, and candidate bubble residence time.

[0045] During the genetic algorithm iteration, the processing device sorts the population individuals according to the candidate gas-liquid contact efficiency index and retains the candidate injection angle sequence with the higher gas-liquid contact efficiency index as the parent individuals. When performing crossover operations between parent individuals, some angle values ​​can be exchanged with the control cycle as the boundary, so that the new candidate injection angle sequence can inherit the angle segments with higher contact efficiency from different parent individuals. The mutation operation is used to make small changes to the angle values ​​within individual control cycles. The mutation range is limited by the angle adjustment constraint to avoid angle jumps that cannot be followed by the actuator. After each iteration, the processing device recalculates the candidate gas-liquid contact efficiency index corresponding to the new generation of population individuals. When the preset number of iterations is reached, the change of the candidate gas-liquid contact efficiency index is less than the iteration convergence range for multiple consecutive rounds, or a candidate injection angle sequence that meets the contact efficiency threshold appears, the processing device ends the iteration and determines the target injection angle sequence from the candidate injection angle sequences. The preset number of iterations is determined based on the computing power of the processing device and the angle adjustment cycle, and the iteration convergence range is determined based on the fluctuation range of the gas-liquid contact efficiency index within the historical target operation cycle.

[0046] The candidate injection angle sequence is generated according to the angle adjustment constraint, which includes the injection angle range, the single angle adjustment amplitude, and the angle adjustment period. The target injection angle sequence is the candidate injection angle sequence that satisfies the angle adjustment constraint and has the highest corresponding candidate gas-liquid contact efficiency index among multiple candidate injection angle sequences.

[0047] In one embodiment, the candidate injection angle sequence is further constrained by angle adjustment constraints to ensure that the angle schemes generated by the genetic algorithm meet the mechanical stroke, response speed, and control cycle requirements of the aeration equipment. The angle adjustment constraints include the injection angle range, the single angle adjustment amplitude, and the angle adjustment cycle. The injection angle range is determined based on the allowable rotation range of the aeration nozzle structure, the pool boundary, and the requirement that the injected airflow does not directly impact the pool wall. The single angle adjustment amplitude is determined based on the maximum allowable angle change of the actuator and the bubble distribution stability requirements. The angle adjustment cycle is determined based on the sensor sampling cycle, the model calculation cycle, and the actuator response time, enabling the system to obtain new water state feedback after a single angle change.

[0048] Any angle value in the candidate injection angle sequence must satisfy the following constraint: in, The first in the candidate injection angle sequence The injection angle corresponding to each control cycle This is the lower limit of the injection angle. This is the upper limit of the spray angle. This is used to number the control cycle. This expression limits the executable range of the injection angle within each control cycle, preventing the candidate injection angle sequence from exceeding the mechanical boundaries of the aeration nozzle.

[0049] The change in injection angle between adjacent control cycles satisfies the following constraint: in, The first in the candidate injection angle sequence The injection angle corresponding to each control cycle This represents the upper limit of a single angle adjustment. This expression is used to limit the angle jump between adjacent control cycles, enabling the actuator to complete the action within the angle adjustment cycle and avoiding instability in the bubble trajectory caused by frequent and large changes in the injection angle.

[0050] When generating candidate injection angle sequences, the processing equipment corrects or removes individuals in the population that do not meet the injection angle range or the magnitude of a single angle adjustment. Correction methods include truncating angle values ​​exceeding the injection angle range to boundary values, or regenerating angle segments in the candidate injection angle sequence that do not meet the constraints. Removal methods are applicable to individuals in the population whose consecutive angle values ​​do not meet the constraints, reducing invalid model calculations. After all candidate injection angle sequences meet the angle adjustment constraints, the processing equipment compares the candidate gas-liquid contact efficiency indices corresponding to each candidate injection angle sequence and determines the candidate injection angle sequence with the highest candidate gas-liquid contact efficiency index as the target injection angle sequence. If multiple candidate injection angle sequences have the same candidate gas-liquid contact efficiency index, the candidate injection angle sequence with the smaller total angle adjustment magnitude can be prioritized, ensuring that the target injection angle sequence maintains a relatively stable execution process while meeting the contact efficiency requirements.

