Chemical adding control method, device, product and equipment in water treatment process

Abstract

The invention relates to the technical field of water treatment, and provides a chemical adding control method and device in a water treatment process, a product and equipment. The method comprises the following steps: acquiring inlet water parameters, and determining a target water quality category to which the inlet water parameters belong according to an inlet water quality classification model; determining a first dosage prediction feature and a second dosage prediction feature corresponding to the target water quality category according to a preset mapping relation; inputting the characteristic value of the first dosage prediction characteristic into a dosage regression prediction model corresponding to the target water quality category to obtain a first dosage prediction value; inputting the characteristic value of the second dosage prediction characteristic into a dosage classification prediction model corresponding to the target water quality category to obtain a first dosage prediction grade; and according to the overlapping relationship between the first dosage predicted value and the first dosage predicted grade, determining a target dosage predicted value, and controlling the dosage in the water treatment process based on the target dosage predicted value. According to the scheme, the dosing prediction efficiency and accuracy can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of water treatment, and in particular, to a chemical dosing control method in the water treatment process, a chemical dosing control device in the water treatment process, a computer program product, and an electronic device. Background Art

[0002] Water treatment is a process of regulating raw water through physical, chemical, or biological means to make it meet the water quality standards required for specific uses. For example, adding chemicals to the raw water to remove impurities in the raw water so that it can be used for residents to drink. Chemical dosing is a crucial link in the water treatment process, which determines the final effluent water quality.

[0003] In the related art, a machine learning model can be used to process inlet parameters, floc images, etc., so as to predict and control the chemical dosing amount. However, in the related art, the same model is used for chemical dosing prediction for any water quality to be treated. It is difficult for the same model to adapt to different water quality requirements, and there is a problem of low prediction accuracy for scenarios with large water quality fluctuations. Moreover, in the related art, usually all chemical dosing prediction features are combined to achieve water quality prediction. Obviously, the more features there are, the greater the computational requirement for the model, and the lower the prediction efficiency.

[0004] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present disclosure is to provide a chemical dosing control method, device, computer program product, and electronic device in the water treatment process, so as to at least improve the accuracy and efficiency of chemical dosing control in the water treatment process to a certain extent.

[0006] Other features and advantages of the present disclosure will become apparent through the following detailed description, or be learned in part through the practice of the present disclosure.

[0007] According to a first aspect of the present disclosure, there is provided a chemical dosing control method in a water treatment process, including: obtaining inlet water parameters, and determining a target water quality category to which the inlet water parameters belong according to an inlet water quality classification model; according to a preset mapping relationship, determining a first chemical dosing prediction feature and a second chemical dosing prediction feature associated with the target water quality category from chemical dosing prediction features, where the chemical dosing prediction features include one or more of inlet water parameter features, floc features, and outlet water parameter features; inputting the feature value of the first chemical dosing prediction feature into a chemical dosing regression prediction model in a preset chemical dosing prediction model corresponding to the target water quality category, and obtaining a first chemical dosing prediction value according to the output of the chemical dosing regression prediction model; inputting the feature value of the second chemical dosing prediction feature into a chemical dosing classification prediction model in a preset chemical dosing prediction model corresponding to the target water quality category, and obtaining a first chemical dosing prediction level according to the output of the chemical dosing classification prediction model; determining a target chemical dosing prediction value according to the overlapping relationship between the first chemical dosing prediction value and the first chemical dosing prediction level, and controlling the chemical dosing amount in the water treatment process based on the target chemical dosing prediction value.

[0008] According to a second aspect of the present disclosure, there is provided a chemical dosing control device in a water treatment process, including: a target water quality category determination module configured to obtain inlet water parameters and determine a target water quality category to which the inlet water parameters belong according to an inlet water quality classification model; a chemical dosing prediction feature determination module configured to determine a first chemical dosing prediction feature and a second chemical dosing prediction feature associated with the target water quality category from chemical dosing prediction features according to a preset mapping relationship, where the chemical dosing prediction features include one or more of inlet water parameter features, floc features, and outlet water parameter features; a first prediction module configured to input the feature value of the first chemical dosing prediction feature into a chemical dosing regression prediction model in a preset chemical dosing prediction model corresponding to the target water quality category, and obtain a first chemical dosing prediction value according to the output of the chemical dosing regression prediction model; a second prediction module configured to input the feature value of the second chemical dosing prediction feature into a chemical dosing classification prediction model in a preset chemical dosing prediction model corresponding to the target water quality category, and obtain a first chemical dosing prediction level according to the output of the chemical dosing classification prediction model; a chemical dosing control module configured to determine a target chemical dosing prediction value according to the overlapping relationship between the first chemical dosing prediction value and the first chemical dosing prediction level, and control the chemical dosing amount in the water treatment process based on the target chemical dosing prediction value.

[0009] According to a third aspect of the present disclosure, there is provided a computer program product including instructions, which when running on a computer, causes the computer to execute the steps of the chemical dosing control method in the water treatment process as described in the first aspect.

[0010] According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the chemical dosing control method in the water treatment process as described in the first aspect of the foregoing embodiments.

[0011] According to a fifth aspect of the embodiments of the present disclosure, there is provided an electronic device, including: a processor; and a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the chemical dosing control method in the water treatment process as described in the first aspect of the foregoing embodiments.

[0012] As can be seen from the above technical solutions, the chemical dosing control method in the water treatment process, the chemical dosing control device in the water treatment process, the computer program product and the electronic device for implementing the chemical dosing control method in the water treatment process in the exemplary embodiments of the present disclosure at least have the following advantages and positive effects: In the technical solutions provided in some embodiments of the present disclosure, the influent water quality is classified based on water quality parameters to obtain the target water quality category to which the influent parameters belong, and then the first chemical dosing prediction feature and the second chemical dosing prediction feature associated with the target water quality category are selected from all the chemical dosing prediction features. Then, the first chemical dosing prediction feature and the second chemical dosing prediction feature are respectively input into the chemical dosing regression prediction model and the chemical dosing classification prediction model corresponding to the target water quality category to obtain the first chemical dosing prediction value and the first chemical dosing prediction level. Thus, according to the overlapping relationship between the first chemical dosing prediction value and the first chemical dosing level, the target chemical dosing prediction value is determined, and the chemical dosing in the water treatment process is controlled according to the target chemical dosing prediction value. Compared with the related art, on the one hand, the present disclosure can use the corresponding chemical dosing prediction model for chemical dosing prediction for different water quality categories, thereby improving the accuracy of chemical dosing prediction in different water quality environments; on the other hand, by classifying the water quality, the present disclosure can select appropriate chemical dosing prediction features according to the characteristics of the water quality, without using all the chemical dosing prediction features for chemical dosing prediction, reducing the input complexity of the chemical dosing prediction model, thereby reducing the computational amount of the model, and further improving the efficiency of chemical dosing prediction; on the still other hand, the present disclosure jointly performs chemical dosing prediction through the chemical dosing classification prediction model and the chemical dosing regression prediction model, and the classification model can verify the prediction result of the regression model, thereby improving the reliability of determining the chemical dosing prediction value.

[0013] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0015] Figure 1 A schematic flowchart showing a chemical dosing control method in a water treatment process according to an exemplary embodiment of the present disclosure; Figure 2 A schematic diagram showing a water treatment system according to an exemplary embodiment of the present disclosure; Figure 3 A schematic flowchart showing a method for determining an influent water quality classification model according to an exemplary embodiment of the present disclosure; Figure 4 A schematic flowchart showing a method for determining a preset chemical dosing prediction model according to an exemplary embodiment of the present disclosure; Figure 5 A schematic flowchart showing a method for determining a target chemical dosing prediction value according to an overlapping relationship according to an exemplary embodiment of the present disclosure; Figure 6 A schematic flowchart showing a method for chemical dosing control according to a target chemical dosing prediction value according to an exemplary embodiment of the present disclosure; Figure 7 A schematic diagram showing the composition of a chemical dosing control device in a water treatment process according to an exemplary embodiment of the present disclosure; Figure 8 A schematic diagram showing the structure of an electronic device according to an exemplary embodiment of the present disclosure. Detailed implementation manners

[0016] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art. The features, structures, or characteristics described can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring the various aspects of the present disclosure.

[0017] As used in this specification, the terms "a", "an", "the", and "said" are used to indicate the presence of one or more elements / components / etc.; the terms "comprising" and "having" are used to mean an open inclusion and refer to the existence of additional elements / components / etc. in addition to the listed elements / components / etc.; the terms "first", "second", etc. are used only as labels and are not a limitation on the quantity of their objects.

[0018] In addition, the drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0019] In the related art, the same chemical dosing prediction features and the same model are used for chemical dosing prediction for any water quality to be treated. For example, all the features related to the chemical dosing are spliced together to form a chemical dosing prediction feature, and then a model is trained based on this one chemical dosing prediction feature, and the chemical dosing is predicted according to the trained model using this one spliced chemical dosing prediction feature. Obviously, it is difficult for one model to adapt to different water quality scenarios, and in scenarios with large water quality fluctuations, there is a problem of low prediction accuracy. Moreover, the method of splicing all the chemical dosing prediction features will increase the input complexity of the model, thereby increasing the computational burden of the model and further resulting in low prediction efficiency of the model.

[0020] In order to solve at least one or more of the above problems, the present disclosure provides a chemical dosing control solution in the water treatment process.

