Flocculant intelligent selection system and method based on water quality conditions

Abstract

The invention discloses an intelligent flocculant selection system and method based on water quality conditions, and the system comprises a water quality condition detection module which collects water quality data; the data analysis module is used for preprocessing and analyzing the collected water quality data; the intelligent selection module is used for quickly recommending the type and dosage of the flocculating agent most suitable for the current water quality condition according to the water quality parameters in combination with a database and a prediction model. The system has the advantage of rapidly and quantitatively selecting the flocculating agent, and solves the problems that in the using process of an existing intelligent flocculating agent selection system, the corresponding flocculating agent is inconvenient to automatically select according to different water quality conditions, an automatic selection mechanism is lacked, an operator needs to frequently and manually adjust the type and the adding amount of the flocculating agent, and the cost is high. The workload and the operation difficulty are increased, and if adjustment is not carried out according to specific water quality conditions, the problem that the flocculation effect is not ideal is possibly caused.

Description

Technical Field

[0001] The present invention relates to the technical field of water treatment, and in particular to a system and method for intelligently selecting a flocculant based on water quality conditions. Background Art

[0002] The intelligent flocculant selection system and selection method based on water quality conditions is a technology that uses information technology and data analysis to optimize the selection and use of flocculants in water treatment processes. In water treatment processes, flocculants are chemical substances used to help remove suspended matter, colloids and other impurities in water. Different water sources have a significant impact on the selection and effect of flocculants. Therefore, in order to improve water treatment efficiency and quality while reducing costs, it is crucial to rationally select flocculants and their dosage.

[0003] Different flocculants show different effectiveness in different pH ranges. For example, aluminum salt flocculants work best at pH 6.5-7.5, while iron salt flocculants work better at pH 5-8. If the pH value is outside the appropriate range, the flocculation effect will be significantly reduced. Temperature affects the solubility and reaction rate of the flocculant. Some flocculants dissolve slowly at low temperatures, affecting the flocculation effect. High turbidity water has more suspended particles and requires more flocculants to form larger flocs. Low turbidity water requires less flocculants.

[0004] The existing intelligent flocculant selection system is not convenient for automatically selecting the corresponding flocculant according to different water quality conditions during use. Due to the lack of an automated selection mechanism, operators need to frequently manually adjust the type and dosage of flocculants, which increases the workload and difficulty of operation. Moreover, if the system is not adjusted according to specific water quality conditions, it may lead to unsatisfactory flocculation effects. Summary of the invention

[0005] The purpose of the present invention is to provide a system and method for intelligent flocculant selection based on water quality conditions. The present invention has the advantage of rapid quantitative selection of flocculants, and solves the problem that the existing intelligent flocculant selection system is not convenient for automatically selecting the corresponding flocculant according to different water quality conditions during use. Due to the lack of an automated selection mechanism, operators need to frequently manually adjust the type and dosage of flocculants, which increases the workload and difficulty of operation. Moreover, if the adjustment is not made according to specific water quality conditions, it may lead to unsatisfactory flocculation effect.

[0006] To achieve the above object, the present invention provides the following technical solution: a flocculant intelligent selection system based on water quality conditions, comprising:

[0007] A water quality condition detection module, wherein the water quality condition detection module collects water quality data;

[0008] A data analysis module, which pre-processes and analyzes the collected water quality data;

[0009] Intelligent selection module, which quickly recommends the type and dosage of flocculant that best suits the current water quality conditions based on water quality parameters, combined with databases and prediction models;

[0010] An automatic feeding device, which feeds the flocculant according to the type and dosage of the flocculant selected by the intelligent selection module;

[0011] The treatment effect feedback module performs water quality detection on the flocculated water and transmits the detected water quality data to the data analysis module.

[0012] As a preferred embodiment of the intelligent flocculant selection system based on water quality conditions of the present invention, the water quality data includes pH value, temperature, conductivity, turbidity, chromaticity, organic matter and inorganic matter content of water.

[0013] As a preferred embodiment of the water quality condition-based flocculant intelligent selection system of the present invention, the intelligent selection module uses statistical methods and machine learning algorithms to establish a prediction model, and trains and verifies the prediction model to evaluate the flocculation effects of different flocculants under specific water quality conditions.

[0014] As a preferred embodiment of the intelligent flocculant selection system based on water quality conditions of the present invention, the database includes a water quality database, a flocculant database and a treatment effect database. The water quality database stores real-time data and historical data of water quality, the flocculant database stores information of different types of flocculants, and the treatment effect database stores real-time treatment effects and historical treatment effects.

