Control system based on flocculation sensor

Abstract

The invention relates to the technical field of sewage treatment, and discloses a control system based on a flocculation sensor, which comprises a control module used for controlling the flocculation sensor, an acquisition module used for obtaining a flocculation image of a flocculation basin, a judgment module used for comparing the number of suspended particles with the number of standard flocculation particles, and judging whether a putting mode is started or not according to a comparison result, comparing the historical control record with the flocculation flow rate, judging whether to determine the opening and closing angle of the valve according to the historical control record or not according to a comparison result, and determining the opening and closing angle of the valve based on a valve angle model when the opening and closing angle of the valve is not determined according to the historical control record; the treatment module determines whether to adjust the flocculation putting amount or not according to the number of suspension treatment particles, the number of suspended particles and the number of standard flocculation particles, and the control module sets putting control factors of the flocculation putting amount according to the flocculation temperature and conducts putting adjustment on the flocculation basin according to the optimization result. The sewage treatment stability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and more particularly, to a control system based on a flocculation sensor. Background Art

[0002] With the development of infrastructure and population, higher requirements are put forward for the recycling of water resources. In the process of sewage treatment, flocculants promote the formation of flocs from suspended solids and colloidal particles in water, so that the particles settle or are filtered out, thereby improving the purity of water. In the process of sewage treatment, the application of flocculants plays a crucial role. However, the dosage of flocculants needs to be accurately controlled. Excessive dosing not only increases costs, but also has a negative impact on water quality, resulting in the inability to completely remove harmful substances in the water body, while insufficient dosing leads to unsatisfactory sewage treatment effects and the inability to effectively remove suspended particles. Traditional flocculant dosing usually relies on manual experience and cannot accurately control the dosage of flocculants in real time according to changes in water quality, resulting in excessive or insufficient flocculant dosing, making it difficult to effectively treat sewage and leading to unstable sewage treatment effects.

[0003] Therefore, how to provide a control system based on a flocculation sensor is an urgent technical problem to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides a control system based on a flocculation sensor, aiming to solve the problem that the dosing of flocculants depends on manual experience and cannot accurately control the dosage of flocculants in real time according to changes in water quality, resulting in excessive or insufficient flocculant dosing, making it difficult to effectively treat sewage and leading to unstable sewage treatment effects.

[0005] The present invention provides a control system based on a flocculation sensor, comprising: a flocculation sensor and a control module, the control module is used to control the flocculation sensor, and the control module comprises: an acquisition module, configured to divide a flocculation tank into several flocculation detection areas, obtain detection images of the several flocculation detection areas, process all the detection images to obtain a flocculation image of the flocculation tank, and analyze the flocculation image to determine the number of suspended particles in the flocculation tank; A judgment module, configured to compare the number of suspended particles with the number of flocculation standard particles, judge whether to start the dosing mode according to the comparison result, when it is determined to start the dosing mode, obtain the historical control record and the flocculation flow rate of the flocculation tank, compare the historical control record with the flocculation flow rate, and judge whether to determine the valve opening and closing angle according to the historical control record according to the comparison result. When it is determined not to determine the valve opening and closing angle according to the historical control record, determine the valve opening and closing angle based on the valve angle model; A processing module, configured to determine the flocculation dosing amount according to the number of suspended particles in the flocculation tank, perform dosing on the flocculation tank according to the valve opening and closing angle and the flocculation dosing amount, obtain a second flocculation image after a preset time, and analyze the second flocculation image to determine the number of suspended processed particles in the flocculation tank, and determine whether to adjust the flocculation dosing amount according to the number of suspended processed particles, the number of suspended particles, and the number of flocculation standard particles; A control module, configured to, when it is determined to adjust the flocculation dosing amount, obtain the flocculation temperature of the flocculation tank, set a dosing control factor for the flocculation dosing amount according to the flocculation temperature, optimize the flocculation dosing amount based on the dosing control factor, perform dosing adjustment on the flocculation tank according to the optimization result, and transmit the dosing result to the processing module.

[0006] Further, when obtaining the detection images of several flocculation detection areas, processing all the detection images to obtain the flocculation image of the flocculation tank, including: The acquisition module preprocesses the detection images of several flocculation detection areas, and the preprocessing includes image denoising and brightness adjustment; Extract feature points from the preprocessed detection images according to the first image algorithm, match the extracted feature points according to the second image algorithm, determine the overlapping area between each detection image, register the detection images according to the overlapping area, and merge the registered detection images based on the third image algorithm to determine the flocculation mosaic image of the flocculation tank; Perform image adjustment on the flocculation mosaic image, and the image adjustment includes edge smoothing and histogram equalization, and determine the flocculation image of the flocculation tank according to the image adjustment result.

[0007] Further, when comparing the number of suspended particles with the number of flocculation standard particles and judging whether to start the dosing mode according to the comparison result, it includes: When the number of suspended particles is greater than the number of flocculation standard particles, the judgment module determines to start the dosing mode; When the number of the suspended particles is less than or equal to the number of the flocculation standard particles, the judgment module determines not to turn on the feeding mode, and determines the number of predicted suspended particles and the prediction time of the flocculation tank based on a pre-trained prediction model, and the prediction time and the number of predicted particles correspond one by one; If there is a situation where the number of predicted suspended particles is greater than the number of flocculation standard particles, the judgment module turns on the feeding mode according to the prediction time; If there is no situation where the number of predicted suspended particles is greater than the number of flocculation standard particles, the judgment module determines not to turn on the feeding mode.