[0021] Exemplarily, Figure 1 shows a chemical dosing control method in a water treatment process in an exemplary embodiment of the present disclosure. Refer to Figure 1 , the method may include: Step S110, obtaining inlet water parameters, and determining the target water quality category to which the inlet water parameters belong according to the inlet water quality classification model; Step S120, determining a first chemical dosing prediction feature and a second chemical dosing prediction feature associated with the target water quality category from the chemical dosing prediction features according to a preset mapping relationship, where the chemical dosing prediction features include one or more of inlet water parameter features, floc features, and outlet water parameter features; Step S130: Input the eigenvalue of the first chemical dosage prediction feature into the chemical dosage regression prediction model in the preset chemical dosage prediction model corresponding to the target water quality category, and obtain the first chemical dosage prediction value according to the output of the chemical dosage regression prediction model; Step S140: Input the eigenvalue of the second chemical dosage prediction feature into the chemical dosage classification prediction model in the preset chemical dosage prediction model corresponding to the target water quality category, and obtain the first chemical dosage prediction level according to the output of the chemical dosage classification prediction model; Step S150: Determine the target chemical dosage prediction value according to the overlapping relationship between the first chemical dosage prediction value and the first chemical dosage prediction level, and control the chemical dosage in the water treatment process based on the target chemical dosage prediction value.

[0022] Figure 1 Compared with the related technologies, the technical solution provided by the illustrated embodiment has the following advantages. On the one hand, the present disclosure can use the corresponding chemical dosage prediction model for different water quality categories to predict the chemical dosage, thereby improving the accuracy of chemical dosage prediction in different water quality environments. On the other hand, by classifying the water quality, the present disclosure can select appropriate chemical dosage prediction features according to the characteristics of the water quality, without using all chemical dosage prediction features for chemical dosage prediction, reducing the input complexity of the chemical dosage prediction model, thereby reducing the computational amount of the model, and further improving the efficiency of chemical dosage prediction. On the other hand, the present disclosure jointly predicts the chemical dosage through the chemical dosage classification prediction model and the chemical dosage regression prediction model. The classification model can verify the prediction results of the regression model, thereby improving the reliability of determining the chemical dosage prediction value.

[0023] Next, a detailed description will be given of the specific implementation of "Step S110: Obtain the influent parameters and determine the target water quality category to which the influent parameters belong according to the influent water quality classification model".

[0024] To more clearly illustrate the water treatment process of the present disclosure, first in combination with Figure 2 for illustration. Exemplarily, Figure 2 shows a schematic diagram of a water treatment system in an exemplary embodiment of the present disclosure. Refer to Figure 2, the water treatment system may include an inlet water tank 21, a flocculation tank 22, a sedimentation tank 23, a filtration tank 24, and an outlet water tank 25. The inlet water tank 21 can adjust the water volume of the incoming water and play a certain buffering role in the sudden change of the raw water quality. The flocculation tank 22 is used to add flocculants such as polyaluminum chloride to the water, so that fine suspended particles, colloids and other impurities in the water are destabilized and aggregated with each other to form larger flocs for subsequent sedimentation and separation. The sedimentation tank 23, through the action of gravity, makes the large flocs formed in the flocculation tank settle to the bottom of the tank, realizes solid-liquid separation, and removes most of the suspended solids, sediment and other impurities in the water. The filtration tank 24 further removes the remaining fine suspended particles, colloids, bacteria and other impurities in the water through filtration media such as quartz sand and activated carbon, improves the clarity and transparency of the water, and makes the water quality meet higher standards. The outlet water tank 25 is used to store the treated clear water, providing a stable water supply guarantee for the water use link. At the same time, the water quality of the treated water can also be monitored in the outlet water tank 25, such as detecting indicators such as pH, turbidity, and suspended solids, to ensure that the outlet water quality meets relevant standards and usage requirements.

[0025] Exemplarily, inlet water parameter detection devices such as an inlet water pH detection device, an inlet water temperature detection device, an inlet water turbidity detection device, and an inlet water flow detection device can be arranged between the inlet water tank 21 and the flocculation tank 22. An underwater camera can be arranged in the flocculation tank 22 to capture floc images and obtain floc characteristics. An underwater camera can also be arranged in the sedimentation tank 23 to capture floc sedimentation videos. A chemical dosing pump can be arranged between the inlet water tank 21 and the flocculation tank 22, and the flocculant can be added to the water through the chemical dosing pump.

[0026] In an exemplary embodiment, the inlet water parameters may include the inlet water pH value, the inlet water flow rate, the inlet water turbidity, and the inlet water temperature. As mentioned above, inlet water parameter detection devices such as an inlet water pH value detection device, an inlet water flow rate detection device, an inlet water turbidity detection device, and an inlet water temperature detection device can be installed between the inlet water tank and the flocculation tank. These inlet water parameter detection devices can send the detected inlet water parameters to the server. Different inlet water parameters characterize different water qualities to be treated, so the water quality can be classified according to the inlet water parameters to determine the target water quality category corresponding to the water quality to be treated.

[0027] Exemplarily, Figure 3 The flowchart showing the determination method of an inlet water quality classification model in an exemplary embodiment of the present disclosure is referred to Figure 3 , this method may include step S310 to step S330. Among them: In step S310, historical inlet water parameters are obtained and the historical inlet water parameters are subjected to the first clustering.

[0028] For example, historical influent parameters can be collected, such as the historical pH value, historical influent flow rate, historical influent turbidity, and historical influent temperature at the same historical moment, and they can be spliced to generate historical influent parameters. Then, according to a clustering algorithm, such as the K-means clustering algorithm, the historical influent parameters are subjected to a first clustering, where the value of K represents the number of clustering categories, and the value of K can be determined according to the test results. For example, different initial K values are given respectively, and then the K value with the best test result is selected as the final K value. Of course, the value of K can also be determined manually according to requirements.

[0029] In step S320, different categories of water quality parameters are determined according to the clustering results of the first clustering, and water quality category labels are added to the different categories of water quality parameters to generate a water quality category training data set.

[0030] For example, each clustering category in the clustering results of the first clustering can be used as only one water quality category, so as to determine different water quality categories, and corresponding water quality category labels are added to the different water quality parameters. Among them, the water quality category label can be a mark, such as the first category, the second category, or the water quality category label can also be a mark related to water quality characterization, such as high-turbidity low-temperature water, low-turbidity high-pH water, etc. This exemplary embodiment does not make special limitations on this.

[0031] In step S330, a machine learning model is trained according to the water quality category training data set to obtain an influent water quality classification model.

[0032] In an exemplary embodiment, the machine learning model in step S330 can be any machine learning model that can achieve classification, such as a support vector machine model, etc. It should be noted that the role of the machine learning model in step S330 is to perform water quality classification, so it can be trained through a simple classification model such as a support vector machine to obtain.

[0033] Through the above steps S310 to S330, an influent water quality classification model can be obtained. In this way, when the chemical dosage prediction is required, the current influent parameters can be input into the water quality classification model first, and the current influent water quality category can be determined according to the water quality classification model, so as to determine the chemical dosage prediction features and chemical dosage prediction model used according to the determined water quality category.

[0034] In an exemplary embodiment, the influent parameters, floc images, and effluent parameters can be collected regularly, and then the relevant data is sent to the server. The server processes the received data according to the method of the present disclosure to obtain the target chemical dosage prediction value, so as to predict and control the chemical dosage regularly.

[0035] Next, a detailed description will be given of the specific implementation manner of "step S120, determining the first chemical dosage prediction feature and the second chemical dosage prediction feature associated with the target water quality category from the chemical dosage prediction features according to the preset mapping relationship".

[0036] In an exemplary implementation manner, the preset mapping relationship is used to characterize the first association relationship between the first chemical dosage prediction features used by the chemical dosage regression prediction models corresponding to different water quality categories and the second association relationship between the second chemical dosage prediction features used by the chemical dosage classification prediction models corresponding to different water quality categories. In other words, the preset mapping relationship is related to the preset chemical dosage prediction models corresponding to different water quality categories.

[0037] Exemplarily, Figure 4 The flowchart of a method for determining a preset chemical dosage prediction model in an exemplary embodiment of the present disclosure is shown. Refer to Figure 4 and this method may include step S410 to step S460.

[0038] In step S410, each single chemical dosage prediction feature is used as the first candidate chemical dosage prediction feature.

[0039] In an exemplary implementation manner, the chemical dosage prediction features may include one or more of floc characteristics, influent parameter characteristics, and effluent parameter characteristics. The chemical dosage prediction features can be understood as being associated with the chemical dosage and can be used to reflect or affect the chemical dosage. Of course, the chemical dosage prediction features may also include other features related to the chemical dosage, and this exemplary implementation manner does not make special limitations on this.

[0040] For example, the first candidate chemical dosage prediction features include these 3 types of features: floc characteristics, influent parameter characteristics, and effluent parameter characteristics. That is, each first candidate chemical dosage prediction feature is of a single type or a single kind of feature.

[0041] In an exemplary implementation manner, the floc characteristics can be determined from the floc images captured by a camera. For example, an underwater camera is configured in the flocculation tank, and the underwater camera can be installed on the inner wall of the flocculation tank. Through the underwater camera, the floc images of the water in the flocculation tank can be captured. The camera can send the captured floc images to the server. After the server receives the floc images, it performs feature extraction processing on the floc images to obtain the floc characteristics. Among them, the floc characteristics may include floc area distribution characteristics, the number of flocs per unit area, the average floc diameter, etc.

