Intelligent in-situ remediation control method and system for black and odorous water body

Abstract

The application discloses a kind of black and odorous water intelligent in-situ remediation control method and system.Method includes: obtaining water dissolved oxygen concentration value D (t) ;D (t) With lower threshold D L =2mg / L and upper threshold D H =5mg / L comparison;When D (t) ≤D L , start micro-nano aeration;When D L <D (t) <D H , according to PID algorithm to calculate aeration control amount and adjust operating power;When D (t) ≥D H , power down or stop.In the control of lift, according to three-stage S-shaped speed curve control rising speed, including accelerating section exponential rise, uniform speed section and deceleration section exponential decline.In bacteria agent dosing, based on dissolved oxygen, pH, temperature three parameters to calculate weighted score, greater than threshold value, start to add.The application realizes on-demand aeration, precision lift control and bacteria agent automatic dosing.

Description

Technical Field

[0001] This invention belongs to the field of water environment management technology, specifically relating to an intelligent in-situ remediation control method and system for black and odorous water bodies. Background Technology

[0002] Black and odorous water bodies are one of the most prominent problems facing urban water environments today. They mainly appear in rivers, lakes, and other water bodies within urban built-up areas, exhibiting an unpleasant color and emitting a foul odor. The main cause of black and odorous water bodies is insufficient dissolved oxygen in the water, leading to the anaerobic decomposition of organic matter and the production of blackening and odor-causing substances such as hydrogen sulfide and ammonia nitrogen.

[0003] For the treatment of black and odorous water bodies, technologies such as aeration, microbial remediation, and ecological floating beds have been widely applied. Among them, micro-nano aeration technology is increasingly used in the treatment of black and odorous water bodies due to its advantages such as small bubble size, slow rising speed, large gas-liquid contact area, and high oxygen mass transfer efficiency. However, existing treatment devices generally have the following shortcomings: most aeration devices adopt timed start-stop or manual operation modes, lacking intelligent control and unable to automatically adjust the aeration intensity according to changes in water quality, resulting in high energy consumption and low treatment efficiency; they lack online water quality monitoring modules, making it impossible to evaluate the treatment effect in real time and achieve precise remediation; aeration components are submerged in water for a long time, making them prone to being clogged by bottom sediment, and cleaning and maintenance require removing the entire device, which is cumbersome; in addition, existing devices mostly have a single aeration function and lack a systematic design that integrates multiple treatment methods such as aeration, microbial enhancement, and water quality monitoring.

[0004] To address the aforementioned issues, some technical solutions have attempted multi-functional integration. Reference 11 (Zhao Xiaofei. A control method for an integrated system of in-situ ecological restoration technology for black and odorous rivers and lakes: CN105481206B[P]. 2018-03-27) discloses an integrated system of in-situ ecological restoration technology for black and odorous rivers and lakes and its control method. This system includes water purification microbial agents, micro-nano aerators, sensor units, wireless network units, and a solar photovoltaic power station, enabling online water quality monitoring and automatic correction of control commands based on water quality changes. Reference 22 (China Metallurgical Huatian Nanjing Engineering Technology Co., Ltd., China Metallurgical Huatian Engineering Technology Co., Ltd. Photovoltaic-driven comprehensive water treatment device: CN217780871U[P]. 2022-11-11) discloses a photovoltaic-driven comprehensive water treatment device, including a PLC control system, a solar photovoltaic device, an aeration device, an ecological floating bed, and a microbial biofilm device. Based on the PLC control system, it achieves real-time monitoring and acquisition of water quality information and automated, intelligent operation of the device. Reference 33 (Shen Xiaoxiao. An automatic lifting device for the aeration pipe of a floating aerator: CN205933413U[P]. 2017-02-08) discloses an automatic lifting device for the aeration pipe of a floating aerator, including a slide rail and an X-shaped mechanical arm fixed on the float of the floating aerator. The aeration pipe can be adjusted at different water depths by extending and retracting the mechanical arm.

[0005] However, the aforementioned existing technologies still have the following shortcomings: In Reference 11, the aeration components are fixedly installed in the river channel without a lifting and adjustment mechanism. During cleaning and maintenance, the entire aeration component still needs to be removed, resulting in inconvenience. Reference 22 also lacks a lifting and maintenance design for the aeration components. Although Reference 33 achieves the lifting and lowering of the aeration pipe, the lifting device has a relatively complex structure and lacks integrated online water quality monitoring and intelligent control functions. It cannot automatically adjust the aeration rate according to changes in water quality, resulting in low functional integration. Furthermore, none of the above references address speed control strategies during the lifting and lowering of the aeration components, nor do they address microbial agent dosing decision algorithms based on multi-parameter comprehensive scoring.

[0006] It is evident that existing in-situ remediation technologies for black and odorous water bodies still have shortcomings in terms of intelligent automatic control, online water quality monitoring and feedback, convenient maintenance of aeration components, and system integration of multiple treatment functions. Summary of the Invention

[0007] This invention provides an intelligent in-situ remediation control method and system for black and odorous water bodies, aiming to solve the following technical problems: (1) to achieve precise automatic adjustment of aeration intensity and reduce energy consumption; (2) to achieve online real-time monitoring and feedback of water quality; (3) to facilitate the cleaning and maintenance of aeration components; and (4) to integrate multiple treatment functions and improve remediation efficiency.

[0008] Control methods The intelligent in-situ remediation and control method for black and odorous water bodies provided by this invention includes the following steps: Obtain the dissolved oxygen concentration value D(t) of the water body collected in real time by the water quality monitoring module; compare the dissolved oxygen concentration value D(t) with a preset lower threshold value D. L and upper limit threshold D H Comparison, where D L =2mg / L, D H =5mg / L; when D(t)≤D L At that time, the controller sends a start command to the micro-nano aeration generator; when D L <D(t)<D H When D(t) ≥ D H At that time, the controller sends a power reduction command or a stop command to the micro-nano aeration generator.

[0009] Acceleration and deceleration curve control

[0010] When cleaning of the micro / nano aeration heads is required, the controller directs the drive motor to raise the sliding support, aeration pipe fixed to it, and the micro / nano aeration heads via a lead screw. During the raising process, the controller controls the raising speed of the sliding support according to a three-segment S-shaped speed curve. Let the preset maximum raising speed be v. max The acceleration time constant is t rise The deceleration time constant is t decel If the acceleration phase ends at time t1, the constant velocity phase ends at time t2, and the ascent is completed at time t3, then the ascent velocity v up (t) is determined according to the following piecewise function: Acceleration segment (0≤t) <t1):

[0011] Uniform speed segment (t1≤t) <t2):

[0012] Deceleration phase (t2≤t) <t3):

[0013] Where v max The value range is from 0.01 m / s to 0.05 m / s, t rise The value range is from 1 second to 3 seconds, t decel The value ranges from 1 second to 3 seconds.