[0008] Further, when comparing the historical control record with the flocculation flow rate and determining whether to determine the valve opening and closing angle according to the historical control record based on the comparison result, it includes: The historical control record includes the historical flocculation flow rate and the historical valve opening and closing angle, and the historical flocculation flow rate and the historical valve opening and closing angle correspond one by one; When there is a historical flocculation flow rate identical to the flocculation flow rate in the historical control record, the judgment module determines to determine the valve opening and closing angle according to the historical control record; The judgment module takes the historical valve opening and closing angle corresponding to the historical flocculation flow rate identical to the flocculation flow rate as the valve opening and closing angle of the flocculation tank. If there are multiple historical flocculation flow rates identical to the flocculation flow rate in the historical control record, the judgment module determines to take the average value of the historical valve opening and closing angles corresponding to each historical flocculation flow rate as the valve opening and closing angle of the flocculation tank; When there is no historical flocculation flow rate identical to the flocculation flow rate in the historical control record, the judgment module determines not to determine the valve opening and closing angle according to the historical control record.

[0009] Further, when it is determined not to determine the valve opening and closing angle according to the historical control record and the valve opening and closing angle is determined based on the valve angle model, it includes: The judgment module establishes a model data set according to the historical control record; The judgment module samples the model data set according to a preset ratio to obtain a training subset and a test subset; The judgment module obtains a pre-selected random forest model, iteratively trains the random forest model based on the training subset, and performs deviation evaluation on the iteratively trained random forest model according to the test subset, and determines whether to stop the iterative training according to the evaluation result to obtain the valve angle model; If the model deviation of the currently iteratively trained random forest model is less than the model deviation of the random forest model after the previous iterative training, continue the iterative training until the preset number of iterations is reached; If the model deviation of the random forest model after the current iterative training is greater than or equal to the model deviation of the random forest model after the previous iterative training, stop the iterative training and use the random forest model after the current iterative training as the valve angle model; The judgment module substitutes the flocculation flow rate into the valve angle model to determine the valve opening and closing angle; When the judgment module determines the valve opening and closing angle according to the historical control record or based on the valve angle model, it further includes: Preset a water sample collection interval in advance, and re-obtain the flocculation flow rate based on the water sample collection interval; When the flocculation flow rate is inconsistent with the re-obtained flocculation flow rate, the judgment module determines to continue to determine the valve opening and closing angle according to the re-obtained flocculation flow rate; When the flocculation flow rate is consistent with the re-obtained flocculation flow rate, the judgment module determines not to change the valve opening and closing angle.

[0010] Further, when determining the flocculation dosage according to the number of suspended particles in the flocculation tank, it includes: The processing module determines the flocculation dosage according to the following formula: ; where, represents the flocculation dosage, is the pH dosage coefficient, represents the pH value of the flocculation tank, represents the volume of the flocculation tank, represents the number of suspended particles, represents the number of standard flocculation particles.

[0011] Further, when determining whether to adjust the flocculation dosage according to the number of suspended treatment particles, the number of suspended particles, and the number of standard flocculation particles, it includes: The processing module presets a preset ratio of dosage; Determine the difference between the number of suspended particles and the number of suspended treatment particles as the flocculation compensation amount, determine the difference between the number of suspended particles and the number of standard flocculation particles as the flocculation change amount, and determine the ratio of the flocculation compensation amount to the flocculation change amount as the dosage ratio; When the dosage ratio is greater than or equal to the preset dosage ratio, the processing module determines not to adjust the flocculation dosage and determines that the current flocculation treatment is completed; When the dosage ratio is less than the preset dosage ratio, the processing module determines to adjust the flocculation dosage.

[0012] Further, when setting the dosing control factor of the flocculation dosing amount according to the flocculation temperature, it includes: The control module preset a first preset flocculation temperature and a second preset flocculation temperature, and the first preset flocculation temperature is greater than the second preset flocculation temperature; The control module preset a first preset dosing control factor, a second preset dosing control factor and a third preset dosing control factor, the first preset dosing control factor is greater than the second preset dosing control factor, and the second preset dosing control factor is greater than the third preset dosing control factor; When the flocculation temperature is greater than the first preset flocculation temperature, the control module uses the first preset dosing control factor as the dosing control factor of the flocculation dosing amount; When the flocculation temperature is less than or equal to the first preset flocculation temperature and greater than the second preset flocculation temperature, the control module uses the second preset dosing control factor as the dosing control factor of the flocculation dosing amount; When the flocculation temperature is less than or equal to the second preset flocculation temperature, the control module uses the third preset dosing control factor as the dosing control factor of the flocculation dosing amount.

[0013] Further, when optimizing the flocculation dosing amount based on the dosing control factor, adjusting the dosing of the flocculation tank according to the optimization result, and transmitting the dosing result to the processing module, it includes: The flocculation dosing amount is in a direct proportion relationship with the dosing control factor; Determine the target dosing amount according to the optimization result, and send the target dosing amount to the processing module, and the processing module monitors the control module based on the target dosing amount.

