Intelligent wastewater treatment system based on Internet of Things

Abstract

The invention discloses an intelligent wastewater treatment system based on Internet of Things, and relates to the technical field of wastewater treatment, and the system comprises the following modules: a flocculation control module for obtaining the content value of to-be-treated wastewater, setting the stirring speed and the stirring time, obtaining the stirring rate through the product of the stirring speed and the stirring time, and controlling the flocculation control module; according to the scheme, suspended matters in the added wastewater are detected through the flocculation control module, so that the stirring speed, the stirring time and the adding amount of a flocculating agent are set according to the condition of the acrylic emulsion wastewater, and a proper flocculating agent is selected for flocculation according to the pH value of the wastewater, so that the situation that the flocculation effect of the flocculating agent is influenced by the pH value of the wastewater is avoided; according to the invention, the flocculating agent is added into the wastewater, so that the flocculating agent can better agglomerate and gather suspended matters, the addition amount of the flocculating agent can be automatically and intelligently set according to the actual condition of the wastewater, waste caused by excessive addition of the flocculating agent can be avoided, and influence on the flocculation effect caused by too little addition of the flocculating agent can be avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of wastewater treatment, and in particular to an intelligent wastewater treatment system based on the Internet of Things. Background Art

[0002] Acrylic emulsion is a milky white or nearly transparent viscous liquid. It is an emulsion copolymerized from pure acrylic ester monomers. It is a small-particle, multi-purpose, and high-performance emulsion suitable for a variety of coating formulations. It has outstanding water resistance and weather resistance. Acrylic emulsion will generate a large amount of wastewater during the production process. If the wastewater is not purified and filtered and discharged directly, it will seriously affect the safety of soil and water sources. The treatment process of acrylic emulsion wastewater is generally divided into five stages: pretreatment, physical treatment, biochemical treatment, deep treatment and sludge treatment. In physical treatment, the wastewater needs to be physically filtered and separated by flotation to separate the sediment and suspended matter in the wastewater. The flotation method is to add flocculants to the wastewater, and then blow small bubbles of gas into the wastewater, so that the small bubbles float up and drive the flocculated impurities to float on the surface of the wastewater, thereby completing the separation of suspended matter; In the process of separating suspended solids from acrylic emulsion wastewater by flotation, the amount of flocculant added needs to be calculated manually based on the amount of wastewater and the concentration of suspended solids. The working time of the air pump will also affect the effect of flotation separation, which is a great test of the management personnel's experience level. If the flocculant is excessive, it will cause waste. Insufficient flocculant will affect the flotation separation effect. If the air pump works too long, it will cause waste of resources. If the air pump works insufficiently, it will be difficult to completely lift and separate the suspended solids after flocculation. For this reason, we propose a smart wastewater treatment system based on the Internet of Things. Summary of the invention

[0003] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides an intelligent wastewater treatment system based on the Internet of Things to solve the above-mentioned problems in the prior art.

[0004] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a smart wastewater treatment system based on the Internet of Things, including the following modules: The flocculation control module obtains the content value of the wastewater to be treated, sets the stirring speed and stirring time, obtains the stirring rate by multiplying the stirring speed and stirring time, obtains the amount of flocculant added by the content value and the stirring rate, obtains the pH value of the wastewater, selects the flocculant according to the pH value of the wastewater, adds the selected flocculant into the wastewater according to the amount of flocculant added, and stirs and flocculates the wastewater at the stirring rate; The air pump control module obtains the bubble stability value, the air pump pressure and the flow rate, obtains the air pump output prediction time through the bubble stability value, the air pump pressure, the air pump flow rate, the wastewater content value and the flocculant addition amount, and blows air into the wastewater at the air pump output prediction time; The visual monitoring module takes a picture of the wastewater after flotation to obtain a contrast ratio, sets a preset threshold value of the contrast ratio, obtains an additional blowing time according to a comparison result between the preset threshold value of the contrast ratio and the contrast ratio, feeds back the additional blowing time to the air pump control module, and controls the air pump to blow additional air according to the additional blowing time; The data recording module obtains the predicted air pump output time and the additional air pump blowing time, sums the predicted air pump output time and the additional air pump blowing time to obtain the total air pump blowing time, establishes a historical database, and includes the total air pump blowing time, wastewater content value, stirring rate and flocculant addition amount into the historical database.

