Sewage treatment flocculant feeding method and system

Abstract

The embodiment of the invention relates to the technical field of green environmental protection-sewage treatment, in particular to a sewage treatment flocculant putting method and system. The method comprises the following steps: S1, establishing a flocculant putting control model; s2, testing to obtain a particle index standard value of the target sewage reservoir; s3, determining the dosage adding range, the maintaining range and the dosage reducing range of the chemical dosage per unit time; and S4, carrying out sewage treatment on the target sewage reservoir, carrying out comparison according to particle indexes, and carrying out regulation and control on addition, maintenance and reduction. According to the sewage treatment flocculating agent putting method, sewage treatment can be monitored in real time, then the putting amount of the flocculating agent is effectively controlled in real time, intelligent putting of the flocculating agent is completed, a preset value is obtained through testing of professionals at the beginning of the treatment process, blending monitoring of the professionals is not needed subsequently, the stability is high, and compared with manual monitoring, the efficiency is high. According to the method, the sewage treatment effect can be effectively improved, the putting amount of the flocculating agent is reasonably planned, and the cost is saved.

Description

Technical Field

[0001] This invention relates to the technical field of green and environmentally friendly wastewater treatment, specifically to a method and system for adding flocculants for wastewater treatment. Background Technology

[0002] As cities become increasingly industrialized, factories are proliferating, generating various wastes during production, such as wastewater. This wastewater, especially from chemical plants, can contain large amounts of toxic and harmful substances. Direct discharge of this industrial wastewater into the natural environment can cause widespread plant decay and fish deaths, severely damaging the ecosystem. Furthermore, light, water, and air are three essential resources for human survival; the lack of any one of them makes long-term human existence impossible. If untreated industrial wastewater is discharged directly into the environment and consumed, it will harm human health. Therefore, industrial wastewater must be treated before discharge to minimize environmental damage and improve the water cycle. In addition to industrial wastewater, domestic sewage from daily life also requires treatment before entering the natural water cycle.

[0003] One step in wastewater treatment is removing pollutants from the wastewater. This is typically done by adding flocculants to the wastewater, causing the numerous pollutants to gradually coagulate into particles, which are then separated from the wastewater, thus achieving the goal of pollutant removal. In traditional wastewater treatment, the addition of flocculants is often controlled based on experience or fixed proportions, which is easily affected by fluctuations in wastewater quality and quantity, leading to problems such as waste of flocculants, unstable effluent quality, and excessive sludge production. Furthermore, flocculant dosage is usually based on the experience of the treatment personnel. Therefore, the treatment process requires highly qualified professionals to control the dosage and monitor it in real time. Wastewater treatment has high labor costs, and the treatment effect is linked to the skill level of the personnel, resulting in significant instability.

[0004] Currently, some enterprises and universities have also developed methods for drug delivery in wastewater treatment. For example, patent CN120260704A discloses a method for controlling the flocculation and thickening process based on multi-scale characteristics. It mainly analyzes the real-time microscale index, real-time mesoscale index, real-time macroscale index, real-time comprehensive flow pattern index, and real-time charge synergy effect index in the wastewater treatment pond to obtain a control index, thereby implementing control measures.

[0005] However, this complex identification method is out of step with the realities of wastewater treatment. For example, in practical applications, the pre-set dosage and standard of flocculant cannot be calculated based on the type of the target wastewater tank. Furthermore, during the dosage control process, the treatment of the target wastewater tank needs to be carried out under high-speed stirring, resulting in significant turbulence and a substantial impact on particle density. Therefore, a reliable control measure cannot be obtained simply through static data analysis. Dynamic control cannot be achieved solely through the three control states (normal, alarm, and abnormal) described in the aforementioned patent; professional personnel are still required for control, and the problems of high treatment costs and poor stability and effectiveness persist. Summary of the Invention

[0006] To address the problems of insufficient intelligent control of flocculant dosing in current wastewater treatment processes, poor reliability and feasibility of existing algorithm-based control methods, and high labor costs in wastewater treatment, this invention provides a method and system for flocculant dosing in wastewater treatment. This invention provides a method and system for flocculant dosing in wastewater treatment.