[0014] The values ​​of the parameters for each segment of the above velocity curve are based on the following: v max=0.03m / s. The theoretical maximum speed of 0.04m / s is calculated from the stepper motor's rated speed of 600rpm and the lead screw of 4mm. The final speed is determined after comprehensively considering the motor's torque-speed characteristics (maximum torque in the low-speed range) and engineering safety margins. rise ≥1s Ensure that the maximum stress at the screw-sliding bracket connection does not exceed 35% of the material's yield strength (finite element analysis results), t decel ≥1s Ensure that the shear stress on the quick-release connector does not exceed 45% of the design value.

[0015] When the sliding bracket triggers the limit switch mounted on the upper end of the lead screw, the controller sends a stop command to the drive motor. After cleaning is complete, the controller sends a reverse command to the drive motor, lowering the sliding bracket, aeration pipe, and micro-nano aeration head to the set depth.

[0016] This three-stage S-shaped speed curve achieves soft start by using an exponential rise in the acceleration phase and a soft stop by using an exponential fall in the deceleration phase. This effectively avoids the mechanical shock caused by the traditional direct full-speed start-stop method and extends the service life of the lead screw, drive motor, and aeration pipe connectors.

[0017] PID control algorithm When the dissolved oxygen concentration is within the normal range D L <D(t)<D H At that time, the controller calculates the aeration control quantity u(t) according to the following formula:

[0018] Where e(t) = D set -D(t), D set To preset the target dissolved oxygen value (preferably 4 mg / L), K p T is the proportionality coefficient. i Let T be the integration time constant. d U is the differential time constant. The controller adjusts the operating power of the micro / nano aeration generator according to the aeration control quantity u(t).

[0019] The initial value K of the above PID parameters p0 T i0 T d0 The following method was used to determine the transfer function model of the micro / nano aeration system: First, a step response experiment was conducted to establish the transfer function model of the micro / nano aeration system. The process gain K = 2.8 L·m 3 / min, time constant T=180 s, pure time delay τ=25 s (goodness of fit R) 2=0.96, model validation mean absolute error 0.15mg / L); then based on this model, the PID parameters were calculated using the Ziegler-Nichols tuning method and the Cohen-Coon tuning method, respectively. After verification by MATLAB / Simulink simulation, the optimal initial parameter K was determined comprehensively. p0 =10、T i0 =60、T d0 =12s. Under these parameters, the closed-loop system's response rise time to a step change in dissolved oxygen setpoint is approximately 120s, the overshoot is approximately 8%, and the steady-state error is less than ±0.1mg / L.

[0020] Adaptive adjustment of PID parameters To further improve control accuracy, this invention also provides an adaptive adjustment scheme for PID parameters. The proportional coefficient K... p Integration time constant T i and differential time constant T d Adaptive adjustment is performed according to the following formula:

[0021]

[0022]

[0023] Where K p0 T is the initial scaling factor, and its value ranges from 5 to 20. i0 T is the initial integration time constant, ranging from 30 seconds to 120 seconds. d0 α is the initial differential time constant, ranging from 0.1 seconds to 2 seconds; α is the proportional coefficient adjustment factor, ranging from 1 to 5; β is the integral time adjustment factor, ranging from 5 to 30; γ is the differential time adjustment factor, ranging from 0.1 to 0.5; and ε is the zero-prevention constant (value 0.01).

[0024] The adaptive adjustment coefficients α, β, and γ are determined using a particle swarm optimization algorithm: the comprehensive performance index J = 0.4·MSE + 0.3·OS + 0.3·T under five typical operating conditions (low load, normal load, high load shock, fluctuating load, and sudden shock). s With minimization as the objective, an optimization was performed with a population size of 50 and 200 iterations, yielding α=3.2, β=12.5, and γ=0.28. This adaptive adjustment strategy can increase the proportional action to accelerate the response speed when the dissolved oxygen deviation is large, weaken the integral action to prevent integral saturation when the deviation is small, and enhance the differential action to suppress oscillations when the rate of change of deviation is large.

[0025] Feedforward compensation algorithm This invention also provides a feedforward compensation scheme based on pH and temperature. The pH value P(t) collected by the pH sensor and the temperature value T collected by the temperature sensor are obtained. emp (t), calculate the dissolved oxygen target correction value ΔD according to the following formula. set (t):

[0026] Where P opt The optimal pH value is between 6.5 and 7.5, T opt The optimal temperature value is λ, which ranges from 20℃ to 30℃. P The pH influence coefficient ranges from 0.3 to 0.8 mg / L·pH. -1 , λ T The value is the temperature influence coefficient, ranging from 0.05 to 0.15 mg / L·℃. -1 .

[0027] The above influence coefficient λ P , λ T The following experimental fitting method was used to determine: λ P The effect of pH was determined through experiments (9 pH values, 3 replicates per group, univariate linear regression), and the regression analysis yielded λ. P =0.53 mg / L·pH -1 (R) 2 =0.98); λ T The effect of temperature was determined through experiments (8 temperature points, 3 replicates per group, univariate linear regression), yielding λ. T =0.10=0.10 mg / L·℃ -1 (R) 2 =0.97). Verification experiments show that, after adopting the above fitting coefficients, the dissolved oxygen control accuracy is improved by approximately 40% compared to the case without feedforward compensation.

[0028] The revised dissolved oxygen target value =D set +ΔD set (t) replaces the preset dissolved oxygen target value D set Calculate the dissolved oxygen deviation value e(t). This feedforward compensation can adjust the dissolved oxygen target value in advance when the pH or temperature deviates from the optimal conditions. It forms a feedforward-feedback composite control with PID feedback control, improving the response speed and anti-interference capability of the control system.

[0029] Microbial agent dosing decision algorithm This invention also provides a microbial agent dosing decision-making scheme based on multi-parameter comprehensive scoring. It acquires the pH value P(t) collected by a pH sensor and the temperature value T collected by a temperature sensor. empThe necessity score S(t) for microbial agent addition is calculated according to the following formula:

[0030] Where D L The lower limit threshold for dissolved oxygen is 2 mg / L, P opt The optimal pH value is between 6.5 and 7.5, T opt The optimal temperature value is ω, which ranges from 20℃ to 30℃. D ω is the dissolved oxygen weighting coefficient, with a value ranging from 0.5 to 0.7. P ω is the pH weighting coefficient, ranging from 0.15 to 0.25. T ω is a temperature weighting coefficient with a value ranging from 0.15 to 0.25. D +ω P +ω T =1.

[0031] The aforementioned weighting coefficients were determined using the analytic hierarchy process (AHP): five experts in the field of black and odorous water body treatment were invited to conduct pairwise comparisons and judgments, constructing a judgment matrix. After a consistency test (CR < 0.1, CR < 0.1), the weight vector was calculated, and the geometric mean was taken to obtain ω. D =0.60、ω P =0.22、ω T =0.18. The verification experiment (12 water samples with different pollution levels) showed that the Pearson correlation coefficient between the objective score and the expert subjective score was r=0.91 (p<0.001), with a mean absolute error of 0.07.