[0014] Further, the flocculation sensor includes: A sensor body and a dosing block; The sensor body is provided with two cameras, the upper surface of the sensor body is fixedly connected with two rotating blocks, and a rotating rod is inserted between the rotating blocks; The dosing block is provided with a plurality of nozzles for spraying flocculant, the lower part of the dosing block is fixedly connected with two valve blocks, the valve blocks are inserted with the rotating rod, and the rotating rod drives the valve blocks to rotate.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: By analyzing the flocculation image and the number of suspended particles, the actual needs of the flocculant to be put in are met, accurately determining whether to turn on the dosing mode, avoiding errors caused by manual operation, improving the reliability of sewage treatment. Using historical control records to determine the valve opening and closing angle, and if not satisfied, determining the valve opening and closing angle according to the valve angle model, enhancing the unity of the system and historical control. Using a machine learning model to intelligently determine the valve opening and closing angle, avoiding the accumulation of manual judgment errors, and further improving the control accuracy. Analyzing the change in the number of suspended particles and deciding whether to adjust the flocculation dosing amount of the flocculant, ensuring the continuous optimization of the flocculation effect, thus avoiding waste caused by over-dosing of the flocculant, improving the quality of sewage treatment. The dosing control factor ensures the efficient utilization of the flocculant, reduces the interference of human factors on the treatment effect, and can adjust the flocculation dosing amount of the flocculant according to the actual state of the flocculation tank by comprehensively considering factors such as the temperature, flow rate, and particle number of the flocculation tank, flexibly coping with changes in different water qualities, and ensuring the sewage treatment effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings: Figure 1 is a structural block diagram of a control system based on a flocculation sensor provided by an embodiment of the present invention; Figure 2 is a schematic structural diagram of a flocculation sensor provided by an embodiment of the present invention.

[0017] In the figure: 1, sensor body; 10, camera; 11, rotating block; 12, rotating rod; 2, dosing block; 20, nozzle; 21, valve block. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The following will describe the exemplary embodiments of the present disclosure in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be completely conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0019] Refer to Figure 1As shown, in some embodiments of the present application, this embodiment provides a control system based on a flocculation sensor, including: A flocculation sensor and a control module, where the control module is used to control the flocculation sensor. The control module includes: An acquisition module, configured to divide the flocculation tank into several flocculation detection areas, obtain detection images of the several flocculation detection areas, process all the detection images to obtain a flocculation image of the flocculation tank, and analyze the flocculation image to determine the number of suspended particles in the flocculation tank; A judgment module, configured to compare the number of suspended particles with the number of standard flocculation particles, judge whether to start the dosing mode according to the comparison result. When it is determined to start the dosing mode, obtain the historical control record and the flocculation flow rate of the flocculation tank, compare the historical control record with the flocculation flow rate, and judge whether to determine the valve opening and closing angle according to the historical control record. When it is determined not to determine the valve opening and closing angle according to the historical control record, determine the valve opening and closing angle based on the valve angle model; A processing module, configured to determine the flocculation dosing amount according to the number of suspended particles in the flocculation tank, perform dosing on the flocculation tank according to the valve opening and closing angle and the flocculation dosing amount, obtain a second flocculation image after a preset time, analyze the second flocculation image to determine the number of suspended treated particles in the flocculation tank, and determine whether to adjust the flocculation dosing amount according to the number of suspended treated particles, the number of suspended particles, and the number of standard flocculation particles; A control module, configured to when it is determined to adjust the flocculation dosing amount, obtain the flocculation temperature of the flocculation tank, set a dosing control factor for the flocculation dosing amount according to the flocculation temperature, optimize the flocculation dosing amount based on the dosing control factor, perform dosing adjustment on the flocculation tank according to the optimization result, and transmit the dosing result to the processing module.

[0020] Specifically, the control module is integrated in the flocculation sensor and is used to control the flocculation sensor. Among them, the acquisition module of the control module divides the flocculation tank into several flocculation detection areas. Preferably, there are 20 flocculation detection areas, which can be specifically adjusted according to the actual sewage environment of the flocculation tank. The acquisition module uses the camera of the flocculation sensor to take pictures of each flocculation detection area, so as to obtain the detection images of the flocculation detection areas. These detection images reflect the sewage environment of the flocculation tank and show the distribution of suspended particles in the water. The acquisition module processes the images of all flocculation detection areas to generate an overall flocculation image. Separately dividing the flocculation detection areas and taking pictures avoids the risk of incomplete picture taking caused by direct camera shooting. Using image analysis algorithms to analyze the flocculation image to determine the number of suspended particles in the flocculation tank. This process is long and mature and will not be elaborated here. The judgment module compares the number of suspended particles with the number of standard flocculation particles. The number of standard flocculation particles represents the number of particles that should exist in the flocculation tank. If there is a difference between the number of suspended particles and the number of standard flocculation particles, it means that flocculant needs to be added to the flocculation tank. The judgment module then activates the dosing mode. During the process of activating the dosing mode, for example, when the flocculation flow rate is too fast, the opening and closing angle of the valve will be interfered by the flow rate, resulting in an error in the opening. Determining the opening and closing angle of the valve using historical control records prevents the angle error during direct opening and maintains unity with historical control. When it is determined not to determine the opening and closing angle of the valve according to historical control records, the opening and closing angle of the valve is determined based on the valve angle model. Using a machine learning model to determine the opening and closing angle of the valve can obtain a good opening and closing angle. At this time, the processing module determines the flocculation dosing amount according to the number of suspended particles in the flocculation tank and sprinkles the flocculant according to the flocculation dosing amount and the opening and closing angle of the valve. After a preset time, the number of suspended treated particles after being treated with the flocculant is obtained from the acquisition module. Moreover, whether to adjust the flocculation dosing amount is determined according to the number of suspended treated particles, the number of suspended particles, and the number of standard flocculation particles. When an adjustment occurs, it means that the flocculation dosing amount fails to meet the expected treatment effect. The flocculation dosing amount is affected by the temperature in the flocculation tank and does not effectively treat the sewage. When the flocculation dosing amount needs to be adjusted, the control module will dynamically optimize the flocculation dosing amount according to the flocculation temperature in the flocculation tank and perform re-dosing, thereby effectively controlling the dosing of the flocculant and improving the stability and reliability of sewage treatment.