[0015] As a preferred embodiment of the intelligent flocculant selection system based on water quality conditions of the present invention, when recommending the type and dosage of flocculant, the intelligent selection module obtains the current water quality parameters from the data analysis module in real time, and then inputs the current water quality parameters into the trained prediction model. The prediction model outputs the type and dosage of flocculant suitable for the current water quality conditions, and then displays the recommended type and dosage of flocculant through the user interface for the operator's reference.

[0016] As a preferred embodiment of the intelligent flocculant selection system based on water quality conditions of the present invention, the automatic feeding device includes a weighing mechanism, a conveying mechanism is fixedly installed on the top of the conveying mechanism, a feeding mechanism connected thereto is fixedly connected to the top of the surface of the conveying mechanism, a feeding end of the feeding mechanism is connected to a flocculant storage mechanism, and the flocculant storage mechanism is fixedly installed on the top of the weighing mechanism.

[0017] As a preferred embodiment of the intelligent flocculant selection system based on water quality conditions of the present invention, the weighing mechanism includes a base, the bottom of the base is fixedly connected to a leg, the top of the base is fixedly connected to a column and a weighing sensor, the top of the column is fixedly connected to a fixing ring, the top of the weighing sensor is fixedly connected to a weighing frame, a weighing bucket is installed inside the weighing frame, and the bottom of the weighing bucket is connected to a discharge valve.

[0018] As a preferred embodiment of the intelligent flocculant selection system based on water quality conditions of the present invention, the conveying mechanism includes a conveying pipe, the fixing ring is fixedly sleeved on the surface of the conveying pipe, the bottom of the conveying pipe is connected to a connecting bucket, the bottom of the connecting bucket extends to the inner cavity of the weighing bucket, the top of the conveying pipe is fixedly connected to a first motor, the output shaft of the first motor passes through the inner cavity of the conveying pipe and is fixedly connected to a spiral rod, and a through hole is opened on the top of the surface of the conveying pipe.

[0019] As a preferred embodiment of the flocculant intelligent selection system based on water quality conditions of the present invention, the feeding mechanism includes a shell, one side of the shell is connected with a feeding pipe, the feeding pipe is fixedly installed in the through hole, the other side of the shell is connected with a connecting pipe, the inner wall of the shell is rotatably connected with a rotating drum, the surface of the rotating drum is fixedly connected with a plurality of material shifting plates, the top of the shell is fixedly connected with a mounting plate, the top of the mounting plate is fixedly connected with a second motor, the output shaft of the second motor is fixedly connected to the top of the rotating drum, the flocculant storage mechanism includes a storage cylinder, the bottom of the storage cylinder is connected with a bent pipe, the bent pipe is connected with the connecting pipe, the top of the storage cylinder is threadedly sleeved with a threaded cover, the top of the threaded cover is connected with a feed port, the surface of the storage cylinder is movably sleeved with a support ring, the surface of the support ring is fixedly connected with a connecting piece, and the connecting piece is fixedly installed on the top of the conveying pipe.

[0020] The intelligent flocculant selection method based on water quality conditions includes the following steps:

[0021] S1. Water quality condition detection: Use water quality monitoring equipment to detect water quality conditions. The water quality monitoring equipment includes pH meter, thermometer, conductivity meter, turbidity meter and colorimeter. The water quality data including pH value, temperature, conductivity, turbidity, color, organic and inorganic content are collected in real time through the water quality monitoring equipment, and the collected water quality data is transmitted to the central control system via wired or wireless means;

[0022] S2. Data preprocessing and analysis: The central control system removes outliers from the data and fills in missing values ​​to ensure the accuracy and completeness of the data. It extracts the combination and time series characteristics of water quality parameters from the raw data and stores the preprocessed data in the water quality database, including real-time data and historical data.