[0007] This invention provides a method for adding flocculants to wastewater treatment, the method comprising:

[0008] S1. Establish a flocculant dosing control model;

[0009] S2. Collect wastewater samples from the target wastewater reservoir, and use a flocculant dosing control model to test the flocculation effect of the wastewater samples to obtain the standard values ​​of particle index of the target wastewater reservoir.

[0010] S3. Determine the range for increasing, maintaining, and decreasing the dosage per unit time based on the standard value of the particle index.

[0011] S4. Treat the target sewage reservoir by using a flocculant dosing control model to control the dosing of flocculants based on the preset dosage.

[0012] S5. During wastewater treatment, if the current particulate matter index is in the range of increasing dosage, increase the flocculant dosage per unit time; if the current particulate matter index is in the range of maintaining dosage, maintain the flocculant dosage per unit time; if the current particulate matter index is in the range of decreasing dosage, decrease the flocculant dosage per unit time.

[0013] Furthermore, in step S1, the calculation process of the flocculant dosing control model includes:

[0014] S11. Within the current time period, multiple collection times are determined at preset intervals, and real-time images of sewage during the sewage treatment process are collected at the collection times.

[0015] S12. Perform image quantization processing on the real-time sewage images at each collection time to obtain the particle index at each collection time.

[0016] S13. Perform multiple rounds of data filtering based on the particle index at each collection time to obtain the filtered collection data group;

[0017] S14. Calculate the average particle index for the current time period based on the collected data set.

[0018] Furthermore, S12 specifically includes:

[0019] S121. Preprocess the real-time sewage images at each acquisition time to obtain each sewage feature image;

[0020] S122. Quantitatively analyze the differences between the wastewater feature images and calculate the particle index at each acquisition time.

[0021] Furthermore, in step S13, data filtering includes extreme value filtering and threshold filtering. Extreme value filtering includes removing collection times when the particle index deviates significantly from the mean; threshold filtering includes removing collection times when the particle index is above the mean.

[0022] Furthermore, step S2 specifically includes:

[0023] S21. Collect a unit volume of wastewater sample, conduct a flocculation test on the wastewater sample, and obtain the standard scheme of flocculant dosage with the best flocculation effect.

[0024] S22. The average particle index of the flocculant dosage standard scheme is calculated using the flocculant dosing control model and used as the standard value of particle index for the target sewage reservoir.

[0025] S23. Based on the flow velocity of the target sewage reservoir and the dosage of the flocculant in the standard scheme, calculate the preset dosage for the sewage treatment process.

[0026] Furthermore, in step S3, the addition range is the particle index standard value * addition multiple range, the maintenance range is the particle index standard value * maintenance multiple range, and the reduction range is the particle index standard value * reduction multiple range.

[0027] Furthermore, in step S5, increasing the flocculant dosage per unit time specifically means increasing the dosage by a multiple based on the current dosage; decreasing the flocculant dosage per unit time specifically means decreasing the dosage by a multiple based on the current dosage.

[0028] Furthermore, the method also includes:

[0029] S6. If the current particle index changes beyond the preset extreme value, restore the flocculant dosage to the preset dosage.

[0030] Furthermore, the particle index is used to quantify the particle size in the current wastewater, and the value of the particle index is inversely proportional to the particle size in the current wastewater.

[0031] Furthermore, the present invention also provides a wastewater treatment flocculant dosing system, which includes at least a processing unit, an image acquisition unit, and a dosing unit. The processing unit invokes the image acquisition unit and the dosing unit to execute the aforementioned wastewater treatment flocculant dosing method.

[0032] The beneficial effects of this invention are as follows:

[0033] 1. This wastewater treatment flocculant dosing method establishes a model, uses the model to test and obtain the standard value of particle index, monitors in real time, and controls the corresponding dosing amount according to the particle index collected by the model. This effectively realizes the intelligent dosing of flocculants. At the beginning of the treatment process, professional personnel test and obtain the preset value. No professional personnel are required for subsequent adjustment and monitoring. It has high stability. Compared with manual monitoring, this method can effectively improve the wastewater treatment effect, rationally plan the flocculant dosing amount, and thus save costs.

