Intelligent control method and system for submersible mixer based on multi-sensor cooperative monitoring

Abstract

The application relates to a method and system for intelligent control of a submersible mixer based on multi-sensor cooperative monitoring, and relates to the field of sewage treatment. The method comprises the following steps: multiple-parameter sensors are arranged at the bottom, middle and top of a biochemical tank; the dissolved oxygen concentration, sludge concentration and flow velocity parameters in the biochemical tank are collected through the multiple-parameter sensors; the dissolved oxygen concentration gradient value, sludge concentration gradient value and flow velocity value between adjacent collection points are calculated according to the dissolved oxygen concentration, sludge concentration and flow velocity parameters; the first running parameter of the submersible mixer in the target area is adjusted to the first preset parameter based on the combination relationship of the dissolved oxygen concentration gradient value, sludge concentration gradient value and flow velocity value; the running effect after the adjustment of the running parameter is calculated according to the real-time data collected by the multiple-parameter sensors; and the second running parameter of the submersible mixer in the adjacent area is adjusted to the second preset parameter when the adjustment effect does not reach the preset effect. The method can improve the sewage treatment effect of the submersible mixer.

Description

Technical Field

[0001] This application relates to the field of wastewater treatment, and in particular to an intelligent control method and system for submersible mixers based on multi-sensor collaborative monitoring. Background Technology

[0002] In wastewater treatment processes, the mixing process within the biological treatment tank plays a crucial role in maintaining the suspended state of activated sludge, ensuring sufficient contact between wastewater and activated sludge, and ensuring uniform distribution of dissolved oxygen. As a key piece of equipment in the biological treatment tank, the submersible mixer's performance directly impacts the efficiency and energy consumption of wastewater treatment.

[0003] Currently, the control of submersible mixers in biological treatment tanks mainly adopts a fixed-sequence control method, with the mixer's operating time and speed set based on experience. Some processes use single-point sensors to detect dissolved oxygen or sludge concentration, and adjust the mixer's operating parameters accordingly.

[0004] Because the internal environment of the biological treatment tank is complex and variable, the detection of a single parameter cannot fully reflect the actual operating status of the tank. At the same time, the fixed time sequence control method cannot respond to changes in operating conditions in a timely manner, which can easily cause problems such as insufficient or excessive stirring in local areas, affecting the treatment effect and increasing energy consumption. Summary of the Invention

[0005] This application provides an intelligent control method and system for submersible mixers based on multi-sensor collaborative monitoring, which can improve the sewage treatment effect of submersible mixers.

[0006] In a first aspect, this application provides an intelligent control method for submersible mixers based on multi-sensor collaborative monitoring, applied to an intelligent control system for submersible mixers. The method includes: setting multi-parameter sensors at the bottom, middle, and top of a biological treatment tank to collect dissolved oxygen concentration, sludge concentration, and flow velocity parameters within the tank; calculating the dissolved oxygen concentration gradient, sludge concentration gradient, and flow velocity values ​​between adjacent collection points based on the dissolved oxygen concentration, sludge concentration, and flow velocity parameters, wherein the adjacent collection points include a target area and adjacent areas; adjusting the first operating parameter of the submersible mixer within the target area to a first preset parameter based on the combined relationship of the dissolved oxygen concentration gradient, sludge concentration gradient, and flow velocity values; calculating the operating effect after adjusting the operating parameters based on real-time data collected by the multi-parameter sensors; and adjusting the second operating parameter of the submersible mixer within the adjacent area to a second preset parameter when the adjustment effect does not reach the preset effect.

[0007] In the above embodiments, multi-parameter sensors are installed at different locations in the biological treatment tank to collect dissolved oxygen concentration, sludge concentration, and flow velocity parameters in real time. The parameter gradient values ​​between adjacent collection points are calculated, and the operating parameters of the agitator in the target area are dynamically adjusted based on the parameter combination relationship. When the adjustment effect does not meet expectations, the parameters of the agitators in adjacent areas are adjusted in a coordinated manner. Because a complete parameter monitoring network and linkage control mechanism are established, the agitator can respond promptly to changes in operating conditions and accurately adjust the mixing intensity, avoiding the problem of insufficient or excessive mixing in local areas, thus improving treatment efficiency and reducing energy consumption.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the step of adjusting the first operating parameter of the submersible mixer in the target area to a first preset parameter based on the combined relationship of dissolved oxygen concentration gradient value, sludge concentration gradient value, and flow velocity value specifically includes: when the flow velocity value is greater than a first preset threshold and the dissolved oxygen concentration gradient value is less than a second preset threshold, reducing the rotation speed of the submersible mixer in the target area to a first preset rotation speed; when the flow velocity value is less than the first preset threshold and the dissolved oxygen concentration gradient value is greater than the second preset threshold, increasing the rotation speed of the submersible mixer in the target area to a second preset rotation speed; when the sludge concentration gradient value is greater than a third preset threshold and the flow velocity value is greater than the first preset threshold, adjusting the rotation speed of the submersible mixer in the target area to a preset percentage of the rated rotation speed, and performing start-stop control according to a preset timing sequence.

[0009] In the above embodiments, corresponding agitator control strategies are adopted based on different combinations of flow rate, dissolved oxygen concentration gradient, and sludge concentration gradient. When the flow rate is too high and the dissolved oxygen gradient is low, the rotation speed is reduced; when the flow rate is too low and the dissolved oxygen gradient is high, the rotation speed is increased; and when the sludge concentration gradient is high and the flow rate is high, a preset percentage rotation speed and timed start-stop control are used. This achieves precise adjustment of the mixing intensity, ensuring thorough mixing while avoiding damage to the sludge flocs from excessive mixing, thus keeping the treatment system operating at its optimal state.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of calculating the operating effect after adjusting the operating parameters based on real-time data collected by multi-parameter sensors, and adjusting the second operating parameters of the submersible mixers in adjacent areas to the second preset parameters when the adjustment effect does not reach the preset effect, specifically includes: calculating the uniformity of dissolved oxygen distribution in the target area based on real-time data collected by multi-parameter sensors; determining that the adjustment effect has not reached the preset effect when the uniformity of dissolved oxygen distribution is less than a fourth preset threshold; starting the submersible mixers in adjacent areas and adjusting their speed to the second preset parameter in response to the determination that the adjustment effect has not reached the preset effect; monitoring the uniformity of dissolved oxygen distribution in the target area and adjacent areas, and increasing the speed of the submersible mixers in the target area and adjacent areas to the rated value and maintaining it for a preset duration when a sudden fluctuation is detected.