[0032] When the necessity score S(t) for microbial agent addition is greater than the preset score threshold S th When the value ranges from 0.4 to 0.6, the controller sends a start command to the metering pump, which then adds the microbial agent from the storage tank into the water body through the dosing pipe. Preset scoring threshold S th =0.50 was determined through ROC curve analysis, and the sensitivity at this threshold was 0.88 and the specificity was 0.94.

[0033] This scoring function quantifies three key water quality parameters—dissolved oxygen, pH, and temperature—into a single score indicating the necessity of microbial agent dosing through a weighted summation, providing the controller with a clear decision-making basis. When dissolved oxygen, pH, or temperature are severely insufficient, the score increases, triggering microbial agent dosing and achieving automation and precision in microbial agent dosing.

[0034] System Composition This invention also provides an intelligent in-situ remediation control system for black and odorous water bodies, comprising: a floating platform; a micro-nano aeration subsystem deployed on the floating platform, including a micro-nano aeration generator, an aeration pipe, and micro-nano aeration heads installed on the aeration pipe; a water quality monitoring subsystem deployed at the bottom of the floating platform and submerged in the water, including a dissolved oxygen sensor, a pH sensor, and a temperature sensor; a lifting and adjusting subsystem, including a drive motor, a lead screw, and a sliding bracket, wherein the aeration pipe is fixed on the sliding bracket, and limit switches are respectively provided at the upper and lower ends of the lead screw; a microbial agent dosing subsystem, including a storage tank and a metering pump, wherein the outlet of the metering pump is connected to a dosing pipe, and the end of the dosing pipe extends into the water; and a controller electrically connected to the dissolved oxygen sensor, the pH sensor, the temperature sensor, the micro-nano aeration generator, the drive motor, the limit switches, and the metering pump, wherein the controller is configured to execute the above-described intelligent in-situ remediation control method for black and odorous water bodies.

[0035] Preferably, a quick-release connector is provided between the aeration pipe and the sliding support. Preferably, the aeration pipe has an annular tubular structure, and 4 to 8 micro-nano aeration heads are evenly distributed at the bottom of the aeration pipe, with the bubble particle size generated by the micro-nano aeration heads being 0.1 to 10 micrometers. Preferably, the system further includes a power supply subsystem and a wireless communication subsystem. The power supply subsystem includes a solar photovoltaic panel and a battery, with the battery electrically connected to the solar photovoltaic panel, and the wireless communication subsystem electrically connected to the controller.

[0036] (ii) Beneficial effects

[0037] Compared with the closest prior art, the present invention has the following beneficial effects.

[0038] (1) Intelligent automatic control. The dissolved oxygen concentration in the water is monitored in real time by a dissolved oxygen sensor. The controller uses a PID control algorithm to automatically adjust the operating power and aeration rate of the micro-nano aerator according to the preset dissolved oxygen threshold, so as to realize on-demand aeration and avoid energy waste caused by over-aeration. Compared with the staged control based on stage targets in the prior art, the present invention adopts continuous feedback control based on real-time dissolved oxygen concentration, which has higher control accuracy and faster response speed.

[0039] (2) Adaptive adjustment of PID parameters. By using an adaptive parameter adjustment strategy based on dissolved oxygen deviation, the response speed is accelerated when the deviation is large, integral saturation is prevented when the deviation is small, and oscillation is suppressed when the rate of change of deviation is large, thereby improving the adaptability of the control system to different water quality conditions.

[0040] (3) Feedforward-feedback composite control. The target dissolved oxygen value is corrected by feedforward using data from pH and temperature sensors, and combined with PID feedback control to form a composite control. This allows for advance adjustment of the control quantity when water quality parameters change, thereby improving the response speed and anti-interference capability of the control system.

[0041] (4) Precise lifting and lowering speed control. The lifting and lowering of the aeration component is controlled by a three-stage S-shaped speed curve. The acceleration and deceleration stages adopt an exponential smooth transition, which effectively reduces mechanical impact and extends the service life of the lead screw, drive motor and aeration pipe connector.

[0042] (5) Automatic and precise dosing of microbial agents. By constructing a multi-parameter weighted scoring function, the three key parameters of dissolved oxygen, pH and temperature are quantified into a single score for the necessity of microbial agent dosing, thus realizing automated decision-making and precise control of microbial agent dosing.

[0043] (6) Real-time water quality monitoring and remote transmission. The system integrates multiple water quality sensors such as dissolved oxygen, pH, and temperature, which can monitor changes in water quality in real time and transmit the data to a remote monitoring platform via a wireless communication module, providing data support for governance decisions.

[0044] (7) Anti-clogging and convenient maintenance. The aeration head can be raised above the water surface for cleaning without stopping the machine, which effectively avoids the problem of the aeration head being clogged by bottom mud and extends the service life of the equipment.

[0045] (8) Green energy power supply. Powered by solar photovoltaic panels, no external power supply is required, making it suitable for use in remote rivers, lakes and other places without power facilities, and the operating cost is low. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the flow modules of the control method in the present invention patent application.

[0047] Figure 2 This is a schematic diagram of the intelligent in-situ remediation control system for black and odorous water bodies, as shown in the patent application of this invention.

[0048] Figure 3 This is a block diagram illustrating the control principle of this invention patent application. Detailed Implementation

[0049] The present invention will be further described below with reference to embodiments, but this is not intended to limit the present invention in any way. Any modifications or substitutions made based on the teachings of the present invention shall fall within the protection scope of the present invention.

[0050] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] Example 1: System Structure like Figure 2 As shown, the intelligent in-situ remediation control system for black and odorous water bodies in this embodiment includes a floating platform, a micro-nano aeration subsystem, a water quality monitoring subsystem, a lifting and adjusting subsystem, a controller, a wireless communication subsystem, a power supply subsystem, and a microbial agent dosing subsystem.

[0052] The floating platform consists of two symmetrically arranged pontoons 1 and a frame 2 connecting the two pontoons. The pontoons 1 are made of high-density polyethylene with a sealed hollow structure to provide buoyancy for the entire system. The frame 2 is welded from stainless steel square tubing, providing excellent corrosion resistance and structural strength. Equipment mounting bases 3 are fixedly installed on the frame 2. Anchor ropes 17 are installed at the four corners of the floating platform, with anchor blocks 18 connected to the ends of the anchor ropes 17, which can fix the system at a designated position in the treated water area, preventing the system from drifting with the water flow.

