An intelligent dosing method and system for wastewater treatment based on Internet of Things technology

Abstract

The present invention discloses an intelligent dosing method and system for wastewater treatment based on Internet of Things technology, which relates to the field of sewage treatment technology. The method includes the following steps: collecting wastewater data from various sensors in the wastewater treatment area based on Internet of Things technology; simplifying the wastewater treatment scenario based on a clustering algorithm, and formulating decision rules for intelligent dosing based on the simplified wastewater treatment scenario; gridding the wastewater treatment area; constructing a dosing demand function, and determining several areas with the greatest dosing demand, and dosing in these areas with the greatest dosing demand; the system includes a data collection module, a scenario simplification module, a gridding processing module, and a region determination module. The present invention effectively reduces the complexity of data processing during intelligent dosing, improves data processing efficiency, can dynamically adjust dosing strategies based on real-time data, and improves the flexibility and adaptability of wastewater treatment.

Description

Technical Field

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

[0002] The Internet of Things (IoT) is a technology that connects various devices and systems to enable data exchange and remote control. In wastewater treatment, IoT technology connects various sensors, monitoring equipment, and control units to enable real-time data collection and interconnection.

[0003] Dosing in wastewater treatment refers to the process of adding chemicals to wastewater during the treatment process, which is to change the physical or chemical properties of the wastewater so as to more effectively remove or reduce pollutants. Dosing is usually carried out at different stages of wastewater treatment to meet specific treatment goals. For example, pH adjustment, disinfection, phosphorus removal, etc. The dosing process requires precise control of the dosage of drugs to ensure treatment efficiency and economy. With the development of technology, many wastewater treatment plants have begun to adopt automated dosing systems. These systems can automatically adjust the dosage based on the real-time monitored wastewater quality data to achieve more accurate and effective wastewater treatment.

[0004] For example, Chinese patent 201810315713.5 discloses an online dosing control method and system for wastewater treatment. Its online dosing model is built using principal component analysis, genetic algorithms, and neural network model algorithms, overcoming the difficulty of accurately determining dosing dosage during wastewater treatment. However, this online dosing control method has the following shortcomings: it relies on a combination of principal component analysis, genetic algorithms, and neural network model algorithms, requiring retraining of the model for different wastewater treatment scenarios, which may not be flexible and efficient when treating different or changing wastewater qualities.

[0005] Currently, no effective solutions have been proposed for the problems in related technologies. Summary of the Invention

[0006] In response to the problems in the related art, the present invention proposes an intelligent dosing method and system for wastewater treatment based on Internet of Things technology to overcome the above-mentioned technical problems existing in the existing related technology.

[0007] To this end, the specific technical solutions adopted in the present invention are as follows:

[0008] According to one aspect of the present invention, an intelligent dosing method for wastewater treatment based on Internet of Things technology is provided, and the intelligent dosing method for wastewater treatment based on Internet of Things technology comprises the following steps:

[0009] S1. Collect wastewater data from various sensors in the wastewater treatment area based on the Internet of Things technology and clean the collected wastewater data.

[0010] S2. Simplify the wastewater treatment scenario based on the clustering algorithm, and formulate decision rules for intelligent dosing based on the simplified wastewater treatment scenario.

[0011] S3. Grid the wastewater treatment area and ensure that each dosing point is within the grid range.

[0012] S4. Construct a drug dosing demand function, determine several areas with the greatest drug dosing demand, and perform drug dosing in the areas with the greatest drug dosing demand.

[0013] Furthermore, in order to simplify the decision-making process, reduce unnecessary complexity, improve the accuracy and efficiency of dosing decisions, reduce operating costs, and ensure wastewater treatment results:

[0014] Simplifying the wastewater treatment scenario based on the clustering algorithm and formulating the intelligent dosing decision rule based on the simplified wastewater treatment scenario includes the following steps:

[0015] S21. Normalize the wastewater data, and form the rows of the sample space matrix with the samples of the normalized wastewater data, and form the columns of the sample space matrix with the features of the wastewater data;

[0016] S22. Obtain a normalized matrix using the similarity matrix and the degree matrix;

[0017] S23, extracting the first two or three eigenvectors of the normalized matrix, and mapping the first two or three eigenvectors into a two-dimensional or three-dimensional space;

[0018] S24. Use the k-means algorithm to perform feature clustering, divide the wastewater data into several different categories, and obtain representative wastewater quality parameter combinations;

[0019] S25. Simplify the wastewater treatment scenario based on representative wastewater quality parameter combinations to obtain a typical wastewater treatment scenario;

[0020] S26. Develop decision rules for intelligent dosing based on typical wastewater treatment scenarios.

