Control system and control method for dynamically regulating magnetic coagulation precipitation

Abstract

The present invention discloses a control system and a control method for AI-dynamically controlled low-cost magnetic coagulation and sedimentation, which relates to the technical field of sewage treatment. The system includes: a dynamic water quality monitoring module for dynamically monitoring the water quality of a target water body through a predetermined probe; a sedimentation dosage information composition module for reading the category of products to be added and composing sedimentation dosage information; a real-time sedimentation control strategy acquisition module for dynamically adjusting the sedimentation control benchmark; and a magnetic coagulation and sedimentation control processing module for performing magnetic coagulation and sedimentation control processing on the target water body according to the real-time sedimentation control strategy. The system solves the technical problems existing in the prior art, such as the inability to perceive the microscopic morphology of magnetic flocs in real time, the difficulty in dynamically optimizing flocculation conditions according to changes in water quality, and the reliance on high-precision sensors and controllers, which in turn lead to high costs for magnetic coagulation and sedimentation treatment and insufficient flexibility and stability. The system achieves the technical effect of reducing the cost of magnetic coagulation and sedimentation treatment and improving the flexibility and stability of water quality treatment.

Description

Technical Field

[0001] The present application relates to the technical field related to sewage treatment, and specifically to a control system and control method for AI-dynamically regulated low-cost magnetic coagulation sedimentation. Background Art

[0002] In the field of sewage treatment, magnetic coagulation technology has been widely used in recent years as an important means to improve the efficiency and quality of water treatment. However, the sedimentation control methods of traditional magnetic coagulation equipment mostly adopt a fixed dosing strategy, that is, fixed parameters are used to control the dosage of coagulants and magnetic powder, which cannot perceive the dynamic changes of water quality in real time. In actual sewage environments, water quality fluctuates frequently due to various factors, such as intermittent drainage of industrial production, diurnal changes in the amount of urban domestic sewage, and rainwater mixing. The fixed parameter dosing method cannot adapt to the dynamic changes of water quality in a timely manner. When the water quality deteriorates, insufficient dosage leads to poor treatment effect, and the effluent water quality is difficult to stably meet the standards; when the water quality is good, excessive dosage results in high drug consumption, which greatly increases the cost of sewage treatment. In addition, the current magnetic coagulation equipment lacks the ability to perceive the microscopic morphology of magnetic flocs in real time, making it difficult to dynamically optimize the flocculation conditions according to actual conditions, making it difficult to achieve the best effect of the entire magnetic coagulation process. In addition, upgrading existing magnetic coagulation equipment requires the configuration of high-precision sensors and dedicated controllers to improve equipment performance. For small and medium-sized sewage treatment plants, the transformation cost is high and unaffordable. In addition, the reliance on cloud computing affects the timely response to water quality changes and also brings data privacy risks.

[0003] Therefore, in the current relevant technologies, there are technical problems such as the inability to perceive the microscopic morphology of magnetic flocs in real time, difficulty in dynamically optimizing flocculation conditions according to changes in water quality, and reliance on high-precision sensors and controllers, which in turn lead to high costs of magnetic coagulation sedimentation treatment and insufficient flexibility and stability. Summary of the Invention

[0004] This application solves the technical problems in the prior art of being unable to perceive the microscopic morphology of magnetic flocs in real time, having difficulty in dynamically optimizing flocculation conditions according to changes in water quality, and relying on high-precision sensors and controllers, which in turn lead to high costs and insufficient flexibility and stability in magnetic coagulation sedimentation treatment, by providing a control system and control method for AI-dynamically controlled low-cost magnetic coagulation sedimentation. This achieves the technical effect of reducing the cost of magnetic coagulation sedimentation treatment and improving the flexibility and stability of water quality treatment.

[0005] The present application provides a control system for AI dynamic regulation of low-cost magnetic coagulation and sedimentation, the system comprising: a dynamic water quality monitoring module, for dynamically monitoring the water quality of a target water body through a predetermined probe to obtain real-time water quality information; a sedimentation dosage information composition module, for reading the category to be added, and using the real-time water quality information as a constraint, analyzing and obtaining the first sedimentation dosage of the first category in the category to be added to form sedimentation dosage information; a real-time sedimentation control strategy acquisition module, for forming a dynamic variable constraint based on the real-time water quality information and the sedimentation dosage information, and dynamically adjusting the sedimentation control benchmark under the dynamic variable constraint to obtain a real-time sedimentation control strategy; a magnetic coagulation and sedimentation control processing module, for performing magnetic coagulation and sedimentation control processing on the target water body according to the real-time sedimentation control strategy.

[0006] In a possible implementation, the AI ​​dynamically controls the control system for low-cost magnetic coagulation sedimentation and further performs the following processing: the real-time water quality information includes at least real-time turbidity, real-time pH value, and real-time temperature.

[0007] In a possible implementation, the AI ​​dynamically regulates the control system of low-cost magnetic coagulation sedimentation and also performs the following processing: the categories to be added include flocculants and magnetic powder.

[0008] In a possible implementation, the control system of the AI ​​dynamic regulation of low-cost magnetic coagulation sedimentation also performs the following processing: extracting the predetermined water quality information in the sedimentation control benchmark; comparing the real-time water quality information with the predetermined water quality information to obtain a real-time water quality deviation coefficient; extracting the predetermined dosage information in the sedimentation control benchmark; comparing the sedimentation dosage information with the predetermined dosage information to obtain a dosage deviation coefficient; performing weighted calculation on the real-time water quality deviation coefficient and the dosage deviation coefficient to obtain a dynamic variable factor; adjusting the sedimentation control benchmark based on the dynamic variable factor to obtain the real-time sedimentation control strategy.

[0009] In a possible implementation, the control system for the AI-dynamically controlled low-cost magnetic coagulation sedimentation further performs the following processing: obtaining any water quality index; traversing and matching the real-time water quality information and the predetermined water quality information to obtain the real-time water quality parameters and the predetermined water quality parameters corresponding to the arbitrary water quality index; introducing a deviation evaluation function to evaluate and analyze the real-time water quality parameters and the predetermined water quality parameters to obtain the real-time water quality deviation coefficient; wherein the expression of the deviation evaluation function is:

[0010] ;

[0011] Refers to the real-time water quality deviation coefficient, It refers to the The value of the real-time water quality parameter, It refers to the The value of a predetermined water quality parameter, It refers to the The weight coefficient of the parameter, is the sum of water quality parameters, and is an integer greater than or equal to 3.