[0042] For example, a floc recognition model can be pre-trained to extract floc features through the floc recognition model. For example, a large number of historical floc images can be collected in advance, and then the historical floc images are manually annotated to mark the contour or boundary of each floc in the floc image, the floc area and floc particle size corresponding to the contour or boundary, and the number of flocs in the floc image as training data.

[0043] A machine learning model is trained with the training data so that the machine learning model can recognize the contour or boundary of the flocs in the image, as well as the area of each floc and count the number of flocs in the image according to the recognition situation. It is also possible to fine-tune an existing object detection model with the training data to train the object detection model to detect and recognize flocs, thereby obtaining a floc recognition model. Through the floc recognition model, flocs can be accurately recognized in an image containing flocs, and the area and floc particle size of the recognized flocs, as well as the number of flocs in the image, can be determined. Then, the recognition results of the floc recognition model are analyzed and processed to obtain floc features. For example, the area distribution of the recognized flocs is statistically analyzed (such as the proportion of the number of different areas, etc.), the average of all floc particle sizes is calculated to obtain the average floc particle size, and the number of flocs per unit area feature is determined according to the number of flocs in the image and the actual scene area indicated by the image. Of course, the floc features can include other features, such as floc morphology, edge sharpness, etc., and this exemplary embodiment does not make special limitations on this.

[0044] In an exemplary embodiment, the influent parameter features include influent pH value, influent flow rate, influent turbidity, and influent temperature. As described above, influent parameter detection devices such as an influent pH value detection device, an influent flow rate detection device, an influent turbidity detection device, and an influent temperature detection device can be installed between the influent tank and the flocculation tank. These influent parameter detection devices can send the detected influent parameters to the server, and the server can obtain the influent parameter features according to the data sent by these influent parameter detection devices.

[0045] In an exemplary embodiment, the effluent parameter features include effluent turbidity, effluent pH, and effluent suspended solid content. For example, effluent parameter detection devices such as an effluent turbidity detection device, an effluent pH detection device, and an effluent suspended solid content detection device can be configured in the effluent tank. The effluent parameter features are detected by these effluent parameter detection devices, and these effluent parameter detection devices can send the detected effluent parameter features to the server.

[0046] In step S420, the single chemical dosage features are combined to generate combined chemical dosage features, and according to the splicing result of multiple chemical dosage prediction features in the combined chemical dosage features, a second candidate chemical dosage prediction feature is generated.

[0047] For example, different combinations can be made for the above-mentioned single chemical dosage feature, and a combined chemical dosage feature can be generated according to the combined results corresponding to all the combination methods, that is, the combined chemical dosage feature includes at least two predicted chemical dosage features. For example, the floc feature and the influent parameter feature are combined into a combined chemical dosage feature, and the floc feature and the effluent parameter feature are combined into a combined chemical dosage feature. For all the combined chemical dosage features, they can be characterized by the splicing results of multiple chemical dosage prediction features included therein, and the splicing order can be determined customarily according to requirements. However, once the splicing order is determined, the splicing order is fixed. That is, each second candidate chemical dosage prediction feature is the splicing result of at least two first candidate chemical dosage prediction features.

[0048] In step S430, candidate chemical dosage prediction features are generated based on the first candidate chemical dosage prediction features and the second candidate chemical dosage prediction features.

[0049] For example, the set composed of all the first candidate chemical dosage prediction features and the second candidate chemical dosage prediction features is the candidate chemical dosage prediction feature.

[0050] In step S440, for any water quality category, a first number of candidate classification prediction models are obtained. For any candidate classification prediction model, the candidate classification prediction model is trained respectively according to each candidate chemical dosage prediction feature, and a second number of candidate chemical dosage classification prediction models are obtained according to the training results of the first number of candidate classification prediction models, and the chemical dosage classification prediction model corresponding to the water quality category is determined from the second number of candidate chemical dosage classification prediction models.

[0051] For example, for any water quality category, the feature value of the influent parameter feature, the feature value of the floc feature corresponding to the floc image, the feature value of the effluent parameter feature, and the correct chemical dosage value under the influent parameter can be obtained according to the influent parameter in the water quality category, and the chemical dosage level to which the correct chemical dosage value belongs in different chemical dosage levels corresponding to each candidate chemical dosage prediction feature can be determined. Among them, the correct chemical dosage value can be understood as the chemical dosage that can make the effluent water quality reach the preset standard under the feature value.

[0052] In an exemplary implementation manner, different chemical dosage levels corresponding to each candidate chemical dosage prediction feature can be determined in advance. Different chemical dosage levels correspond to different chemical dosage ranges.

[0053] Exemplarily, the method for determining different dosing levels corresponding to any candidate dosing amount prediction feature includes: collecting historical data of the candidate dosing amount prediction feature and clustering the collected historical data; determining different dosing amount prediction levels corresponding to the candidate dosing amount prediction feature according to the dosing amounts indicated by the historical data in each clustering category in the clustering result.

[0054] For example, according to the magnitude relationship of the means of the dosing amounts indicated by the historical data in each clustering category in the clustering result, the magnitude relationship of the dosing levels corresponding to different clustering categories can be determined; the dosing amount range indicated by the dosing level corresponding to the clustering category can be determined according to the minimum and maximum values of the dosing amounts indicated by the historical data in the clustering category.

[0055] Taking the number of clustering categories as 3 as an example, each clustering category corresponds to one level, and there are a total of 3 levels. Among them, the average value of the correct dosing amounts corresponding to the historical data in clustering category 1 is c1, the average value of the correct dosing amounts corresponding to the historical data in clustering category 2 is c2, and the average value of the correct dosing amounts corresponding to the historical data in clustering category 3 is c3. The magnitude relationship of c1, c2, and c3 is c1 < c2 < c3. Then, clustering category 1 corresponds to the first dosing level, clustering category 2 corresponds to the second dosing level, and clustering category 3 corresponds to the third dosing level. The dosing level of the first dosing level is less than that of the second dosing level, and the dosing level of the second dosing level is less than that of the third dosing level. For example, the first dosing level is the low dosing level, the second dosing level is the medium dosing level, and the third dosing level is the high dosing level.

[0056] For example, according to the minimum and maximum values of the dosing amounts indicated by the historical data in the clustering category corresponding to each dosing level, the dosing amount range indicated by the dosing level can be determined. For example, there are 100 historical data in a certain dosing level, and these 100 historical data correspond to 100 dosing amounts. The interval range composed of the minimum and maximum values of these 100 dosing amounts is the dosing amount range indicated by the dosing level.

[0057] Taking the above first dosing level, second dosing level, and third dosing level as examples, there may be an overlapping interval between the dosing amount ranges indicated by different dosing levels corresponding to the dosing levels determined according to the clustering result. At this time, manual verification and adjustment can be performed to make the dosing amount ranges of adjacent dosing levels continuous and non-overlapping. Or the dosing amount range corresponding to the dosing level obtained according to the clustering result can be used as the initial dosing amount range, and the initial dosing amount range can be adjusted according to the first preset rule to obtain the dosing amount range corresponding to the final dosing level.

[0058] Exemplarily, the first preset rule may include: in the case where there is an overlapping interval in the dosing amount ranges indicated by adjacent dosing amount levels, taking the overlapping interval as a buffer interval, that is, the dosing amount in the buffer interval belongs to two adjacent dosing amount levels simultaneously, and the dosing amount in the non-overlapping part belongs to its respective dosing amount level. That is, the dosing amount range indicated by each dosing amount level consists of a non-overlapping interval plus a buffer interval. In this way, when performing data annotation, there are two labels for the dosing amount level of the historical data with the dosing amount value falling in the buffer interval. Subsequently, when making predictions, the final output result is also selected through the class confidence. For example, if the confidence is greater than 0.9, it is the final predicted class. In this way, the problem of low prediction accuracy caused by unreasonable division of the interval range of the dosing amount level can be avoided to the greatest extent.

[0059] Exemplarily, the first preset rule may also include: evenly dividing the overlapping interval and allocating it to the dosing amount ranges indicated by adjacent dosing amount levels, that is, dividing the overlapping interval into two at the median value of the overlapping interval. The final dosing amount range corresponding to the lower level among adjacent dosing amount levels is from the minimum value in its corresponding initial dosing amount range to the median value of the overlapping interval, and the final dosing amount range corresponding to the higher level among adjacent dosing amount levels is from the median value of the overlapping interval to the maximum value in its corresponding initial dosing amount range.

[0060] Similarly, for the case where the initial dosing amount ranges corresponding to adjacent dosing amount levels are discontinuous, it can also be adjusted by the second preset rule to obtain the final dosing amount range.

[0061] Exemplarily, the second preset rule may include: taking the range interval between the initial dosing amount ranges corresponding to adjacent dosing amount levels as a buffer interval, and the buffer interval belongs to these adjacent dosing amount levels simultaneously. Similarly, in this way, when performing data annotation, there are two labels for the dosing amount level of the historical data with the dosing amount value falling in the buffer interval.

[0062] Exemplarily, the second preset rule may also include: taking the interval between the range intervals between the initial dosing amount ranges corresponding to adjacent dosing amount levels as a buffer interval, and dividing this buffer interval into two at the median value of the buffer interval and allocating it to these adjacent dosing amount levels. That is, the final dosing amount range corresponding to the lower level among adjacent dosing amount levels is from the minimum value in its corresponding initial dosing amount range to the median value of the buffer interval, and the final dosing amount range corresponding to the higher level among adjacent dosing amount levels is from the median value of the buffer interval to the maximum value in the initial dosing amount range corresponding to this dosing amount level.