[0051] The target bubble distribution information is obtained by updating the bubble trajectory distribution information according to the target injection angle sequence; After determining the target injection angle sequence, the treatment equipment inputs this sequence as the new injection angle into the micro / nano bubble dynamics model. The model retains the effluent state data within the same operating cycle and replaces the original injection angle data with the target injection angle sequence, recalculating the trajectory of micro / nano bubbles within the effluent treatment tank. The calculation results include bubble transit records within each spatial grid, the target bubble coverage area, and the target bubble residence time distribution. Based on these calculation results, the treatment equipment updates the original bubble trajectory distribution information and generates target bubble distribution information. This target bubble distribution information represents the spatial coverage and residence state of micro / nano bubbles within the pollutant distribution area after executing the target injection angle sequence, and is then incorporated into the subsequent analysis of water disturbance intensity and aeration energy consumption.

[0052] The target bubble distribution information is obtained by updating the bubble trajectory distribution information according to the target injection angle sequence, including: inputting the target injection angle sequence into the micro-nano bubble dynamics model to obtain the target bubble trajectory distribution information; and determining the target bubble coverage area and target bubble residence time distribution based on the target bubble trajectory distribution information.

[0053] In one embodiment, the generation process of target bubble distribution information is further defined as a continuous process of model input replacement, trajectory recalculation, region filtering, and data archiving. This ensures that the target injection angle sequence is not merely a control command output, but rather bubble distribution data that can be used in subsequent stages after bubble trajectory simulation and verification. When the processing device reads the target injection angle sequence, it breaks it down into multiple angle values ​​corresponding to the angle adjustment cycle. Each angle value corresponds to a control cycle, and the control cycle maintains a correspondence with the sampling window of the tailwater state data. If the target injection angle sequence contains angle values ​​from multiple aeration nozzles, the processing device writes them into the model input table according to the aeration nozzle number and the control cycle number, ensuring that each aeration nozzle has a unique target injection angle in each control cycle.

[0054] During model execution, the processing equipment maintains the spatial grid, flow velocity, pollutant concentration, and pool boundary conditions unchanged, only replacing the original injection angle data with the target injection angle sequence. In this way, the differences between the calculated results of different bubble trajectories mainly stem from changes in the injection angle, facilitating the assessment of the impact of the target injection angle sequence on the spatial distribution of bubbles. The micro / nano bubble dynamics model updates bubble positions progressively according to the spatial grid, recording the order in which bubbles enter and pass through the spatial grid, their residence time within the spatial grid, and the corresponding control cycle. If a bubble trajectory reaches a non-flowing area due to pool boundaries, guide vanes, or water level limitations, the processing equipment marks this trajectory segment as invalid and excludes it from the target bubble trajectory distribution information, preventing areas that cannot participate in gas-liquid contact from being included in the target bubble coverage area.

[0055] The target bubble coverage area is determined based on the spatial grid coverage records in the target bubble trajectory distribution information. When a bubble trajectory enters a spatial grid and the bubble residence time reaches the minimum effective residence time, that spatial grid is marked as the target effective coverage area; when a bubble trajectory enters a spatial grid but the bubble residence time does not reach the minimum effective residence time, that spatial grid is marked as the target weak coverage area. The minimum effective residence time can be the value set in the previous modeling stage to avoid using different contact judgment scales within the same operating cycle. The processing equipment generates the target bubble coverage area based on the target effective coverage area and the target weak coverage area, and retains the target bubble coverage area and the pollutant concentration distribution data under the same spatial grid reference, which facilitates subsequent determination of the overlap between the bubble coverage area and the pollutant distribution area.

[0056] The target bubble residence time distribution is formed based on the bubble residence time corresponding to each spatial grid. The processing device summarizes the residence time of all target bubble trajectories within each spatial grid and records at least one of the maximum residence time, average residence time, and effective residence count. For scenarios involving multiple aeration nozzles, if bubbles from different aeration nozzles simultaneously enter the same spatial grid, the bubble residence time within the same spatial grid can be accumulated, or it can be saved separately according to the aeration nozzle number. When using the accumulation method, the target bubble residence time distribution represents the overall bubble contact duration within that spatial grid; when using the separate nozzle method, the target bubble residence time distribution is used to determine the contribution sources of different aeration nozzles to the same spatial grid. The processing device writes the target bubble coverage area, target bubble residence time distribution, target bubble trajectory number, and corresponding target injection angle sequence into the target bubble distribution information. If the target bubble distribution information shows that areas with high pollutant concentrations have not yet formed an effective target coverage area, the processing device configures an uncovered marker in the result record for subsequent feedback control processes to identify spatial areas requiring further adjustment.