[0012] In a possible implementation, the AI ​​dynamically controls the control system for low-cost magnetic coagulation sedimentation and also performs the following processing: extracting the mixing control information in the sedimentation control benchmark, wherein the mixing control information includes a predetermined stirring intensity and a predetermined reaction time; performing weighted adjustment on the predetermined stirring intensity and the predetermined reaction time using the dynamic variable factor as a weight to obtain the real-time stirring intensity and the real-time reaction time, respectively; and forming the real-time sedimentation control strategy based on the real-time stirring intensity and the real-time reaction time.

[0013] In a possible implementation, the control system for the AI ​​dynamic regulation of low-cost magnetic coagulation sedimentation also performs the following processing: dynamically collects real-time microscopic images of target magnetic flocs in the target water body; performs feature evaluation and analysis on the real-time microscopic images according to a microscopic state evaluation mechanism to obtain a real-time microscopic state; obtains an arbitrary information value coefficient of the real-time microscopic state for the arbitrary water quality index; performs predictive analysis on the real-time water quality parameters in combination with the arbitrary information value coefficient to obtain predicted real-time water quality parameters; and calibrates the real-time water quality parameters with the predicted real-time water quality parameters.

[0014] In a possible implementation, the control system for the AI ​​dynamic regulation of low-cost magnetic coagulation precipitation also performs the following processing: collecting real-time image feature information of the real-time microscopic image; normalizing and weighting the real-time image color parameters and real-time image structure parameters in the real-time image feature information to obtain a real-time image feature value; and standardizing and correcting the real-time image feature value in combination with the microscopic state coefficient stored in the microscopic state evaluation mechanism to obtain the real-time microscopic state.

[0015] In a possible implementation, the AI ​​dynamically controls the control system for low-cost magnetic coagulation and sedimentation, and also performs the following processing: extracting a first historical data set from the magnetic coagulation and sedimentation database; performing mutual information analysis on the first historical microscopic state of the first magnetic floc in the first historical data set and the first historical indicator parameter corresponding to the first historical arbitrary water quality indicator to obtain a first historical mutual information coefficient; and using the first historical mutual information coefficient as the arbitrary information value coefficient.

[0016] The present application also provides a control method for low-cost magnetic coagulation sedimentation with dynamic AI regulation, including: dynamically monitoring the water quality of a target water body through a predetermined probe to obtain real-time water quality information; reading the category to be added, and using the real-time water quality information as a constraint, analyzing to obtain a first sedimentation dosage of the first category in the category to be added, and forming sedimentation dosage information; forming a dynamic variable constraint based on the real-time water quality information and the sedimentation dosage information, and dynamically adjusting the sedimentation control benchmark under the dynamic variable constraint to obtain a real-time sedimentation control strategy; and performing magnetic coagulation sedimentation control treatment on the target water body according to the real-time sedimentation control strategy.

[0017] The control system and control method for low-cost magnetic coagulation and sedimentation that is dynamically controlled by AI proposed in this application include a dynamic water quality monitoring module for dynamically monitoring the water quality of the target water body through a predetermined probe; a sedimentation dosage information composition module for reading the category to be added and composing the sedimentation dosage information; a real-time sedimentation control strategy acquisition module for dynamically adjusting the sedimentation control benchmark to obtain a real-time sedimentation control strategy; and a magnetic coagulation and sedimentation control treatment module for performing magnetic coagulation and sedimentation control treatment on the target water body according to the real-time sedimentation control strategy. This solves the technical problems in the prior art of being unable to perceive the microscopic morphology of magnetic flocs in real time, difficulty in dynamically optimizing flocculation conditions according to changes in water quality, and reliance on high-precision sensors and controllers, which in turn lead to high costs for magnetic coagulation and sedimentation treatment and insufficient flexibility and stability. This achieves the technical effect of reducing the cost of magnetic coagulation and sedimentation treatment and improving the flexibility and stability of water quality treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0019] Figure 1 Schematic diagram of the control system structure for AI dynamic regulation of low-cost magnetic coagulation sedimentation provided in the embodiment of the present application.

[0020] Figure 2 Schematic diagram of the flow chart of the control method for AI dynamic regulation of low-cost magnetic coagulation sedimentation provided in the embodiment of the present application.

[0021] Description of the accompanying drawings: dynamic water quality monitoring module 10, sedimentation dosage information composition module 20, real-time sedimentation control strategy acquisition module 30, magnetic coagulation sedimentation control processing module 40. DETAILED DESCRIPTION

[0022] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0023] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0024] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0025] The embodiment of the present application provides a control system for AI dynamic regulation of low-cost magnetic coagulation precipitation, such as Figure 1 As shown, the system includes:

[0026] The dynamic water quality monitoring module 10 is used to perform dynamic water quality monitoring on the target water body through a predetermined probe to obtain real-time water quality information.

[0027] Furthermore, the specific configuration of the dynamic water quality monitoring module 10 also includes that the real-time water quality information at least includes real-time turbidity, real-time pH value and real-time temperature.

[0028] Preferably, a predetermined probe device (all-in-one water quality probe) is used to continuously and automatically detect and collect data on the target water body to obtain real-time water quality information. The all-in-one water quality probe integrates multiple sensors such as turbidity sensors, pH sensors, and temperature sensors, and can simultaneously measure multiple water quality parameters, including turbidity, pH value, and temperature of the water body. The all-in-one water quality probe can replace single-parameter sensors, reducing the number of probes and lowering costs. Specifically, dynamic water quality monitoring refers to the predetermined probe detecting the target water body at certain time intervals or continuously (for example, collecting data every 5 minutes) to timely capture the changes in water quality over time and reflect the dynamic characteristics of water quality. Through the measurement of the all-in-one water quality probe, the real-time data of the target water body is obtained as real-time water quality information, including real-time turbidity, real-time pH value, and real-time temperature. Turbidity is used to reflect the content of suspended particles in the water body. By measuring the degree of light scattering or absorption by the water body, the turbidity sensor in the all-in-one water quality probe can obtain the turbidity information of the water body in real time, thereby understanding the water quality. The pH value is used to measure the acidity and alkalinity of water. The pH sensor in the all-in-one water quality probe reacts with hydrogen ions in the water to generate a corresponding electrical signal, thereby measuring the pH value of the water in real time. In wastewater treatment, different treatment processes require a specific pH range to achieve optimal results. The temperature sensor in the all-in-one water quality probe typically uses components such as thermistors or thermocouples to obtain real-time water temperature information by measuring changes in water temperature. Changes in water temperature can affect the solubility of chemical substances in the water, the activity of microorganisms, and the rate of chemical reactions. Dynamic water quality monitoring of the target water body through the all-in-one water quality probe provides accurate data support for magnetic coagulation and sedimentation control, allowing timely adjustment of treatment strategies based on changes in water quality to improve treatment effectiveness and stability.