[0034] 2. This method utilizes a flocculant dosing control model for quantifiable monitoring of wastewater conditions. This model includes a pre-defined wastewater particulate matter identification scheme. By monitoring wastewater conditions at different time periods using images, a quantifiable particulate matter index is obtained. Based on this index and pre-tested standard values, the flocculant dosage per unit time is controlled. This quantifiable monitoring is reliable and effective, achieving optimal quantification even under conditions of high-speed stirring and significant wastewater turbulence, greatly improving the reliability and effectiveness of the entire dosing control system. Attached Figure Description

[0035] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0036] Figure 1 A structural flowchart of the wastewater treatment flocculant dosing method in an embodiment of the present invention is shown.

[0037] Figure 2 This invention illustrates a flowchart of step S1 in the wastewater treatment flocculant dosing method according to an embodiment of the present invention.

[0038] Figure 3 The diagram shows the structural flow of step S2 in the wastewater treatment flocculant dosing method in an embodiment of the present invention.

[0039] Figure 4 A structural block diagram of a wastewater treatment flocculant dosing system according to an embodiment of the present invention is shown. Detailed Implementation

[0040] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0041] Example 1:

[0042] Figure 1 A structural flowchart of the wastewater treatment flocculant dosing method provided in this embodiment is shown.

[0043] Please see Figure 1 This embodiment provides a method for dispensing flocculants in wastewater treatment. This method is mainly used to achieve intelligent dispensing of flocculants during wastewater treatment, improve wastewater treatment efficiency, rationally plan the amount of flocculant dispensed, and save costs. Specifically, the method is based on a flocculant dispensing control model, which includes a pre-set wastewater particulate index identification scheme. This model uses image monitoring of wastewater conditions at different time periods to obtain a quantitative index—the particulate index. Based on the particulate index and pre-tested standard values ​​of the particulate index, the amount of flocculant dispensed per unit time is controlled.

[0044] In general, the method includes the following steps:

[0045] S1. Establish a flocculant dosing control model;

[0046] S2. Collect wastewater samples from the target wastewater reservoir, and use a flocculant dosing control model to test the flocculation effect of the wastewater samples to obtain the standard values ​​of particle index of the target wastewater reservoir.

[0047] S3. Determine the range for increasing, maintaining, and decreasing the dosage per unit time based on the standard value of the particle index.

[0048] S4. Treat the target sewage reservoir by using a flocculant dosing control model to control the dosing of flocculants based on the preset dosage.

[0049] S5. During wastewater treatment, if the current particulate matter index is in the range of increasing dosage, increase the flocculant dosage per unit time; if the current particulate matter index is in the range of maintaining dosage, maintain the flocculant dosage per unit time; if the current particulate matter index is in the range of decreasing dosage, decrease the flocculant dosage per unit time.

[0050] This method, through steps S1-S5, can adapt to different wastewater treatment scenarios. During wastewater treatment of a specific target wastewater reservoir, it enables intelligent and efficient control of flocculant dosing, improving wastewater treatment efficiency and reducing costs. Furthermore, compared to existing wastewater treatment methods, this method can achieve accurate judgment even under high turbulence, ensuring the reliability and precision of the flocculant dosing control process.

[0051] To provide a better user experience, some specific implementation methods of this embodiment are provided below.

[0052] Preferably, in some embodiments, the method further includes

[0053] S6. If the current particle index changes beyond the preset extreme value, restore the flocculant dosage to the preset dosage.

[0054] This step primarily addresses special situations arising in wastewater treatment, such as image recognition anomalies, abnormal dosage, or abnormal wastewater conditions. By identifying the degree of drastic change, it improves the effectiveness and reliability of the flocculant dosage control method.

[0055] Figure 2 The structural flowchart of step S1 of the wastewater treatment flocculant dosing method provided in this embodiment is shown.