[0011] In the above embodiments, the uniformity of dissolved oxygen distribution in the target area is calculated based on real-time monitoring data. When the uniformity is lower than a threshold, the stirrers in adjacent areas are activated for coordinated adjustment, and the dissolved oxygen distribution in both areas is continuously monitored. By establishing a multi-level linkage control mechanism, combined with normal adjustment and emergency adjustment strategies for sudden operating conditions, the stirring system can quickly respond to changes in operating conditions, always maintain a uniform distribution of dissolved oxygen, and ensure the stable progress of the biochemical reaction.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of setting multi-parameter sensors at the bottom, middle and top of the biological treatment tank and collecting dissolved oxygen concentration, sludge concentration and flow velocity parameters in the biological treatment tank through the multi-parameter sensors, the method further includes: constructing a dynamic flow field model based on the dissolved oxygen concentration, sludge concentration and flow velocity parameters; identifying low flow velocity regions in the biological treatment tank according to the dynamic flow field model; determining the linked submersible mixers in adjacent regions that need to be linked according to the location information of the low flow velocity regions; and issuing control commands to the linked submersible mixers.

[0013] In the above embodiments, a dynamic flow field model is constructed based on monitoring parameters to identify low-velocity regions within the biochemical tank in real time. Based on the location information of these low-velocity regions, the system determines which agitators need to be activated and issues control commands. Because a dynamic flow field analysis mechanism based on real-time data has been established, the system can accurately locate stirring dead zones and precisely eliminate these dead zones through the coordinated operation of multiple agitators, forming a complete flow field optimization and control system that effectively improves the mixing uniformity and treatment efficiency of the biochemical tank.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of installing multi-parameter sensors at the bottom, middle, and top of the biological treatment tank and collecting dissolved oxygen concentration, sludge concentration, and flow velocity parameters within the biological treatment tank through the multi-parameter sensors, the method further includes: generating a dissolved oxygen isosurface cloud map based on the dissolved oxygen data collected by the multi-parameter sensors; identifying anoxic and hyperoxic regions within the biological treatment tank based on the dissolved oxygen isosurface cloud map; setting differentiated PID parameters for the anoxic and hyperoxic regions using a fuzzy sliding mode control method, wherein the differentiated PID parameters include different dissolved oxygen concentration control ranges set for the surface, middle, and bottom of the biological treatment tank; and adjusting the dissolved oxygen concentration of each anoxic and hyperoxic region according to the differentiated PID parameters.

[0015] In the above embodiments, isosurface cloud maps are generated based on dissolved oxygen monitoring data to identify anoxic and hyperoxic areas within the tank. Fuzzy sliding mode control is used to set differentiated PID parameters for different areas, implementing different dissolved oxygen control strategies for the tank surface, middle, and bottom. By establishing a layered and zoned precise control mechanism, a dissolved oxygen gradient distribution favorable to biochemical reactions is formed in the biological treatment tank. This promotes nitrification while avoiding interference with the denitrification process, thus improving the treatment efficiency of the biological system.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after calculating the operating effect after adjusting the operating parameters based on real-time data collected by multi-parameter sensors, and adjusting the second operating parameters of the submersible mixer in the adjacent area to the second preset parameters when the adjustment effect does not reach the preset effect, the method further includes: establishing a dissolved oxygen gradient curve and a sludge concentration gradient curve based on real-time data collected by multi-parameter sensors; establishing an over-stirring early warning model based on the dissolved oxygen gradient curve and the sludge concentration gradient curve; and reducing the operating parameters of the submersible mixer when the over-stirring early warning model triggers an early warning.

[0017] In the above embodiments, dissolved oxygen and sludge concentration gradient curves are established based on real-time data, and an over-stirring early warning model is constructed. When the model triggers an early warning, the operating parameters of the agitator are promptly reduced. Because an early warning mechanism based on multi-parameter correlation analysis is established, the system can detect and prevent over-stirring in advance, effectively avoiding problems such as sludge floc breakage and unstable sludge index, thus ensuring the good operating condition and settling performance of the activated sludge system.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, after calculating the operating effect after adjusting the operating parameters based on real-time data collected by multi-parameter sensors, and when the adjustment effect does not reach the preset effect, adjusting the second operating parameters of the submersible mixer in the adjacent area to the second preset parameters, the method further includes: predicting the future sludge growth trend based on historical flow rate data and historical sludge concentration data; and adjusting the stirring intensity of the submersible mixer based on the sludge growth trend.

[0019] In the above embodiments, the future sludge growth trend is predicted based on historical flow rate data and sludge concentration data, and the stirring intensity of the agitator is adjusted in advance. Because a trend prediction mechanism based on historical data analysis is established, when it is predicted that the sludge concentration is about to exceed the threshold, the system will reduce the stirring intensity in advance and operate in a low-speed homogenization mode. Through speed adjustment and alternating group operation, both the uniformity of mixing and the damage to the sludge structure caused by strong stirring are ensured, maintaining the good settling performance and treatment effect of the sludge.

[0020] In a second aspect, embodiments of this application provide an intelligent control system for a submersible mixer, the intelligent control system for a submersible mixer comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors calling the computer instructions to cause the intelligent control system for a submersible mixer to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a submersible mixer intelligent control system, cause the submersible mixer intelligent control system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a submersible mixer intelligent control system, cause the submersible mixer intelligent control system to perform the method described in the first aspect and any possible implementation thereof.

[0023] Understandably, the intelligent control system for the submersible mixer provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. This application uses multi-parameter sensors installed at different locations in the biological treatment tank to collect dissolved oxygen concentration, sludge concentration, and flow velocity parameters in real time. It calculates the parameter gradient values ​​between adjacent collection points and dynamically adjusts the operating parameters of the agitator in the target area based on parameter combinations. When the adjustment effect does not meet expectations, the parameters of the agitators in adjacent areas are adjusted in conjunction. Because a complete parameter monitoring network and linkage control mechanism are established, the agitator can respond promptly to changes in operating conditions and accurately adjust the mixing intensity, avoiding problems of insufficient or excessive mixing in local areas, thus improving treatment efficiency and reducing energy consumption.