[0029] The precipitation dosage information composition module 20 is used to read the category of products to be added, and based on the real-time water quality information, analyze and obtain the first precipitation dosage of the first category in the category of products to be added to form precipitation dosage information.

[0030] Furthermore, the specific configuration of the precipitation dosage information composition module 20 also includes that the categories of products to be added include flocculants and magnetic powders.

[0031] Preferably, in the magnetic coagulation sedimentation treatment process, specific substances are added to the target water body to promote the sedimentation effect. The categories to be added mainly include flocculants and magnetic powder. Among them, flocculants can make the suspended particles in the water condense into larger flocs, which are convenient for subsequent sedimentation and separation; magnetic powder can increase the specific gravity of the flocs, accelerate the sedimentation speed, and improve the treatment efficiency. Specifically, under different water quality conditions, the dosage of the agent required to achieve the ideal sedimentation effect is different. The real-time water quality information of the target water body obtained by the predetermined probe is used as a constraint to determine the dosage. For example, high turbidity means that there are many suspended particles in the water, and more is needed. More flocculants and magnetic powder are used to promote particle aggregation and precipitation. If the turbidity is low, the required dosage can be reduced; the pH value affects the hydrolysis and ionization process of the flocculant. Different flocculants have different flocculation effects in different pH value ranges. For example, some flocculants work well in a weakly acidic environment, while others are more effective in an alkaline environment; water temperature affects the rate of chemical reaction and the hydrolysis speed of the flocculant. When the temperature is low, the chemical reaction slows down and the hydrolysis of the flocculant is incomplete. The dosage may need to be increased to ensure the flocculation effect. When the temperature is high, the reaction speed is accelerated and the dosage may be appropriately reduced.

[0032] Preferably, based on the real-time water quality information and with reference to historical data, the first precipitation dosage corresponding to the first category of the categories to be added is analyzed and calculated, that is, the dosage required to achieve the best precipitation effect under the current water quality conditions, wherein the first category is one of the categories to be added (flocculant or magnetic powder). Specifically, a large amount of historical water quality data and corresponding dosage data are collected, and pre-processing operations such as cleaning and normalization are performed on the data, and the data are divided into training sets and test sets. Then, a suitable machine learning algorithm, such as artificial neural network (ANN), support vector machine (SVM), etc., is selected to use historical water quality parameters (turbidity) to predict the precipitation effect. , pH value, temperature, etc.) as input features and the dosage of the first category as the output label to train the model. During training, the model parameters are continuously adjusted so that the model can accurately learn the mapping relationship between water quality parameters and dosage, thereby obtaining a dosage prediction model. Real-time water quality information is then input into the trained dosage prediction model to output the first precipitation dosage of the first category. For example, after inputting real-time turbidity, real-time pH value, and real-time temperature, the trained artificial neural network model outputs a flocculant dosage of 6 mg / L, which is the first precipitation dosage. Similarly, the precipitation dosage corresponding to the magnetic powder is calculated to form precipitation dosage information, which may include the dosage value, the corresponding water quality parameters, and the calculation time. This is used to guide the chemical addition operation during the magnetic coagulation and sedimentation process.

[0033] The real-time sedimentation control strategy acquisition module 30 is used to form a dynamic variable constraint based on the real-time water quality information and the sedimentation dosage information, and dynamically adjust the sedimentation control benchmark under the dynamic variable constraint to obtain a real-time sedimentation control strategy.

[0034] Preferably, real-time water quality information (such as real-time turbidity, real-time pH, and real-time temperature) and sedimentation dosage information (precipitation dosage for each category) are combined to form dynamic variable constraints to reflect the actual current water condition and the required chemical dosage. For example, high real-time turbidity indicates that the flocculant dosage needs to be increased to achieve a better precipitation effect. Real-time pH and temperature affect the reactivity of the flocculant, thus placing certain restrictions and requirements on the dosage. The sedimentation dosage information directly specifies the current recommended chemical dosage, which cannot be arbitrarily exceeded or lowered within this range, otherwise it may lead to poor precipitation effect or chemical waste. The sedimentation control benchmark is then dynamically adjusted based on the dynamic variable constraints. The sedimentation control benchmark is the control parameters and target values ​​set under ideal operating conditions (such as the standard dosage of flocculant and magnetic powder, precipitation time, stirring speed, etc. under normal water quality conditions) to guide the operation of the magnetic coagulation sedimentation process.

[0035] Preferably, the sedimentation control benchmark is adjusted based on the dynamic variable constraint. For example, if the real-time water quality information indicates that the turbidity of the water body increases, according to the relationship between turbidity and dosage in the dynamic variable constraint, the dosage of the flocculant needs to be increased from the standard value. The increase is the dosage adjustment value calculated based on the turbidity change. At the same time, other control parameters such as the sedimentation time and the stirring speed may also need to be adjusted accordingly according to the real-time water quality information and the sedimentation dosage information. For example, the sedimentation time may be appropriately extended or the stirring speed may be adjusted to allow the added flocculant to fully mix and react with the water body. Then, a real-time sedimentation control strategy is obtained, including various information such as the adjusted agent dosage, sedimentation time, stirring speed, equipment operating parameters, as well as response measures and adjustment methods under different water quality changes. For example, when the turbidity is between 50-80 NTU, the flocculant dosage is adjusted to 6-8 mg / L, the sedimentation time is extended to 30-40 minutes, the stirring speed is increased to 150-200 r / min in the initial stage, and then gradually reduced to 50-80 r / min, etc. The real-time sedimentation control strategy can be dynamically adjusted according to the constantly changing real-time water quality information and sedimentation dosage information, thereby achieving a more efficient and stable magnetic coagulation sedimentation process, improving the effluent quality, and reducing drug consumption and operating costs.