[0053] The micro-nano aeration subsystem includes a micro-nano aerator 4, an aeration pipe 5, and several micro-nano aeration heads 6. The micro-nano aerator 4 is fixedly installed on the equipment mounting base 3, and its air outlet is connected to one end of the aeration pipe 5 via a pipe. The other end of the aeration pipe 5 extends into the water and is connected to the micro-nano aeration heads 6. In this embodiment, the aeration pipe 5 has an annular tubular structure, with six micro-nano aeration heads 6 evenly connected to its bottom. The micro-nano aeration heads 6 are made of ceramic microporous material, producing bubbles with a particle size of 0.1 to 10 micrometers, which can effectively improve the oxygen mass transfer efficiency of the water.

[0054] The water quality monitoring subsystem includes a dissolved oxygen sensor 7, a pH sensor 8, and a temperature sensor 9, all of which are installed at the bottom of the floating platform and submerged in the water. Each sensor is an industrial-grade online sensor, characterized by fast response and high measurement accuracy.

[0055] The lifting and adjusting subsystem includes a drive motor 10, a lead screw 11, and a sliding bracket 12. The drive motor 10 is a waterproof stepper motor, fixedly mounted on the equipment mounting base 3. The lead screw 11 is vertically positioned, its upper end connected to the output end of the drive motor 10 via a coupling. The sliding bracket 12 is fitted onto the lead screw 11, achieving vertical movement through a threaded connection. The aeration pipe 5 is fixed to the sliding bracket 12 via a quick-release connector 23. Limit switches 22 are installed at the upper and lower ends of the lead screw 11. When the sliding bracket 12 moves to its limit position, the limit switches 22 are triggered. Upon receiving the signal, the controller 13 stops the drive motor 10, ensuring safety.

[0056] The controller 13 is fixedly installed on the equipment mounting base 3 and can be a PLC programmable controller or a microcontroller. The controller 13 is electrically connected to the dissolved oxygen sensor 7, pH sensor 8, temperature sensor 9, micro / nano aeration generator 4, drive motor 10, and metering pump 20. The controller 13 has a built-in data acquisition module, data processing module, and control output module. The controller 13 is configured to execute the intelligent in-situ remediation control method for black and odorous water bodies of the present invention.

[0057] The wireless communication subsystem 14 is electrically connected to the controller 13 and is used to transmit water quality monitoring data and equipment operating status to a remote monitoring platform in real time. The wireless communication subsystem 14 can use a 4G communication module, a 5G communication module, or a LoRa communication module.

[0058] The power supply subsystem includes a solar photovoltaic panel 15 and a battery 16. The solar photovoltaic panel 15 is installed at an angle above the equipment mounting base 3, facing south to obtain maximum sunlight. The battery 16 is a lithium iron phosphate battery, electrically connected to the solar photovoltaic panel 15 through a charging controller, and supplies power to the micro-nano aerator 4, drive motor 10, controller 13, wireless communication subsystem 14, and metering pump 20.

[0059] The microbial agent dosing subsystem includes a storage tank 19 and a metering pump 20. The storage tank 19 contains liquid microbial agent. The inlet of the metering pump 20 is connected to the outlet of the storage tank 19, and the outlet of the metering pump 20 is connected to a dosing pipe 21, the end of which extends into the water. The metering pump 20 is electrically connected to the controller 13.

[0060] Example 2: Aeration Control Method This embodiment details the aeration control method of the present invention.

[0061] After the system is deployed to the black and odorous water treatment area, it is fixed in the set position by anchor rope 17 and anchor block 18. Dissolved oxygen sensor 7, pH sensor 8 and temperature sensor 9 collect water quality data in real time at a preset sampling frequency (e.g., once every 10 seconds) and transmit it to controller 13.

[0062] After receiving the dissolved oxygen concentration value D(t), the controller 13 compares it with a preset lower threshold value D. L =2mg / L and upper limit threshold D H Comparison at 5 mg / L: Operating Condition 1: Low Oxygen State (D(t)≤2mg / L) When the dissolved oxygen concentration falls below the lower threshold, it indicates that the water body is in a state of severe hypoxia, the anaerobic decomposition of organic matter intensifies, and a large amount of blackening and odor-causing substances are produced. The controller 13 immediately sends a start command to the micro-nano aerator 4, which operates at the first power P1 (e.g., 100% of the rated power) to replenish dissolved oxygen to the water body at maximum capacity.

[0063] Operating Condition 2: Normal State (2mg / L) <D(t)<5mg / L) When the dissolved oxygen concentration is within the normal range, controller 13 starts the PID control algorithm and calculates the aeration control quantity u(t) according to the following formula:

[0064] Where e(t) = D set -D(t), D set The preset target dissolved oxygen value is 4 mg / L.

[0065] In this embodiment, the scaling factor K p The initial value is set to 10, and the integration time constant is T. i The initial value is set to 60 seconds, and the differential time constant is T. d The initial value is set to 0.5 seconds. The controller adjusts the operating power of the micro-nano aerator 4 according to the calculated aeration control amount u(t) to make the dissolved oxygen concentration approach the target value of 4 mg / L.

[0066] To further improve control accuracy, this embodiment also employs a PID parameter adaptive adjustment strategy. Let the dissolved oxygen deviation at the current moment be e(t), and the rate of change of deviation be de(t) / dt, then the proportional coefficient K... p Integration time constant T i and differential time constant T d Make real-time adjustments according to the following formula:

[0067]

[0068]

[0069] The above adaptive adjustment means that when the dissolved oxygen deviation is large (e.g., the deviation exceeds 1 mg / L), the proportional coefficient is increased to more than 13 to speed up the response speed; when the deviation is small, the integral time constant is increased (the integral effect is weakened) to prevent overshoot caused by integral saturation; when the rate of change of deviation is large (e.g., the dissolved oxygen concentration drops rapidly), the derivative time constant is increased (the derivative effect is enhanced) to suppress oscillation.

[0070] Operating Condition 3: High Oxygen State (D(t)≥5mg / L) When the dissolved oxygen concentration reaches or exceeds the upper limit threshold, it indicates that the dissolved oxygen in the water body is fully saturated, and continued aeration will result in energy waste. Controller 13 sends a power reduction command (reducing to below 30% of the rated power) or a stop command to the micro-nano aerator 4 to achieve energy-saving operation.

[0071] Furthermore, when the micro / nano aeration generator 4 is in a stopped state and the dissolved oxygen concentration value D(t) is lower than the lower limit threshold D L When the delay exceeds a preset delay time τ (e.g., 60 seconds), the controller 13 sends a start command to the micro / nano aeration generator 4. This delayed start mechanism avoids frequent start-stop cycles caused by instantaneous fluctuations in the sensor.

[0072] Tuning and Fitting of PID Control Parameters This embodiment details the initial parameter K of the PID controller. p0 T i0 T d0 And the method for tuning and fitting the adaptive adjustment coefficients α, β, and γ.

[0073] 1. Establishment of the transfer function model for dissolved oxygen controlled object To determine the initial parameters of the PID controller, a transfer function model of the micro-nano aeration system was first established through a step response experiment. The experiment was conducted under standard operating conditions: water temperature 25℃, pH 7.0, and initial dissolved oxygen concentration 1.0 mg / L.