[0063] In an exemplary factual manner, if there is an overlapping interval in the dosing amount ranges indicated by non-adjacent dosing amount levels, such as an overlapping interval between the dosing amount ranges corresponding to the low dosing amount level and the high dosing amount level as described above, then after adjusting the K value, clustering is performed again, and the dosing amount level is determined again based on the clustering result. It is also possible to adjust the dosing amount ranges indicated by non-adjacent dosing amount levels based on the dosing amount range indicated by the intermediate dosing amount level between non-adjacent dosing amount levels, so as to ensure that there is no overlapping interval in the dosing amount ranges indicated by non-adjacent dosing amount levels. For example, take the minimum value of the intermediate dosing amount level as the upper limit value of the lower dosing amount level among non-adjacent dosing amount levels, and take the maximum value of the intermediate dosing amount level as the lower limit value of the higher dosing amount level among non-adjacent dosing amount levels.

[0064] Adjusting the initial dosing amount range based on the above method can ensure the rationality of the initial dosing amount range and ensure that the initial dosing amount range is continuous without any breaks. In this way, the target dosing amount prediction value can be determined according to the overlapping relationship between the first dosing amount prediction value and the first dosing amount prediction level in the subsequent steps.

[0065] Taking a certain water quality category 1 as an example, through the above method, clustering can be performed on the historical data corresponding to each candidate dosing amount feature under the water quality category 1 respectively, so as to generate the dosing amount ranges indicated by different dosing amount levels corresponding to each candidate dosing amount prediction feature under the water quality category. That is, the candidate dosing amount prediction feature 1 under the water quality category 1 corresponds to a dosing amount range indicated by a different dosing amount level, and the candidate dosing amount prediction feature 1 under the water quality category 2 corresponds to a dosing amount range indicated by a different dosing amount level, and so on.

[0066] In an exemplary implementation manner, the dosing amount ranges indicated by the same dosing amount level under the same candidate dosing amount prediction feature for different water quality categories can be the same or different. For example, taking the dosing amount levels corresponding to each candidate dosing amount prediction feature under each water quality category including a total of 3 levels: low, medium, and high as an example, the dosing amount range indicated by the low-level dosing amount corresponding to the candidate dosing amount prediction feature 1 under the water quality category 1 and the dosing amount range indicated by the low-level dosing amount corresponding to the candidate dosing amount prediction feature 1 under the water quality category 2 can be the same or different, which is determined according to the specific clustering result.

[0067] According to the dosing amount ranges indicated by different dosing amount levels corresponding to each candidate dosing amount prediction feature under each water quality category, determine the dosing amount level to which the correct dosing amount value corresponding to the historical data of each candidate dosing amount prediction feature under the water quality category belongs, so as to generate the first label training set corresponding to the candidate dosing amount prediction feature under the water quality category.

[0068] For any candidate dosing prediction feature in any water quality category, a first number of candidate classification prediction models can be trained based on the first label training set corresponding to the candidate dosing prediction feature, so as to obtain a candidate dosing classification prediction model corresponding to each candidate dosing prediction feature.

[0069] Taking the candidate dosing prediction features including 7 and the first number being 3 as an example, that is, each candidate dosing prediction feature can train 3 candidate dosing classification prediction models. For any water quality category, a total of 21 candidate dosing classification prediction models can be obtained.

[0070] Exemplarily, for a certain water quality category, the 21 candidate dosing classification prediction models can be tested, and the candidate dosing classification prediction model with the best test result can be selected as the dosing classification prediction model corresponding to the water quality category. For example, the 21 models can be tested according to the test data set for the prediction speed and prediction accuracy, and the candidate dosing classification prediction model with the best comprehensive result of the prediction speed and prediction accuracy can be selected as the dosing classification prediction model corresponding to the water quality category.

[0071] In step S450, for any water quality category, a third number of candidate regression prediction models are obtained. For any candidate regression prediction model, the candidate regression prediction model is trained respectively according to each candidate dosing prediction feature, and a fourth number of candidate dosing regression prediction models are obtained according to the training results of the third number of candidate regression prediction models, and the dosing regression prediction model corresponding to the water quality category is determined from the fourth number of candidate dosing regression prediction models.

[0072] Exemplarily, the specific implementation manner of step S450 can refer to the relevant description in step S440, and will not be elaborated here. Different from step S440, in step S450, the label in the label training data set is the correct dosing value.

[0073] In step S460, according to the dosing classification prediction model and the dosing regression prediction model corresponding to the water quality category, the preset dosing prediction model corresponding to the water quality category is obtained.

[0074] For example, the preset dosing prediction model for each water quality category is composed of the dosing regression prediction model and the dosing classification prediction model corresponding to the water quality category.

[0075] Through the above steps S410 to S460, a corresponding preset chemical dosage classification prediction model can be generated for each water quality category, and the preset chemical dosage classification prediction model corresponding to each water quality category is selected after model training with a variety of different candidate chemical dosage prediction features. Then, the input of the finally determined chemical dosage prediction model is the chemical dosage prediction feature suitable for predicting this water quality category.

[0076] Based on this, by way of example, the determination method of the preset mapping relationship includes: obtaining the first training feature indicated by the chemical dosage regression prediction model corresponding to each water quality category and the second training feature indicated by the chemical dosage classification prediction model corresponding to each water quality category; generating the preset mapping relationship according to the corresponding relationship between the water quality category and the first training feature and the corresponding relationship between the water quality category and the second training feature.

[0077] For example, if the training feature used by the chemical dosage classification prediction model corresponding to a certain water quality category 1 is the floc feature, then the first training feature corresponding to this water quality category 1 is the floc feature, and the training feature used by the chemical dosage regression prediction model corresponding to water quality category 1 is the concatenated feature composed of the floc feature and the influent parameter feature, then the second training feature corresponding to this water quality category 1 is the concatenated feature composed of the floc feature and the influent parameter feature. Then, a mapping relationship such as "water quality category 1, the first chemical dosage prediction feature is the floc feature, and the second chemical dosage prediction feature is the concatenated feature of the floc feature and the influent parameter feature" can be stored in the database.

[0078] Next, a detailed description will be given of the specific implementation manner of "step S130, inputting the feature value of the first chemical dosage prediction feature into the chemical dosage regression prediction model in the preset chemical dosage prediction model corresponding to the target water quality category, and obtaining the first chemical dosage prediction value according to the output of the chemical dosage regression prediction model".

[0079] In an exemplary implementation manner, the feature value of the chemical dosage prediction feature can be understood as the specific value of each feature determined at the sampling moment, such as the influent temperature value, the influent pH value, etc.

[0080] For example, the current feature value corresponding to the first chemical dosage prediction feature can be input into the chemical dosage regression prediction model corresponding to the target water quality category, so as to obtain the first chemical dosage prediction value.

[0081] Next, a detailed description will be given of the specific implementation manner of "step S140, inputting the feature value of the second chemical dosage prediction feature into the chemical dosage classification prediction model in the preset chemical dosage prediction model corresponding to the target water quality category, and obtaining the first chemical dosage prediction grade according to the output of the chemical dosage classification prediction model".

[0082] For example, the current feature value corresponding to the second chemical dosage prediction feature can be input into the chemical dosage classification prediction model corresponding to the target water quality category, so as to obtain the first chemical dosage prediction level.

[0083] Next, a detailed description will be given of the specific implementation manner of "step S150, determining the target chemical dosage prediction value according to the overlapping relationship between the first chemical dosage prediction value and the first chemical dosage prediction level, and controlling the chemical dosage in the water treatment process based on the target chemical dosage prediction value".

[0084] Exemplarily, the overlapping relationship between the first chemical dosage prediction value and the first chemical dosage prediction level may include that the first chemical dosage prediction value belongs to the chemical dosage range indicated by the first chemical dosage prediction level or the first chemical dosage prediction value does not belong to the chemical dosage range indicated by the first chemical dosage prediction level.

[0085] Exemplarily, Figure 5 The flowchart shows a method for determining the target chemical dosage prediction value according to the overlapping relationship in an exemplary embodiment of the present disclosure. Refer to Figure 5 , this method may include steps S510 to S540. Among them: In step S510, it is judged whether the first chemical dosage prediction value is within the first chemical dosage range indicated by the first chemical dosage prediction level. If so, go to step S520; otherwise, go to step S530.

[0086] In an exemplary implementation manner, the chemical dosage ranges indicated by each chemical dosage level corresponding to different chemical dosage prediction features are pre-stored in the database. For the purpose of differential expression, the chemical dosage ranges stored in the database are the first chemical dosage ranges, and the chemical dosage ranges obtained after adjusting the chemical dosage ranges in the database are the second chemical dosage ranges.

[0087] In step S520, the first chemical dosage prediction value is determined as the target chemical dosage prediction value.

[0088] For example, when the first chemical dosage prediction value is within the first chemical dosage range indicated by the first chemical dosage prediction level, it indicates that the first chemical dosage prediction value is consistent with the prediction result of the first chemical dosage prediction level, that is, the chemical dosage values predicted by different types of models are consistent, which means that the first chemical dosage prediction value is reliable. Therefore, the first chemical dosage prediction value can be directly determined as the target chemical dosage prediction value.