[0057] The jet angle control configuration is output based on the water disturbance intensity data, aeration energy consumption, target bubble distribution information, and reacquired tailwater state data after executing the target jet angle sequence.

[0058] After the target spray angle sequence is sent to the aeration equipment, the treatment equipment re-collects water flow velocity data and pollutant concentration distribution data in the effluent treatment tank, and reads the gas flow rate, operating power, and operating time of the aeration equipment during execution. The treatment equipment determines the water disturbance intensity data based on the water flow velocity changes before and after executing the target spray angle sequence, determines the aeration energy consumption based on the gas flow rate, operating power, and operating time, and correlates the newly acquired effluent state data with the target bubble distribution information. The target bubble distribution information represents the bubble coverage and residence in the spatial grid, and the newly acquired effluent state data represents the water state changes after executing the target spray angle sequence. Based on the above data, the treatment equipment determines whether the target spray angle sequence needs to be maintained or adjusted, and outputs the spray angle control configuration, which includes at least the spray angle sequence and angle execution time for the next operating cycle.

[0059] The water disturbance intensity data is determined based on the change in water flow velocity after executing the target spray angle sequence; the aeration energy consumption is determined based on the gas flow rate, operating power, and operating time of the aeration equipment when executing the target spray angle sequence.

[0060] In one embodiment, the determination method for water disturbance intensity data and aeration energy consumption is further defined to ensure that the output basis of the spray angle control configuration has a clear engineering source. The treatment device uses the water flow velocity data before executing the target spray angle sequence as the reference flow velocity data and the water flow velocity data after executing the target spray angle sequence as the feedback flow velocity data. Both the reference flow velocity data and the feedback flow velocity data are stored according to a spatial grid. The treatment device compares the flow velocity values ​​of the two operating stages within the same spatial grid to obtain the water flow velocity change corresponding to the spatial grid. The water flow velocity change can be represented by changes in flow velocity magnitude, changes in flow velocity direction, or a combination of both. When there are strong disturbance areas near local backflow, guide plates, or aeration nozzles in the effluent treatment tank, only the target bubble coverage area and its adjacent spatial grids can be selected for calculation to avoid the static areas far from the aeration influence range weakening the disturbance evaluation results.

[0061] Water disturbance intensity data can be represented by the average, weighted average, or zonal statistical values ​​of the water velocity changes within each participating spatial grid. When using the weighted average, spatial grids with higher pollutant concentrations can be assigned higher weights, making the disturbance intensity data more reflective of the flow improvement in pollutant-rich areas. When generating water disturbance intensity data, the treatment equipment simultaneously retains the spatial grid number, velocity change, sampling time, and data source marker. If a spatial grid lacks effective velocity sampling results after executing the target injection angle sequence, the treatment equipment can use the velocity values ​​from adjacent spatial grids and the previous sampling window to complete the data, and a completion marker is configured in the water disturbance intensity data. Spatial grids continuously lacking effective velocity sampling results are not included in the disturbance intensity statistics to avoid abnormal data affecting the injection angle control configuration.

[0062] Aeration energy consumption is determined based on the gas flow rate, operating power, and operating time of the aeration equipment. Operating power directly reflects the electrical energy consumed by the aeration equipment when executing the target spray angle sequence. Gas flow rate is used to confirm whether the aeration equipment is in an effective air supply state, and operating time is used to limit the cumulative energy consumption time. Aeration energy consumption can be expressed by the following expression: in, For aeration energy consumption, The number of aeration devices participating in the execution of the target spray angle sequence. For the number The operating power of the aeration equipment when executing the target spray angle sequence. For the number The expression calculates the runtime of the aeration equipment while executing the target spray angle sequence. The inputs to this expression are operating power and runtime, and the output is the aeration energy consumption during the current operating cycle. Gas flow rate is not directly used as the energy consumption product, but rather as a basis for verifying aeration effectiveness. When the gas flow rate of an aeration device is lower than the lower limit of the gas supply, the treatment equipment marks the corresponding data for that aeration device as an aeration anomaly and excludes this abnormal data or triggers equipment verification when configuring the output spray angle control. The lower limit of the gas supply is determined based on the rated gas supply capacity of the aeration equipment, the nozzle orifice diameter, and the designed aeration volume of the effluent treatment tank, and is used to distinguish between normal low-load operation and gas supply failure.