[0023] S3, intelligent selection, load the trained prediction model, which is established using statistical methods and machine learning algorithms, and has been trained and verified. The current water quality parameters are obtained from the data analysis module in real time, and the current water quality parameters are input into the trained prediction model. The model outputs the type and dosage of flocculants suitable for the current water quality conditions, and displays the recommended type and dosage of flocculants through the user interface for the operator's reference;

[0024] S4, automatic feeding, generating feeding instructions according to the recommendation results of the intelligent selection module, and the automatic feeding device accurately adds the recommended type and dosage of flocculant from the corresponding storage structure according to the instructions, records the feeding time and dosage, and stores them in the treatment effect database;

[0025] S5. Feedback on treatment effect. After flocculation treatment, the treated water is tested by water quality monitoring equipment, and the treated water quality data is collected and transmitted to the data analysis module. The data analysis module analyzes the treated water quality data to evaluate whether the treatment effect meets the expected standard. According to the feedback on the treatment effect, the recommendation strategy of the intelligent selection module is adjusted to optimize the prediction model.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. The present invention can accurately measure the weight of the flocculant by setting a weighing sensor, ensuring that the amount of flocculant added each time meets the recommended value of the intelligent selection module to avoid overdosage or underdosage. The spiral rod can evenly and continuously transport the flocculant to ensure the stability and accuracy of the dosing process. The entire feeding process is coordinated by the feeding mechanism, the conveying mechanism and the weighing mechanism, which realizes full automation, reduces manual intervention and improves work efficiency.

[0028] 2. The present invention can automatically select the appropriate type and dosage of flocculant according to real-time water quality parameters by setting an intelligent selection module, and control the automatic feeding device to perform the feeding operation, thereby realizing closed-loop control. The storage cylinder can store different types of flocculants, and the flocculants can be conveniently replaced and supplemented through the feed port to meet the needs of different water quality conditions. The dosage of the flocculant can be flexibly adjusted through the recommendation of the intelligent selection module to ensure the optimal treatment effect.

[0029] 3. The present invention provides a threaded cover, which makes it easy to open the storage cylinder for internal cleaning and maintenance. The treatment effect feedback module can detect the water quality data after flocculation in real time, and transmit the data back to the data analysis module for evaluating the treatment effect and optimizing the prediction model. According to the feedback on the treatment effect, the intelligent selection module can continuously adjust and optimize the prediction model to improve the accuracy and reliability of the recommendation. The precise dosage can reduce the waste of flocculants, reduce the treatment cost, avoid excessive addition of flocculants, reduce secondary pollution to the environment, and improve the environmental friendliness of the treatment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the system of the present invention;

[0031] Figure 2 This is a schematic diagram of the structure of the automatic feeding device of the present invention;

[0032] Figure 3 The weighing mechanism structure of the present invention is shown in FIG. Figure 1 ;

[0033] Figure 4 The weighing mechanism structure of the present invention is shown in FIG. Figure 2 ;

[0034] Figure 5 It is a schematic cross-sectional view of the conveying mechanism of the present invention;

[0035] Figure 6 It is an exploded view of the feeding mechanism of the present invention;

[0036] Figure 7 It is a schematic diagram of the structure of the flocculant storage mechanism of the present invention.

[0037] In the figure: 1. weighing mechanism; 101. base; 102. weighing frame; 103. column; 104. fixing ring; 105. weighing bucket; 106. weighing sensor; 107. supporting leg; 108. unloading valve; 2. conveying mechanism; 201. conveying pipe; 202. through hole; 203. first motor; 204. screw rod; 205. connecting bucket; 3. feeding mechanism; 301. shell; 302. rotating drum; 303. connecting pipe; 304. second motor; 305. mounting plate; 306. material shifting plate; 307. feeding pipe; 4. flocculant storage mechanism; 401. storage cylinder; 402. threaded cover; 403. feed port; 404. connecting piece; 405. supporting ring; 406. elbow. DETAILED DESCRIPTION

[0038] Example 1

[0039] See also Figure 1-Figure 7 , intelligent flocculant selection system based on water quality conditions, including:

[0040] A water quality condition detection module, which collects water quality data;

[0041] Data analysis module, which pre-processes and analyzes the collected water quality data;

[0042] Intelligent selection module: The intelligent selection module quickly recommends the most suitable type and dosage of flocculant for the current water quality conditions based on water quality parameters, combined with database and prediction model;

[0043] An automatic feeding device, which feeds the flocculant according to the type and dosage of the flocculant selected by the intelligent selection module;

[0044] The treatment effect feedback module performs water quality testing on the flocculated water and transmits the tested water quality data to the data analysis module.

[0045] Furthermore, water quality data include water pH, temperature, conductivity, turbidity, color, organic matter and inorganic matter content.

[0046] Furthermore, the intelligent selection module uses statistical methods and machine learning algorithms to establish a prediction model, and trains and verifies the prediction model to evaluate the flocculation effect of different flocculants under specific water quality conditions.