[0089] In step S530, the water flow rate and water turbidity detected at multiple detection points from the raw water point to the inlet point are obtained. When the water flow rate is greater than the first preset value and / or the water turbidity is greater than the second preset value, the first chemical dosage range indicated by the second chemical dosage prediction level is adjusted according to historical experience to obtain the second chemical dosage range.

[0090] Under normal circumstances, the prediction results of different types of models should be consistent. When the prediction results of different types of models are inconsistent, it is necessary to re-evaluate the prediction results of the models.

[0091] For example, in the present disclosure, the dosing prediction value is constrained and verified by the dosing prediction level, so as to ensure the reliability and accuracy of the determined target dosing prediction value. Therefore, the division of the dosing level is crucial for the prediction result. When the prediction results of the classification prediction model and the regression prediction model are inconsistent, it can be preferentially considered whether there is a sudden change in the current water quality, such as a sudden change in the influent turbidity, and the dosing ranges of each dosing level determined in advance may not cover the current water quality mutation situation, resulting in the problem that the prediction results of the classification prediction model and the regression prediction model are inconsistent. Therefore, in the present disclosure, when the first dosing prediction value does not belong to the first dosing range indicated by the first dosing prediction level, the first dosing range indicated by the first dosing prediction level determined in advance can be dynamically adjusted to satisfy as many complex emergencies as possible and improve the accuracy of the dosing prediction.

[0092] In an exemplary embodiment, a plurality of detection points can be set between the raw water point and the influent point to determine whether there is a water quality mutation caused by a sudden change in weather or other emergencies. Among them, the raw water point refers to the source point of the water to be treated in the water treatment system. For example, if the water treatment system treats the water in a reservoir, then the reservoir is the raw water point, and the influent point refers to the inlet of the influent tank in the water treatment system.

[0093] For example, a plurality of detection points can be intermittently set between the raw water point and the influent point, and each detection point is equipped with a turbidimeter and a flowmeter. Whether there is a sudden change in turbidity or flow can be detected by the turbidimeter and the flowmeter. When the flow detected by the flowmeter is greater than the first preset value and / or the flow detected by the turbidimeter is greater than the second preset value, it is determined that there is a water quality mutation situation, and then the first dosing range indicated by the second dosing prediction level can be adjusted.

[0094] Among them, the first preset value and the second preset value can be determined according to requirements, and this exemplary embodiment does not make special limitations on this. For example, the first preset value can be the maximum value of the influent flow rate of all influent parameter characteristics in the training data multiplied by a coefficient greater than 1.

[0095] For example, the first dosing range indicated by the first dosing prediction level corresponding to the second dosing prediction feature can be adjusted based on historical experience. Taking the second dosing prediction value as an example of the influent parameter feature, the similarity between the current influent parameter feature value and the historical influent parameter feature values stored in the database can be calculated, and multiple historical influent parameter feature values similar to the current influent parameter feature value can be selected. The range between the minimum and maximum values of the historical dosing amounts corresponding to the multiple historical influent parameter feature values is used as the second dosing range indicated by the first dosing prediction level corresponding to the influent parameter feature.

[0096] In step S540, based on the overlapping relationship between the second dosing range and the first dosing prediction value, a target dosing prediction value is determined.

[0097] Exemplarily, one implementation of step S540 may include: when the first dosing prediction value is within the second dosing range, the first dosing prediction value is determined as the target dosing prediction value; when the first dosing prediction value is not within the second dosing range, a target dosing prediction value is determined based on the first dosing prediction value and the minimum value in the first dosing range indicated by the second dosing prediction level. In this way, the dosing amount can be initially adjusted and controlled through the target dosing prediction value, and then it can be determined whether further adjustment is needed through the method shown in Figure 6 to timely adjust the dosing amount while avoiding the situation of secondary pollution to the water caused by excessive dosing amount.

[0098] It should be noted that adjusting the first dosing range indicated by the second dosing prediction level to the second dosing range is only for using the second dosing range in this comparison. The first dosing range stored in the database remains unchanged. When predicting the dosing amount value next time, the first dosing range in the database is still used for determination.

[0099] In the present disclosure, dosing prediction can be performed through different types of models. The prediction results of different types of models can be mutually verified and restricted, thereby improving the accuracy and reliability of the predicted dosing amount prediction value.

[0100] Exemplarily, Figure 6 shows a flowchart of a method for dosing control according to a target dosing prediction value in an exemplary embodiment of the present disclosure; Exemplarily, Figure 6 shows a flowchart of a method for dosing control according to a target dosing prediction value in an exemplary embodiment of the present disclosure. Referring to Figure 6 , the method may include steps S610 to S660. Among them: In step S610, control the chemical dosing pump to perform the chemical dosing operation in the water treatment process according to the first target chemical dosing amount indicated by the target chemical dosing amount prediction value.

[0101] In an exemplary embodiment, the chemical dosing pump can control the chemical dosing amount by frequency. There is a mapping relationship between the frequency of the chemical dosing pump and the chemical dosing amount. Based on this mapping relationship, the frequency of the chemical dosing pump corresponding to the target chemical dosing amount prediction value can be determined, and then this frequency can be sent to the chemical dosing pump through an instruction to control the chemical dosing pump to perform the chemical dosing operation according to this frequency.

[0102] In step S620, in response to monitoring that the turbidity of the water in the sedimentation tank decreases and for the first time the turbidity difference between adjacent sampling times is less than the third preset value, simultaneously start the radar device and the video acquisition device acting on the sedimentation tank.

[0103] For example, after controlling the chemical dosing pump to perform the chemical dosing operation according to the target chemical dosing amount prediction value, the chemical dosing effect can be detected through the floc state in the sedimentation tank, so as to monitor whether the target chemical dosing amount prediction value is appropriate, and determine whether it is necessary to adjust the chemical dosing amount according to the monitoring situation.

[0104] In an exemplary embodiment, while sending a chemical dosing control instruction to the chemical dosing pump, an opening control instruction can be sent to the turbidity detector in the sedimentation tank to turn on the turbidity detector in the sedimentation tank, and the turbidity in the sedimentation tank can be obtained through the turbidity detector.

[0105] When it is monitored by the turbidity meter that the turbidity of the water in the sedimentation tank decreases and for the first time the turbidity difference between adjacent sampling times is less than the third preset value, simultaneously send an opening control instruction to the radar device and the video acquisition device acting on the sedimentation tank.

[0106] For example, when the turbidity of the water in the sedimentation tank shows a downward trend and first tends to be stable, it indicates that the flocs in the sedimentation tank have begun to settle. At this time, the chemical dosing effect can be determined by the sedimentation speed of the flocs.

[0107] Exemplarily, in the present disclosure, the sedimentation speed of the flocs can be measured by the radar and the video simultaneously to ensure the accuracy of the determination of the sedimentation speed. Therefore, it is necessary for the radar and the video to measure the sedimentation speed of the flocs in the same area within the same time period. So the radar device and the video acquisition device can be started simultaneously. At the same time, it is necessary to layout the installation positions of the radar device and the video acquisition device to ensure that the action ranges of the radar device and the video acquisition device overlap to the greatest extent, so as to ensure as much as possible that the radar device and the video acquisition device measure the sedimentation speed of the flocs in the same area, which is convenient for subsequent accurate fusion.

[0108] In step S630, the first average sedimentation velocity of the flocs in the sedimentation tank is measured by a radar device within a first preset time period.

[0109] In an exemplary implementation, the first preset time period can be determined according to the radar activation time and a first preset duration, that is, the first preset time period is the time period within the first preset duration after the radar is activated. Taking the first preset time period as 2 minutes as an example, the first preset time period is 2 minutes after the radar is activated.

[0110] For example, the radar device can emit electromagnetic waves to the sedimentation tank. The flocs in the sedimentation tank will reflect the electromagnetic waves, and the radar will receive the reflected waves. According to the Doppler effect, when the target object moves relative to the radar, the frequency of the reflected wave will change. If the target object moves towards the radar, the frequency of the reflected wave will increase; if the target object moves away from the radar, the frequency of the reflected wave will decrease. The amount of frequency change is proportional to the movement speed of the target object. By measuring the frequency difference between the transmitted wave and the reflected wave, that is, the Doppler frequency shift, and combining the parameters of the radar system, the movement speed of the target object can be determined.

[0111] Exemplarily, the radar signal can be filtered to avoid the interference of water surface fluctuations on the radar signal. After filtering, according to the intensity distribution of the radar signal, the signals in different regions are subjected to gain adjustment, so as to facilitate subsequent analysis. Next, the radar signal in the time domain can be converted into a signal in the frequency domain, and the frequency spectrum distribution of the signal is obtained through Fourier transform. Extreme points are searched in the frequency spectrum, and the reflection signals of floc groups with different speeds correspond to different extreme points. The speed of each extreme point is determined according to the reflection frequency of each extreme point, and the speeds of all extreme points are weighted and averaged to obtain the average sedimentation velocity of the flocs within the detection range during this measurement process. Among them, the weight of the speed of each extreme point can be determined according to the energy of each extreme point. The greater the energy of the extreme point, the greater the weight of the speed corresponding to the extreme point. Thus, the speeds of different extreme points are weighted by the weight to obtain the average sedimentation velocity.