[0036] Furthermore, the specific configuration of the real-time sedimentation control strategy acquisition module 30 also includes extracting the predetermined water quality information in the sedimentation control benchmark; comparing the real-time water quality information with the predetermined water quality information to obtain a real-time water quality deviation coefficient; extracting the predetermined dosage information in the sedimentation control benchmark; comparing the sedimentation dosage information with the predetermined dosage information to obtain a dosage deviation coefficient; performing weighted calculation on the real-time water quality deviation coefficient and the dosage deviation coefficient to obtain a dynamic variable factor; and adjusting the sedimentation control benchmark based on the dynamic variable factor to obtain the real-time sedimentation control strategy.

[0037] Preferably, predetermined water quality information is extracted from the sedimentation control benchmark, including predetermined water quality indicators, such as predetermined turbidity, predetermined pH value, predetermined temperature, etc., which represent the water quality state expected to be achieved under standard conditions. For example, in the sedimentation control benchmark, the predetermined turbidity may be set to 50NTU, the predetermined pH value is 7.5, and the predetermined temperature is 22°C; then the real-time water quality information is compared with the predetermined water quality information to calculate the deviation degree of each water quality indicator. For example, if the real-time turbidity is 60NTU and the predetermined turbidity is 50NTU, the turbidity deviation is 60-50=10NTU. The water quality indicator deviation degree is then converted into a deviation coefficient to take into account the deviation impact of different water quality indicators. For example, the deviation value is divided by the predetermined value to obtain a relative deviation coefficient. The relative deviation coefficient of turbidity is 10 / 50=0.2. Finally, the deviation coefficients of each water quality indicator are comprehensively processed (such as weighted average, etc.) to obtain a real-time water quality deviation coefficient, which reflects the degree of deviation of the current real-time water quality from the predetermined water quality.

[0038] Preferably, the predetermined dosage information is extracted from the sedimentation control benchmark, that is, the amount of various agents (such as flocculants, magnetic powder, etc.) required to achieve the ideal sedimentation effect under predetermined water quality conditions. For example, the sedimentation control benchmark may stipulate that the predetermined dosage of flocculants is 5 mg / L, and the predetermined dosage of magnetic powder is 10 mg / L; the sedimentation dosage information is then compared with the predetermined dosage information to calculate the degree of deviation of the dosage. For example, the actual analysis shows that the sedimentation dosage of flocculants is 6 mg / L, and the predetermined dosage is 5 mg / L. The deviation of the flocculant dosage is 6-5=1 mg / L. Similarly, the dosage deviation is converted into a deviation coefficient, such as the relative deviation coefficient of the flocculant dosage is 1 / 5=0.2; the dosage deviation coefficients of various agents are then comprehensively processed to obtain the dosage deviation coefficient, which reflects the degree of deviation of the current actual dosage from the predetermined dosage. For example, Figure 2 The figure shows the quarterly chemical and electricity consumption saving curve of a sewage treatment plant.

[0039] Preferably, the real-time water quality deviation coefficient and the dosage deviation coefficient are weightedly calculated. Specifically, different weights are assigned to the real-time water quality deviation coefficient and the dosage deviation coefficient based on actual experience and experimental data. For example, water quality has a greater impact on the sedimentation effect. The weight assigned to the real-time water quality deviation coefficient is 0.6, and the weight assigned to the dosage deviation coefficient is 0.4. The two deviation coefficients are multiplied by their respective weights and then added together to obtain a dynamic variable factor. Assuming that the real-time water quality deviation coefficient is 0.3 and the dosage deviation coefficient is 0.2, the dynamic variable factor is 0.3×0.6+0.2×0.4=0.26. The dynamic variable factor comprehensively considers the changes in water quality and dosage, and can more comprehensively reflect the dynamic changes of the current magnetic coagulation sedimentation process.

[0040] Preferably, the sedimentation control benchmark is finally adjusted based on the dynamic variable factor, that is, the various parameters in the sedimentation control benchmark are adjusted. For example, if the dynamic variable factor is greater than 1, it means that the changes in the current water quality and dosage have caused the actual situation to deviate significantly from the predetermined standard, and the dosage of the reagent is increased, the sedimentation time is extended, or the stirring speed is adjusted. If the dynamic variable factor is less than 1, the dosage of the reagent is appropriately reduced or the sedimentation time is shortened. By dynamically adjusting the sedimentation control benchmark, a real-time sedimentation control strategy is obtained, which can timely and accurately adjust the various parameters of the magnetic coagulation sedimentation process according to the real-time changes in water quality and dosage, thereby improving the sedimentation effect and reducing treatment costs.

[0041] Furthermore, the specific configuration of the real-time sedimentation control strategy acquisition module 30 also includes obtaining any water quality index; traversing and matching the real-time water quality information and the predetermined water quality information to obtain the real-time water quality parameter and the predetermined water quality parameter corresponding to the arbitrary water quality index; introducing a deviation evaluation function to evaluate and analyze the real-time water quality parameter and the predetermined water quality parameter to obtain the real-time water quality deviation coefficient; wherein the expression of the deviation evaluation function is:

[0042] ;

[0043] Refers to the real-time water quality deviation coefficient, It refers to the The value of the real-time water quality parameter, It refers to the The value of a predetermined water quality parameter, It refers to the The weight coefficient of the parameter, is the sum of water quality parameters, and is an integer greater than or equal to 3.

[0044] Preferably, any one of the water quality indicators is selected, such as turbidity, and then the specific value corresponding to the selected water quality indicator is found in the real-time water quality information by searching, finding, etc., that is, the real-time water quality parameter. For example, the turbidity of the water body currently monitored in real time is 20NTU. Similarly, the predetermined value corresponding to the same water quality indicator is found in the predetermined water quality information (the water quality parameter pre-set in the sedimentation control benchmark), that is, the predetermined water quality parameter. For example, the turbidity specified in the sedimentation control benchmark should not exceed 15NTU, and 15NTU is the predetermined water quality parameter. Using the deviation evaluation function The degree of deviation between the real-time water quality parameters and the predetermined water quality parameters is measured, and the real-time water quality deviation coefficient is calculated, where n is an integer greater than or equal to 3, indicating that at least three different water quality parameters will be considered in the evaluation.