[0047] Furthermore, the database includes a water quality database, a flocculant database and a treatment effect database. The water quality database stores real-time and historical data of water quality, the flocculant database stores information of different types of flocculants, and the treatment effect database stores real-time and historical treatment effects.

[0048] Furthermore, when the intelligent selection module recommends the type and dosage of flocculants, it obtains the current water quality parameters from the data analysis module in real time, and then inputs the current water quality parameters into the trained prediction model. The prediction model outputs the type and dosage of flocculants suitable for the current water quality conditions, and then displays the recommended type and dosage of flocculants through the user interface for the operator's reference.

[0049] Furthermore, the automatic feeding device includes a weighing mechanism 1, a conveying mechanism 2 is fixedly installed on the top of the conveying mechanism 2, a feeding mechanism 3 connected thereto is fixedly connected to the top of the surface of the conveying mechanism 2, a feeding end of the feeding mechanism 3 is connected to a flocculant storage mechanism 4, and the flocculant storage mechanism 4 is fixedly installed on the top of the weighing mechanism 1.

[0050] Furthermore, the weighing mechanism 1 includes a base 101, a leg 107 is fixedly connected to the bottom of the base 101, a column 103 and a weighing sensor 106 are fixedly connected to the top of the base 101, a fixing ring 104 is fixedly connected to the top of the column 103, a weighing frame 102 is fixedly connected to the top of the weighing sensor 106, a weighing bucket 105 is installed inside the weighing frame 102, and a discharge valve 108 is connected to the bottom of the weighing bucket 105.

[0051] Furthermore, the conveying mechanism 2 includes a conveying pipe 201, a fixing ring 104 is fixedly sleeved on the surface of the conveying pipe 201, a connecting bucket 205 is connected to the bottom of the conveying pipe 201, the bottom of the connecting bucket 205 extends to the inner cavity of the weighing bucket 105, a first motor 203 is fixedly connected to the top of the conveying pipe 201, an output shaft of the first motor 203 passes through the inner cavity of the conveying pipe 201 and is fixedly connected to a screw rod 204, and a through hole 202 is opened on the top of the surface of the conveying pipe 201.

[0052] Furthermore, the feeding mechanism 3 includes a shell 301, one side of the shell 301 is connected to a feeding pipe 307, the feeding pipe 307 is fixedly installed in the through hole 202, the other side of the shell 301 is connected to a connecting pipe 303, the inner wall of the shell 301 is rotatably connected to a rotating drum 302, the surface of the rotating drum 302 is fixedly connected to a plurality of material-diverting plates 306, the top of the shell 301 is fixedly connected to a mounting plate 305, the top of the mounting plate 305 is fixedly connected to a second motor 304, the output of the second motor 304 The output shaft is fixedly connected to the top of the rotating drum 302, and the flocculant storage mechanism 4 includes a storage cylinder 401. The bottom of the storage cylinder 401 is connected to a curved pipe 406, and the curved pipe 406 is connected to the connecting pipe 303. A threaded cover 402 is threadedly sleeved on the top of the storage cylinder 401, and the top of the threaded cover 402 is connected to a feed port 403. A support ring 405 is movably sleeved on the surface of the storage cylinder 401, and a connecting piece 404 is fixedly connected to the surface of the support ring 405. The connecting piece 404 is fixedly installed on the top of the conveying pipe 201.

[0053] When the automatic feeding device feeds the flocculant, the feeding mechanism 3, the conveying mechanism 2 and the conveying mechanism 3 are controlled to work according to the flocculant selected by the intelligent selection module and the dosage used. During the operation of the feeding mechanism 3, the flocculant previously stored in the storage cylinder 401 enters the shell 301 through the bent pipe 406 and the connecting pipe 303, the output shaft of the second motor 304 drives the rotating drum 302 to rotate, and the rotating drum 302 drives a plurality of material-dispensing plates 306 to rotate, so as to convey the flocculant in the shell 301 to the feeding pipe 307, and the flocculant in the feeding pipe 307 enters the interior of the conveying pipe 201 through the through hole 202. When it is necessary to add flocculant to the interior of the storage cylinder 401, different types of flocculants are fed into the interior of the storage cylinder 401 through the feed port 403. By setting the threaded cover 402, it is convenient for the staff to clean the interior of the storage cylinder 401, and the storage cylinder 401 can be supported by the coordinated use of the connecting piece 404 and the support ring 405.