[0005] Preferably, the content value of the wastewater to be treated is obtained in the flocculation control module, specifically: the amount of wastewater to be treated is obtained, the turbidity of the wastewater is obtained, the concentration of suspended solids in the wastewater is obtained, and the content value of the wastewater is obtained by the amount of wastewater to be treated, the turbidity of the wastewater and the concentration of suspended solids in the wastewater; The calculation method of wastewater content value is as follows: , Among them, Hz represents the content value of wastewater, As represents the amount of wastewater to be treated, Zd represents the turbidity of wastewater, and Nd represents the concentration of suspended solids in wastewater. , and are weights, , , .

[0006] Preferably, the stirring speed and stirring time are set in the flocculation control module, and the stirring rate is obtained by multiplying the stirring speed and the stirring time, specifically: Step 1: Obtain the content value of the wastewater to be treated, arrange all the recorded content values ​​in the historical database in ascending order, and capture the reference content value closest to the content value of the wastewater to be treated from all the recorded content values ​​in the historical database; Step 2: Obtain the reference stirring rate corresponding to the reference content value in the historical database, set the stirring speed, and obtain the stirring time by dividing the reference stirring rate by the stirring speed.

[0007] Preferably, the method for calculating the amount of flocculant added in the flocculation control module is as follows: , Among them, Xt represents the amount of flocculant added, Hz represents the content value of wastewater, Ja represents the stirring rate, and Xs represents the preset threshold value of flocculant. , and are weights, , .

[0008] Preferably, the bubble stability value is obtained in the air pump control module, specifically: Step 1: Start the experimental air pump to blow air into the wastewater, obtain the time from the bubble floating to the bursting and mark it as the bubble holding value, set the experimental reference time, blow air into the wastewater for the experimental reference time, record the bubble thickness on the water surface within the experimental reference time and mark it as the bubble thickness value; Step 2: Obtain the bubble retention value, obtain the bubble thickness value, and obtain the bubble stability value by weighted summation of the bubble retention value and the bubble thickness value.

[0009] Preferably, in the air pump control module, the calculation method for obtaining the predicted air pump output time is performed by using the bubble stability value, the air pump pressure, the air pump flow rate, the wastewater content value and the flocculant addition amount, specifically as follows:

[0010] Among them, Ys represents the predicted time of air pump output, Hz represents the content value of wastewater, Xt represents the amount of flocculant added, Qd represents the bubble stability value, Qy represents the air pump pressure, Qg represents the air pump flow rate, and Sz represents the time preset threshold. , and are weights, , , .

[0011] Preferably, in the visual inspection module, the wastewater after flotation is photographed to obtain a contrast ratio, specifically: Step 1: Obtain multiple images of wastewater after flotation, filter out duplicate images with the same viewing angle, filter out unclear images, and finally obtain multiple reference images, and perform grayscale processing on the multiple reference images; Step 2: Set a preset brightness intersection threshold, use the preset brightness intersection threshold as a standard to mark multiple brightness intersection points in each grayscale processed reference image, set a preset intersection range threshold, use each brightness intersection point as a circle center and the intersection range as a radius to mark multiple marking circles in each grayscale processed reference image, record multiple intersecting marking circles as one, and count the marking circles in each reference image to obtain the number of markings; Step 3: Arrange the number of marks of all reference images in ascending order, delete the reference image with the smallest number of marks and the reference image with the largest number of marks, and take the average of the number of marks of the remaining reference images to obtain the contrast ratio.