[0063] The output jet angle control configuration includes: determining the effective aeration area based on the target bubble distribution information and pollutant concentration distribution data. The effective aeration area is the overlapping area of ​​the bubble coverage area represented by the target bubble distribution information and the pollutant distribution area represented by the pollutant concentration distribution data. When the re-acquired tailwater state data meets the feedback update conditions, the output jet angle control configuration is based on the effective aeration area, water body disturbance intensity data, and aeration energy consumption. The feedback update conditions include the water body flow velocity change exceeding the flow velocity change threshold or the pollutant concentration change exceeding the concentration change threshold.

[0064] In one embodiment, the output process of the jet angle control configuration is further defined to form a feedback loop between the target bubble distribution information, water disturbance intensity data, aeration energy consumption, and reacquired effluent state data. The treatment equipment determines the bubble coverage area based on the target bubble distribution information and the pollutant distribution area based on the pollutant concentration distribution data. The pollutant distribution area can be composed of a spatial grid where the pollutant concentration reaches a concentration threshold, which is set based on the emission control requirements of the target pollutant, the influent pollution load, and historical compliance operation data. The treatment equipment spatially overlaps the bubble coverage area with the pollutant distribution area to obtain the effective aeration area. The effective aeration area represents the spatial grid area actually covered by micro / nano bubbles and where there is a need for pollutant treatment; bubble coverage areas that do not overlap with the pollutant distribution area are not included in the effective aeration area.

[0065] The treatment equipment reads the newly acquired effluent status data and compares it with the effluent status data before executing the target spray angle sequence. If the change in water flow velocity exceeds the flow velocity change threshold, it indicates a significant change in the flow field within the effluent treatment tank, and the bubble trajectory corresponding to the original target spray angle sequence may no longer stably cover the pollutant distribution area. If the change in pollutant concentration exceeds the concentration change threshold, it indicates that the pollutant distribution area has migrated or the pollution load has fluctuated, and the original target spray angle sequence also needs to be re-evaluated. The flow velocity change threshold is set based on the measurement error of the flow velocity detection unit, the normal operating fluctuation range of the effluent treatment tank, and the adjustment cycle of the aeration nozzles; the concentration change threshold is set based on the detection accuracy of the water quality detection unit, the allowable fluctuation range of the target pollutant, and the effluent water quality control requirements. The two thresholds correspond to different physical quantities to avoid using the same judgment standard for flow field changes and pollutant concentration changes.

[0066] The output of the spray angle control configuration includes one or more of the following: maintaining the current target spray angle sequence, shortening the angle adjustment cycle, re-triggering the spray angle sequence search, or outputting a device verification prompt. When the effective aeration area covers the pollutant distribution area, the water disturbance intensity data reaches the disturbance judgment limit, and the aeration energy consumption does not exceed the energy consumption limit, the treatment device outputs a spray angle control configuration that maintains the current target spray angle sequence. The disturbance judgment limit is determined based on the change in water flow velocity and the decreasing trend of target pollutant concentration in the target bubble coverage area during historical compliance operating cycles. When the effective aeration area shrinks, or the re-acquired effluent status data meets the feedback update conditions, the treatment device outputs a spray angle control configuration that re-triggers the spray angle sequence search. When the aeration energy consumption exceeds the energy consumption limit but the gas-liquid contact effect does not improve synchronously, the treatment device outputs a spray angle control configuration that reduces the angle adjustment frequency or limits the spray angle change amplitude. The energy consumption limit is set based on the rated power of the aeration equipment, the allowable power consumption per operating cycle, and the effluent treatment process energy consumption target to avoid exchanging local disturbances for excessive aeration. The treatment equipment writes the spray angle control configuration into the control command cache and simultaneously records the effective aeration area, water disturbance intensity data, aeration energy consumption, and the judgment results of feedback update conditions, so that the next operating cycle can directly read the configuration source and execute the corresponding angle control.