[0026] 2. This application employs corresponding agitator control strategies based on different combinations of flow rate, dissolved oxygen concentration gradient, and sludge concentration gradient. When the flow rate is too high and the dissolved oxygen gradient is low, the rotation speed is reduced; when the flow rate is too low and the dissolved oxygen gradient is high, the rotation speed is increased; and when the sludge concentration gradient is high and the flow rate is high, a preset percentage rotation speed and timed start-stop control are used. This achieves precise adjustment of the mixing intensity, ensuring thorough mixing while avoiding damage to the sludge flocs from excessive mixing, thus keeping the treatment system operating at its optimal state.

[0027] 3. This application calculates the uniformity of dissolved oxygen distribution in the target area based on real-time monitoring data. When the uniformity falls below a threshold, the stirrers in adjacent areas are activated for coordinated adjustment, and the dissolved oxygen distribution in both areas is continuously monitored. By establishing a multi-level linkage control mechanism, combined with normal adjustment and emergency adjustment strategies for sudden operating conditions, the stirring system can quickly respond to changes in operating conditions, always maintaining a uniform distribution of dissolved oxygen and ensuring the stable progress of the biochemical reaction. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating an intelligent control method for a submersible mixer based on multi-sensor collaborative monitoring in an embodiment of this application.

[0029] Figure 2 This is another flowchart illustrating the intelligent control method for submersible mixers based on multi-sensor collaborative monitoring in this application embodiment;

[0030] Figure 3 This is a schematic diagram of the equipment installation of the intelligent control method for submersible mixers based on multi-sensor collaborative monitoring in the embodiments of this application;

[0031] Figure 4 This is a flowchart of the control system of the intelligent control method for submersible mixers based on multi-sensor collaborative monitoring in the embodiments of this application;

[0032] Figure 5 This is an installation diagram of the intelligent control method for a submersible mixer based on multi-sensor collaborative monitoring in an embodiment of this application;

[0033] Figure 6 This is a schematic diagram of the physical device structure of an intelligent control system for a submersible mixer in this application embodiment. Detailed Implementation

[0034] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0035] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0036] To facilitate understanding, the application scenarios of the embodiments of this application are described below.

[0037] In the biological treatment tank of a wastewater treatment plant, the influent water quality and volume fluctuate greatly, with COD concentrations ranging from 200 to 800 mg / L. Furthermore, the diurnal temperature range causes significant differences in sludge activity. Traditional stationary submersible mixers, operating at a constant speed, cannot adapt to these dynamic changes. Especially during low-load periods at night, excessive mixing leads to energy waste; while during high-load periods, insufficient mixing causes sludge accumulation at the bottom. Simultaneously, the uneven distribution of dissolved oxygen within the biological treatment tank, reaching 3.5 mg / L at the top and only 0.8 mg / L at the bottom, severely impacts the efficiency of the biological reaction. Moreover, the independent operation of multiple mixers frequently results in localized dead zones, leading to sludge floc breakage and unstable sludge index.

[0038] A wastewater treatment plant employed a stirrer control system with online single-point dissolved oxygen monitoring. The system adjusts the stirrer speed by placing a dissolved oxygen sensor in the tank. When the dissolved oxygen level at the monitoring point is below 1.5 mg / L, the system automatically increases the stirrer speed; when it is above 2.5 mg / L, it decreases the speed. However, relying solely on single-point monitoring data fails to capture the overall dissolved oxygen distribution within the tank, resulting in delayed and inaccurate adjustments. Furthermore, the system cannot identify dead zones in the flow field and lacks real-time monitoring of sludge concentration, frequently leading to sludge accumulation in corner areas or breakage of sludge flocs within the tank. In addition, the lack of a coordinated linkage mechanism between the stirrers sometimes results in interference between adjacent stirrers, creating eddies that actually reduce stirring efficiency.

[0039] After applying the intelligent control system of this invention, a wastewater treatment plant installed a multi-layered sensor array in the biological treatment tank, achieving comprehensive parameter monitoring. The system constructs a real-time dynamic flow field model by analyzing the three-dimensional gradient distribution of dissolved oxygen, sludge concentration, and flow velocity. When a flow velocity below 0.15 m / s and a sludge concentration gradient greater than 500 mg / L / m are detected in a certain area at the bottom of the tank, the system automatically triggers a linkage control strategy for the agitators near that area, eliminating dead zones by adjusting the speed and direction of multiple agitators. Simultaneously, based on dissolved oxygen isosurface cloud maps, the system employs differentiated control strategies for different water depths: dissolved oxygen at the tank surface is controlled at 2.8-3.0 mg / L to promote nitrification, maintained in the middle of the tank within a stable range of 2.0 ± 0.3 mg / L, and at the bottom of the tank, ensured to be no less than 1.5 mg / L to prevent denitrification.

[0040] To facilitate understanding, the method provided in this implementation will be described in detail below, using the above scenario as an example. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating an intelligent control method for a submersible mixer based on multi-sensor collaborative monitoring in an embodiment of this application.

[0041] S101. Multi-parameter sensors are installed at the bottom, middle and top of the biological treatment tank to collect dissolved oxygen concentration, sludge concentration and flow rate parameters in the biological treatment tank.

[0042] Among them, the biological treatment tank refers to the treatment unit used for biochemical reactions, including rectangular tanks, circular tanks, or racetrack-shaped tanks; the multi-parameter sensor refers to the detection device that can simultaneously measure multiple water quality parameters, including dissolved oxygen meter, sludge concentration meter, flow rate meter, etc.; dissolved oxygen concentration represents the content of dissolved oxygen per unit volume of water, with the unit being mg / L; sludge concentration refers to the content of activated sludge per unit volume of water, expressed as MLSS; and flow rate parameter represents the velocity characteristics of water movement.