[0012] Preferably, a preset threshold value of the contrast ratio is set in the visual monitoring module, and the additional blowing time is obtained according to the comparison result between the preset threshold value of the contrast ratio and the contrast ratio, specifically: the preset threshold value of the contrast ratio is set, the contrast ratio is obtained, and the size relationship between the contrast ratio and the preset threshold value of the contrast ratio is compared; if the contrast ratio is less than the preset threshold value of the contrast ratio, the additional blowing time is 0; if the contrast ratio is greater than the preset threshold value of the contrast ratio, the contrast ratio of the wastewater image before flotation and the predicted time of the air pump output are obtained; the additional blowing time is obtained through the contrast ratio of the wastewater image after flotation, the contrast ratio of the wastewater image before flotation and the predicted time of the air pump output.

[0013] Preferably, the additional blowing time is calculated as follows: , Among them, Es represents the additional blowing time, Qv represents the wastewater image contrast ratio before flotation, Qh represents the wastewater image contrast ratio after flotation, and Ys represents the air pump output prediction time.

[0014] (III) Beneficial effects The present invention provides a smart wastewater treatment system based on the Internet of Things, which has the following beneficial effects: (1) In this scheme, the suspended solids in the added wastewater are detected through the flocculation control module, so as to set the stirring speed, stirring time and the amount of flocculant added according to the situation of the acrylic emulsion wastewater, and select a suitable flocculant for flocculation according to the pH value of the wastewater, which is beneficial to avoid the pH value of the wastewater affecting the flocculation effect of the flocculant, thereby facilitating the flocculant to better aggregate the suspended solids, and thus automatically and intelligently setting the amount of flocculant added in combination with the actual situation of the wastewater, thereby avoiding waste caused by excessive addition of flocculants, and also avoiding the flocculation effect affected by insufficient addition of flocculants; (2) In this scheme, the air pump control module is used to control the blowing time of the bubbles. The air pump output time is predicted by combining the bubble stability value, air pump pressure and flow rate, wastewater content value and flocculant addition amount. This is helpful to avoid the waste of resources caused by the air pump working time being too long, and also to avoid the difficulty of completely lifting and separating the suspended matter after flocculation due to insufficient working time of the air pump. The wastewater after flotation is visually inspected by the visual inspection module, and the contrast ratio of the image is compared with the set contrast ratio preset threshold. According to the comparison result, it is determined whether the air pump needs to replenish air. If the air pump needs to replenish air, the replenishment time is calculated and the air pump is controlled to output additional air, so as to facilitate the addition of the air pump output prediction time in the air pump control module, which is further helpful to avoid the difficulty of completely lifting and separating the suspended matter after flocculation due to insufficient blowing time of the air pump. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a structural schematic diagram of the intelligent wastewater treatment system based on the Internet of Things of the present invention; Figure 2 This is a flowchart of the smart wastewater treatment system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1-Figure 2 The present invention provides a smart wastewater treatment system based on the Internet of Things, comprising the following modules: The flocculation control module obtains the content value of the wastewater to be treated, sets the stirring speed and stirring time, obtains the stirring rate by multiplying the stirring speed and stirring time, obtains the amount of flocculant added by the content value and the stirring rate, obtains the pH value of the wastewater, selects the flocculant according to the pH value of the wastewater, adds the selected flocculant into the wastewater according to the amount of flocculant added, and stirs and flocculates the wastewater at the stirring rate; The air pump control module obtains the bubble stability value, the air pump pressure and the flow rate, obtains the air pump output prediction time through the bubble stability value, the air pump pressure, the air pump flow rate, the wastewater content value and the flocculant addition amount, and blows air into the wastewater at the air pump output prediction time; The visual monitoring module takes a picture of the wastewater after flotation to obtain a contrast ratio, sets a preset threshold value of the contrast ratio, obtains an additional blowing time according to a comparison result between the preset threshold value of the contrast ratio and the contrast ratio, feeds back the additional blowing time to the air pump control module, and controls the air pump to blow additional air according to the additional blowing time; The data recording module obtains the predicted output time of the air pump and the additional blowing time of the air pump, sums the predicted output time of the air pump and the additional blowing time of the air pump to obtain the total blowing time of the air pump, establishes a historical database, and includes the total blowing time of the air pump, the wastewater content value, the stirring rate and the amount of flocculant added into the historical database; The calculation method of the amount of flocculant added in the flocculation control module is as follows: , Among them, Xt represents the amount of flocculant added, Hz represents the content value of wastewater, Ja represents the stirring rate, and Xs represents the preset threshold value of flocculant. , and are weights, , ; In the air pump control module, the calculation method for the predicted air pump output time is obtained by using the bubble stability value, air pump pressure, air pump flow, wastewater content value and flocculant addition amount, specifically: , Among them, Ys represents the predicted time of air pump output, Hz represents the content value of wastewater, Xt represents the amount of flocculant added, Qd represents the bubble stability value, Qy represents the air pump pressure, Qg represents the air pump flow rate, and Sz represents the time preset threshold. , and are weights, , , .