[0067] like Figure 2 As shown, a multi-sensor fusion-based monitoring system for micro-nano aerated wastewater treatment processes is used to implement a multi-sensor fusion-based monitoring method for micro-nano aerated wastewater treatment processes. The system includes: The data acquisition module is used to acquire water flow velocity data, pollutant concentration distribution data, and jet angle data in the effluent treatment tank, and to fuse the water flow velocity data and pollutant concentration distribution data to obtain effluent status data. The data acquisition module consists of a flow velocity sensor, a water quality detection sensor, a jet angle encoder, a data acquisition unit, and a communication interface. It is used to collect water flow velocity data, pollutant concentration distribution data, and jet angle data in the effluent treatment tank, and to transmit the collected data to the treatment unit.

[0068] The model building module is used to establish a micro-nano bubble dynamics model based on tailwater state data and jet angle data, and to determine bubble trajectory distribution information. The model building module consists of an industrial processor, memory and model computing chip, which is used to read tailwater state data and jet angle data, and run the micro-nano bubble dynamics model to obtain bubble trajectory distribution information.

[0069] The index calculation module is used to calculate the gas-liquid contact efficiency index based on bubble trajectory distribution information and pollutant concentration distribution data. The index calculation module consists of an industrial processor, a data buffer, and a numerical calculation unit, which is used to call bubble trajectory distribution information and pollutant concentration distribution data, and calculate the gas-liquid contact efficiency index.

[0070] The angle determination module is used to determine the target injection angle sequence based on a genetic algorithm when the gas-liquid contact efficiency index is lower than the contact efficiency threshold. The angle determination module consists of an industrial processor, a genetic algorithm operation unit, a parameter memory, and a control interface, and is used to generate the target injection angle sequence when the gas-liquid contact efficiency index is lower than the contact efficiency threshold.

[0071] The distribution update module is used to update the bubble trajectory distribution information according to the target injection angle sequence to obtain the target bubble distribution information. The distribution update module consists of an industrial processor, a model recalculation unit and a data storage unit. It is used to re-input the target injection angle sequence into the micro-nano bubble dynamics model and update the target bubble distribution information.

[0072] The configuration output module is used to output the spray angle control configuration based on the water disturbance intensity data, aeration energy consumption, target bubble distribution information, and the reacquired tailwater state data after executing the target spray angle sequence. The configuration output module consists of an industrial controller, an execution control interface, an aeration equipment power acquisition unit, and a communication output interface.

[0073] It should be noted that, in this document, the terms "comprising," "including," and any other variations are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Specific examples have been used in this document to illustrate the principles and implementation methods of the technical solutions of this application. The above examples are only for the purpose of helping to understand the methods and core ideas of this application. The above descriptions are merely preferred embodiments of this application. It should be pointed out that, due to the limitations of written expression and the objective existence of infinite specific structures, those skilled in the art can make several improvements, modifications, or changes without departing from the principles of this application, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes, or combinations, or the direct application of the concept and technical solutions of this application to other situations without modification, should all be considered within the scope of protection of this application.

Claims

1. A multi-sensor fusion method for monitoring micro-nano aeration effluent treatment processes, characterized in that, The method includes: Acquire water flow velocity data, pollutant concentration distribution data, and jet angle data of the effluent treatment tank, and fuse the water flow velocity data and the pollutant concentration distribution data to obtain effluent state data; A micro / nano bubble dynamics model is established based on the tailwater state data and the jet angle data, and the bubble trajectory distribution information is determined. The gas-liquid contact efficiency index is calculated based on the bubble trajectory distribution information and the pollutant concentration distribution data. When the gas-liquid contact efficiency index is lower than the contact efficiency threshold, the target injection angle sequence is determined based on a genetic algorithm; The target bubble distribution information is obtained by updating the bubble trajectory distribution information according to the target injection angle sequence; The jet angle control configuration is output based on the water disturbance intensity data, aeration energy consumption, target bubble distribution information, and the reacquired tailwater state data after executing the target jet angle sequence.

2. The method according to claim 1, characterized in that, The process of fusing the water flow velocity data and the pollutant concentration distribution data to obtain the tailwater state data includes: The tailwater treatment tank is divided into multiple spatial grids according to the tank area; Time alignment is performed on the water flow velocity data and the pollutant concentration distribution data; Determine the flow velocity and pollutant concentration values ​​corresponding to each of the spatial grids; The tailwater state data is generated based on the flow velocity value and the pollutant concentration value corresponding to each of the spatial grids.