[0112] In an exemplary implementation, the average sedimentation velocity of the flocs within the detection range can be measured multiple times by the radar signal within the first preset time period, and the average sedimentation velocities measured multiple times within the first preset time period are averaged again to obtain the first average sedimentation velocity of the flocs in the sedimentation tank within the first preset time period.

[0113] In an exemplary embodiment, the radar device can also be used to measure the separation interface between the water in the sedimentation tank and the mud at the bottom of the sedimentation tank, and determine whether to perform the sludge discharge operation of the sedimentation tank according to the height of the separation interface. For example, after continuous floc precipitation, sludge will gradually accumulate at the bottom of the sedimentation tank. The radar device can determine the stratification interface between the sludge and the water according to the different electromagnetic wave reflection characteristics of different media, so as to obtain the height of the sludge interface. When the height of the sludge interface reaches a preset height, the alarm device can be triggered to remind to perform the sewage discharge operation on the sedimentation tank, avoiding excessive accumulation of sludge and affecting the precipitation effect.

[0114] In other words, in the present disclosure, the radar device installed in the sedimentation tank can be used to measure the sedimentation speed of the flocs and also to remind of the sewage discharge of the sedimentation tank. In this way, by reusing the radar device, while improving the water treatment effect, the hardware cost in the water treatment process can be reduced.

[0115] In step S640, the video in the sedimentation tank within the first preset period is collected by the video acquisition device, the floc target tracking and video depth estimation are performed on the video, and the second average sedimentation speed of the flocs in the sedimentation tank within the first preset period is determined according to the results of the floc target tracking and video depth estimation.

[0116] For example, the floc video can be sampled to obtain a sequence of floc image frames. Then, based on the object detection algorithm, all the flocs are detected in the first frame of the floc image frame sequence, and then based on the multi-object tracking algorithm, the flocs detected in the first frame are tracked in the second frame and subsequent floc image frames of the floc image frame sequence, and the two-dimensional pixel coordinates of the same floc in each floc image frame are determined through the object tracking results. At the same time, the video depth estimation can be performed on the floc image frame sequence, and the depth value of the same floc in each floc image frame is determined according to the results of the video depth estimation. Through the camera calibration parameters, the pixel coordinates and depth values of the flocs are converted into three-dimensional spatial positions in the world coordinate system, the position component in the floc sedimentation direction in the three-dimensional spatial positions is extracted, and the sedimentation displacement of the flocs is determined according to the absolute value of the difference between the position components of the current frame and the reference frame of the current frame. According to the sedimentation displacement and the time interval between the current frame and the reference frame, the sedimentation speed between the current frame and the reference frame is obtained. The average value of the sedimentation speeds between each current frame and the reference frame is used to obtain the average value of the sedimentation speeds of the flocs within the first preset period, and the second average sedimentation speed of the flocs within the first preset period is obtained according to the average value of the sedimentation speeds of each successfully tracked floc within the first preset period.

[0117] Among them, the reference frame of the current frame can be the floc image frame located before the current frame and separated from the current frame by N frames, where N is determined according to experience or requirements. For example, N is 1, N is 2, N is 0, etc. This exemplary embodiment does not make special limitations on this. N being 0 means that the reference frame of the current frame is the previous frame of the current frame.

[0118] In step S650, the first average sedimentation velocity and the second average sedimentation velocity are fused to obtain the target average sedimentation velocity.

[0119] In an exemplary embodiment, the first average sedimentation velocity and the second average sedimentation velocity can be fused according to the first weight corresponding to the first average sedimentation velocity and the second weight corresponding to the second average sedimentation velocity to obtain the target average sedimentation velocity. Among them, the first weight and the second weight can be determined according to requirements and experience. This exemplary embodiment does not make special limitations on this.

[0120] In step S660, according to the difference between the target average sedimentation velocity and the preset sedimentation velocity, the first target chemical addition amount is adjusted to obtain the second target chemical addition amount, and the chemical addition pump is controlled to perform the chemical addition operation in the water treatment process according to the second target chemical addition amount.

[0121] In an exemplary embodiment, the preset sedimentation velocity is determined according to the floc sedimentation velocity corresponding to the target effluent water quality of the sedimentation tank. Among them, the target effluent water quality can include the effluent water quality that meets the expected requirements, which is custom-defined according to requirements. This exemplary embodiment does not make special limitations on this.

[0122] Exemplarily, the preset sedimentation velocity can be determined according to the corresponding relationship between the historical sedimentation velocity and the effluent water quality. For example, the sedimentation velocity calculated each time and the corresponding effluent water quality can be recorded to obtain the first recorded data. Then, the second recorded data with the effluent water quality meeting the target effluent water quality is selected from the first recorded data, and then the preset sedimentation velocity is determined according to the sedimentation velocity in the second recorded data.

[0123] For example, the preset sedimentation rate can be determined according to the average value of the sedimentation rates in the second recorded data, or the preset sedimentation rate range can be determined according to the range formed by the minimum and maximum values of the sedimentation rates in the second recorded data. That is, the preset sedimentation rate can also be a range. When the preset sedimentation rate is a range, if the target average sedimentation rate falls within this range, it is considered that there is no difference between the target average sedimentation rate and the preset sedimentation rate, and in this case, the chemical dosing amount does not need to be adjusted. If the target average sedimentation rate is less than the minimum value of the preset sedimentation rate range and the absolute value of the difference from this minimum value is greater than the fourth preset value, the first adjustment strategy is executed; if the target average sedimentation rate is greater than the maximum value of the preset sedimentation rate range and the absolute value of the difference from this maximum value is greater than the fourth preset value, the second adjustment strategy is executed. When the preset sedimentation rate is a specific value, if the target average sedimentation rate is less than the preset sedimentation rate and the absolute value of the difference from the preset sedimentation rate is greater than the fourth preset value, the first adjustment strategy is executed; if the target average sedimentation rate is greater than the preset sedimentation rate and the absolute value of the difference from the preset sedimentation rate is greater than the fourth preset value, the second adjustment strategy is executed.

[0124] In an exemplary embodiment, the first adjustment strategy includes: increasing the current chemical dosing amount of the chemical dosing pump according to a preset increase ratio coefficient. For example, the preset increase ratio coefficient can be greater than 1, such as 1.05. In this way, each time the value obtained by multiplying the current chemical dosing amount by 1.05 is determined as the new chemical dosing amount. After re-dosing, the target average sedimentation rate is re-determined according to the above method, and then it is determined whether further adjustment is needed. This cycle continues until the difference between the newly determined target average sedimentation rate and the preset sedimentation rate meets the preset conditions, and then the adjustment stops.

[0125] In an exemplary embodiment, the second adjustment strategy includes: reducing the current chemical dosing amount of the chemical dosing pump according to a preset decrease ratio coefficient. For example, the preset decrease ratio coefficient can be less than 1, such as 0.95. In this way, each time the value obtained by multiplying the current chemical dosing amount by 0.95 is determined as the new chemical dosing amount. After re-dosing, the target average sedimentation rate is re-determined according to the above method, and then it is determined whether further adjustment is needed. This cycle continues until the difference between the newly determined target average sedimentation rate and the preset sedimentation rate meets the preset conditions, and then the adjustment stops.

[0126] In other words, if the sedimentation rate of the flocs is fast after chemical dosing, it indicates that the large chemical dosing amount causes the flocs to further aggregate into larger flocs, and the sedimentation rate may increase. If the sedimentation rate of the flocs is slow, it indicates that the chemical dosing amount is small.

[0127] Among them, the preset conditions include that the absolute value of the difference between the target average sedimentation rate and the preset sedimentation rate is less than the fourth preset value.

[0128] In an exemplary embodiment, the chemical dosage and influent parameter characteristics, floc characteristics, and effluent water quality parameter characteristics at the time of stopping adjustment can be associated to generate an updated database, and each model in the above-mentioned preset chemical dosage prediction model can be updated regularly according to this updated database.

[0129] Through the above steps S610 to S660, the sedimentation velocity of the flocs can be determined in various ways, improving the accuracy of determining the sedimentation velocity of the flocs. Based on the sedimentation velocity of the flocs, the dosing treatment effect of the target chemical dosage prediction value can be judged, and thus the chemical dosage can be adjusted according to the judgment result to control the chemical dosage more accurately. At the same time, adjusting the chemical dosage according to the floc situation in the sedimentation stage can improve the timeliness of adjusting the chemical dosage.

[0130] In the present disclosure, the chemical dosage prediction models under different water quality categories can be determined in advance. At the same time, each chemical dosage prediction model under each category is selected after being trained according to different training characteristics, so that the chemical dosage prediction characteristics and chemical dosage prediction models suitable for chemical dosage prediction under different water quality categories can be determined, improving the accuracy and efficiency of chemical dosage prediction under different water qualities. In addition, the method of the present disclosure improves the accuracy of chemical dosage control, thereby reducing the degree of manual participation in the chemical dosing process and realizing a higher degree of automation in the water treatment process.

[0131] In addition, it should be noted that the above drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present invention, rather than for limiting purposes. It is easy to understand that the processes shown in the above drawings do not indicate or limit the time sequence of these processes. Additionally, it is also easy to understand that these processes can be executed synchronously or asynchronously in, for example, multiple modules.