[0045] Furthermore, the specific configuration of the real-time sedimentation control strategy acquisition module 30 also includes extracting the mixing control information in the sedimentation control benchmark, wherein the mixing control information includes a predetermined stirring intensity and a predetermined reaction time; using the dynamic variable factor as a weight to perform weighted adjustment on the predetermined stirring intensity and the predetermined reaction time to obtain the real-time stirring intensity and the real-time reaction time respectively; and forming the real-time sedimentation control strategy based on the real-time stirring intensity and the real-time reaction time.

[0046] Preferably, mixing control information is extracted from the sedimentation control benchmark, specifically including a predetermined stirring intensity and a predetermined reaction time, wherein the predetermined stirring intensity refers to the standard of stirring equipment operation intensity under normal circumstances, so that the precipitant and the water body are fully mixed, for example, the speed of the stirrer is set to 200 revolutions per minute, which is the predetermined stirring intensity; the predetermined reaction time refers to the time required for the precipitant to react with the water body under normal conditions to achieve the expected precipitation effect, such as setting the reaction time to 30 minutes, which is the predetermined reaction time; and the dynamic variable factor is used as a weight to perform weighted adjustment on the predetermined stirring intensity and the predetermined reaction time. Specifically, the predetermined stirring intensity is multiplied by the dynamic variable factor. If the dynamic variable factor is greater than 1, it means that the current water quality or dosage change requires more intense stirring during the mixing process, and the real-time stirring intensity will be higher than the predetermined stirring intensity. If the dynamic variable factor is less than 1, the real-time stirring intensity will be reduced. For example, if the predetermined stirring intensity is 200 revolutions per minute and the dynamic variable factor is 1.2, then the real-time stirring intensity is 200×1.2=240 revolutions per minute.

[0047] Preferably, the predetermined reaction time is multiplied by the dynamic variable factor. If the dynamic variable factor is greater than 1, the precipitation reaction may take longer to fully proceed due to complex water quality or changes in dosage, and the real-time reaction time will be extended; if the dynamic variable factor is less than 1, the real-time reaction time will be shortened. For example, if the predetermined reaction time is 30 minutes and the dynamic variable factor is 0.8, the real-time reaction time is 30×0.8=24 minutes; finally, the adjusted real-time stirring intensity and real-time reaction time, together with other parameters in the precipitation process (such as the determined precipitant dosage, etc.), constitute a real-time precipitation control strategy, which is a specific operation plan for the current actual water quality and precipitant dosage conditions, and is used to guide the operation of the precipitation equipment. For example, the real-time precipitation control strategy may stipulate that in the next precipitation operation, the stirring equipment operates at a real-time stirring intensity of 240 rpm, the reaction time is controlled to 24 minutes, and the reagent is added according to the calculated precipitant dosage to achieve an efficient and stable precipitation effect.

[0048] Furthermore, the specific configuration of the real-time sedimentation control strategy acquisition module 30 also includes dynamically collecting a real-time microscopic image of the target magnetic flocs in the target water body; performing feature evaluation and analysis on the real-time microscopic image according to a microscopic state evaluation mechanism to obtain a real-time microscopic state; obtaining an arbitrary information value coefficient of the real-time microscopic state for the arbitrary water quality index; predicting and analyzing the real-time water quality parameters in combination with the arbitrary information value coefficient to obtain predicted real-time water quality parameters; and calibrating the real-time water quality parameters with the predicted real-time water quality parameters.

[0049] Preferably, a USB microscope with a price of no more than 5,000 yuan and a resolution of 5 μm is used, combined with a mobile phone camera, to replace an industrial-grade microscope camera to dynamically capture the target magnetic flocs in the target water body and obtain real-time microscopic images. Specifically, the modified imaging unit can achieve image acquisition functions similar to those of an industrial-grade microscope camera at a lower cost. For example, in the sedimentation process of sewage treatment, the microscopic morphology of the magnetic flocs in the water can be photographed to observe information related to the sedimentation effect. Then, a PLC-embedded AI module is used to evaluate and analyze the characteristics of the target magnetic flocs in the image (such as size, shape, and degree of aggregation) according to a pre-set microscopic state evaluation mechanism, thereby determining the current real-time microscopic state of the target water body. The AI ​​module uses a lightweight neural network model (MobileNetV3). For example, if the magnetic flocs are large and tightly clustered, the sedimentation effect is good; if the magnetic flocs are small and dispersed, the sedimentation effect may be poor. For example, Table 1 shows relevant data for the reuse and renovation of a certain urban sewage treatment plant (with a treatment capacity of 10,000 tons / day):

[0050] Table 1 A list of hardware modifications for a certain process

[0051]

[0052] Preferably, the information value coefficient of the real-time microscopic state for any water quality index is then obtained. Specifically, different real-time microscopic states have different reference values ​​for each water quality index, i.e., information value coefficients. The real-time microscopic image is analyzed according to the microscopic state evaluation mechanism to determine the information value coefficient of the current real-time microscopic state for any water quality index. For any water quality index (such as turbidity, pH value, etc.), the information value coefficient of the current real-time microscopic state for the index is determined. For example, for the turbidity index, when the microscopic state shows that the magnetic flocs are tightly aggregated, its information value coefficient for turbidity reduction may be higher; conversely, if the magnetic flocs are dispersed, the information value coefficient is lower.

[0053] Preferably, the obtained information value coefficient is combined with the real-time water quality parameters for predictive analysis. Specifically, through a large amount of experiments or historical data, a relationship model (such as a linear regression model, a neural network model, etc.) between the information value coefficient corresponding to the real-time microstate and the water quality parameters is established. For example, the information value coefficient corresponding to the microstate of the target magnetic flocculent (such as size, degree of aggregation, etc.) is used as the independent variable, and the water quality index (such as turbidity, dissolved oxygen, etc.) is used as the dependent variable. Historical data is used for training to obtain a mathematical relationship between the two; the information value coefficient is then substituted into the mathematical relationship to calculate the predicted real-time water quality parameters; the predicted real-time water quality parameters are then compared with the real-time water quality parameters obtained by monitoring, and the difference between the two is calculated. Finally, an adjustment ratio is determined based on historical data for calibrating the real-time water quality parameters to improve the accuracy of water quality monitoring and treatment.

[0054] Furthermore, the specific configuration of the real-time sedimentation control strategy acquisition module 30 also includes collecting real-time image feature information of the real-time microscopic image; normalizing and weighting the real-time image color parameters and real-time image structure parameters in the real-time image feature information to obtain a real-time image feature value; and standardizing and correcting the real-time image feature value in combination with the microscopic state coefficient stored in the memory of the microscopic state evaluation mechanism to obtain the real-time microscopic state.