[0074] The experimental procedure is as follows: The controller sends a step command to the micro / nano aerator, increasing the aeration rate from 0 to 60% of the rated flow rate. The response curve of dissolved oxygen concentration over time is recorded. After normalizing the response data, the step response curve is fitted using the least squares method. According to the classic modeling method for dissolved oxygen control in wastewater treatment aeration systems, the dynamic response of dissolved oxygen concentration to aeration rate can be described by a first-order inertial plus pure time-delay model.

[0075] Where K is the process gain (mg / L·m 3 / min), T is the time constant (s), and τ is the pure time delay (s). Nonlinear least squares fitting (using the Levenberg-Marquardt algorithm) was performed on the experimental data, and the model parameters obtained are:

[0076] Goodness of fit R 2=0.966, indicating that the model interprets the experimental data to a high degree. Model validation used independent step response experimental data (aeration rate stepping from 60% to 90%), and the mean absolute error between the predicted and measured values ​​was 0.15 mg / L, with a maximum error of 0.28 mg / L.

[0077] 2. Tuning the initial parameters of the PID controller Based on the above transfer function model, the initial parameters of the PID are calculated using the Ziegler-Nichols tuning rule:

[0078] Since the Ziegler-Nichols tuning rule usually results in large overshoot, the Cohen-Coon tuning method is used for correction:

[0079] Based on the calculation results of the two tuning methods described above, and after closed-loop response verification using the MATLAB / Simulink simulation platform, the final determined initial parameters are: K p0 =10、T i0 =60s, T d0 =12s. Closed-loop simulation results show that, under the above parameters, the system's response rise time to a step change in dissolved oxygen setpoint is approximately 120s, the overshoot is approximately 8%, the settling time is approximately 280s, and the steady-state error is less than ±0.1mg / L.

[0080] 3. Fitting of adaptive adjustment coefficients The adaptive adjustment coefficients α, β, and γ were determined through multi-condition simulation optimization. Five typical operating conditions were set:

[0081] The comprehensive performance index J = w1·MSE + w2·OS + w3·T s (MSE is mean square error, OS is overshoot, T) s To adjust the time (w1=0.4, w2=0.3, w3=0.3), and with the objective of minimizing this time, a particle swarm optimization algorithm (population size 50, iterations 200) was used to optimize α, β, and γ. The optimized coefficients are: α=3.2, β=12.5, γ=0.28 The verification results show that, under the above coefficients, the comprehensive performance index J of each working condition is lower than that of the fixed parameter PID controller (reduction of 18%-25%).

[0082] Example 3: Feedforward Compensation Control Method This embodiment details the feedforward compensation control method of the present invention.

[0083] pH and temperature are important factors affecting oxygen solubility in water and the oxygen consumption rate of microorganisms. When the pH deviates from neutral conditions or the temperature deviates from the optimal range, microbial activity decreases and the oxygen consumption rate decreases. In this case, the target dissolved oxygen value can be appropriately reduced to save energy. Conversely, when the pH and temperature are at the optimal conditions, microbial activity is high and the oxygen consumption rate is large, so a higher target dissolved oxygen value needs to be maintained.

[0084] In this embodiment, the controller 13 acquires the pH value P(t) collected by the pH sensor 8 and the temperature value T collected by the temperature sensor 9. emp (t), calculate the dissolved oxygen target correction value ΔD according to the following formula. set (t): ΔD set (t) = 0.5·(P(t) - 7.0) + 0.1·(T) emp (t)-25) Where pH influence coefficient λ P =0.5 mg / L·pH -1 Temperature influence coefficient λ T =0.1 mg / L·℃ -1 λ, optimal pH value P opt =7.0, optimal temperature value T opt =25℃.

[0085] The revised dissolved oxygen target value =D set +ΔD set (t) replaces the preset dissolved oxygen target value D set =4mg / L. Calculate the dissolved oxygen deviation value e(t).

[0086] For example, when the measured pH value is 6.5 and the temperature is 30℃: ΔD set (t)=0.5×(6.5-7.0)+0.1×(30-25)=-0.25+0.5=0.25mg / L The revised target value for dissolved oxygen is =4.0 + 0.25 = 4.25 mg / L. Because the higher temperature leads to a decrease in oxygen solubility and changes in the rate of oxygen consumption by microorganisms, the target dissolved oxygen value should be appropriately increased to compensate for the temperature effect.

[0087] For example, when the measured pH value is 8.0 and the temperature is 15℃: ΔD set (t)=0.5×(8.0-7.0)+0.1×(15-25)=0.5-1.0=-0.5mg / L The revised target value for dissolved oxygen is =4.0-0.5=3.5mg / L. Due to the alkaline pH and low temperature, microbial activity is reduced, and the oxygen consumption rate decreases. Therefore, the target dissolved oxygen value should be appropriately lowered to save energy.

[0088] This feedforward compensation, together with the PID feedback control in Example 2, constitutes a feedforward-feedback composite control, which can adjust the control quantity in advance when pH or temperature changes, thereby improving the response speed of the control system.

[0089] Fitting process of feedforward compensation coefficients This embodiment details the feedforward compensation coefficient λ. P (pH influence coefficient) and λ T Fitting method for (temperature influence coefficient).

[0090] 1. pH influence coefficient λ P Fitting Design a pH effect experiment: Under constant temperature conditions (25℃), nine groups of water samples with pH values ​​of 4.0, 5.0, 6.0, 6.5, 7.0, 7.5, 8.0, 9.0, and 10.0 were prepared. For each group, the initial dissolved oxygen concentration was stabilized at 1.0 mg / L by nitrogen stripping under sealed conditions. Then, a micro-nano aeration system was started and operated at a constant aeration rate. The equilibrium time required for the dissolved oxygen concentration to reach 4.0 mg / L and the steady-state dissolved oxygen concentration were recorded. Each experiment was repeated three times, and the average value was taken.

[0091] With pH 7.0 (optimal pH value P) opt Using the steady-state dissolved oxygen concentration under certain conditions as a baseline, calculate the dissolved oxygen deviation ΔD at each pH value. pH A univariate linear regression model ΔD was adopted. pH =λ P ·(PP opt Fit the data using )+b:

[0092] Regression analysis results: λ P =0.53 mg / L·pH -1 Intercept b = 0.02 mg / L (close to zero), coefficient of determination R 2 =0.98, indicating a strong linear correlation between pH and dissolved oxygen deviation. Based on literature data and multiple repeated experiments, λ P The reasonable range for pH is 0.3 to 0.8 mg / L. -1 .