[0056] In some implementations, please refer to Figure 2 In step S1, the internal calculation process of the flocculant dosing control model includes:

[0057] S11. Within the current time period, multiple acquisition times are determined at preset intervals, and real-time images of sewage during the sewage treatment process are acquired at the acquisition times.

[0058] The length of the time period can be adjusted according to the situation of the target sewage reservoir. For example, if the sewage flow velocity and turbulence are large, and the image acquisition is difficult, the time period can be appropriately lengthened.

[0059] The current time period refers to the time period at the current moment. For example, in this sewage treatment process, the duration of the time period is defined as 4 minutes. After the sewage treatment starts, 0-4 minutes is the first collection cycle, and 4-8 minutes is the second collection cycle. If the current moment is the 5th minute after the sewage treatment starts, then the current time period refers to the second collection cycle.

[0060] During the acquisition process, multiple image acquisitions are performed within a single time period. The final granularity index is determined by processing the acquired images. The timing of each acquisition is determined by a preset interval. For example, if the next acquisition time is t0 in the current time period, then t0 plus the preset interval will be the next acquisition time.

[0061] Real-time images of wastewater are mainly acquired through image acquisition units, such as microscopic high-precision cameras or infrared cameras. The acquired images are then stored and processed.

[0062] S12. Perform image quantization processing on the real-time images of sewage at each collection time to obtain the particle index at each collection time.

[0063] Preferably, step S12 specifically includes:

[0064] S121. Preprocess the real-time sewage images at each acquisition time to obtain each sewage feature image;

[0065] S122. Quantitatively analyze the differences between the wastewater feature images and calculate the particle index at each acquisition time.

[0066] Specifically, the quantization processing step S121 includes: performing grayscale processing on the real-time sewage image to obtain a sewage grayscale image; dividing each of the sewage grayscale images into multiple regions; performing binarization processing on each region to obtain a sewage feature image, wherein sewage particles in the sewage feature image are uniformly colored to the same color, and sewage gaps are uniformly colored to another uniform color.

[0067] The difference values ​​of the wastewater pretreatment images in step S122 specifically include the difference values ​​of each pixel in each of the wastewater feature images. The quantization calculation includes obtaining the difference between each pixel and its corresponding upper, lower, left, and right adjacent pixels, and summing these differences to determine the difference value of each pixel; obtaining the mean difference value of each wastewater feature image based on the difference values ​​of each pixel; the mean difference value is used to characterize the morphology of particulate matter; and obtaining the morphological changes of particulate matter in wastewater based on the mean difference values.

[0068] For the acquisition process and quantitative execution standards of real-time sewage images, please refer to the specific acquisition process in the patent document "Sewage State, Identification Method and Device Based on Image Processing and Quantitative Statistical Differences" (Publication No.: CN116580304B). This embodiment will not elaborate on this process.

[0069] S13. Perform multiple rounds of data filtering based on the particle index at each collection time to obtain the filtered collection data group;

[0070] Given that granular metrics can suffer from data distortion, this step is primarily used for data denoising to obtain granular metrics that match and represent the data within that time period. Specifically, this step involves data filtering, which includes extreme value filtering and threshold filtering. Extreme value filtering removes data collection times where the granular metrics deviate significantly from the mean; threshold filtering removes data collection times where the granular metrics are above the mean.

[0071] Extreme value screening is mainly used to remove obviously distorted data, while threshold screening is a screening method derived from experience in actual wastewater treatment experiments. Specifically, in actual wastewater treatment, the wastewater image acquisition area is affected by turbulence. When the water flow is rapid, it causes greater damage to particulate flocs, resulting in smaller particles and larger particle size indicators. Therefore, particle size indicators above the mean are mostly from acquisition moments when high-speed water flow causes significant damage, and these particle size indicators should be removed to obtain more accurate image data and further improve the reliability of particle size indicators.

[0072] It is worth noting that extreme value screening and threshold screening do not involve the order of data, and each type of screening is not limited to one screening. For example, in actual calculation, extreme value screening is performed on the data set first, then threshold screening is performed, and finally extreme value screening is performed again to obtain the final data set.