[0055] Preferably, after dynamically collecting real-time microscopic images of target magnetic flocs in the target water body using a modified USB microscope and a mobile phone camera, characteristic information in the image is extracted, including real-time image color parameters and real-time image structure parameters. Extracting the real-time image color parameters specifically includes obtaining hue information by converting the image from the RGB color space to the HSV (hue, saturation, value) color space, and similarly obtaining saturation information in the HSV color space, wherein saturation (S) reflects the vividness of the color, and the value range is 0~1. Colors with higher saturation indicate higher purity, while colors with lower saturation are more inclined to gray; in the value (V) in the HSV space and in the RGB color space, the brightness information of the image is obtained by calculating the average value of the pixel values, etc., to reflect the overall brightness of the image. Factors such as the concentration and size of the magnetic flocs and the turbidity of the surrounding water body will affect the brightness of the image.

[0056] Preferably, extracting real-time image structural parameters specifically includes using an edge detection algorithm, such as the Canny algorithm, to extract edge information of the magnetic flocs, thereby analyzing and obtaining their shape characteristics; converting the pixel size in the image to actual physical dimensions based on the image resolution and the known microscope magnification to obtain size characteristics; describing the texture information of the magnetic flocs by calculating statistics of the gray-level co-occurrence matrix (GLCM), such as contrast, correlation, energy, and entropy; and calculating parameters such as the average distance between magnetic flocs and clustering density to analyze the distribution characteristics of the magnetic flocs in the image. Because the value ranges and dimensions of real-time image color parameters and real-time image structural parameters may differ, different real-time image feature parameters have different importance in determining the microscopic state of the magnetic flocs. Then, the real-time image color parameters and real-time image structural parameters in the real-time image feature information are normalized and weighted, that is, different weights are assigned to the color parameters and structural parameters, and the real-time image feature values ​​are calculated to preliminarily reflect the characteristics of the magnetic flocs presented in the image.

[0057] Preferably, the microstate evaluation mechanism pre-stores microstate coefficients corresponding to different microstates. These coefficients are standard reference values ​​determined through a large number of experiments and analyses. Finally, the calculated real-time image characteristic values ​​are compared and calculated with these microstate coefficients (such as performing linear transformation), and the real-time image characteristic values ​​are standardized and corrected. The corrected result is the real-time microstate, which more accurately corresponds to the actual microstate of the target magnetic flocs in the target water body, such as whether it is in a good cohesion state, a state of beginning to disperse, etc.

[0058] Furthermore, the specific configuration of the real-time sedimentation control strategy acquisition module 30 also includes extracting a first historical data set from the magnetic coagulation sedimentation database; performing mutual information analysis on the first historical microscopic state of the first magnetic floc in the first historical data set and the first historical indicator parameter corresponding to the first historical arbitrary water quality indicator to obtain a first historical mutual information coefficient; and using the first historical mutual information coefficient as the arbitrary information value coefficient.

[0059] Preferably, a first historical data set is extracted from a magnetic coagulation sedimentation database, wherein the magnetic coagulation sedimentation database stores a large amount of data related to the magnetic coagulation sedimentation process, and the first historical data set includes various information about the first magnetic flocs, and related data of the corresponding first historical arbitrary water quality indicators, including the state of the magnetic flocs and the changes in the water quality indicators under different conditions; then, a mutual information analysis is performed on the first historical microstate of the first magnetic flocs and the first historical indicator parameters corresponding to the first historical arbitrary water quality indicators in the first historical data set, that is, the degree of correlation between the two variables is determined by calculating the mutual information between them, wherein the mutual information is used to measure the degree of mutual dependence between two random variables, and the first historical microstate of the first magnetic flocs and the first historical indicator parameters corresponding to the first historical arbitrary water quality indicators are two random variables. For example, if the particle size distribution of the magnetic flocs in the first historical microstate is different from that in the first historical indicator parameters. There is a high mutual information between the turbidity and the particle size distribution of the magnetic flocs, which means that the microscopic state of the magnetic flocs can reflect the changes in the water quality indicators to a certain extent, and then the first historical mutual information coefficient is obtained, which quantifies the degree of mutual dependence between the two. The value range is usually 0 to positive infinity. The larger the value, the higher the degree of mutual dependence. Finally, the calculated first historical mutual information coefficient is used as the arbitrary information value coefficient, which is used to measure the importance or value of the real-time microscopic state of the first magnetic flocs for predicting any water quality indicator when predicting and analyzing real-time water quality parameters. For example, if the first historical mutual information coefficient is high, it means that the microscopic state of the first magnetic flocs has a high information value for the prediction of the corresponding water quality indicator. When predicting real-time water quality parameters, it is necessary to pay more attention to the information provided by the real-time microscopic state. On the contrary, if the coefficient is low, it means that the predictive effect of the microscopic state on the water quality indicator is relatively small.

[0060] The magnetic coagulation and sedimentation control processing module 40 is used to perform magnetic coagulation and sedimentation control processing on the target water body according to the real-time sedimentation control strategy.