[0018] In this embodiment, in this scheme, the suspended matter in the added wastewater is detected by the flocculation control module, so as to set the stirring speed, stirring time and the amount of flocculant added according to the condition of the acrylic emulsion wastewater, and select a suitable flocculant for flocculation according to the pH value of the wastewater, so as to avoid the pH value of the wastewater affecting the flocculation effect of the flocculant, thereby facilitating the flocculant to better aggregate the suspended matter, thereby helping to automatically and intelligently set the amount of flocculant added in combination with the actual condition of the wastewater, thereby helping to avoid waste caused by excessive addition of flocculants, and also helping to avoid the flocculation effect affected by too little addition of flocculants; In this scheme, the air pump control module is used to control the blowing time of the bubbles, and the air pump output time is predicted by combining the bubble stability value, air pump pressure and flow, wastewater content value and flocculant addition amount, which is beneficial to avoid the waste of resources caused by too long working time of the air pump, and also to avoid the difficulty of achieving complete lifting and separation of the suspended matter after flocculation due to insufficient working time of the air pump; In this scheme, the visual inspection module is used to visually inspect the wastewater after flotation, and the contrast ratio of the image is compared with the preset contrast ratio threshold, so as to judge whether the air pump is needed to replenish air according to the comparison result. If the air pump is needed to replenish air, the replenishment time is calculated and the air pump is controlled to output additional air, so as to facilitate the addition of the air pump output prediction time in the air pump control module, which is further beneficial to avoid the difficulty of achieving complete lifting and separation of suspended matter after flocculation due to insufficient air pump blowing time; In this scheme, the total air pump blowing time, wastewater content value, stirring rate and flocculant addition amount in this wastewater treatment are collected through the data recording module and included in the historical database, so as to provide historical data for calculating the flocculant addition amount and air pump blowing time in the subsequent wastewater treatment process, which is beneficial to the calculation results in the subsequent wastewater treatment process to be more accurate; It is worth mentioning that the weight values ​​in this scheme can be obtained through the hierarchical analysis method, and the values ​​of the preset thresholds in this scheme can be obtained through the weight analysis method, which will not be elaborated here.

[0019] The content value of the wastewater to be treated is obtained in the flocculation control module, specifically: the amount of wastewater to be treated, the turbidity of the wastewater, the concentration of suspended solids in the wastewater are obtained, and the content value of the wastewater is obtained through the amount of wastewater to be treated, the turbidity of the wastewater and the concentration of suspended solids in the wastewater; The calculation method of wastewater content value is as follows: , Among them, Hz represents the content value of wastewater, As represents the amount of wastewater to be treated, Zd represents the turbidity of wastewater, and Nd represents the concentration of suspended solids in wastewater. , and are weights, , , .

[0020] In this embodiment, the content value of the wastewater is obtained by combining the amount of wastewater to be treated, the turbidity of the wastewater and the concentration of suspended matter in the wastewater, which is beneficial to more accurate calculation results of the amount of flocculant added, and thus facilitates targeted addition of a corresponding amount of flocculant to the wastewater, and thus is beneficial to automatically and intelligently set the amount of flocculant added in combination with the actual situation of the wastewater, and thus is beneficial to avoid waste caused by excessive addition of flocculant, and also helps to avoid affecting the flocculation effect by adding too little flocculant.