[0073] S14. Calculate the average particle index for the current time period based on the collected data set.

[0074] The granularity index for that time period is determined by the last acquired array. For example, if the granularity index of multiple consecutively acquired images within a time period is {95, 81, 100, 90, 90, 89, 92, 93}, the average granularity index of these multiple acquired images is first calculated to be 91.25. Extreme value filtering is then performed to remove {81, 100}, leaving the data {95, 90, 90, 89, 92, 93}. Then, threshold filtering is performed on the remaining array to remove data above the average of the remaining data (91.5), resulting in the valid data {90, 90, 89}, which is calculated to be 89.67.

[0075] It should be noted that the above data is for illustrative purposes only and should not be considered as having a corresponding relationship with actual operations.

[0076] Figure 3 The flowchart of step S2 of the wastewater treatment flocculant dosing method provided in this embodiment is shown.

[0077] In some implementations, in step S2, the target wastewater reservoir refers to a wastewater pond awaiting wastewater treatment. Since wastewater treatment typically targets different types of wastewater, the dosage of flocculants, treatment effects, and treatment times vary. Therefore, the standard value for particulate matter needs to be customized based on the specific target wastewater reservoir. For example, if the wastewater is industrial wastewater, customized testing is conducted based on the substances that may be present in the wastewater during the production process to determine the standard value for particulate matter.

[0078] For more details, please refer to Figure 3 Step S2 specifically includes:

[0079] S21. Collect a unit volume of wastewater sample, conduct a flocculation test on the wastewater sample, and obtain the standard scheme of flocculant dosage with the best flocculation effect.

[0080] S22. The average particle index of the flocculant dosage standard scheme is calculated using the flocculant dosing control model and used as the standard value of particle index for the target sewage reservoir.

[0081] S23. Based on the flow velocity of the target sewage reservoir and the dosage of the flocculant in the standard scheme, calculate the preset dosage for the sewage treatment process.

[0082] To better illustrate this, a specific test procedure is provided below:

[0083] 1. Take 1 liter of wastewater (without flocculant); 2. Add flocculant solution (taken from the dosing tank) to the wastewater until the wastewater achieves good flocculation (faster sedimentation, clearer water above); 3. If the amount of flocculant used in step 2 is n ml, and the rate at which the wastewater enters the flocculation tank is m tons per hour, then the amount of flocculant needed per hour in the flocculation tank is K liters (K = n × m); 4. Perform a particle standard preset value test, check the dosing pump "scale value / discharge" comparison table, and check the scale value corresponding to the discharge rate K liters according to the dosing pump model installed in the equipment; 5. If the scale value corresponding to step 4 is Y, then set the preset dosing rate to Y in the main interface; 6. Use the flocculant dosing control model to perform a particle standard test, run it for a period of time (more than 30 minutes), check the recent sets of particle index values, and set the "particle standard" in the settings panel to an integer value close to the recent sets of particle index values.

[0084] It is worth noting that the above testing procedures are for illustrative purposes only and can be adjusted according to the target wastewater reservoir. They do not represent all the content or fixed procedures of the particle index standard value and preset dosage test in this embodiment.

[0085] In some implementations, the addition range in step S3 is the particle index standard value * addition multiple range, the maintenance range is the particle index standard value * maintenance multiple range, and the reduction range is the particle index standard value * reduction multiple range.

[0086] For example, if the standard value of the particle index is 90, and +120% to +150% is defined as the dosage multiplier, then the dosage range is 90 * {+120% - +150%} = 108 - 135. Similarly, in step S6 above, if the preset extreme value range is defined as 50%, then if the absolute value of (current particle index - standard particle index value) / standard particle index value exceeds 50%, it is considered to have exceeded the preset extreme value range.

[0087] In some implementations, regarding step S5, increasing the flocculant dosage per unit time specifically means increasing the dosage by a multiple based on the current dosage; decreasing the flocculant dosage per unit time specifically means decreasing the dosage by a multiple based on the current dosage.