[0043] This step is performed when the biological treatment tank is put into operation and real-time monitoring of water quality parameters is required. Specifically, three or more multi-parameter sensors are placed on the bottom, middle, and surface of the tank along the same vertical line. Alternatively, the sensor array can be arranged in the middle or around the tank's horizontal plane, in areas with dead zones for stirring or where sludge easily accumulates. These sensors upload monitoring data in real time to the intelligent control platform via an IoT module to establish a dynamic flow field model for the biological treatment tank.

[0044] In some embodiments, multi-point monitoring of biochemical tank parameters can be achieved in various ways: Optionally, multi-parameter sensor modules can be installed in preset locations to construct a complete monitoring network with the intelligent control box and intelligent control platform; a data acquisition channel can be established; and various parameter data can be collected and analyzed in real time. Optionally, a suitable sensor deployment scheme can be selected according to the characteristics of different tank types; sensor arrays can be deployed in key locations prone to dead zones and sedimentation; and a corresponding control system can be configured for data acquisition and transmission. It is understood that other reasonable monitoring schemes can also be used to achieve three-dimensional monitoring of tank parameters, which are not limited here.

[0045] S102. Based on the dissolved oxygen concentration, sludge concentration, and flow velocity parameters, calculate the dissolved oxygen concentration gradient, sludge concentration gradient, and flow velocity values ​​between adjacent sampling points. These adjacent sampling points include the target area and the adjacent area.

[0046] Among them, the dissolved oxygen concentration gradient value represents the difference in dissolved oxygen concentration between adjacent sensors, with a key judgment threshold of about 0.5 mg / L; the sludge concentration gradient value refers to the difference in sludge concentration between adjacent areas; the flow velocity value represents the water flow velocity at a specific monitoring point, with a key control threshold of 0.3 m / s; the target area refers to the main area that needs parameter adjustment; and the adjacent area refers to the affected area adjacent to the target area.

[0047] This step is performed after parameter acquisition is complete. Specifically, the system receives real-time data uploaded by multi-parameter sensors via the IoT module and calculates the parameter differences between adjacent monitoring points. By analyzing the three-dimensional gradient distribution of these parameters, the system can dynamically output a real-time flow vector map to guide subsequent adjustments to the control strategy.

[0048] In some embodiments, parameter gradient calculation and analysis can be achieved in various ways: Optionally, real-time data collected by a multi-parameter sensor array, including agitator speed / power, dissolved oxygen, sludge concentration, flow velocity, etc., can be analyzed to determine the three-dimensional gradient distribution of these parameters; a dynamic flow field model can be established; and a real-time flow vector diagram can be generated. Optionally, real-time data from a dissolved oxygen meter array can be used to generate a dissolved oxygen isosurface cloud map; the parameter gradient between adjacent regions can be calculated; and the target region requiring regulation can be identified. It is understood that other reasonable methods can also be used to calculate and analyze parameter gradients, which are not limited here.

[0049] S103. Based on the combined relationship between dissolved oxygen concentration gradient, sludge concentration gradient and flow rate, adjust the first operating parameter of the submersible mixer in the target area to the first preset parameter.

[0050] Among them, the dissolved oxygen concentration gradient value represents the rate of change of dissolved oxygen content between adjacent measuring points, which is used to reflect the uniformity of oxygen distribution; the sludge concentration gradient value refers to the amount of change of sludge content per unit distance, which is used to characterize the sludge distribution state; the flow velocity value represents the magnitude of water flow speed, which is used to measure the stirring intensity; the combination relationship refers to the mutual influence and constraint relationship between these three parameters; the first operating parameter represents the working parameters of the submersible mixer, including speed, start-stop sequence, etc.; the first preset parameter refers to the optimal operating parameter value determined according to process requirements.

[0051] This step is executed after obtaining the parameter gradient data and is used to optimize the agitator's operating status. Specifically, when the flow rate is greater than 0.3 m / s and the dissolved oxygen concentration gradient is less than 0.5 mg / L, it indicates that the agitation intensity is too high, and the agitator speed in the target area needs to be reduced to the first preset speed; when the flow rate is less than 0.3 m / s and the dissolved oxygen concentration gradient is greater than 0.5 mg / L, it indicates that the agitation intensity is insufficient, and the speed needs to be increased to the second preset speed; when the sludge concentration gradient is greater than the set threshold and the flow rate is greater than 0.3 m / s, the agitator speed is adjusted to a preset percentage of the rated speed, and timed start-stop control is adopted.

[0052] In some embodiments, the dynamic adjustment of agitator parameters can be achieved in several ways: Optionally, a multi-parameter comprehensive evaluation model can be established, and the weight coefficients of each parameter can be set; a comprehensive score value can be calculated to determine the current mixing effect; and the agitator operating parameters can be automatically adjusted based on the score result. Optionally, a fuzzy control rule base can be constructed to map parameter gradient values ​​to mixing intensity adjustment amounts; fuzzy inference can be performed to obtain control commands; and the agitator operating state can be adjusted in real time. It is understood that other intelligent algorithms can also be used to optimize and adjust agitator parameters, which are not limited here.

[0053] In some embodiments, this step may further include the following steps:

[0054] When the flow rate is greater than the first preset threshold and the dissolved oxygen concentration gradient is less than the second preset threshold, reduce the rotation speed of the submersible mixer in the target area to the first preset rotation speed.

[0055] When the flow rate is less than the first preset threshold and the dissolved oxygen concentration gradient is greater than the second preset threshold, the rotation speed of the submersible mixer in the target area is increased to the second preset rotation speed.

[0056] When the sludge concentration gradient value is greater than the third preset threshold and the flow rate value is greater than the first preset threshold, the speed of the submersible mixer in the target area is adjusted to a preset percentage of the rated speed, and the start-stop control is performed according to the preset timing sequence.

[0057] S104. Calculate the operating effect after adjusting the operating parameters based on the real-time data collected by the multi-parameter sensor. If the adjustment effect does not reach the preset effect, adjust the second operating parameter of the submersible mixer in the adjacent area to the second preset parameter.

[0058] Among them, the operating effect represents the actual improvement after the agitator parameters are adjusted; the preset effect refers to the ideal operating state required by the process; the second operating parameter represents the working parameters of the agitators in the adjacent area; the second preset parameter refers to the optimal operating parameter value of the agitators in the adjacent area; and the adjustment effect represents the degree of improvement after the parameter adjustment.