[0021] The stirring speed and stirring time are set in the flocculation control module, and the stirring rate is obtained by multiplying the stirring speed and stirring time, which is: Step 1: Obtain the content value of the wastewater to be treated, arrange all the recorded content values ​​in the historical database in ascending order, and capture the reference content value closest to the content value of the wastewater to be treated from all the recorded content values ​​in the historical database; Step 2: Obtain the reference stirring rate corresponding to the reference content value in the historical database, set the stirring speed, and obtain the stirring time by dividing the reference stirring rate by the stirring speed.

[0022] In this embodiment, by setting the stirring speed and then calculating the stirring time according to the corresponding historical stirring rate in the historical database of the data recording module, it is beneficial to avoid the flocculant being unable to fully dissolve in the wastewater and agglomerate the suspended matter due to the stirring time being too short, and it is also beneficial to avoid the agglomerated suspended matter being dispersed again due to the stirring time being too long, thereby facilitating the sufficient agglomeration and separation of the suspended matter in the wastewater.

[0023] Get the bubble stability value in the air pump control module, specifically: Step 1: Start the experimental air pump to blow air into the wastewater, obtain the time from the bubble floating to the bursting and mark it as the bubble holding value, set the experimental reference time, blow air into the wastewater for the experimental reference time, record the bubble thickness on the water surface within the experimental reference time and mark it as the bubble thickness value; Step 2: Obtain the bubble retention value, obtain the bubble thickness value, and obtain the bubble stability value by weighted summation of the bubble retention value and the bubble thickness value.

[0024] In the present embodiment, the retention value and thickness value of the bubbles in the wastewater are collected by blowing an experimental air pump, so that the stability of the bubbles can be comprehensively judged based on the retention value and thickness value of the bubbles, thereby facilitating the mastering of the characteristics of the bubbles in different acrylic acid wastewaters, and further facilitating the prediction of the air pump blowing time based on the characteristics of the bubbles in different acrylic acid wastewaters, which is further beneficial to avoid the waste of resources caused by the air pump working for too long, and also helps to avoid the difficulty in achieving complete lifting and separation of suspended matter after flocculation due to insufficient working time of the air pump.

[0025] In the visual inspection module, the wastewater after flotation is photographed to obtain the contrast ratio, which is: Step 1: Obtain multiple images of wastewater after flotation, filter out duplicate images with the same viewing angle, filter out unclear images, and finally obtain multiple reference images, and perform grayscale processing on the multiple reference images; Step 2: Set a preset brightness intersection threshold, use the preset brightness intersection threshold as a standard to mark multiple brightness intersection points in each grayscale processed reference image, set a preset intersection range threshold, use each brightness intersection point as a circle center and the intersection range as a radius to mark multiple marking circles in each grayscale processed reference image, record multiple intersecting marking circles as one, and count the marking circles in each reference image to obtain the number of markings; Step 3: Arrange the number of marks of all reference images in ascending order, delete the reference image with the smallest number of marks and the reference image with the largest number of marks, and take the average of the number of marks of the remaining reference images to obtain the contrast ratio.

[0026] In this embodiment, by marking the brightness difference area in the picture after flotation, it is convenient to mark the position of the suspended matter in the wastewater, so as to understand the amount of remaining flocculated suspended matter after flotation, and then judge whether the remaining flocculated suspended matter needs additional blowing of the air pump to drive the flocculated suspended matter to float and separate according to the contrast ratio, and then add the predicted time of the air pump output in the air pump control module, which is further beneficial to avoid the difficulty of completely lifting and separating the flocculated suspended matter due to insufficient blowing time of the air pump.