[0088] It is worth noting that since the preset dosage obtained from the test is already representative, only minor adjustments to the dosage are needed during the dosage control process to achieve effective dosage control. Therefore, in the dosage control method of this embodiment, the upper limit of the dosage control is (1+30%) times the standard dosage, and the lower limit of the dosage control is (1-30%) times the standard dosage.

[0089] For example, if the current particulate matter index value is within the range of the standard value, the preset dosage will be increased by 10% based on the current dosage. This achieves dynamic dosage and ensures wastewater treatment efficiency.

[0090] As a preferred embodiment, the particle index in this embodiment is used to quantify the particle size in the current wastewater, and the value of the particle index is inversely proportional to the particle size in the current wastewater. That is to say, if the particle index is higher over a period of time, it means that the flocculant particles in the wastewater are smaller, which may indicate that the flocculant effect is poor, and the dosage needs to be increased accordingly.

[0091] Example 2:

[0092] Figure 4 A structural block diagram of the wastewater treatment flocculant dosing system provided in this embodiment is shown.

[0093] Please see Figure 4 This embodiment provides a wastewater treatment flocculant dosing system 400, which includes at least a processing unit 401, an image acquisition unit 402, and a dosing unit 403. The processing unit 401 can call the image acquisition unit 402 and the dosing unit 403 to execute the wastewater treatment flocculant dosing method in Embodiment 1.

[0094] Specifically, the execution steps include:

[0095] S1. The treatment unit 401 establishes a flocculant dosing control model, wherein the flocculant dosing control model acquires images of wastewater through the image acquisition unit 402.

[0096] S2. Take sewage samples from the target sewage reservoir, use the flocculant dosing control model of the treatment unit 401 to test the flocculation effect of the sewage samples, call the image acquisition unit 402 to acquire images, and perform calculation processing on the acquired images to obtain the particle index standard value of the target sewage reservoir.

[0097] S3. The treatment unit 401 determines the range of dosage increase, maintenance range, and reduction range of the dosage per unit time based on parameters such as the standard value of particulate matter index, the amount of wastewater sample during the test process, and the flow rate during the wastewater treatment process.

[0098] S4. Based on external input, the treatment unit 401 begins to treat the target sewage reservoir. Based on the preset dosage, the treatment unit 401 uses the flocculant dosing control model to control the dosing of flocculant.

[0099] S5. During the wastewater treatment process, if the treatment unit 401 detects that the current particulate index is within the dosing range, it increases the flocculant dosage per unit time through the dosing unit 403; if the current particulate index is within the maintenance range, it maintains the flocculant dosage per unit time; if the current particulate index is within the decrement range, it reduces the flocculant dosage per unit time.

[0100] It is worth noting that the wastewater treatment flocculant dosing system 400 also includes multiple sensing mechanisms, alarm mechanisms, human-machine interaction platforms, chemical storage mechanisms, and wastewater mixing equipment.

[0101] Specifically, the sensing mechanism is used for data acquisition and may include a liquid level monitoring sensor. The alarm mechanism may include an alarm light and a buzzer to work in conjunction with the alarm mechanism to trigger an alarm when the liquid level in the medicine storage tank is too low. For example, when the flocculant solution in the medicine tank is less than 10 cm high, the alarm will be triggered, the blue circle above will turn red, and the alarm light will turn on (the alarm light flashes and sounds).

[0102] Similarly, the alarm mechanism can issue an alarm for abnormal dosing according to the requirements of the processing unit 401. For example, when the particle index in the current time period becomes large enough, the gray circle above the alarm light will turn red. When the particle index increases further, the alarm light will be turned on and an inspection prompt will appear on the main interface.

[0103] The human-computer interaction platform is used to enable the operator to interact with the input and output of the system's internal processing unit 401. Specifically, the human-computer interaction platform may include a display screen, which displays the system's settings interface. The settings interface is used by the operator to perform operations or monitor data.