[0021] It can be understood that by real-time monitoring the changes of suspended particles in the flocculation tank, the system can dynamically adjust the dosing of the flocculant according to real-time treatment requirements, avoiding over-dosing or under-dosing situations, ensuring good flocculation treatment effects. Moreover, through precise control of the opening and closing angle of the valve and adjustment of the flocculation dosing amount, it reduces the waste of chemicals and treatment costs, effectively reduces the dependence on manual operation, and improves the efficiency of flocculation treatment.

[0022] In some embodiments of the present application, when obtaining the detection images of several flocculation detection regions and processing all the detection images to obtain the flocculation image of the flocculation tank, it includes: the acquisition module preprocesses the detection images of several flocculation detection regions, and the preprocessing includes image denoising and brightness adjustment. Feature points are extracted from the preprocessed detection images according to the first image algorithm, and the extracted feature points are matched according to the second image algorithm to determine the overlapping regions between each detection image. The detection images are registered according to the overlapping regions, and the registered detection images are merged based on the third image algorithm to determine the flocculation mosaic image of the flocculation tank. The flocculation mosaic image is adjusted, and the image adjustment includes edge smoothing and histogram equalization. The flocculation image of the flocculation tank is determined according to the image adjustment result.

[0023] In some embodiments of the present application, when comparing the number of suspended particles with the number of flocculation standard particles and determining whether to turn on the dosing mode according to the comparison result, it includes: when the number of suspended particles is greater than the number of flocculation standard particles, the judgment module determines to turn on the dosing mode; when the number of suspended particles is less than or equal to the number of flocculation standard particles, the judgment module determines not to turn on the dosing mode, and determines the predicted number of suspended particles and the prediction time of the flocculation tank based on a pre-trained prediction model. The prediction time and the predicted number of particles correspond one by one. If there is a predicted number of suspended particles greater than the number of flocculation standard particles, the judgment module turns on the dosing mode according to the prediction time; if there is no predicted number of suspended particles greater than the number of flocculation standard particles, the judgment module determines not to turn on the dosing mode.

[0024] Specifically, for preprocessing image denoising, Gaussian filtering or median filtering is used to achieve it. And for brightness adjustment, the exposure of the image is changed to ensure consistency on the image, eliminating unnecessary information that affects subsequent processing, thereby improving the image quality. The first image algorithm represents Scale-Invariant Feature Transform (SIFT), the second image algorithm is the RANSAC algorithm, which can determine the relative positions between images, thereby determining the overlapping areas between the detected images. The third image algorithm represents the weighted mean. The system merges the registered detected images, performs edge smoothing and histogram equalization on the flocculation spliced images to adjust the deviation during merging, improving the quality of merging, thus ensuring that the flocculation images conform to the actual situation of the flocculation tank. When the number of suspended particles is greater than the number of standard flocculation particles, it indicates that a flocculant needs to be added for treatment in the flocculation tank, and the judgment module determines to activate the dosing mode. When the number of suspended particles is less than or equal to the number of standard flocculation particles, the dosing mode is not activated. Moreover, the number of suspended particles in the flocculation tank is trained, and a prediction model is used to determine the predicted number of suspended particles and the prediction time in the flocculation tank, avoiding the risk of untimely response to sudden sewage situations. If the predicted number of suspended particles obtained by the prediction model is greater than the number of standard flocculation particles, the judgment module activates the dosing mode according to the prediction time. And the subsequent processing process is consistent with the processing method of the number of suspended particles, treating the predicted number of suspended particles as the number of suspended particles. This improves the reliability of the control of flocculant dosing and avoids dosing errors caused by relying on human experience.

[0025] In some embodiments of the present application, when comparing the historical control record with the flocculation flow rate and determining whether to determine the valve opening and closing angle according to the historical control record based on the comparison result, it includes: The historical control record includes the historical flocculation flow rate and the historical valve opening and closing angle, and the historical flocculation flow rate and the historical valve opening and closing angle correspond one by one. When there is a historical flocculation flow rate in the historical control record that is the same as the flocculation flow rate, the judgment module determines to determine the valve opening and closing angle of the flocculation tank according to the historical control record, and the judgment module uses the historical valve opening and closing angle corresponding to the historical flocculation flow rate that is the same as the flocculation flow rate as the valve opening and closing angle of the flocculation tank. If there are multiple historical flocculation flow rates in the historical control record that are the same as the flocculation flow rate, the judgment module determines to use the mean value of the historical valve opening and closing angles corresponding to each historical flocculation flow rate as the valve opening and closing angle of the flocculation tank. When there is no historical flocculation flow rate in the historical control record that is the same as the flocculation flow rate, the judgment module determines not to determine the valve opening and closing angle according to the historical control record.