[0021] Furthermore, obtaining a normalized matrix using the similarity matrix and the degree matrix includes the following steps:

[0022] S221, construct a similarity matrix, and each element W in the similarity matrix ij Represents the similarity between sample i and sample j;

[0023] S222. Construct a degree matrix, where the elements on the diagonal of the degree matrix are the sum of the elements in any row of the similarity matrix;

[0024] S223, obtaining an unnormalized Laplace matrix by calculating the difference between the degree matrix and the similarity matrix;

[0025] S224. Obtain a normalized matrix using a symmetric normalization calculation formula.

[0026] Furthermore, when the normalized matrix is obtained using the symmetric normalization calculation formula, the symmetric normalization calculation formula is:

[0027] ;

[0028] Where, represents a normalized matrix;

[0029] D represents the degree matrix, W represents the similarity matrix;

[0030] represents the inverse square root matrix of the degree matrix, I Represents the identity matrix.

[0031] Furthermore, in order to locate areas with high dosing demand and ensure maximum dosing effect:

[0032] Constructing a dosing demand function, determining several areas with the greatest dosing demand, and performing dosing in the areas with the greatest dosing demand includes the following steps:

[0033] S41. Define a dosing demand function, where the dosing demand function reflects the degree of dosing demand in each grid area, and obtain a dosing demand function plane;

[0034] S42, obtaining several areas with the greatest dosing demand based on the expansion operation;

[0035] S43, adding medicine in several areas with the greatest demand for medicine addition according to the decision rules of intelligent medicine addition;

[0036] Wherein, when obtaining the dosing requirement function plane, the dosing requirement function value is calculated for each grid point in the wastewater treatment area to form the dosing requirement function plane.

[0037] Furthermore, obtaining several areas with the greatest dosing demand based on the expansion operation includes the following steps:

[0038] S421. Determine the initial point of dosing based on the wastewater data, and determine the size and shape of the neighborhood where the dosing point expands;

[0039] S422, performing an expansion operation on the dosing demand function plane, and comparing the dosing demand function value of each grid point with the dosing demand function values of the grid points in its neighborhood, and taking the maximum dosing demand function value in the neighborhood to replace the dosing demand function value of the current grid point;

[0040] S423. Compare the expanded dosing demand function plane with the original unexpanded dosing demand function plane, extract several areas with the largest dosing demand, and determine them as dosing points.

[0041] Furthermore, the calculation formula of the dosing demand function value is:

[0042] ;

[0043] Where, f ( x ) represents the value of the dosing demand function;

[0044] n Indicates the number of samples of wastewater data;

[0045] w i Indicates the i The weight of the samples;

[0046] P i Indicates the i The actual measured value of the samples, T i Indicates the i The target value of each sample;

[0047] g i Represents the deviation calculation function.

[0048] Furthermore, the weights of samples in the calculation formula of the dosing demand function value were preliminarily set through expert scoring and adjusted through decision analysis.

[0049] Furthermore, the weights of the samples in the calculation formula of the dosing demand function value are preliminarily set by expert scoring and adjusted by the decision analysis method, including the following steps:

[0050] Construct a decision matrix, where the rows represent different typical wastewater treatment scenarios and the columns represent different sample parameters;

[0051] The initial weight of each sample is determined by the expert scoring method, and the decision matrix is multiplied by the initial weight to obtain the weighted decision matrix;

[0052] Determine the ideal solution and the negative ideal solution, and use the Euclidean distance to calculate the distance between the sample parameter value of each typical wastewater treatment scenario and the ideal solution and the negative ideal solution;

[0053] For each typical wastewater treatment scenario, the relative proximity is obtained by calculating the ratio of the distance between each sample parameter value of the typical wastewater treatment scenario and the negative ideal solution to the sum of the distances between each sample parameter value of the typical wastewater treatment scenario and the ideal solution and the negative ideal solution;

[0054] The weight of each sample is adjusted according to the relative proximity.

[0055] According to another aspect of the present invention, an intelligent dosing system for wastewater treatment based on the Internet of Things technology is provided. The intelligent dosing system for wastewater treatment based on the Internet of Things technology includes a data collection module, a scene simplification module, a grid processing module and an area determination module; wherein the data collection module is connected to the scene simplification module and the grid processing module, and the scene simplification module and the grid processing module are both connected to the area determination module.

[0056] The data collection module is used to collect wastewater data from various sensors in the wastewater treatment area based on the Internet of Things technology and clean the collected wastewater data.