[0059] This step is executed after the stirrer parameters for the target area are adjusted. Specifically, the system first calculates the uniformity of dissolved oxygen distribution in the target area based on sensor data. When the uniformity is less than a preset threshold, it is determined that the adjustment effect has not met the requirements. Then, the stirrer in the adjacent area is started and its speed is adjusted to the second preset parameter. At the same time, the dissolved oxygen distribution in the target area and the adjacent area is continuously monitored. When a sudden fluctuation is detected, the speed of the stirrer in both areas is increased to the rated value and maintained for a preset duration.

[0060] In some embodiments, the evaluation and optimization of operational performance can be achieved in various ways: Optionally, a dynamic flow field model can be constructed to analyze fluid motion characteristics; the location of low-velocity regions can be identified; adjacent agitators requiring linkage can be determined; and corresponding control commands can be issued for coordinated adjustment. Optionally, dissolved oxygen isosurface cloud maps can be generated to identify anoxic and hyperoxic regions; fuzzy sliding mode control can be used to set differentiated PID parameters; and precise adjustments can be made according to the dissolved oxygen control requirements of different water depth regions. It is understood that other control strategies can also be used to optimize the agitation effect, which are not limited here.

[0061] In some embodiments, this step may further include the following steps:

[0062] The uniformity of dissolved oxygen distribution in the target area is calculated based on real-time data collected by multi-parameter sensors.

[0063] When the uniformity of dissolved oxygen distribution is less than the fourth preset threshold, it is determined that the adjustment effect has not achieved the preset effect.

[0064] In response to the determination that the adjustment effect does not achieve the preset effect, the submersible mixer in the adjacent area is started and its speed is adjusted to the second preset parameter;

[0065] Monitor the uniformity of dissolved oxygen distribution in the target area and adjacent areas. When a sudden fluctuation is detected, increase the rotation speed of the submersible mixers in the target area and adjacent areas to the rated value and maintain it for a preset time.

[0066] The following provides a more detailed description of the process of the method provided in this implementation. Please refer to [link / reference]. Figure 2 This is another flowchart illustrating the intelligent control method for submersible mixers based on multi-sensor collaborative monitoring in this application.

[0067] S201. Construct a dynamic flow field model based on dissolved oxygen concentration, sludge concentration, and flow velocity parameters.

[0068] Dissolved oxygen concentration indicates the level of dissolved oxygen in water, measured in mg / L; sludge concentration refers to the sludge content per unit volume of water, measured in mg / L; flow velocity parameters include information on the magnitude and direction of water flow velocity, measured in m / s; the dynamic flow field model is a mathematical model reflecting the fluid motion state within the biological treatment tank, including elements such as spatial coordinates, velocity field, and concentration field.

[0069] Based on real-time data collected by multi-parameter sensors, a flow field distribution function in a three-dimensional coordinate system is established using an interpolation algorithm. First, the dissolved oxygen concentration and sludge concentration data at each monitoring point are substituted into the three-dimensional spatial coordinates to form a discrete data point set. Then, the continuous distribution function within the entire spatial domain is calculated using Kriging interpolation, yielding a three-dimensional field reflecting the distribution of sludge concentration and dissolved oxygen. Simultaneously, the velocity vector data at each point is decomposed to establish velocity component fields in the x, y, and z directions, resulting in a comprehensive fluid motion model. This model can be updated in real time, dynamically reflecting changes in the flow regime within the tank.

[0070] S202. Identify low-velocity regions within the biochemical pool based on the dynamic flow field model.

[0071] The dynamic flow field model includes information such as velocity field and concentration field; the low flow velocity region refers to the region in the pool where the flow velocity is lower than a set threshold, usually 0.3 m / s as the judgment standard.

[0072] Using a constructed dynamic flow field model, the flow velocity at each point within the tank is calculated. Specifically, based on a three-dimensional mesh, the actual flow velocity at each grid point is calculated through vector synthesis of velocity components. When the flow velocity in a certain area consistently falls below 0.3 m / s, that area is marked as a low-velocity region. Simultaneously, considering the sludge concentration trend in that area, if a gradual increase in sludge concentration is observed, the presence of a dead zone is further confirmed. Through continuous monitoring and data analysis, all low-velocity regions within the tank requiring optimization can be accurately identified.

[0073] S203. Determine the submersible mixers in adjacent areas that need to be linked based on the location information of the low flow rate area.

[0074] Location information for low-flow-rate areas includes spatial coordinates and size; coordinated submersible mixers refer to mixing equipment in adjacent areas that need to work together.

[0075] After identifying the low-velocity region, the spatial relationship between this region and surrounding agitators is analyzed. By calculating the center coordinates of the low-velocity region, the nearest agitators are determined. Considering the effective operating range of the agitators and the superposition effect of the flow field, adjacent agitators capable of effectively mixing the target area are selected. For example, when a flow velocity below 0.3 m / s is detected in a region exceeding 2 square meters, agitators within a 3-meter radius of this region are selected for coordinated control. By adjusting the operating parameters of multiple agitators, a synergistic mixing effect is achieved.

[0076] S204. Send control commands to the linked submersible mixer.

[0077] Control commands represent specific parameters and commands for adjusting the operating status of the agitator, including start / stop control, speed adjustment, etc.; a linkage submersible agitator refers to a mixing device that needs to work in coordination with the target area.

[0078] Control commands are sent to the designated agitators via an IoT module to adjust their operating status. These commands include equipment start-up commands, speed adjustment values, and operating sequences. Differentiated control strategies are employed for different characteristics of low-flow-rate areas: when the dissolved oxygen concentration gradient in the area exceeds 0.5 mg / L, the agitator speed is increased to 80% of its rated speed; when the sludge concentration gradient exceeds a set threshold, an intermittent operation mode is adopted, controlling the agitator with a 10-minute start-10-minute stop sequence. These specific control parameters are sent to the agitator's variable frequency drive module via serial communication for precise adjustment.

[0079] S205. Generate a dissolved oxygen isosurface cloud map based on dissolved oxygen data collected by multi-parameter sensors.