[0054] During the operation of the conveying mechanism 2, the output shaft of the first motor 203 drives the screw rod 204 to rotate, and the screw rod 204 conveys the flocculant to the connecting bucket 205. The flocculant discharged from the connecting bucket 205 enters the inside of the weighing bucket 105. The weighing sensor 106 weighs the flocculant inside the weighing bucket 105 through the weighing frame 102. When the set weight is reached, the conveying mechanism 2 and the feeding mechanism 3 are closed, and the discharge valve 108 is started. The flocculant inside the weighing bucket 105 enters the sewage through the discharge valve 108 to flocculate the impurities in the sewage.

[0055] Example 2

[0056] See also Figure 1-Figure 7 , the intelligent flocculant selection method based on water quality conditions includes the following steps:

[0057] S1. Water quality condition detection: Use water quality monitoring equipment to detect water quality conditions. The water quality monitoring equipment includes pH meter, thermometer, conductivity meter, turbidity meter and colorimeter. The water quality data including pH value, temperature, conductivity, turbidity, color, organic and inorganic content are collected in real time through the water quality monitoring equipment, and the collected water quality data is transmitted to the central control system via wired or wireless means;

[0058] S2. Data preprocessing and analysis: The central control system removes outliers from the data and fills in missing values ​​to ensure the accuracy and completeness of the data. It extracts the combination and time series characteristics of water quality parameters from the raw data and stores the preprocessed data in the water quality database, including real-time data and historical data.

[0059] S3, intelligent selection, load the trained prediction model, which is established using statistical methods and machine learning algorithms, and has been trained and verified. The current water quality parameters are obtained from the data analysis module in real time, and the current water quality parameters are input into the trained prediction model. The model outputs the type and dosage of flocculants suitable for the current water quality conditions, and displays the recommended type and dosage of flocculants through the user interface for the operator's reference;

[0060] S4, automatic feeding, generating feeding instructions according to the recommendation results of the intelligent selection module, and the automatic feeding device accurately adds the recommended type and dosage of flocculant from the corresponding storage structure according to the instructions, records the feeding time and dosage, and stores them in the treatment effect database;

[0061] S5. Feedback on treatment effect. After flocculation treatment, the treated water is tested by water quality monitoring equipment, and the treated water quality data is collected and transmitted to the data analysis module. The data analysis module analyzes the treated water quality data to evaluate whether the treatment effect meets the expected standard. According to the feedback on the treatment effect, the recommendation strategy of the intelligent selection module is adjusted to optimize the prediction model.

[0062] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Intelligent flocculant selection system based on water quality conditions, characterized in that: include: A water quality condition detection module, wherein the water quality condition detection module collects water quality data; A data analysis module, which pre-processes and analyzes the collected water quality data; Intelligent selection module, which quickly recommends the type and dosage of flocculant that best suits the current water quality conditions based on water quality parameters, combined with databases and prediction models; An automatic feeding device, which feeds the flocculant according to the type and dosage of the flocculant selected by the intelligent selection module; The treatment effect feedback module performs water quality detection on the flocculated water and transmits the detected water quality data to the data analysis module.

2. The water quality condition-based flocculant intelligent selection system according to claim 1, characterized in that: The water quality data include pH value, temperature, conductivity, turbidity, color, organic matter and inorganic matter content of water.

3. The water quality-based flocculant intelligent selection system according to claim 2, characterized in that: The intelligent selection module uses statistical methods and machine learning algorithms to establish a prediction model, and trains and verifies the prediction model to evaluate the flocculation effects of different flocculants under specific water quality conditions.

4. The water quality-based flocculant intelligent selection system according to claim 3 is characterized in that: The database includes a water quality database, a flocculant database and a treatment effect database. The water quality database stores real-time data and historical data of water quality, the flocculant database stores information of different types of flocculants, and the treatment effect database stores real-time treatment effects and historical treatment effects.

5. The water quality-based flocculant intelligent selection system according to claim 4, characterized in that: When recommending the type and dosage of flocculants, the intelligent selection module obtains the current water quality parameters from the data analysis module in real time, and then inputs the current water quality parameters into the trained prediction model. The prediction model outputs the type and dosage of flocculants suitable for the current water quality conditions, and then displays the recommended type and dosage of flocculants through the user interface for reference by the operator.

6. The water quality-based flocculant intelligent selection system according to claim 5, characterized in that: The automatic feeding device comprises a weighing mechanism (1), a conveying mechanism (2) is fixedly mounted on the top of the conveying mechanism (2), a feeding mechanism (3) connected thereto is fixedly connected to the top of the surface of the conveying mechanism (2), a feeding end of the feeding mechanism (3) is connected to a flocculant storage mechanism (4), and the flocculant storage mechanism (4) is fixedly mounted on the top of the weighing mechanism (1).