[0132] Furthermore, the exemplary embodiment of the present disclosure further provides a chemical dosage control device in the water treatment process. Refer to Figure 7As shown in the figure, the chemical dosage control device in the water treatment process includes the following program modules: a target water quality category determination module 710, configured to obtain influent parameters and determine the target water quality category to which the influent parameters belong according to the influent water quality classification model; a chemical dosage prediction feature determination module 720, configured to determine, according to a preset mapping relationship, a first chemical dosage prediction feature and a second chemical dosage prediction feature associated with the target water quality category from the chemical dosage prediction features, where the chemical dosage prediction features include one or more of influent parameter features, floc features, and effluent parameter features; a first prediction module 730, configured to input the feature value of the first chemical dosage prediction feature into the chemical dosage regression prediction model in the preset chemical dosage prediction model corresponding to the target water quality category, and obtain a first chemical dosage prediction value according to the output of the chemical dosage regression prediction model; a second prediction module 740, configured to input the feature value of the second chemical dosage prediction feature into the chemical dosage classification prediction model in the preset chemical dosage prediction model corresponding to the target water quality category, and obtain a first chemical dosage prediction grade according to the output of the chemical dosage classification prediction model; a chemical dosage control module 750, configured to determine a target chemical dosage prediction value according to the overlapping relationship between the first chemical dosage prediction value and the first chemical dosage prediction grade, and control the chemical dosage in the water treatment process based on the target chemical dosage prediction value.

[0133] In an exemplary implementation manner, the determination method of the influent water quality classification model includes: obtaining historical influent parameters and performing first clustering on the historical influent parameters; determining water quality parameters of different categories according to the clustering results, adding water quality category labels to the water quality parameters of different categories, and generating a water quality category training data set; training a machine learning model according to the water quality category training data set to obtain an influent water quality classification model.

[0134] In an exemplary implementation manner, the determination method of the preset dosing amount prediction model corresponding to any water quality category includes: taking each single dosing amount prediction feature as the first candidate dosing amount prediction feature; combining the single dosing amount features to generate combined dosing amount features, and generating the second candidate dosing amount prediction feature according to the splicing result of multiple dosing amount prediction features in the combined dosing amount features; generating candidate dosing amount prediction features based on the first candidate dosing amount prediction feature and the second candidate dosing amount prediction feature; for any water quality category, obtaining a first number of candidate classification prediction models, and for any candidate classification prediction model, training the candidate classification prediction model according to each candidate dosing amount prediction feature respectively, obtaining a second number of candidate dosing amount classification prediction models according to the training results of the first number of candidate classification prediction models, and determining the dosing amount classification prediction model corresponding to the water quality category from the second number of candidate dosing amount classification prediction models; for any water quality category, obtaining a third number of candidate regression prediction models, and for any candidate regression prediction model, training the candidate regression prediction model according to each candidate dosing amount prediction feature respectively, obtaining a fourth number of candidate dosing amount regression prediction models according to the training results of the third number of candidate regression prediction models, and determining the dosing amount regression prediction model corresponding to the water quality category from the fourth number of candidate dosing amount regression prediction models; obtaining the preset dosing amount prediction model corresponding to the water quality category according to the dosing amount classification prediction model and the dosing amount regression prediction model corresponding to the water quality category.

[0135] In an exemplary implementation manner, the determination method of the preset mapping relationship includes: obtaining the first training feature indicated by the dosing amount regression prediction model corresponding to each water quality category and the second training feature indicated by the dosing amount classification prediction model corresponding to each water quality category; generating the preset mapping relationship according to the corresponding relationship between the water quality category and the first training feature and the corresponding relationship between the water quality category and the second training feature.

[0136] In an exemplary embodiment, determining the target chemical dosage prediction value according to the overlapping relationship between the first chemical dosage prediction value and the first chemical dosage prediction level includes: when the first chemical dosage prediction value is within the first chemical dosage range indicated by the first chemical dosage prediction level, determining the first chemical dosage prediction value as the target chemical dosage prediction value; when the first chemical dosage prediction value is not within the first chemical dosage range indicated by the first chemical dosage prediction level, obtaining the water flow rate and water turbidity detected at multiple detection points during the process from the raw water point to the inlet point, and in the case where the water flow rate is greater than a first preset value and / or the water turbidity is greater than a second preset value, adjusting the first chemical dosage range indicated by the first chemical dosage prediction level according to historical experience to obtain a second chemical dosage range, and determining the target chemical dosage prediction value according to the overlapping relationship between the second chemical dosage range and the first chemical dosage prediction value.

[0137] In an exemplary embodiment, determining the target chemical dosage prediction value according to the overlapping relationship between the second chemical dosage range and the first chemical dosage prediction value includes: when the first chemical dosage prediction value is within the second chemical dosage range, determining the first chemical dosage prediction value as the target chemical dosage prediction value; when the first chemical dosage prediction value is not within the second chemical dosage range, determining the target chemical dosage prediction value according to the minimum value in the first chemical dosage prediction value and the first chemical dosage range indicated by the first chemical dosage level.

[0138] In an exemplary embodiment, controlling the chemical dosage in the water treatment process according to the chemical dosage prediction value includes: controlling the chemical dosing pump to perform the chemical dosing operation in the water treatment process according to the first chemical dosage indicated by the chemical dosage prediction value; in response to monitoring that the water turbidity in the sedimentation tank decreases and for the first time the turbidity difference between adjacent sampling times is less than a third preset value, simultaneously starting the radar device and the video acquisition device acting on the sedimentation tank; measuring the first average sedimentation velocity of the flocs in the sedimentation tank within a first preset period by the radar device; collecting the video within the first preset period in the sedimentation tank by the video acquisition device, performing floc target tracking and video depth estimation on the video, and determining the second average sedimentation velocity of the flocs in the sedimentation tank within the first preset period according to the results of the floc target tracking and video depth estimation; fusing the first average sedimentation velocity and the second average sedimentation velocity to obtain the target average sedimentation velocity; adjusting the first chemical dosage according to the difference between the target average sedimentation velocity and the preset sedimentation velocity to obtain a second chemical dosage, and controlling the chemical dosing pump to perform the chemical dosing operation in the water treatment process according to the second chemical dosage; wherein the preset sedimentation velocity is determined according to the sedimentation velocity of the flocs corresponding to the target effluent water quality of the sedimentation tank.

[0139] The specific details of each part in the above device have been described in detail in the implementation manners of the method part. For the details not disclosed, reference can be made to the implementation manners in the method part, so they will not be elaborated here.

[0140] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the exemplary embodiments of the present disclosure, the features and functions of two or more of the above-mentioned modules or units can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0141] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that these steps must be executed in that specific order, or that all the steps shown must be executed to achieve the desired result. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution, etc.

[0142] The exemplary embodiments of the present disclosure also provide a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the dosing control method in the above water treatment process.

[0143] In one embodiment, the computer program product can be a tangible product containing the computer program, such as a computer-readable storage medium storing the computer program. The readable storage medium can be a storage medium based on signals such as electricity, magnetism, light, electromagnetic, infrared, etc., including but not limited to: random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory (Flash), hard disk drive (HDD), solid state drive (SSD), etc. Exemplarily, the computer program product can be implemented as a non-volatile storage medium storing the computer program, such as read-only memory, Nand Flash, etc.

[0144] In one embodiment, the computer program product can be an intangible product containing the computer program. Exemplarily, the computer program product can be implemented as a virtual digital product, such as an executable file storing the computer program, an installation package and other digital files.

[0145] The code of a computer program can be written in one or more programming languages. Examples of programming languages include C, Java, C++, Python, etc. The program code can be executed entirely on the user's computing device, or partially on the user's computing device, or as an independent software package, or partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, such as a local area network (LAN), wide area network (WAN), etc., or it can be connected to an external computing device (e.g., through an Internet connection provided by an operator).

[0146] A computer program can be carried or transmitted by signals such as electricity, magnetism, light, electromagnetic, infrared, etc. An electronic device can convert the signal carrying the computer program into a digital signal and then run the computer program. When the computer program runs on an electronic device, its code is used to cause the electronic device to execute (more specifically, to cause the processor of the electronic device to execute) the method steps of various exemplary embodiments of the present disclosure, such as the chemical dosing control method in the above-mentioned water treatment process.

[0147] Exemplary embodiments of the present disclosure also provide an electronic device, which may include a processor and a memory. The memory stores executable instructions for the processor, such as a computer program. The processor executes the method steps of various exemplary embodiments of the present disclosure by executing the executable instructions. In addition, the electronic device may further include a display for displaying a graphical user interface.

[0148] The following refers to Figure 8 , and an electronic device is exemplarily described in the form of a general-purpose computing device. It should be understood that Figure 8 the electronic device 800 shown is merely an example and should not impose limitations on the functions and scope of use of the embodiments of the present disclosure.

[0149] As Figure 8 shown, the electronic device 800 may include: a processor 810, a memory 820, a bus 830, an I / O (input / output) interface 840, a network adapter 850, and a display 860.

[0150] The memory 820 may include volatile memory such as RAM 821 and cache unit 822, and may also include non-volatile memory such as ROM 823. The memory 820 may also include one or more program modules 824. Such program modules 824 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. For example, the program module 824 may include each module in the above device.

[0151] The processor 810 may include one or more processing units. For example, the processor 810 may include an AP (Application Processor), a modem processor, a GPU (Graphics Processing Unit), an ISP (Image Signal Processor), a controller, an encoder, a decoder, a DSP (Digital Signal Processor), a baseband processor, and / or an NPU (Neural-Network Processing Unit), etc.

[0152] The processor 810 can be used to execute the executable instructions stored in the memory 820, such as the dosing control method in the above water treatment process.