[0093] 2. Temperature influence coefficient λ T Fitting Temperature effect experiment: Under constant pH (7.0), experiments were conducted at eight temperature points: 5℃, 10℃, 15℃, 20℃, 25℃, 30℃, 35℃, and 40℃. At each temperature point, the water sample was kept at the target temperature, and the initial dissolved oxygen concentration was stabilized at 1.0 mg / L by nitrogen stripping. Then, the micro-nano aeration system was started and operated at a constant aeration rate. The equilibrium time required for the dissolved oxygen concentration to reach 4.0 mg / L and the steady-state dissolved oxygen concentration were recorded. Each experiment was repeated three times, and the average value was taken.

[0094] At 25℃ (optimal temperature value T) opt Using the steady-state dissolved oxygen concentration under the given conditions as a baseline, calculate the dissolved oxygen deviation ΔD at each temperature. temp A univariate linear regression model ΔD was adopted. temp =λ T ·(T emp -T opt Fit the data using )+b:

[0095] Regression analysis results: λ T =0.10 mg / L·℃ -1 Intercept b = 0.01 mg / L, coefficient of determination R 2 =0.97. The physical meaning of this fitted value is: for every 1℃ deviation of the temperature from the optimum temperature, the target dissolved oxygen value needs to be adjusted by approximately 0.10 mg / L. Based on literature data and multiple repeated experiments, λ T The reasonable range for this value is 0.05 to 0.15 mg / L·℃. -1 .

[0096] Example 4: Lifting Control Method This embodiment details the lifting control method of the present invention.

[0097] When the micro-nano aeration head 6 needs cleaning and maintenance (e.g., according to a preset maintenance cycle or based on aeration efficiency monitoring data), the controller 13 sends a forward rotation command to the drive motor 10. The drive motor 10 drives the lead screw 11 to rotate via a coupling, and the sliding bracket 12 moves upward in the vertical direction through a threaded engagement with the lead screw 11, causing the aeration pipe 5 and the micro-nano aeration head 6 fixed on the sliding bracket 12 to rise.

[0098] During the ascent, controller 13 controls the ascent speed v of sliding support 12 according to a three-segment S-shaped speed curve. up (t). In this embodiment, the preset maximum ascent speed v max =0.03m / s, acceleration time constant t rise =2s, deceleration time constant t decel=2s. Assuming the total upward stroke is H=0.6m, the parameters for each stage are calculated as follows: Acceleration phase (0≤t) <t1):

[0099] When t = t1 = 2s, v up (2) = 0.03·(1-e -1 The velocity is approximately 0.019 m / s, and the displacement during the acceleration phase is approximately 0.019 m.

[0100] Uniform speed phase (t1≤t) <t2): v up (t) = 0.03 m / s The duration of the uniform velocity phase is approximately (0.6-0.038) / 0.03≈18.7s.

[0101] Deceleration phase (t2≤t) <t3):

[0102] The entire ascent takes approximately 23 seconds.

[0103] The aforementioned speed curve is characterized by: an exponential increase from zero during the acceleration phase, avoiding the impact load caused by traditional direct full-speed starts; and an exponential decrease to zero during the deceleration phase, avoiding the inertial impact caused by traditional sudden stops. Through this S-shaped speed curve control, the force on the lead screw and sliding support is stable, the current surge of the drive motor 10 is small, and the quick-release connector 23 between the aeration pipe 5 and the sliding support 12 will not bear excessive alternating loads due to sudden acceleration and deceleration, effectively extending the service life of each mechanical component.

[0104] When the sliding bracket 12 rises to trigger the limit switch 22 installed on the upper end of the lead screw 11, the controller 13 receives the limit signal and immediately sends a stop command to the drive motor 10 to prevent the sliding bracket 12 from exceeding the travel range and causing equipment damage.

[0105] After the aeration components rise above the water surface, the operator can remove the aeration pipe 5 for cleaning using the quick-release connector 23. The quick-release connector 23 can be a snap-on or rotary quick-connect fitting, allowing for assembly and disassembly without tools.

[0106] After cleaning, the operator reinstalls the aeration pipe 5 back into the sliding bracket 12 via the quick-release connector 23. The controller 13 sends a reverse command to the drive motor 10, which lowers the aeration component to the set depth according to the same S-shaped speed curve (downward direction).

[0107] Throughout the lifting and lowering process, the limit switches 22 at the upper and lower ends of the lead screw 11 provide dual stroke protection: the upper limit switch prevents overtravel during lifting, and the lower limit switch prevents the aeration head from hitting the bottom of the water during lowering.

[0108] Determination of parameters for acceleration / deceleration curve This embodiment details the acceleration / deceleration curve parameter v. max t rise t decel The basis for determination.

[0109] The drive motor is a waterproof stepper motor with a rated speed of 600 rpm, a lead screw of 4 mm, and a theoretical maximum linear speed of:

[0110] The parameter range is determined by taking into account the following factors: (1) Motor torque-speed characteristics: The stepper motor outputs the maximum torque in the low-speed range (<300rpm), corresponding to a linear speed of approximately 0.02m / s; the torque decreases linearly in the 300-600rpm range. Let v max =0.03m / s (corresponding to a motor speed of approximately 450rpm) can achieve a balance between speed and torque.

[0111] (2) Mechanical shock analysis: acceleration time constant t rise The impact load on startup is affected. Finite element analysis shows that when t... rise At <0.5s, the maximum stress at the screw-sliding support connection exceeds 60% of the material's yield strength; when t rise When the time is ≥1s, the maximum stress drops to below 35% of the yield strength. Therefore, t is determined. rise The value ranges from 1s to 3s.

[0112] (3) Inertia and braking analysis: deceleration time constant t decel Affects braking distance and impact. When t decel At <0.5s, the braking inertia force causes the shear stress on the quick-release connector to exceed 80% of the design value; when t decel When the time is ≥1s, the shear stress drops to below 45% of the design value. Therefore, t is determined. decel The value ranges from 1s to 3s.

[0113] Example 5: Method for Controlling the Dosage of Microbial Agents This embodiment details the method for controlling the addition of microbial agents according to the present invention.

[0114] The controller 13 comprehensively determines whether microbial agents need to be added based on water quality monitoring data. In this embodiment, the controller 13 acquires the dissolved oxygen concentration value D(t) collected by the dissolved oxygen sensor 7, the pH value P(t) collected by the pH sensor 8, and the temperature value T collected by the temperature sensor 9. emp The necessity score S(t) for microbial agent addition is calculated according to the following formula:

[0115] Where the dissolved oxygen weighting coefficient ω D =0.6, pH weighting coefficient ω P =0.2, temperature weighting coefficient ω T =0.2, optimal pH value P opt =7.0, optimal temperature value T opt =25℃, preset scoring threshold S th =0.5.

[0116] The meanings of each item in this scoring function are as follows: Dissolved oxygen When the dissolved oxygen concentration is below 2 mg / L, this item takes a value between 0 and 1, with the value increasing as the dissolved oxygen level decreases, reflecting the contribution of the water's oxygen deficiency to the necessity of bacterial agent addition. When the dissolved oxygen is 0 mg / L, this item takes a value of 1; when the dissolved oxygen reaches 2 mg / L or above, this item takes a value of 0.