[0027] A preset threshold value of the contrast ratio is set in the visual monitoring module, and the additional blowing time is obtained according to the comparison result between the preset threshold value of the contrast ratio and the contrast ratio, specifically: a preset threshold value of the contrast ratio is set, the contrast ratio is obtained, and the magnitude relationship between the contrast ratio and the preset threshold value of the contrast ratio is compared. If the contrast ratio is less than the preset threshold value of the contrast ratio, the additional blowing time is 0. If the contrast ratio is greater than the preset threshold value of the contrast ratio, the contrast ratio of the wastewater image before flotation and the predicted output time of the air pump are obtained, and the additional blowing time is obtained through the contrast ratio of the wastewater image after flotation, the contrast ratio of the wastewater image before flotation, and the predicted output time of the air pump; The calculation method of the additional blowing time is as follows: , Among them, Es represents the additional blowing time, Qv represents the wastewater image contrast ratio before flotation, Qh represents the wastewater image contrast ratio after flotation, and Ys represents the air pump output prediction time.

[0028] In this embodiment, the amount of suspended matter separation is grasped by the change in the contrast ratio in the pictures before and after flotation, and the separation efficiency of the suspended matter is obtained according to the change in the contrast ratio and the predicted air pump output time. Finally, the additional blowing time is reasonably planned according to the separation efficiency and the amount of remaining suspended matter, so as to facilitate the addition of the predicted air pump output time in the air pump control module, which is further beneficial to avoid the difficulty in achieving complete lifting and separation of the flocculated suspended matter due to insufficient air pump blowing time.

[0029] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0030] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0031] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. The intelligent wastewater treatment system based on the Internet of Things is characterized by: Includes the following modules: The flocculation control module obtains the content value of the wastewater to be treated, sets the stirring speed and stirring time, obtains the stirring rate by multiplying the stirring speed and stirring time, obtains the amount of flocculant added by the content value and the stirring rate, obtains the pH value of the wastewater, selects the flocculant according to the pH value of the wastewater, adds the selected flocculant into the wastewater according to the amount of flocculant added, and stirs and flocculates the wastewater at the stirring rate; The air pump control module obtains the bubble stability value, the air pump pressure and the flow rate, obtains the air pump output prediction time through the bubble stability value, the air pump pressure, the air pump flow rate, the wastewater content value and the flocculant addition amount, and blows air into the wastewater at the air pump output prediction time; The visual monitoring module takes a picture of the wastewater after flotation to obtain a contrast ratio, sets a preset threshold value of the contrast ratio, obtains an additional blowing time according to a comparison result between the preset threshold value of the contrast ratio and the contrast ratio, feeds back the additional blowing time to the air pump control module, and controls the air pump to blow additional air according to the additional blowing time; The data recording module obtains the predicted air pump output time and the additional air pump blowing time, sums the predicted air pump output time and the additional air pump blowing time to obtain the total air pump blowing time, establishes a historical database, and includes the total air pump blowing time, wastewater content value, stirring rate and flocculant addition amount into the historical database.

2. The smart wastewater treatment system based on the Internet of Things according to claim 1 is characterized in that: The content value of the wastewater to be treated is obtained in the flocculation control module, specifically: the amount of wastewater to be treated, the turbidity of the wastewater, the concentration of suspended solids in the wastewater are obtained, and the content value of the wastewater is obtained through the amount of wastewater to be treated, the turbidity of the wastewater and the concentration of suspended solids in the wastewater; The calculation method of wastewater content value is as follows: ， Among them, Hz represents the content value of wastewater, As represents the amount of wastewater to be treated, Zd represents the turbidity of wastewater, and Nd represents the concentration of suspended solids in wastewater. , and are weights, , , .

3. The smart wastewater treatment system based on the Internet of Things according to claim 1 is characterized in that: The stirring speed and stirring time are set in the flocculation control module, and the stirring rate is obtained by multiplying the stirring speed and stirring time, which is: Step 1: Obtain the content value of the wastewater to be treated, arrange all the recorded content values ​​in the historical database in ascending order, and capture the reference content value closest to the content value of the wastewater to be treated from all the recorded content values ​​in the historical database; Step 2: Obtain the reference stirring rate corresponding to the reference content value in the historical database, set the stirring speed, and obtain the stirring time by dividing the reference stirring rate by the stirring speed.