[0026] Specifically, by judging the matching degree between the flocculation flow rate and the historical flocculation flow rate, these data can be directly used to determine the opening and closing angle of the valve, so as to ensure the reliability and consistency of the valve opening and closing. When there are multiple historical flocculation flow rates that are the same as the current flocculation flow rate, the corresponding historical valve opening and closing angles are summed and averaged, which improves the accuracy of the valve opening and closing angle, reduces the dependence on human experience and intuition, and reduces the uncertainty and dosing risk brought by human judgment. Thus, the automation level of the system and the dosing accuracy are improved. Moreover, the system can learn and optimize from historical experience, so as to continuously improve the accuracy of the valve opening and closing angle and ensure the reliability of system control.

[0027] In some embodiments of the present application, when it is determined not to determine the valve opening and closing angle according to the historical control record and the valve opening and closing angle is determined based on the valve angle model, it includes: the judgment module establishes a model data set according to the historical control record, the judgment module samples the model data set according to a preset ratio to obtain a training subset and a test subset, the judgment module obtains a pre-selected random forest model, performs iterative training on the random forest model based on the training subset, and performs deviation evaluation on the iteratively trained random forest model according to the test subset. According to the evaluation result, it is judged whether to stop the iterative training to obtain the valve angle model. If the model deviation of the current iteratively trained random forest model is smaller than the model deviation of the previous iteratively trained random forest model, the iterative training continues until the preset number of iterations is reached. If the model deviation of the current iteratively trained random forest model is greater than or equal to the model deviation of the previous iteratively trained random forest model, the iterative training stops, and the current iteratively trained random forest model is used as the valve angle model. The judgment module substitutes the flocculation flow rate into the valve angle model to determine the valve opening and closing angle. When the judgment module determines to determine the valve opening and closing angle according to the historical control record or determine the valve opening and closing angle based on the valve angle model, it further includes: presetting a water sample collection interval, and re-obtaining the flocculation flow rate based on the water sample collection interval. When the flocculation flow rate is inconsistent with the re-obtained flocculation flow rate, the judgment module determines to continue to determine the valve opening and closing angle according to the re-obtained flocculation flow rate. When the flocculation flow rate is consistent with the re-obtained flocculation flow rate, the judgment module determines not to change the valve opening and closing angle.

[0028] Specifically, after the flocculation flow rate consistent with the flocculation tank cannot be found in the historical control records, a model dataset is established from the historical control records, and a training subset and a test subset are obtained by sampling according to a preset ratio. Usually, 70-90% of the data is used as the training subset, and the rest is used as the test subset, so that both the training subset and the test subset can include the states of the flocculation tank in different periods. The random forest model contains multiple nodes and branches and aims to capture the complex relationships in the data. By training the random forest model, the valve opening and closing angle of the flocculation flow rate can be accurately output, improving the control accuracy. If the model deviation of the random forest model after the current iterative training is less than the model deviation of the random forest model after the previous iterative training, continue the iterative training until the preset number of iterations is reached. In each iteration, the random forest model will try to learn the patterns and relationships in the data to improve its prediction or classification ability. After each iterative training, the data in the test subset is used to evaluate the deviation of the model. The deviation evaluation indicators include the loss function value and the recall rate, etc., which are used to measure the comprehensive performance of the model. If the model deviation after the current iterative training is less than the previous model deviation, it means that the model performance has decreased and iterative training needs to continue, which helps the model to stably approach the global optimal solution. If the model deviation after the current iterative training is greater than or equal to the previous model deviation, it means that the model performance has improved or remained stable. At this time, the iterative training can be stopped, and it is considered that the model has reached a satisfactory performance level. This trained model can be used as the valve angle model to output the valve opening and closing angle of the flocculation flow rate, ensuring the control accuracy of the system.

[0029] It can be understood that the water sample collection interval is preset, and the water sample collection interval is preferably once every 30S. After determining the valve opening and closing angle according to the historical control records or according to the valve angle model, the flocculation flow rate may still change. For example, external force disturbs the flocculation tank or the wind speed drives the flow rate of the flocculation tank to change. Therefore, it is necessary to re-determine whether the flocculation flow rate of the flocculation tank has changed according to the water sample collection interval. If the re-determined flocculation flow rate is inconsistent with the initially measured flocculation flow rate, the valve opening and closing angle at this time needs to be adjusted accordingly. The re-determined flocculation flow rate is still used to re-determine the valve opening and closing angle by using the historical control records or the valve angle model, ensuring that the valve opening and closing angle can adapt to the flocculation flow rate of the flocculation tank in real time, thereby improving the control accuracy of the system.

[0030] In some embodiments of the present application, when determining the flocculation dosage according to the number of suspended particles in the flocculation tank, it includes: The processing module determines the flocculation dosage according to the following formula: ; Wherein, represents the flocculation dosage, is the pH dosing coefficient, represents the pH value of the flocculation tank, represents the volume of the flocculation tank, represents the number of suspended particles, represents the number of standard flocculation particles.

[0031] In some embodiments of the present application, when determining whether to adjust the flocculation dosage according to the number of suspended treatment particles, the number of suspended particles, and the number of standard flocculation particles, it includes: the processing module presetting a dosing preset ratio in advance, determining the flocculation compensation amount as the difference between the number of suspended particles and the number of suspended treatment particles, determining the flocculation change amount as the difference between the number of suspended particles and the number of standard flocculation particles, determining the ratio of the flocculation compensation amount to the flocculation change amount as the dosing ratio. When the dosing ratio is greater than or equal to the dosing preset ratio, the processing module determines not to adjust the flocculation dosage and determines that the current flocculation treatment is completed. When the dosing ratio is less than the dosing preset ratio, the processing module determines to adjust the flocculation dosage.