[0080] Dissolved oxygen data collected by multi-parameter sensors refers to the real-time dissolved oxygen concentration value at the monitoring point; dissolved oxygen isosurface cloud map refers to a three-dimensional graphic used to represent the spatial distribution of dissolved oxygen, with different colors representing different concentration areas.

[0081] Dissolved oxygen data is collected using a sensor array distributed at different locations in the biochemical pool. These discrete monitoring data are then converted into continuously distributed isosurfaces using spatial interpolation methods. The specific implementation process is as follows: First, the spatial coordinates and corresponding dissolved oxygen values ​​of each monitoring point are obtained to establish a three-dimensional data point set. Then, a three-dimensional interpolation algorithm is used to calculate the dissolved oxygen distribution function within the entire spatial domain. Finally, the calculation results are displayed in the form of a cloud map, where high-concentration areas are shown in red, low-concentration areas in blue, and the transition between different colors represents the gradient change of dissolved oxygen.

[0082] S206. Identify anoxic and hyperoxic regions in the biochemical tank based on dissolved oxygen isosurface cloud maps.

[0083] Anoxic areas refer to regions where the dissolved oxygen concentration is lower than the process requirements; hyperoxic areas refer to regions where the dissolved oxygen concentration exceeds the process requirements; the dissolved oxygen isosurface cloud map shows the spatial distribution characteristics of dissolved oxygen in the pool.

[0084] By analyzing the color distribution in the dissolved oxygen isosurface map, the location and extent of anoxic and hyperoxic regions were determined. Based on process requirements, areas with dissolved oxygen concentrations less than 1.5 mg / L were marked as anoxic regions, and areas greater than 3.0 mg / L were marked as hyperoxic regions. Different criteria were applied to different depths of the tank: dissolved oxygen at the surface was allowed to reach 3.0 mg / L to promote nitrification; within the tank, it was controlled within the range of 2.0 ± 0.3 mg / L; and at the bottom, it was required to be no less than 1.5 mg / L to prevent denitrification. This stratified identification method clarified the dissolved oxygen control targets for each region.

[0085] S207. A fuzzy sliding mode control method is used to set differentiated PID parameters for the anoxic and hyperoxic regions. The differentiated PID parameters include different dissolved oxygen concentration control ranges set for the surface, middle and bottom of the biological tank.

[0086] Fuzzy sliding mode control refers to a hybrid control strategy that combines fuzzy control and sliding mode control; differentiated PID parameters represent the proportional, integral, and derivative parameters set for different regions; dissolved oxygen concentration control range refers to the allowable range of dissolved oxygen variation at each layer, specifically 3.0 mg / L at the surface, 2.0 ± 0.3 mg / L in the middle, and not less than 1.5 mg / L at the bottom.

[0087] Control parameters were set for the identified anoxic and hyperoxic zones. For the surface zone, to promote nitrification, the upper limit of dissolved oxygen concentration was set at 3.0 mg / L, using a larger proportional coefficient and a shorter integral time for faster control response. For the mid-zone, dissolved oxygen was strictly controlled within the range of 2.0 ± 0.3 mg / L, using a moderate proportional coefficient and integral time to maintain dissolved oxygen stability. For the bottom zone, the lower limit of dissolved oxygen concentration was set at 1.5 mg / L, using a smaller proportional coefficient and a larger integral time to avoid excessive fluctuations in dissolved oxygen that could affect the denitrification process.

[0088] S208. Adjust the dissolved oxygen concentration of each anoxic and hyperoxic zone according to the differentiated PID parameters.

[0089] Anoxic and hyperoxic regions refer to areas with abnormal dissolved oxygen concentrations; dissolved oxygen concentration refers to the amount of dissolved oxygen in the water; differentiated PID parameters include control parameter settings for different regions.

[0090] Dissolved oxygen regulation is performed based on predefined differentiated PID parameters. When the dissolved oxygen level in a certain area is detected to be below 1.5 mg / L, the speed of the agitator in that area is increased to improve oxygen transfer efficiency; when the dissolved oxygen level exceeds 3.0 mg / L, the agitation intensity is reduced to decrease surface reoxygenation. If the dissolved oxygen level in a region of the tank deviates from 2.0 mg / L, the adjustment amount is calculated based on the magnitude of the deviation to precisely control the agitator speed. By adjusting the agitator operating parameters in real time, the dissolved oxygen concentration in each region is gradually brought to the set range, forming a stratified distribution of dissolved oxygen conducive to biochemical reactions.

[0091] S209. Establish dissolved oxygen gradient curves and sludge concentration gradient curves based on real-time data collected by multi-parameter sensors.

[0092] The dissolved oxygen gradient curve represents the spatial variation trend of dissolved oxygen concentration; the sludge concentration gradient curve represents the spatial distribution variation of sludge concentration; real-time data refers to the monitoring data continuously collected by multi-parameter sensors.

[0093] Using data collected by sensors deployed at different locations, gradient curves characterizing the spatial distribution of parameters were constructed. First, dissolved oxygen data from each monitoring point was collected, and the concentration difference between adjacent points was calculated. A dissolved oxygen gradient curve was plotted with distance on the x-axis and concentration gradient on the y-axis. Sludge concentration data was processed in the same way to obtain a sludge concentration gradient curve. These gradient curves visually display the spatial variation characteristics of the parameters, which can be used for subsequent control strategy optimization and over-stirring early warning.

[0094] S210. Establish an over-stirring early warning model based on the dissolved oxygen gradient curve and the sludge concentration gradient curve.

[0095] The dissolved oxygen gradient curve represents the relationship between dissolved oxygen concentration and spatial location; the sludge concentration gradient curve reflects the spatial distribution of sludge concentration; and the over-stirring early warning model is a mathematical model used to identify excessive stirring intensity.

[0096] An over-mixing early warning model was constructed using established dissolved oxygen gradient curves and sludge concentration gradient curves as input parameters. When the dissolved oxygen gradient value is less than 0.5 mg / L and the flow velocity is greater than 0.3 m / s, it indicates excessive mixing. When the sludge concentration gradient value exceeds a set threshold and the flow velocity is greater than 0.3 m / s, it indicates that the sludge flocs are undergoing shear failure. The early warning model uses a combination of these parameters to monitor whether the mixing intensity exceeds a reasonable range in real time, and immediately triggers an early warning signal if any deviation occurs.