[0061] Preferably, the target water body is subjected to magnetic coagulation sedimentation control treatment according to the real-time sedimentation control strategy to achieve better sedimentation effect and water quality treatment goals. Specifically, according to the precipitant dosage (including flocculant, magnetic powder, etc.) determined in the real-time sedimentation control strategy, the dosage of the reagents is accurately controlled. For example, the strategy stipulates that under the current real-time water quality conditions, the dosage of flocculant is 8 mg per liter of water, and the dosage of magnetic powder is 12 mg per liter of water. Then, the corresponding reagents are accurately added to the target water body according to this amount. By accurately controlling the dosage of the reagents, the sedimentation effect can be guaranteed, the waste of reagents can be avoided, and the treatment cost can be reduced. According to the requirements of the real-time stirring intensity, the operating parameters of the stirring equipment are adjusted. If the real-time stirring intensity determined by the real-time sedimentation control strategy is a stirrer speed of 250 revolutions per minute, the stirring equipment will be controlled. The speed of the equipment is adjusted to this set value. Appropriate stirring intensity helps to fully mix the agent with the water body, so that the magnetic flocs can be better formed and aggregated, and the sedimentation efficiency is improved; strictly control the time of the magnetic coagulation reaction in the target water body. For example, the real-time sedimentation control strategy stipulates that the reaction time is 25 minutes. Then, from the beginning of the agent addition, the timing device is started. When the reaction time reaches 25 minutes, the subsequent sedimentation and separation operations are entered. The accurate reaction time can ensure that the agent fully reacts with the impurities in the water body to form larger, easy-to-precipitate magnetic floc particles; after the reaction is completed, according to the guidance of the real-time sedimentation control strategy, the sedimentation and separation operation is carried out, including controlling the operating parameters of the sedimentation equipment, such as the liquid level height of the sedimentation tank, the drainage speed, etc., to ensure that the magnetic flocs can be smoothly settled to the bottom of the sedimentation tank and the treated supernatant is discharged. During the entire magnetic coagulation sedimentation control treatment process, water quality parameters (such as turbidity, pH value, dissolved oxygen, etc.) and equipment operating status (such as the current and voltage of the stirring equipment, the liquid level of the sedimentation equipment, etc.) are continuously monitored in real time. If the actual situation is found to be inconsistent with the expectations of the real-time sedimentation control strategy, for example, the monitored turbidity does not achieve the expected reduction effect, the system will re-analyze and calculate based on the new monitoring data and adjust the real-time sedimentation control strategy to ensure the stability and effectiveness of the treatment process, and ultimately achieve the ideal water quality treatment effect for the target water body. For example, Table 2 shows the economic advantages of this application:

[0062] Table 2 Data related to the economic advantages of this application

[0063]

[0064] In the above, refer to Figure 1 The control system of low-cost magnetic coagulation precipitation dynamically controlled by AI according to the embodiment of the present invention is described in detail. Figure 2 The present invention describes a control method for AI-dynamically regulating low-cost magnetic coagulation precipitation according to an embodiment of the present invention.

[0065] AI dynamically controls low-cost magnetic coagulation and sedimentation control methods, such as Figure 2 As shown, the method includes: dynamically monitoring the water quality of the target water body through a predetermined probe to obtain real-time water quality information; reading the category to be added, and using the real-time water quality information as a constraint, analyzing to obtain a first sedimentation dosage of the first category in the category to be added, and forming sedimentation dosage information; forming a dynamic variable constraint based on the real-time water quality information and the sedimentation dosage information, and dynamically adjusting the sedimentation control benchmark under the dynamic variable constraint to obtain a real-time sedimentation control strategy; performing magnetic coagulation sedimentation control treatment on the target water body according to the real-time sedimentation control strategy.

[0066] In a possible implementation, the AI ​​dynamic control method for low-cost magnetic coagulation precipitation also includes: the real-time water quality information includes at least real-time turbidity, real-time pH value and real-time temperature.

[0067] In a possible implementation, the AI ​​dynamic control method for low-cost magnetic coagulation sedimentation further includes: the categories to be added include flocculants and magnetic powder.

[0068] In one possible implementation, the AI ​​dynamic control method for low-cost magnetic coagulation precipitation also includes: extracting predetermined water quality information from the sedimentation control benchmark; comparing the real-time water quality information with the predetermined water quality information to obtain a real-time water quality deviation coefficient; extracting predetermined dosage information from the sedimentation control benchmark; comparing the sedimentation dosage information with the predetermined dosage information to obtain a dosage deviation coefficient; performing weighted calculation on the real-time water quality deviation coefficient and the dosage deviation coefficient to obtain a dynamic variable factor; and adjusting the sedimentation control benchmark based on the dynamic variable factor to obtain the real-time sedimentation control strategy.

[0069] In one possible implementation, the AI ​​dynamic control method for low-cost magnetic coagulation precipitation further includes: obtaining an arbitrary water quality index; traversing and matching the real-time water quality information and the predetermined water quality information to obtain a real-time water quality parameter and a predetermined water quality parameter corresponding to the arbitrary water quality index; introducing a deviation evaluation function to evaluate and analyze the real-time water quality parameter and the predetermined water quality parameter to obtain the real-time water quality deviation coefficient; wherein the expression of the deviation evaluation function is:

[0070] ;

[0071] Refers to the real-time water quality deviation coefficient, It refers to the The value of the real-time water quality parameter, It refers to the The value of a predetermined water quality parameter, It refers to the The weight coefficient of the parameter, is the sum of water quality parameters, and is an integer greater than or equal to 3.

[0072] In one possible implementation, the AI ​​dynamic control method for low-cost magnetic coagulation precipitation also includes: extracting mixing control information from the precipitation control benchmark, wherein the mixing control information includes a predetermined stirring intensity and a predetermined reaction time; using the dynamic variable factor as a weight to perform weighted adjustment on the predetermined stirring intensity and the predetermined reaction time to obtain real-time stirring intensity and real-time reaction time, respectively; and forming the real-time precipitation control strategy based on the real-time stirring intensity and the real-time reaction time.

[0073] In one possible implementation, the AI ​​dynamic control method for low-cost magnetic coagulation precipitation also includes: dynamically acquiring a real-time microscopic image of the target magnetic flocs in the target water body; performing feature evaluation and analysis on the real-time microscopic image according to a microscopic state evaluation mechanism to obtain a real-time microscopic state; obtaining an arbitrary information value coefficient of the real-time microscopic state for the arbitrary water quality index; predicting and analyzing the real-time water quality parameters in combination with the arbitrary information value coefficient to obtain predicted real-time water quality parameters; and calibrating the real-time water quality parameters with the predicted real-time water quality parameters.

[0074] In one possible implementation, the AI ​​dynamic control method for low-cost magnetic coagulation precipitation also includes: collecting real-time image feature information of the real-time microscopic image; normalizing and weighting the real-time image color parameters and real-time image structure parameters in the real-time image feature information to obtain a real-time image feature value; and standardizing and correcting the real-time image feature value in combination with the microscopic state coefficient stored in the microscopic state evaluation mechanism to obtain the real-time microscopic state.

[0075] In one possible implementation, the AI ​​dynamic control method for low-cost magnetic coagulation precipitation also includes: extracting a first historical data set from a magnetic coagulation precipitation database; performing mutual information analysis on a first historical microscopic state of a first magnetic floc in the first historical data set and a first historical indicator parameter corresponding to a first historical arbitrary water quality indicator to obtain a first historical mutual information coefficient; and using the first historical mutual information coefficient as the arbitrary information value coefficient.