[0117] pH item When the pH deviates from the optimum value of 7.0, this value reflects the contribution of the degree of pH deviation to the necessity of adding the microbial agent. When the pH is 7.0, this value is 0; when the pH is 4.0 or 10.0, this value is 0.43.

[0118] Temperature Item When the temperature deviates from the optimum value by 25℃, this value reflects the contribution of the degree of temperature deviation to the necessity of adding microbial agents. When the temperature is 25℃, this value is 0; when the temperature is 0℃ or 50℃, this value is 1.

[0119] For example, when the measured dissolved oxygen concentration D(t) = 1.0 mg / L, pH value P(t) = 6.5, and temperature value T emp When (t) = 30℃: Dissolved oxygen:

[0120] pH value:

[0121] Temperature item:

[0122] S(t)=0.6×0.5+0.2×0.071+0.2×0.2=0.3+0.014+0.04=0.354 Since S(t) = 0.354 th =0.5, which does not meet the dosing conditions, so controller 13 does not start metering pump 20.

[0123] For example, when the measured dissolved oxygen concentration D(t) = 0.5 mg / L, pH value P(t) = 6.0, and temperature value T... emp When (t) = 35℃: Dissolved oxygen:

[0124] pH value:

[0125] Temperature item:

[0126] S(t)=0.6×0.75+0.2×0.143+0.2×0.4=0.45+0.029+0.08=0.559 Since S(t) = 0.559 > S th =0.5, meeting the dosing conditions, the controller 13 sends a start command to the metering pump 20. After the metering pump 20 starts, it adds the liquid microbial agent in the storage tank 19 to the water body through the dosing pipe 21 at a preset flow rate (e.g., 2L / min). The dosing duration is determined according to the score value; the higher the score, the greater the dosing amount.

[0127] When the score S(t) falls below the threshold, the controller 13 sends a stop command to the metering pump 20 to end the dosing of the microbial agent.

[0128] The advantage of this microbial agent dosing decision algorithm lies in its ability to quantify three key water quality parameters—dissolved oxygen, pH, and temperature—into a single microbial agent dosing necessity score through a weighted summation. This provides the controller with a clear basis for automatic decision-making, avoiding the subjectivity and uncertainty of traditional methods that rely on human experience. When dissolved oxygen, pH, or temperature are severely insufficient, the score increases, triggering microbial agent dosing, thus achieving automation, precision, and intelligence in microbial agent dosing.

[0129] Determination of the weighting coefficient for microbial agent dosing scoring This embodiment details the three weighting coefficients ω in the necessity score S(t) for microbial agent addition. D ω P ω T The method for determining the score, and the preset scoring threshold S th The method for determining it. ​

[0130] 1. Methods for determining weighting coefficients The weighting coefficients were determined using the analytic hierarchy process (AHP). Five experts in the field of black and odorous water treatment were invited to conduct pairwise comparisons of the influence of dissolved oxygen, pH, and temperature on the necessity of bacterial agent application, and a judgment matrix was constructed.

[0131] Take an expert's judgment matrix as an example:

[0132] Calculate the largest eigenvalue λ of the judgment matrix. max =3.02, consistency ratio CR=0.02<0.1, the judgment matrix has satisfactory consistency. The normalized eigenvector is the weight vector: ω D =0.62、ω P =0.24、ω T =0.14.

[0133] The final weighting coefficient is obtained by taking the geometric mean of the judgments of the five experts. ω D =0.60,ω P =0.22,ω T =0.18 2. Verification of weighting coefficients To verify the rationality of the weighting coefficients, a verification experiment was designed. Twelve black and odorous water samples with different pollution levels (four each for mild, moderate, and severe pollution) were selected. Three experts independently evaluated the necessity of adding the microbial agent to each sample subjectively (0-1 points), and the average score was used as the subjective scoring benchmark. Simultaneously, the scoring function (ω) of this invention was employed. D =0.60、ω P =0.22、ω T =0.18) Calculate the objective score S(t) for each water sample.

[0134] The comparison results show that the Pearson correlation coefficient between objective and subjective ratings is r=0.91 (p<0.001), the mean absolute error is 0.07, and the maximum error is 0.14, indicating that the weighting coefficient is set reasonably and effectively.

[0135] 3. Preset scoring threshold S th The determination Preset scoring threshold S th ROC curve analysis was used to determine the optimal response. The gold standard was determined by expert subjective judgment (three experts unanimously agreeing that "addition is necessary" was used as the positive criterion). Different S values ​​were plotted. th The ROC curve is plotted under the given values, and the Youden index (Youden index = sensitivity + specificity - 1) is calculated. When S... thThe Youden index reaches its maximum value (0.82) when the value is 0.48. Considering the cost of misjudgment in practical applications, the threshold is set to S. th =0.50, at which point the sensitivity is 0.88 and the specificity is 0.94.

[0136] Example 6: Remote Monitoring and Data Transmission This embodiment illustrates the remote monitoring and data transmission method of the present invention.

[0137] The controller 13 transmits real-time collected water quality monitoring data and equipment operating status to a remote monitoring platform via the wireless communication subsystem 14. The transmitted data includes, but is not limited to: dissolved oxygen concentration D(t), pH value P(t), and temperature value T. emp (t), aeration control amount u(t), necessity score of microbial agent addition S(t), operating status of drive motor 10 (forward / reverse / stop), operating power of micro-nano aeration generator 4, trigger status of limit switch 22, etc.

[0138] The wireless communication subsystem 14 can employ a 4G communication module, a 5G communication module, or a LoRa communication module, selected based on the network coverage of the water area being treated. For areas with good 4G / 5G signal coverage, such as urban waterways, 4G / 5G communication modules are preferred to achieve high-speed data transmission; for remote areas or areas with weak signals, LoRa communication modules can be used to achieve low-power, long-distance transmission.

[0139] The remote monitoring platform can be deployed on a cloud server or a local monitoring center, and has functions such as data storage, real-time display, historical query, trend analysis, and alarm push. Managers can view water quality data and equipment operating status at each monitoring point in real time through a web page or mobile application. When water quality parameters exceed preset alarm thresholds, the platform automatically pushes alarm information to the manager's terminal.

[0140] This remote monitoring function allows managers to monitor the water quality and equipment operation of the treated water area without having to be physically present on-site, significantly reducing the cost of manual inspections and improving management efficiency.

[0141] Algorithm Parameter Summary Table

[0142] Industrial applicability The intelligent in-situ remediation control method and system for black and odorous water bodies provided by this invention can be widely applied to the in-situ remediation and treatment of black and odorous water bodies such as urban rivers, lakes, and landscape water bodies. The system is powered by solar photovoltaic panels, eliminating the need for an external power source, making it particularly suitable for use in remote rivers and lakes without electricity. Through intelligent aeration control, precise lifting speed control, and automated microbial agent dosing decisions, it can achieve efficient, energy-saving, and automated remediation of black and odorous water bodies, demonstrating good industrial practical value and promising prospects for widespread application.