3. The method according to claim 2, characterized in that, The process of establishing a micro / nano bubble dynamics model based on the tailwater state data and the injection angle data, and determining the bubble trajectory distribution information, includes: Based on the jetting angle data and the flow velocity value corresponding to each spatial grid, the movement path, bubble coverage area, and bubble residence time of the micro-nano bubbles in each spatial grid are determined. The bubble trajectory distribution information is generated based on the movement path, the bubble coverage area, and the bubble dwell time.

4. The method according to claim 3, characterized in that, The calculation of the gas-liquid contact efficiency index based on the bubble trajectory distribution information and the pollutant concentration distribution data includes: The pollutant concentration weight corresponding to each spatial grid is determined based on the pollutant concentration distribution data. The bubble coverage probability corresponding to each spatial grid is determined based on the bubble trajectory distribution information. The gas-liquid contact efficiency index is calculated based on the pollutant concentration weight, the bubble coverage probability, and the bubble residence time corresponding to each spatial grid.

5. The method according to claim 4, characterized in that, The determination of the target injection angle sequence based on the genetic algorithm includes: Construct multiple candidate jet angle sequences, and use each candidate jet angle sequence as an individual in the population of the genetic algorithm; Each candidate injection angle sequence is input into the micro / nano bubble dynamics model to obtain the candidate bubble trajectory distribution information corresponding to each candidate injection angle sequence; Based on the candidate bubble trajectory distribution information corresponding to each candidate injection angle sequence, calculate the candidate gas-liquid contact efficiency index corresponding to each candidate injection angle sequence; The target injection angle sequence is determined from a plurality of candidate injection angle sequences based on the candidate gas-liquid contact efficiency index.

6. The method according to claim 5, characterized in that, The candidate injection angle sequence is generated according to angle adjustment constraints, which include injection angle range, single angle adjustment magnitude, and angle adjustment period. The target injection angle sequence is the candidate injection angle sequence that satisfies the angle adjustment constraint and has the highest corresponding candidate gas-liquid contact efficiency index among the multiple candidate injection angle sequences.

7. The method according to claim 1, characterized in that, The step of updating the bubble trajectory distribution information according to the target injection angle sequence to obtain target bubble distribution information includes: The target injection angle sequence is input into the micro / nano bubble dynamics model to obtain the target bubble trajectory distribution information; The target bubble coverage area and target bubble dwell time distribution are determined based on the target bubble trajectory distribution information to obtain the target bubble distribution information.

8. The method according to claim 1, characterized in that, The water disturbance intensity data is determined based on the change in water flow velocity after executing the target injection angle sequence; The aeration energy consumption is determined based on the gas flow rate, operating power, and operating time of the aeration equipment when executing the target spray angle sequence.

9. The method according to claim 8, characterized in that, The output injection angle control configuration includes: The effective aeration area is determined based on the target bubble distribution information and the pollutant concentration distribution data. The effective aeration area is the overlapping area of ​​the bubble coverage area represented by the target bubble distribution information and the pollutant distribution area represented by the pollutant concentration distribution data. When the reacquired tailwater state data meets the feedback update conditions, the spray angle control configuration is output based on the effective aeration area, the water body disturbance intensity data, and the aeration energy consumption. The feedback update conditions include the water body flow velocity change exceeding the flow velocity change threshold, or the pollutant concentration change exceeding the concentration change threshold.

10. A multi-sensor fusion-based monitoring system for micro-nano aerated wastewater treatment processes, used to implement the multi-sensor fusion-based monitoring method for micro-nano aerated wastewater treatment processes according to any one of claims 1 to 9, characterized in that, The system includes: The data acquisition module is used to acquire water flow velocity data, pollutant concentration distribution data and spray angle data of the effluent treatment tank, and to fuse the water flow velocity data and the pollutant concentration distribution data to obtain effluent state data. The model building module is used to establish a micro-nano bubble dynamics model based on the tailwater state data and the injection angle data, and to determine the bubble trajectory distribution information. The index calculation module is used to calculate the gas-liquid contact efficiency index based on the bubble trajectory distribution information and the pollutant concentration distribution data; An angle determination module is used to determine the target injection angle sequence based on a genetic algorithm when the gas-liquid contact efficiency index is lower than the contact efficiency threshold. The distribution update module is used to update the bubble trajectory distribution information according to the target injection angle sequence to obtain target bubble distribution information; The configuration output module is used to output the jet angle control configuration based on the water disturbance intensity data, aeration energy consumption, target bubble distribution information, and the reacquired tailwater state data after executing the target jet angle sequence.

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