[0076] The control system for AI dynamic control of low-cost magnetic coagulation precipitation provided in an embodiment of the present invention can execute the control method for AI dynamic control of low-cost magnetic coagulation precipitation provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0077] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0078] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A control system for dynamically regulating magnetic coagulation precipitation, characterized in that: The system comprises: Dynamic water quality monitoring module, used to dynamically monitor the water quality of the target water body through a predetermined probe to obtain real-time water quality information; A precipitation dosage information composition module is used to read the category of products to be added, and based on the real-time water quality information, analyze and obtain a first precipitation dosage of a first category in the category of products to be added to form precipitation dosage information; A real-time sedimentation control strategy acquisition module is used to form a dynamic variable constraint based on the real-time water quality information and the sedimentation dosage information, and dynamically adjust the sedimentation control benchmark under the dynamic variable constraint to obtain a real-time sedimentation control strategy; A magnetic coagulation and sedimentation control processing module is used to perform magnetic coagulation and sedimentation control processing on the target water body according to the real-time sedimentation control strategy; The real-time precipitation control strategy acquisition module includes: A predetermined water quality information extraction unit, configured to extract predetermined water quality information from the sedimentation control benchmark, wherein the predetermined water quality information includes predetermined water quality indicators; a real-time water quality deviation coefficient obtaining unit, configured to compare the real-time water quality information with the predetermined water quality information to obtain a real-time water quality deviation coefficient; a predetermined dosage information extraction unit, configured to extract predetermined dosage information from the sedimentation control benchmark; a dosage deviation coefficient obtaining unit, configured to compare the precipitation dosage information with the predetermined dosage information to obtain a dosage deviation coefficient; A dynamic variable factor obtaining unit is used to perform weighted calculation on the real-time water quality deviation coefficient and the dosage deviation coefficient to obtain a dynamic variable factor; A real-time sedimentation control strategy obtaining unit, configured to adjust the sedimentation control benchmark based on the dynamic variable factor to obtain the real-time sedimentation control strategy; An arbitrary water quality index obtaining unit, used for obtaining any water quality index among predetermined water quality indexes; a water quality parameter obtaining unit, configured to traverse and match the real-time water quality information and the predetermined water quality information to obtain the real-time water quality parameter and the predetermined water quality parameter corresponding to the arbitrary water quality index; a water quality parameter evaluation and analysis unit, configured to introduce a deviation evaluation function to evaluate and analyze the real-time water quality parameter and the predetermined water quality parameter to obtain the real-time water quality deviation coefficient; Wherein, the expression of the deviation evaluation function is: ； Refers to the real-time water quality deviation coefficient, It refers to the The value of the real-time water quality parameter, It refers to the The value of a predetermined water quality parameter, It refers to the The weight coefficient of the parameter, is the sum of water quality parameters, and is an integer greater than or equal to 3; A real-time microscopic image acquisition unit, used for dynamically acquiring a real-time microscopic image of the target magnetic flocs in the target water body; a feature evaluation and analysis unit, configured to perform feature evaluation and analysis on the real-time microscopic image according to a microscopic state evaluation mechanism to obtain a real-time microscopic state; An information value coefficient acquisition unit, configured to acquire any information value coefficient of the real-time microscopic state to any water quality index; A prediction and analysis unit, configured to perform prediction and analysis on the real-time water quality parameter in combination with the arbitrary information value coefficient to obtain a predicted real-time water quality parameter; a water quality parameter calibration unit, configured to calibrate the real-time water quality parameter using the predicted real-time water quality parameter; A real-time image feature information acquisition unit, configured to acquire real-time image feature information of the real-time microscopic image; A real-time image feature value obtaining unit, configured to perform normalized weighting on the real-time image color parameter and the real-time image structure parameter in the real-time image feature information to obtain a real-time image feature value; The standardization correction unit is used to perform standardization correction on the real-time image feature value in combination with the microscopic state coefficient stored in the memory of the microscopic state evaluation mechanism to obtain the real-time microscopic state.

2. The control system for dynamically controlling magnetic coagulation precipitation according to claim 1, characterized in that: The dynamic water quality monitoring module includes: The real-time water quality information includes at least real-time turbidity, real-time pH value and real-time temperature.

3. The control system for dynamically controlling magnetic coagulation and sedimentation according to claim 1, characterized in that: The products to be added include flocculants and magnetic powder.

4. The control system for dynamically controlling magnetic coagulation and sedimentation according to claim 1, characterized in that: The real-time precipitation control strategy acquisition module includes: a mixing control information extraction unit, configured to extract mixing control information from the sedimentation control benchmark, wherein the mixing control information includes a predetermined stirring intensity and a predetermined reaction time; a weighted adjustment unit, configured to perform weighted adjustment on the predetermined stirring intensity and the predetermined reaction time using the dynamic variable factor as a weight, to obtain a real-time stirring intensity and a real-time reaction time, respectively; A sedimentation control strategy forming unit is used to form the real-time sedimentation control strategy based on the real-time stirring intensity and the real-time reaction time.

5. The control system for dynamically controlling magnetic coagulation and sedimentation according to claim 1, characterized in that: The real-time precipitation control strategy acquisition module also includes: A first historical data set extraction unit is used to extract a first historical data set from a magnetic coagulation sedimentation database; a mutual information analysis unit, configured to perform mutual information analysis on a first historical microscopic state of the first magnetic floc in the first historical data set and a first historical indicator parameter corresponding to a first historical arbitrary water quality indicator to obtain a first historical mutual information coefficient; The arbitrary information value coefficient obtaining unit is used to use the first historical mutual information coefficient as the arbitrary information value coefficient.

6. A method for dynamically regulating magnetic coagulation precipitation, characterized in that: The method is applied to the control system for dynamically controlling magnetic coagulation precipitation according to any one of claims 1 to 5, and the method comprises: Dynamically monitor the water quality of the target water body through predetermined probes to obtain real-time water quality information; Read the category of products to be added, and using the real-time water quality information as a constraint, analyze and obtain a first precipitation dosage of a first category in the category of products to be added, to form precipitation dosage information; Based on the real-time water quality information and the sedimentation dosage information, a dynamic variable constraint is formed, and the sedimentation control benchmark is dynamically adjusted under the dynamic variable constraint to obtain a real-time sedimentation control strategy; The target water body is subjected to magnetic coagulation sedimentation control treatment according to the real-time sedimentation control strategy.

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