Claims

1. A method for intelligent in-situ remediation and control of black and odorous water bodies, characterized in that, Includes the following steps: Obtain the dissolved oxygen concentration value D(t) of the water body in real time collected by the water quality monitoring module; The dissolved oxygen concentration value D(t) Compared with the preset lower threshold D L and upper limit threshold D H Comparison, where D L =2mg / L, D H =5mg / L; When D(t) ≤ D L At that time, the controller sends a start command to the micro-nano aeration generator; When D L <D(t)<D H At that time, the controller calculates the aeration control quantity u(t) according to the following formula: ， Where e(t) = D set -D(t), D set To preset the target dissolved oxygen value, K p T is the proportionality coefficient. i Let T be the integration time constant. d The time constant is a differential value; the controller adjusts the operating power of the micro / nano aeration generator according to the aeration control quantity u(t); When D(t)≥D H At that time, the controller sends a power reduction command or a stop command to the micro-nano aeration generator; Upon receiving a cleaning command, the controller controls the drive motor to raise the sliding bracket via a lead screw; during the raising process, the controller controls the raising speed v of the sliding bracket according to the following speed curve. up (t): ， ， ， Where v max To preset the maximum ascent speed, t rise Let t1 be the acceleration time constant, t2 be the end time of the acceleration phase, t3 be the end time of the constant velocity phase, and t4 be the time when the ascent is completed. decel The deceleration time constant is defined as follows: when the sliding bracket triggers the limit switch, the controller controls the drive motor to stop running.

2. The intelligent in-situ remediation and control method for black and odorous water bodies according to claim 1, characterized in that, The preset maximum upward speed v max The acceleration time constant t is between 0.01 m / s and 0.05 m / s. rise The deceleration time constant t is between 1 and 3 seconds. decel The duration is 1 to 3 seconds.

3. The intelligent in-situ remediation and control method for black and odorous water bodies according to claim 1, characterized in that, The water quality monitoring module also includes a pH sensor and a temperature sensor, and the method further includes a bacterial agent dosing step: The pH value P(t) collected by the pH sensor and the temperature value T collected by the temperature sensor are obtained. emp (t); The necessity score S(t) for microbial agent addition is calculated using the following formula: ， Where P opt For the optimal pH value, T opt For the optimal temperature value, ω D ω P ω T ω is the weighting coefficient. D +ω P +ω T =1; when the necessity score S(t) for adding the microbial agent is greater than the preset score threshold S th At that time, the controller sends a start command to the metering pump.

4. The intelligent in-situ remediation and control method for black and odorous water bodies according to claim 3, characterized in that, The optimal pH value P opt The optimal temperature value T is between 6.5 and 7.

5. opt The temperature is between 20°C and 30°C; the weighting coefficient ω D The value is between 0.5 and 0.7, ω P The value is between 0.15 and 0.25, ω T The value is between 0.15 and 0.25; the preset scoring threshold S th It ranges from 0.4 to 0.

6.

5. The intelligent in-situ remediation and control method for black and odorous water bodies according to claim 1, characterized in that, The preset dissolved oxygen target value D set The concentration is 4 mg / L; the proportionality coefficient K p Integration time constant T i and differential time constant T d Adaptive adjustment is performed according to the following formula: ， ， ， Where K p0 T is the initial scaling factor, and its value ranges from 5 to 20. i0 T is the initial integration time constant, ranging from 30 seconds to 120 seconds. d0 α is the initial differential time constant, ranging from 0.1 seconds to 2 seconds; α is the proportional coefficient adjustment factor, ranging from 1 to 5; β is the integral time adjustment factor, ranging from 5 to 30; γ is the differential time adjustment factor, ranging from 0.1 to 0.5; and ε is the zero-prevention constant.

6. The intelligent in-situ remediation and control method for black and odorous water bodies according to claim 1, characterized in that, It also includes a feedforward compensation step: The pH value P(t) collected by the pH sensor and the temperature value T collected by the temperature sensor are obtained. emp (t); Calculate the target correction value ΔD for dissolved oxygen using the following formula. set (t): ， Where λ P The pH influence coefficient ranges from 0.3 to 0.8 mg / L·pH. -1 , λ T The value is the temperature influence coefficient, ranging from 0.05 to 0.15 mg / L·℃. -1 ; The revised dissolved oxygen target value =D set +ΔD set (t) replaces the preset dissolved oxygen target value D set Calculate the dissolved oxygen deviation value e(t).

7. The intelligent in-situ remediation and control method for black and odorous water bodies according to claim 1, characterized in that, When the micro / nano aeration generator is in a stopped state and the dissolved oxygen concentration value D(t) is lower than the lower threshold value D L When the time exceeds the preset delay time τ, the controller sends a start command to the micro-nano aeration generator; the preset delay time τ is 30 seconds to 120 seconds.

8. The intelligent in-situ remediation and control method for black and odorous water bodies according to claim 1, characterized in that, The controller transmits the dissolved oxygen concentration D(t), pH value P(t), and temperature value T via a wireless communication module. emp The aeration control quantity u(t) and the necessity score S(t) for adding microbial agent are transmitted to the remote monitoring platform in real time.

9. A smart in-situ remediation control system for black and odorous water bodies, characterized in that, include: floating platform; The micro-nano aeration subsystem, deployed on the floating platform, includes a micro-nano aeration generator, an aeration pipe, and a micro-nano aeration head installed on the aeration pipe; The water quality monitoring subsystem, deployed at the bottom of the floating platform and submerged in water, includes a dissolved oxygen sensor, a pH sensor, and a temperature sensor; The lifting and adjusting subsystem includes a drive motor, a lead screw, and a sliding bracket, with the aeration pipe fixed to the sliding bracket; The microbial agent dosing subsystem includes a storage tank and a metering pump. The outlet of the metering pump is connected to a dosing pipe, and the end of the dosing pipe extends into the water body. The controller is electrically connected to the dissolved oxygen sensor, the pH sensor, the temperature sensor, the micro-nano aeration generator, the drive motor, and the metering pump, respectively, and the controller is configured to execute the intelligent in-situ remediation control method for black and odorous water bodies as described in any one of claims 1 to 8.

10. The intelligent in-situ remediation control system for black and odorous water bodies according to claim 9, characterized in that, Limit switches are respectively provided at the upper and lower ends of the lead screw, and the limit switches are electrically connected to the controller; a quick-release connector is provided between the aeration pipe and the sliding bracket; the aeration pipe is an annular tubular structure, and 4 to 8 micro-nano aeration heads are provided and evenly distributed at the bottom of the aeration pipe, and the bubble particle size generated by the micro-nano aeration heads is 0.1 to 10 micrometers.

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