[0032] Specifically, by combining multiple factors such as the pH value of the flocculation tank, the number of suspended particles, and the volume of the flocculation tank, the system can accurately obtain the dosage of the flocculant, thereby improving the effect of flocculation treatment. It avoids the risks of waste or incomplete treatment caused by excessive or insufficient dosing based on human experience. is the pH dosing coefficient. The system determines the pH dosing coefficient through experimental data and the pH value of the historical flocculation tank. The pH dosing coefficient represents the flocculation dosage required per unit turbidity at the current pH value. The pH dosing coefficient measures the effectiveness of dosing the flocculant at the current pH value, thereby ensuring the reliability of the flocculant during use. The pH dosing coefficient is preferably 3 NTU*L*kg -1 , after the flocculant is dosed, the processing module determines the number of suspended treatment particles through the images of the acquisition module. The number of suspended treatment particles represents the number of remaining suspended particles in the flocculation tank after the flocculant is added. The flocculation compensation amount represents the change in the number of particles after flocculation treatment. The flocculation change amount represents the gap between the initial number of suspended particles in the flocculation tank and the number of standard flocculation particles. By presetting the dosing preset ratio in advance to measure the relationship between the dosing ratios, it can be judged whether the result of the current treatment has reached the expected effect. If not, it means that other factors in the flocculation tank have interfered with the current flocculation dosage, and the flocculation dosage needs to be appropriately adjusted. Through the automated control and adjustment process, the need for manual intervention is reduced, the reliability and stability of the system are improved, and the uncertainty caused by manual dosing errors is reduced.

[0033] In some embodiments of the present application, when setting the dosing control factor for the flocculation dosing amount according to the flocculation temperature, it includes: the control module pre-sets a first preset flocculation temperature and a second preset flocculation temperature, the first preset flocculation temperature is greater than the second preset flocculation temperature, the control module pre-sets a first preset dosing control factor, a second preset dosing control factor and a third preset dosing control factor, the first preset dosing control factor is greater than the second preset dosing control factor, and the second preset dosing control factor is greater than the third preset dosing control factor. When the flocculation temperature is greater than the first preset flocculation temperature, the control module uses the first preset dosing control factor as the dosing control factor for the flocculation dosing amount. When the flocculation temperature is less than or equal to the first preset flocculation temperature and greater than the second preset flocculation temperature, the control module uses the second preset dosing control factor as the dosing control factor for the flocculation dosing amount. When the flocculation temperature is less than or equal to the second preset flocculation temperature, the control module uses the third preset dosing control factor as the dosing control factor for the flocculation dosing amount.

[0034] In some embodiments of the present application, when optimizing the flocculation dosing amount based on the dosing control factor, adjusting the dosing in the flocculation tank according to the optimization result, and transmitting the dosing result to the processing module, it includes: the flocculation dosing amount is directly proportional to the dosing control factor, determining the target dosing amount according to the optimization result, and sending the target dosing amount to the processing module. The processing module monitors the control module based on the target dosing amount.

[0035] Specifically, by comparing the flocculation temperature with the set first preset flocculation temperature and second preset flocculation temperature, the corresponding dosing control factor is dynamically selected, and thus the dynamic adjustment of the flocculation dosing amount can be realized, further improving the accuracy of the control of the flocculation dosing amount. Adjust the flocculation dosing amount according to the dosing control factor. When the flocculation dosing amount is and assuming the dosing control factor is , the adjusted flocculation dosing amount is . When the dosing control factor increases, the flocculation dosing amount will also increase accordingly. The adjusted flocculation dosing amount is the target dosing amount. The control module continues to dose the flocculant according to the target dosing amount and sends the target dosing amount to the processing module. The processing module is responsible for receiving the target dosing amount and monitoring the dosing process of the control module in real time, avoiding the risk of missed dosing or over-dosing, achieving precise control of the flocculant dosing at different flocculation temperatures, improving the reliability and precision of the flocculation process, and further improving the sewage treatment effect.

[0036] Refer to Figure 2As shown, in some embodiments of the present application, the flocculation sensor includes: a sensor body and a dosing block. The sensor body is provided with two cameras. Two rotating blocks are fixedly connected to the upper surface of the sensor body. A rotating rod is inserted between the rotating blocks. The dosing block is provided with a plurality of nozzles for spraying flocculant. Two valve blocks are fixedly connected to the lower part of the dosing block. The rotating rod is inserted through the valve blocks, and the rotating rod drives the valve blocks to rotate.

[0037] Specifically, the two cameras 10 provided on the sensor body 1 are responsible for photographing the environment of the flocculation tank and transmitting real-time detection images to the acquisition module. The dosing block 2 stores the flocculant. Two rotating blocks 11 are fixedly connected to the upper surface of the sensor body 1. The rotating blocks 11 provide a rotating track for the rotating rod 12 so that the rotating rod 12 can rotate. When the dosing mode is not turned on, the dosing block 2 fits against the upper surface of the sensor body 1. When the judgment module obtains the valve opening and closing angle, the rotation of the rotating rod 12 drives the valve block 21 to rotate, causing the dosing block 2 to open according to the valve opening and closing angle. The dosing block 2 is provided with a plurality of nozzles 20, preferably 3 nozzles. The nozzles 20 spray according to the flocculation dosing amount of the processing module and the optimized flocculation dosing amount of the control module. The nozzles 20 can precisely control the dosing of the flocculant, thereby effectively treating the sewage and improving the sewage treatment effect.