[0104] For example, the human-computer interaction platform can be configured with a flow rate indicator. This indicator displays the real-time flow rate of wastewater flowing into the flocculation tank and enables intelligent control of the treatment unit 401 through preset rules. Another example is that operators can set rules such as: during chemical dosing, if the wastewater inflow is less than 5 liters per minute for 4-5 consecutive minutes, the dosing will automatically stop. During chemical dosing, if the flow rate is less than 5 liters per minute, the flow rate value will be displayed with a red background.

[0105] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method of dosing a flocculant for sewage treatment, characterized by, The method comprises: S1, establishing a flocculant dosing control model; S2, fetching sewage samples from a target sewage reservoir, testing the flocculation effect of the sewage samples by the flocculant dosing control model, and obtaining a particle index standard value of the target sewage reservoir; S3, determining an increase range, a maintenance range and a decrease range of the unit time dosage according to the particle index standard value; S4, performing sewage treatment on the target sewage reservoir, and performing flocculant dosing control by the flocculant dosing control model on the basis of a preset dosage; S5, during the sewage treatment process, if the current particle index is in the increase range, the unit time flocculant dosage is increased; if the current particle index is in the maintenance range, the unit time flocculant dosage is maintained; and if the current particle index is in the decrease range, the unit time flocculant dosage is decreased.

2. The method of claim 1, wherein the method is characterized by, In the step S1, the calculation process of the flocculant dosing control model comprises: S11, determining a plurality of collection time points at intervals of a preset time in a current time period, and collecting real-time images of sewage in the sewage treatment process at the collection time points; S12, performing image quantification processing on the real-time images of sewage at each collection time point to obtain the particle index at each collection time point; S13, performing multi-round data screening according to the particle index at each collection time point to obtain a screened collection data set; S14, calculating the average particle index of the current time period according to the collection data set.

3. The method of claim 1, wherein the method is characterized by, The S12 specifically comprises: S121, preprocessing the real-time images of sewage at each collection time point to obtain a plurality of sewage feature images; S122, quantitatively analyzing the differences between the sewage feature images to calculate the particle index at each collection time point.

4. The method of claim 1, wherein the method is characterized by, In the step S13, the data screening comprises extreme value screening and threshold value screening, wherein the extreme value screening comprises removing the collection time points with particle indexes far away from the mean value; and the threshold value screening comprises removing the collection time points with particle indexes above the mean value.

5. The method of claim 2-4, wherein the method is characterized by, The step S2 specifically comprises: S21, fetching a unit volume of sewage sample, performing flocculation test on the sewage sample, and obtaining a flocculant dosage standard scheme with optimal flocculation effect; S22, calculating the average particle index of the flocculant dosage standard scheme by the flocculant dosing control model to obtain the particle index standard value of the target sewage reservoir; S23, calculating the preset dosage of the sewage treatment process according to the flow rate of the target sewage reservoir and the dosage of the flocculant dosage standard scheme.

6. The method of sewage treatment flocculant dosing according to claim 1, characterized in that, In the step S3, the increase range is the particle index standard value*the increase multiple range, the maintenance range is the particle index standard value*the maintenance multiple range, and the decrease range is the particle index standard value*the decrease multiple range.

7. The method of claim 1, wherein the method is characterized by, In the step S5, the increase of the unit time flocculant dosage specifically refers to increasing the dosage by an increase multiple based on the current dosage; and the decrease of the unit time flocculant dosage specifically refers to decreasing the dosage by a decrease multiple based on the current dosage.

8. The method of claim 1, wherein the method further comprises: The method further comprises: S6, if the current particle index change amplitude exceeds a preset extreme value amplitude, restoring the flocculant dosage to the preset dosage.

9. The method of claim 1, wherein the method is characterized by, The particle index is used to quantify the size of the particles in the current sewage, and the value of the particle index is inversely proportional to the size of the particles in the current sewage.

10. A sewage treatment flocculant dosing system characterized by comprising: The sewage treatment flocculant feeding method comprises a processing unit, an image acquisition unit and a drug feeding unit, wherein the processing unit calls the image acquisition unit and the drug feeding unit to execute the sewage treatment flocculant feeding method according to any one of claims 1-9.

Images