7. The water quality-based flocculant intelligent selection system according to claim 6, characterized in that: The weighing mechanism (1) comprises a base (101), the bottom of the base (101) is fixedly connected to a support leg (107), the top of the base (101) is fixedly connected to a column (103) and a weighing sensor (106), the top of the column (103) is fixedly connected to a fixing ring (104), the top of the weighing sensor (106) is fixedly connected to a weighing frame (102), a weighing bucket (105) is installed inside the weighing frame (102), and the bottom of the weighing bucket (105) is connected to a discharge valve (108).

8. The water quality-based flocculant intelligent selection system according to claim 7, characterized in that: The conveying mechanism (2) comprises a conveying pipe (201), the fixing ring (104) is fixedly sleeved on the surface of the conveying pipe (201), the bottom of the conveying pipe (201) is connected to a connecting bucket (205), the bottom of the connecting bucket (205) extends to the inner cavity of the weighing bucket (105), the top of the conveying pipe (201) is fixedly connected to a first motor (203), the output shaft of the first motor (203) passes through the inner cavity of the conveying pipe (201) and is fixedly connected to a spiral rod (204), and a through hole (202) is opened on the top of the surface of the conveying pipe (201).

9. The water quality-based flocculant intelligent selection system according to claim 8, characterized in that: The feeding mechanism (3) comprises a shell (301), one side of the shell (301) is connected to a feeding pipe (307), the feeding pipe (307) is fixedly installed in the through hole (202), the other side of the shell (301) is connected to a connecting pipe (303), the inner wall of the shell (301) is rotatably connected to a rotating drum (302), the surface of the rotating drum (302) is fixedly connected to a plurality of material-diverting plates (306), the top of the shell (301) is fixedly connected to a mounting plate (305), the top of the mounting plate (305) is fixedly connected to a second motor (304), and the output shaft of the second motor (304) The flocculant storage mechanism (4) is fixedly connected to the top of the rotating drum (302), and comprises a storage drum (401). The bottom of the storage drum (401) is connected to a curved pipe (406), and the curved pipe (406) is connected to the connecting pipe (303). The top of the storage drum (401) is threadedly sleeved with a threaded cover (402), and the top of the threaded cover (402) is connected to a feed port (403). The surface of the storage drum (401) is movably sleeved with a support ring (405), and the surface of the support ring (405) is fixedly connected to a connecting piece (404), and the connecting piece (404) is fixedly installed on the top of the conveying pipe (201).

10. The method for intelligently selecting flocculants based on water quality conditions is applicable to the system for intelligently selecting flocculants based on water quality conditions as claimed in claim 9, characterized in that: The following steps are involved: S1. Water quality condition detection: Use water quality monitoring equipment to detect water quality conditions. The water quality monitoring equipment includes pH meter, thermometer, conductivity meter, turbidity meter and colorimeter. The water quality data including pH value, temperature, conductivity, turbidity, color, organic and inorganic content are collected in real time through the water quality monitoring equipment, and the collected water quality data is transmitted to the central control system via wired or wireless means; S2. Data preprocessing and analysis: The central control system removes outliers from the data and fills in missing values ​​to ensure the accuracy and completeness of the data. It extracts the combination and time series characteristics of water quality parameters from the raw data and stores the preprocessed data in the water quality database, including real-time data and historical data. S3, intelligent selection, load the trained prediction model, which is established using statistical methods and machine learning algorithms, and has been trained and verified. The current water quality parameters are obtained from the data analysis module in real time, and the current water quality parameters are input into the trained prediction model. The model outputs the type and dosage of flocculants suitable for the current water quality conditions, and displays the recommended type and dosage of flocculants through the user interface for the operator's reference; S4, automatic feeding, generating feeding instructions according to the recommendation results of the intelligent selection module, and the automatic feeding device accurately adds the recommended type and dosage of flocculant from the corresponding storage structure according to the instructions, records the feeding time and dosage, and stores them in the treatment effect database; S5. Feedback on treatment effect. After flocculation treatment, the treated water is tested by water quality monitoring equipment, and the treated water quality data is collected and transmitted to the data analysis module. The data analysis module analyzes the treated water quality data to evaluate whether the treatment effect meets the expected standard. According to the feedback on the treatment effect, the recommendation strategy of the intelligent selection module is adjusted to optimize the prediction model.

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