[0038] In summary, the beneficial effects of the present invention are as follows: By analyzing the flocculation image and the number of suspended particles, it meets the actual needs of the dosed flocculant, accurately judges whether to turn on the dosing mode, avoids errors caused by manual operation, and improves the reliability of sewage treatment. The historical control records are used to determine the valve opening and closing angle. If not satisfied, the valve opening and closing angle is determined according to the valve angle model, improving the unity of the system and historical control. The machine learning model is used to intelligently determine the valve opening and closing angle, avoiding the accumulation of manual judgment errors and further improving the control accuracy. Analyze the change in the number of suspended particles and decide whether to adjust the flocculation dosing amount of the flocculant, ensuring the continuous optimization of the flocculation effect, thus avoiding waste caused by over-dosing of the flocculant and improving the quality of sewage treatment. The dosing control factor ensures the efficient use of the flocculant, reduces the interference of human factors on the treatment effect, and can adjust the flocculation dosing amount of the flocculant according to the actual state of the flocculation tank by comprehensively considering factors such as the temperature, flow rate, and particle number of the flocculation tank, flexibly coping with changes in different water qualities and ensuring the sewage treatment effect.

[0039] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0040] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0041] These computer program instructions can also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable storage medium generate a manufactured article including instruction means, and the instruction means implements the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0042] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: still can modify the specific implementation manners of the present invention or make equivalent replacements, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A control system based on a flocculation sensor, characterized in that Including: A flocculation sensor and a control module for controlling the flocculation sensor, the control module including: An acquisition module configured to divide a flocculation tank into a plurality of flocculation detection areas, obtain detection images of the plurality of flocculation detection areas, process all the detection images to obtain a flocculation image of the flocculation tank, and analyze the flocculation image to determine the number of suspended particles in the flocculation tank; A judgment module configured to compare the number of suspended particles with the number of flocculation standard particles, judge whether to start a dosing mode according to the comparison result, when it is determined to start the dosing mode, obtain a historical control record and the flocculation flow rate of the flocculation tank, compare the historical control record with the flocculation flow rate, and judge whether to determine the valve opening and closing angle according to the historical control record according to the comparison result. When it is determined not to determine the valve opening and closing angle according to the historical control record, determine the valve opening and closing angle based on a valve angle model; A processing module configured to determine a flocculation dosing amount according to the number of suspended particles in the flocculation tank, dose the flocculation tank according to the valve opening and closing angle and the flocculation dosing amount, obtain a second flocculation image after a preset time, and analyze the second flocculation image to determine the number of suspended treated particles in the flocculation tank, and determine whether to adjust the flocculation dosing amount according to the number of suspended treated particles, the number of suspended particles and the number of flocculation standard particles; A control module configured to, when it is determined to adjust the flocculation dosing amount, obtain the flocculation temperature of the flocculation tank, set a dosing control factor for the flocculation dosing amount according to the flocculation temperature, optimize the flocculation dosing amount based on the dosing control factor, adjust the dosing of the flocculation tank according to the optimization result, and transmit the dosing result to the processing module.

2. The control system based on a flocculation sensor according to claim 1, characterized in that, When obtaining detection images of a plurality of flocculation detection areas, processing all the detection images to obtain a flocculation image of the flocculation tank, it includes: The acquisition module preprocesses the detection images of the plurality of flocculation detection areas, and the preprocessing includes image denoising and brightness adjustment; Extracting feature points from the preprocessed detection images according to a first image algorithm, matching the extracted feature points according to a second image algorithm, determining the overlapping areas between each detection image, registering the detection images according to the overlapping areas, and merging the registered detection images based on a third image algorithm to determine a flocculation mosaic image of the flocculation tank; Adjusting the flocculation mosaic image, the image adjustment including edge smoothing and histogram equalization, and determining the flocculation image of the flocculation tank according to the image adjustment result.

3. The control system based on a flocculation sensor according to claim 2, wherein When comparing the number of suspended particles with the number of flocculation standard particles and judging whether to start a dosing mode according to the comparison result, it includes: When the number of suspended particles is greater than the number of flocculation standard particles, the judgment module determines to start the dosing mode; When the number of the suspended particles is less than or equal to the number of the flocculation standard particles, the judgment module determines not to activate the dosing mode, and determines the predicted number of suspended particles and the predicted time of the flocculation tank based on a pre-trained prediction model, and the predicted time and the predicted number of particles correspond one by one; If there is a situation where the predicted number of suspended particles is greater than the number of flocculation standard particles, the judgment module activates the dosing mode according to the predicted time; If there is no situation where the predicted number of suspended particles is greater than the number of flocculation standard particles, the judgment module determines not to activate the dosing mode.