[0097] S211. When the over-stirring warning model triggers an alarm, reduce the operating parameters of the submersible mixer.

[0098] The over-stirring early warning model is a mathematical model for judging whether the stirring intensity is too high; the early warning trigger refers to the alarm signal generated when the preset threshold is exceeded; the operating parameters include the stirring speed, start-stop sequence and other working parameters.

[0099] When the warning model detects excessive stirring intensity, it automatically implements agitator deceleration control. First, the agitator speed is reduced to a preset percentage of the rated speed, such as 30% of the rated speed. Simultaneously, the operating mode is changed to intermittent operation, with start-stop control following a fixed sequence, such as alternating operation of group A for 10 minutes and group B for 10 minutes. By reducing the stirring intensity, sludge flocs are prevented from breaking down, maintaining good settling performance.

[0100] S212. Predict future sludge growth trends based on historical flow velocity data and historical sludge concentration data.

[0101] Historical flow velocity data refers to the water flow velocity information recorded over a period of time; historical sludge concentration data represents the collected records of sludge concentration changes; sludge growth trend refers to the direction and rate of change in sludge concentration.

[0102] Based on historical data recorded by multi-parameter sensors, the variation pattern of sludge concentration was analyzed. By comparing flow rate and sludge concentration data at different time periods, the correlation between the two was established. When historical data showed that the sludge concentration was continuously rising and approaching 5000 mg / L, while the flow rate remained above 0.3 m / s, it was predicted that the sludge concentration would continue to increase within the next 2 hours. This prediction result was used to guide the advance adjustment of the stirring intensity to prevent excessively high sludge concentration from affecting the treatment effect.

[0103] S213. Adjust the mixing intensity of the submersible mixer based on the sludge growth trend.

[0104] Sludge growth trend indicates the pattern and direction of sludge concentration change over time; mixing intensity refers to the working intensity of the submersible mixer, which is mainly reflected by the rotation speed and operating sequence; a submersible mixer is a device that performs mixing functions.

[0105] Stirring control is implemented based on the predicted sludge growth trend. When the prediction shows that the sludge concentration exceeds 5000 mg / L and the flow rate is greater than 0.3 m / s, a low-speed homogenization mode is activated: the stirrer speed is reduced to 30% of the rated speed, and a group alternating operation mode is adopted, that is, the stirrer group A runs for 10 minutes, then switches to the group B for 10 minutes. By reducing the stirring intensity, the shearing effect on the sludge flocs is reduced, maintaining good sludge settling performance. At the same time, the intermittent operation mode ensures the uniformity of stirring while avoiding the damage to the sludge structure caused by continuous strong stirring. When the sludge concentration drops to the normal range, the regular stirring mode is restored, realizing dynamic optimization and adjustment of the stirring intensity.

[0106] The method provided in this implementation will now be described in more detail. Please refer to [link / reference]. Figure 3 This is a schematic diagram of the equipment installation for the intelligent control method of submersible mixers based on multi-sensor collaborative monitoring in this embodiment of the application. 1 represents the intelligent control platform, 2 the intelligent control box, 3 the multi-parameter sensor module, and 4-1 and 4-2 are paired submersible mixers / pumpers. The multi-parameter sensor module collects dissolved oxygen concentration, sludge concentration, and flow velocity parameters at different locations within the tank. The data is processed by the intelligent control box and transmitted to the intelligent control platform, which then issues control commands to the submersible mixers.

[0107] The method provided in this implementation will now be described in more detail. Please refer to [link / reference]. Figure 4 This is a flowchart of the control system of the intelligent control method for submersible mixers based on multi-sensor collaborative monitoring in this application embodiment. 403 is the intelligent control platform, responsible for executing the fuzzy sliding mode control strategy and setting differentiated PID parameters; 402 is the intelligent control box, used to receive and process sensor data and establish dissolved oxygen and sludge concentration gradient curves; 401 is a multi-parameter sensor module, which collects real-time dissolved oxygen concentration (3.0 mg / L at the surface, 2.0 ± 0.3 mg / L in the middle, and not less than 1.5 mg / L at the bottom), sludge concentration, and flow velocity parameters in the tank; 404 are paired submersible mixers / pumpers, which adjust operating parameters according to control commands, including rotation speed (which can be reduced to 30% of the rated speed) and operating sequence (A and B groups alternate operation for 10 minutes).

[0108] The method provided in this implementation will now be described in more detail. Please refer to [link / reference]. Figure 5 This is an installation diagram of the intelligent control method for a submersible mixer based on multi-sensor collaborative monitoring in this embodiment of the application.

[0109] Figure 5 Part (a) in the diagram is a schematic diagram of the installation of a rectangular biochemical tank. Figure 5 Part (b) is a schematic diagram of the installation of a circular or racetrack-shaped biochemical pool.

[0110] For rectangular pools, the intelligent control box 2 is installed near the pool edge, the submersible mixers 4 are installed diagonally parallel and staggered at a certain distance, and the multi-parameter sensor module 3 is installed in a corner of the pool. Based on experience, this area has a lower flow rate, is a dead zone for mixing, and is prone to sludge deposition and other problems.

[0111] For circular or racetrack-shaped pools, the intelligent control box 2 is installed near the pool edge, the submersible mixers 4 are installed diagonally parallel and staggered at a certain distance, and the multi-parameter sensor module 3 is installed at the center or middle of the submersible. Based on experience, this area has a lower flow rate, is a dead zone for mixing, and is prone to problems such as sludge deposition.

[0112] The intelligent control system for the submersible mixer in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 6 This is a schematic diagram of the physical device structure of an intelligent control system for a submersible mixer in this application embodiment.

[0113] It should be noted that, Figure 6 The structure of the intelligent control system for the submersible mixer shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0114] like Figure 6 As shown, the intelligent control system for the submersible mixer includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM) 603, such as executing the methods described in the above embodiments. The RAM 603 also stores various programs and data required for system operation. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0115] The following components are connected to I / O interface 605: input section 606 including audio input devices, push-button switches, etc.; output section 607 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 608 including a hard disk, etc.; and communication section 609 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 609 performs communication processing via a network such as the Internet. Drive 610 is also connected to I / O interface 605 as needed. Removable media 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 610 as needed so that computer programs read from them can be installed into storage section 608 as needed.