[0153] The bus 830 is used to implement the connection between different components of the electronic device 800, and may include a data bus, an address bus, and a control bus.

[0154] The electronic device 800 can communicate with one or more external devices 900 (such as a keyboard, a mouse, an external controller, etc.) through the I / O interface 840.

[0155] The electronic device 800 can communicate with one or more networks through the network adapter 850. For example, the network adapter 850 can provide mobile communication solutions such as 3G / 4G / 5G, or provide wireless communication solutions such as wireless local area network, Bluetooth, and near field communication. The network adapter 850 can communicate with other modules of the electronic device 800 through the bus 830.

[0156] The electronic device 800 can display a graphical user interface through the display 860, such as a display of the dosing amount prediction value interface, etc.

[0157] Although Figure 8Not shown in the figure, other hardware and / or software modules may also be provided in the electronic device 800, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0158] In addition, the above-mentioned drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, rather than for limiting purposes. It is easy to understand that the processes shown in the above-mentioned drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easy to understand that these processes may be executed synchronously or asynchronously in, for example, multiple modules.

[0159] As can be seen from the above, the technical solution of the present disclosure can be implemented as a method, apparatus, system, computer program product, storage medium, electronic device, etc. Those skilled in the art can understand that various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware implementation manner, a complete software implementation manner (including firmware, microcode, etc.), or an implementation manner combining hardware and software aspects, such as can be respectively referred to as "circuit", "module", or "system".

[0160] It should be understood that the present disclosure is not limited to the specific method steps or structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. Those skilled in the art will easily think of other implementation manners based on the specific implementation manners provided by the present disclosure. Therefore, the specific implementation manners provided by the present disclosure are only exemplary, and the scope and spirit of the present disclosure are pointed out by the claims, which should cover any variations, uses, or adaptation changes of the present disclosure, and these variations, uses, or adaptation changes follow the general principles of the present disclosure and include well-known common knowledge or conventional technical means in the technical field not disclosed by the present disclosure.

Claims

1. A chemical dosing control method in a water treatment process, characterized in that, Including: Obtain the influent parameters, and determine the target water quality category to which the influent parameters belong according to the influent water quality classification model; According to the preset mapping relationship, determine the first chemical dosage prediction feature and the second chemical dosage prediction feature associated with the target water quality category from the chemical dosage prediction features, where the chemical dosage prediction features include one or more of influent parameter features, floc features, and effluent parameter features; Input the feature value of the first chemical dosage prediction feature into the chemical dosage regression prediction model in the preset chemical dosage prediction model corresponding to the target water quality category, and obtain the first chemical dosage prediction value according to the output of the chemical dosage regression prediction model; Input the feature value of the second chemical dosage prediction feature into the chemical dosage classification prediction model in the preset chemical dosage prediction model corresponding to the target water quality category, and obtain the first chemical dosage prediction level according to the output of the chemical dosage classification prediction model; Determine the target chemical dosage prediction value according to the overlapping relationship between the first chemical dosage prediction value and the first chemical dosage prediction level, and control the chemical dosage in the water treatment process based on the target chemical dosage prediction value.

2. The method according to claim 1, characterized in that, The determination method of the influent water quality classification model includes: Obtain the historical influent parameters and perform the first clustering on the historical influent parameters; Determine the water quality parameters of different categories according to the clustering results, add water quality category labels to the water quality parameters of different categories, and generate a water quality category training data set; Train a machine learning model according to the water quality category training data set to obtain the influent water quality classification model.

3. The method according to claim 1, wherein The determination method of the preset chemical dosage prediction model corresponding to any water quality category includes: Take each single chemical dosage prediction feature as the first candidate chemical dosage prediction feature; Combine the single chemical dosage features to generate combined chemical dosage features, and generate the second candidate chemical dosage prediction feature according to the splicing result of multiple chemical dosage prediction features in the combined chemical dosage features; Generate candidate chemical dosage prediction features based on the first candidate chemical dosage prediction feature and the second candidate chemical dosage prediction feature; For any water quality category, obtain the first number of candidate classification prediction models. For any candidate classification prediction model, train the candidate classification prediction model according to each candidate chemical dosage prediction feature respectively, and obtain the second number of candidate chemical dosage classification prediction models according to the training results of the first number of candidate classification prediction models. Determine the chemical dosage classification prediction model corresponding to the water quality category from the second number of candidate chemical dosage classification prediction models; For any water quality category, obtain the third number of candidate regression prediction models. For any candidate regression prediction model, train the candidate regression prediction model according to each candidate chemical dosage prediction feature respectively, and obtain the fourth number of candidate chemical dosage regression prediction models according to the training results of the third number of candidate regression prediction models. Determine the chemical dosage regression prediction model corresponding to the water quality category from the fourth number of candidate chemical dosage regression prediction models; Obtain the preset chemical dosage prediction model corresponding to the water quality category according to the chemical dosage classification prediction model and the chemical dosage regression prediction model corresponding to the water quality category.

4. The method according to claim 3, wherein The determination method of the preset mapping relationship includes: Obtain the first training features indicated by the regression prediction model of the chemical dosage corresponding to each water quality category and the second training features indicated by the classification prediction model of the chemical dosage corresponding to each water quality category; Generate the preset mapping relationship according to the corresponding relationship between the water quality category, the first training feature, the water quality category, and the second training feature.

5. The method according to claim 1, characterized in that, The determining the target chemical dosage prediction value according to the overlapping relationship between the first chemical dosage prediction value and the first chemical dosage prediction level includes: When the first chemical dosage prediction value is within the first chemical dosage range indicated by the first chemical dosage prediction level, determine the first chemical dosage prediction value as the target chemical dosage prediction value; When the first chemical dosage prediction value is not within the first chemical dosage range indicated by the first chemical dosage prediction level, obtain the water flow rate and water turbidity detected at multiple detection points during the process from the raw water point to the inlet point. In the case where the water flow rate is greater than the first preset value and / or the water turbidity is greater than the second preset value, adjust the first chemical dosage range indicated by the first chemical dosage prediction level according to historical experience to obtain a second chemical dosage range, and determine the target chemical dosage prediction value according to the overlapping relationship between the second chemical dosage range and the first chemical dosage prediction value.

6. The method according to claim 5, wherein The determining the target chemical dosage prediction value according to the overlapping relationship between the second chemical dosage range and the first chemical dosage prediction value includes: When the first chemical dosage prediction value is within the second chemical dosage range, determine the first chemical dosage prediction value as the target chemical dosage prediction value; When the first chemical dosage prediction value is not within the second chemical dosage range, determine the target chemical dosage prediction value according to the minimum value of the first chemical dosage prediction value and the first chemical dosage range indicated by the first chemical dosage level.

7. The method according to any one of claims 1 to 6, characterized in that The controlling the chemical dosage in the water treatment process based on the target chemical dosage prediction value includes: Control the chemical dosing pump to perform the chemical dosing operation in the water treatment process according to the target chemical dosage prediction value; In response to monitoring that the water turbidity in the sedimentation tank decreases and for the first time the turbidity difference between adjacent sampling times is less than the third preset value, simultaneously start the radar device and the video acquisition device acting on the sedimentation tank; Measure the first average sedimentation velocity of the flocs in the sedimentation tank within the first preset time period by the radar device; Collect the video within the first preset time period in the sedimentation tank through the video acquisition device, perform floc target tracking and video depth estimation on the video, and determine the second average sedimentation velocity of the flocs in the sedimentation tank within the first preset time period according to the results of the floc target tracking and video depth estimation; Fuse the first average sedimentation velocity and the second average sedimentation velocity to obtain the target average sedimentation velocity; Adjust the first chemical dosage according to the difference between the target average sedimentation velocity and the preset sedimentation velocity to obtain a second chemical dosage, and control the chemical dosing pump to perform the chemical dosing operation in the water treatment process according to the second chemical dosage; Wherein, the preset sedimentation velocity is determined according to the sedimentation velocity of the flocs corresponding to the target effluent water quality of the sedimentation tank.

8. A chemical dosing control device in a water treatment process, characterized in that, Includes: A target water quality category determination module, configured to obtain influent parameters and determine the target water quality category to which the influent parameters belong according to an influent water quality classification model; A chemical dosage prediction feature determination module, configured to determine a first chemical dosage prediction feature and a second chemical dosage prediction feature associated with the target water quality category from chemical dosage prediction features according to a preset mapping relationship, where the chemical dosage prediction features include one or more of influent parameter features, floc features, and effluent parameter features; A first prediction module, configured to input the feature value of the first chemical dosage prediction feature into a chemical dosage regression prediction model in a preset chemical dosage prediction model corresponding to the target water quality category, and obtain a first chemical dosage prediction value according to the output of the chemical dosage regression prediction model; A second prediction module, configured to input the feature value of the second chemical dosage prediction feature into a chemical dosage classification prediction model in a preset chemical dosage prediction model corresponding to the target water quality category, and obtain a first chemical dosage prediction grade according to the output of the chemical dosage classification prediction model; A chemical dosage control module, configured to determine a target chemical dosage prediction value according to the overlapping relationship between the first chemical dosage prediction value and the first chemical dosage prediction grade, and control the chemical dosage in the water treatment process based on the target chemical dosage prediction value.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, Including: One or more processors; A storage device for storing one or more programs, which when executed by the one or more processors cause the one or more processors to implement the method according to any one of claims 1 to 7.

Images