4. The control system based on a flocculation sensor according to claim 3, characterized in that, When comparing the historical control record with the flocculation flow rate and judging whether to determine the valve opening and closing angle according to the historical control record based on the comparison result, it includes: The historical control record includes the historical flocculation flow rate and the historical valve opening and closing angle, and the historical flocculation flow rate and the historical valve opening and closing angle correspond one by one; When there is a historical flocculation flow rate identical to the flocculation flow rate in the historical control record, the judgment module determines to determine the valve opening and closing angle according to the historical control record; The judgment module takes the historical valve opening and closing angle corresponding to the historical flocculation flow rate identical to the flocculation flow rate as the valve opening and closing angle of the flocculation tank. If there are multiple historical flocculation flow rates identical to the flocculation flow rate in the historical control record, the judgment module determines to take the average value of the historical valve opening and closing angles corresponding to each historical flocculation flow rate as the valve opening and closing angle of the flocculation tank; When there is no historical flocculation flow rate identical to the flocculation flow rate in the historical control record, the judgment module determines not to determine the valve opening and closing angle according to the historical control record.

5. The control system based on a flocculation sensor according to claim 4, characterized in that, When it is determined not to determine the valve opening and closing angle according to the historical control record and the valve opening and closing angle is determined based on the valve angle model, it includes: The judgment module establishes a model data set according to the historical control record; The judgment module samples the model data set according to a preset ratio to obtain a training subset and a test subset; The judgment module obtains a pre-selected random forest model, performs iterative training on the random forest model based on the training subset, and performs deviation evaluation on the iteratively trained random forest model according to the test subset, and judges whether to stop the iterative training according to the evaluation result to obtain the valve angle model; If the model deviation of the currently iteratively trained random forest model is less than the model deviation of the previous iteratively trained random forest model, continue the iterative training until the preset number of iterations is reached; If the model deviation of the currently iteratively trained random forest model is greater than or equal to the model deviation of the previous iteratively trained random forest model, stop the iterative training and take the currently iteratively trained random forest model as the valve angle model; The judgment module substitutes the flocculation flow rate into the valve angle model to determine the valve opening and closing angle; When the judgment module determines to determine the valve opening and closing angle according to the historical control record or determine the valve opening and closing angle based on the valve angle model, it further includes: Preset a water sample collection interval, and re-obtain the flocculation flow rate based on the water sample collection interval; When the flocculation flow rate is inconsistent with the newly obtained flocculation flow rate, the judgment module determines to continue to determine the valve opening and closing angle according to the newly obtained flocculation flow rate; When the flocculation flow rate is consistent with the newly obtained flocculation flow rate, the judgment module determines not to change the valve opening and closing angle.

6. The control system based on a flocculation sensor according to claim 5, characterized in that, When determining the flocculation dosage according to the number of suspended particles in the flocculation tank, it includes: The processing module determines the flocculation dosage according to the following formula: ； Among them, represents the flocculant dosage, is the pH dosage coefficient, represents the pH value of the flocculation tank, represents the volume of the flocculation tank, represents the number of suspended particles, represents the number of standard flocculant particles.

7. The control system based on a flocculation sensor according to claim 6, wherein When determining whether to adjust the flocculation dosage according to the number of suspended treatment particles, the number of suspended particles, and the number of flocculation standard particles, it includes: The processing module preset a preset ratio for dosing; Determine the difference between the number of suspended particles and the number of suspended treatment particles as the flocculation compensation amount, determine the difference between the number of suspended particles and the number of flocculation standard particles as the flocculation change amount, and determine the ratio of the flocculation compensation amount to the flocculation change amount as the dosing ratio; When the dosing ratio is greater than or equal to the preset dosing ratio, the processing module determines not to adjust the flocculation dosage and determines that the current flocculation treatment is completed; When the dosing ratio is less than the preset dosing ratio, the processing module determines to adjust the flocculation dosage.

8. The control system based on a flocculation sensor according to claim 7, characterized in that, When setting the dosing control factor of the flocculation dosage according to the flocculation temperature, it includes: The control module preset a first preset flocculation temperature and a second preset flocculation temperature, and the first preset flocculation temperature is greater than the second preset flocculation temperature; The control module preset a first preset dosing control factor, a second preset dosing control factor, and a third preset dosing control factor, the first preset dosing control factor is greater than the second preset dosing control factor, and the second preset dosing control factor is greater than the third preset dosing control factor; When the flocculation temperature is greater than the first preset flocculation temperature, the control module uses the first preset dosing control factor as the dosing control factor of the flocculation dosage; When the flocculation temperature is less than or equal to the first preset flocculation temperature and greater than the second preset flocculation temperature, the control module uses the second preset dosing control factor as the dosing control factor of the flocculation dosage; When the flocculation temperature is less than or equal to the second preset flocculation temperature, the control module uses the third preset dosing control factor as the dosing control factor of the flocculation dosage.

9. The control system based on a flocculation sensor according to claim 8, characterized in that, When optimizing the flocculation dosage based on the dosing control factor, adjusting the dosing of the flocculation tank according to the optimization result, and transmitting the dosing result to the processing module, it includes: The flocculation dosage is directly proportional to the dosing control factor; Determine the target dosing amount according to the optimization result and send the target dosing amount to the processing module, and the processing module monitors the control module based on the target dosing amount.

10. The control system based on a flocculation sensor according to claim 9, characterized in that, The flocculation sensor includes: A sensor body and a dosing block; The sensor body is provided with two cameras, two rotating blocks are fixedly connected to the upper surface of the sensor body, and a rotating rod is inserted between the rotating blocks; The dosing block is provided with a plurality of spray nozzles for spraying flocculant. Two valve blocks are fixedly connected to the lower part of the dosing block, and the valve blocks are inserted into the rotating rod, which drives the valve blocks to rotate.

Images