4. The smart wastewater treatment system based on the Internet of Things according to claim 1 is characterized in that: The calculation method of the amount of flocculant added in the flocculation control module is as follows: ， Among them, Xt represents the amount of flocculant added, Hz represents the content value of wastewater, Ja represents the stirring rate, and Xs represents the preset threshold value of flocculant. , and are weights, , .

5. The smart wastewater treatment system based on the Internet of Things according to claim 1 is characterized in that: Get the bubble stability value in the air pump control module, specifically: Step 1: Start the experimental air pump to blow air into the wastewater, obtain the time from the bubble floating to the bursting and mark it as the bubble holding value, set the experimental reference time, blow air into the wastewater for the experimental reference time, record the bubble thickness on the water surface within the experimental reference time and mark it as the bubble thickness value; Step 2: Obtain the bubble retention value, obtain the bubble thickness value, and obtain the bubble stability value by weighted summation of the bubble retention value and the bubble thickness value.

6. The smart wastewater treatment system based on the Internet of Things according to claim 1 is characterized in that: In the air pump control module, the calculation method for the predicted air pump output time is obtained by using the bubble stability value, air pump pressure, air pump flow, wastewater content value and flocculant addition amount, specifically: ， Among them, Ys represents the predicted time of air pump output, Hz represents the content value of wastewater, Xt represents the amount of flocculant added, Qd represents the bubble stability value, Qy represents the air pump pressure, Qg represents the air pump flow rate, and Sz represents the time preset threshold. , and are weights, , , .

7. The smart wastewater treatment system based on the Internet of Things according to claim 1 is characterized in that: In the visual inspection module, the wastewater after flotation is photographed to obtain the contrast ratio, which is: Step 1: Obtain multiple images of wastewater after flotation, filter out duplicate images with the same viewing angle, filter out unclear images, and finally obtain multiple reference images, and perform grayscale processing on the multiple reference images; Step 2: Set a preset brightness intersection threshold, use the preset brightness intersection threshold as a standard to mark multiple brightness intersection points in each grayscale processed reference image, set a preset intersection range threshold, use each brightness intersection point as a circle center and the intersection range as a radius to mark multiple marking circles in each grayscale processed reference image, record multiple intersecting marking circles as one, and count the marking circles in each reference image to obtain the number of markings; Step 3: Arrange the number of marks of all reference images in ascending order, delete the reference image with the smallest number of marks and the reference image with the largest number of marks, and take the average of the number of marks of the remaining reference images to obtain the contrast ratio.

8. The smart wastewater treatment system based on the Internet of Things according to claim 1 is characterized in that: A preset threshold value of the contrast ratio is set in the visual monitoring module, and the additional blowing time is obtained according to the comparison result between the preset threshold value of the contrast ratio and the contrast ratio, specifically: the preset threshold value of the contrast ratio is set, the contrast ratio is obtained, and the size relationship between the contrast ratio and the preset threshold value of the contrast ratio is compared. If the contrast ratio is less than the preset threshold value of the contrast ratio, the additional blowing time is 0; if the contrast ratio is greater than the preset threshold value of the contrast ratio, the contrast ratio of the wastewater image before flotation and the predicted time of the air pump output are obtained, and the additional blowing time is obtained through the contrast ratio of the wastewater image after flotation, the contrast ratio of the wastewater image before flotation and the predicted time of the air pump output.

9. The smart wastewater treatment system based on the Internet of Things according to claim 8 is characterized in that: The calculation method of the additional blowing time is as follows: ， Among them, Es represents the additional blowing time, Qv represents the wastewater image contrast ratio before flotation, Qh represents the wastewater image contrast ratio after flotation, and Ys represents the air pump output prediction time.

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