[0116] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs the various functions defined in the present invention.

[0117] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0118] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0119] Specifically, the intelligent control system for the submersible mixer in this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the intelligent control method for the submersible mixer based on multi-sensor collaborative monitoring provided in the above embodiment.

[0120] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the intelligent control system for submersible mixers described in the above embodiments; or it may exist independently and not incorporated into the intelligent control system for submersible mixers. The storage medium carries one or more computer programs, which, when executed by a processor of the intelligent control system for submersible mixers, enable the intelligent control system to implement the intelligent control method for submersible mixers based on multi-sensor collaborative monitoring provided in the above embodiments.

[0121] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0122] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0123] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A smart control method for a submersible mixer based on multi-sensor collaborative monitoring, characterized in that, The method, applied to an intelligent control system for submersible mixers, includes: Multi-parameter sensors are installed at the bottom, middle and top of the biological treatment tank to collect dissolved oxygen concentration, sludge concentration and flow rate parameters in the biological treatment tank. A dynamic flow field model is constructed based on the dissolved oxygen concentration, the sludge concentration, and the flow velocity parameters. The low-velocity regions within the biochemical pool are identified based on the dynamic flow field model. Based on the location information of the low flow rate area, determine the linked submersible mixers in the adjacent areas that need to be linked. Send control commands to the linked submersible mixer; A dissolved oxygen isosurface cloud map is generated based on the dissolved oxygen data collected by the multi-parameter sensor; Based on the dissolved oxygen isosurface cloud map, the anoxic and hyperoxic regions in the biological treatment tank were identified. A fuzzy sliding mode control method is used to set differentiated PID parameters for the anoxic region and the hyperoxic region, wherein the differentiated PID parameters include different dissolved oxygen concentration control ranges set for the surface, middle and bottom of the biological tank; The dissolved oxygen concentration in each of the hypoxic and hyperoxic regions is adjusted according to the differentiated PID parameters. Based on the dissolved oxygen concentration, the sludge concentration, and the flow velocity parameters, calculate the dissolved oxygen concentration gradient, sludge concentration gradient, and flow velocity values ​​between adjacent sampling points, where the adjacent sampling points include the target area and adjacent areas. Based on the combined relationship between the dissolved oxygen concentration gradient value, the sludge concentration gradient value, and the flow rate value, the first operating parameter of the submersible mixer in the target area is adjusted to the first preset parameter. The operating effect after adjusting the operating parameters is calculated based on the real-time data collected by the multi-parameter sensor. When the adjustment effect does not reach the preset effect, the second operating parameter of the submersible mixer in the adjacent area is adjusted to the second preset parameter.

2. The method according to claim 1, characterized in that, The step of adjusting the first operating parameters of the submersible mixer in the target area to the first preset parameters based on the combined relationship of the dissolved oxygen concentration gradient value, the sludge concentration gradient value, and the flow velocity value specifically includes: When the flow rate is greater than a first preset threshold and the dissolved oxygen concentration gradient is less than a second preset threshold, the rotation speed of the submersible mixer in the target area is reduced to the first preset rotation speed. When the flow rate is less than the first preset threshold and the dissolved oxygen concentration gradient is greater than the second preset threshold, the rotation speed of the submersible mixer in the target area is increased to the second preset rotation speed. When the sludge concentration gradient value is greater than the third preset threshold and the flow rate value is greater than the first preset threshold, the rotation speed of the submersible mixer in the target area is adjusted to a preset percentage of the rated speed, and start-stop control is performed according to the preset timing sequence.

3. The method according to claim 1, characterized in that, The step of calculating the operating effect after adjusting the operating parameters based on the real-time data collected by the multi-parameter sensor, and adjusting the second operating parameters of the submersible mixer in the adjacent area to the second preset parameters when the adjustment effect does not reach the preset effect, specifically includes: The uniformity of dissolved oxygen distribution in the target area is calculated based on the real-time data collected by the multi-parameter sensor. When the uniformity of dissolved oxygen distribution is less than the fourth preset threshold, it is determined that the adjustment effect has not achieved the preset effect. In response to the determination that the adjustment effect does not achieve the preset effect, the submersible mixer in the adjacent area is started and its speed is adjusted to the second preset parameter; Monitor the uniformity of dissolved oxygen distribution in the target area and the adjacent areas. When a sudden fluctuation is detected, increase the rotation speed of the submersible mixers in the target area and the adjacent areas to the rated value and maintain it for a preset time.

4. The method according to claim 1, characterized in that, After the step of calculating the operating effect after adjusting the operating parameters based on the real-time data collected by the multi-parameter sensor, and adjusting the second operating parameters of the submersible mixer in the adjacent area to the second preset parameters when the adjustment effect does not reach the preset effect, the method further includes: Dissolved oxygen gradient curve and sludge concentration gradient curve are established based on the real-time data collected by the multi-parameter sensor. An over-stirring early warning model was established based on the dissolved oxygen gradient curve and the sludge concentration gradient curve. When the over-stirring warning model triggers an early warning, the operating parameters of the submersible mixer are reduced.

5. The method according to claim 1, characterized in that, After the step of calculating the operating effect after adjusting the operating parameters based on the real-time data collected by the multi-parameter sensor, and adjusting the second operating parameters of the submersible mixer in the adjacent area to the second preset parameters when the adjustment effect does not reach the preset effect, the method further includes: Predict future sludge growth trends based on historical flow velocity data and historical sludge concentration data; Based on the sludge growth trend, the mixing intensity of the submersible mixer is adjusted.

6. A smart control system for a submersible mixer, characterized in that, The intelligent control system for the submersible mixer includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the intelligent control system for the submersible mixer to perform the method as described in any one of claims 1-5.

7. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the intelligent control system of the submersible mixer, the intelligent control system of the submersible mixer performs the method as described in any one of claims 1-5.

8. A computer program product, characterized in that, When the computer program product is run on the intelligent control system of the submersible mixer, the intelligent control system of the submersible mixer performs the method as described in any one of claims 1-5.

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