[0057] The scenario simplification module is used to simplify the wastewater treatment scenario based on the clustering algorithm and formulate decision rules for intelligent dosing based on the simplified wastewater treatment scenario.

[0058] The grid processing module is used to perform grid processing on the wastewater treatment area and ensure that each dosing point is located within the grid range.

[0059] The area determination module is used to construct a drug addition demand function, determine several areas with the greatest drug addition demand, and perform drug addition in the several areas with the greatest drug addition demand.

[0060] The beneficial effects of the present invention are:

[0061] (1) The present invention provides an intelligent dosing method and system for wastewater treatment based on Internet of Things technology, which effectively reduces the complexity of data processing during intelligent dosing, while retaining typical wastewater treatment scenarios and improving data processing efficiency. The constructed dosing demand function can dynamically adjust the dosing strategy according to real-time data, thereby improving the flexibility and adaptability of wastewater treatment.

[0062] (2) Collecting wastewater data through Internet of Things technology provides an accurate basis for intelligent dosing; by simplifying the wastewater treatment scenario and formulating decision rules for intelligent dosing based on the simplified wastewater treatment scenario, and by retaining representative wastewater treatment scenarios, it helps to simplify the decision-making process, reduce unnecessary complexity, improve the accuracy and efficiency of dosing decisions, reduce operating costs, and ensure wastewater treatment effects.

[0063] (3) The present invention defines a dosing demand function and performs an expansion operation on the dosing demand function plane, so that the dosing demand value of each grid point is compared with the dosing demand value of the grid points in its neighborhood, and the maximum value in the neighborhood is taken to replace the value of the current point. The deviation between the wastewater data of several areas with the largest dosing demand and the target value is large, thereby quantifying the dosing demand, ensuring that dosing is carried out in the areas with the largest demand, optimizing resource allocation, and locating areas with high dosing demand through spatial operations to ensure that the dosing effect is maximized. At the same time, by combining the use of expert scoring and decision analysis methods, the weight of each sample is reasonably formulated, making the calculation of the dosing demand function value more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0065] Figure 1 This is a flow chart of an intelligent dosing method for wastewater treatment based on Internet of Things technology according to an embodiment of the present invention;

[0066] Figure 2 The present invention is a block diagram of an intelligent dosing system for wastewater treatment based on Internet of Things technology according to an embodiment of the present invention.

[0067] In the picture:

[0068] 1. Data collection module; 2. Scene simplification module; 3. Grid processing module; 4. Region determination module. DETAILED DESCRIPTION

[0069] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention. They are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. By referring to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0070] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0071] According to an embodiment of the present invention, an intelligent dosing method and system for wastewater treatment based on Internet of Things technology are provided.

[0072] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a smart dosing method for wastewater treatment based on the Internet of Things technology is provided, and the smart dosing method for wastewater treatment based on the Internet of Things technology includes the following steps:

[0073] S1. Based on the Internet of Things technology, wastewater data such as pH, COD, BOD and other parameters are collected from various sensors in the wastewater treatment area, and the collected wastewater data is cleaned, including noise removal, missing value processing, outlier detection and correction, etc., to improve data quality, thereby ensuring the accuracy and availability of wastewater treatment data and providing reliable basic data for subsequent analysis.

[0074] Among them, S1 specifically includes the following steps:

[0075] Sensors deployed in wastewater treatment areas monitor various parameters in wastewater in real time, such as pH, chemical oxygen demand (COD), biological oxygen demand (BOD), etc.

[0076] The sensors send the monitored data to the data collection system.

[0077] The collected wastewater data are subjected to noise removal, missing value processing, outlier detection and correction.

[0078] Verify the cleaned data to ensure that the data quality meets the analysis requirements.

[0079] The cleaned and verified data is stored in a database or data warehouse to provide support for subsequent data analysis and dosing control models.

[0080] S2. Simplify the wastewater treatment scenario based on the clustering algorithm, and formulate decision rules for intelligent dosing based on the simplified wastewater treatment scenario.

[0081] In a further embodiment, simplifying the wastewater treatment scenario based on a clustering algorithm and formulating a decision rule for intelligent dosing based on the simplified wastewater treatment scenario includes the following steps:

[0082] S21. Normalize the wastewater data, and form the rows of the sample space matrix with the samples of the normalized wastewater data, and form the columns of the sample space matrix with the features of the wastewater data;

[0083] S22. Obtain a normalized matrix using the similarity matrix and the degree matrix;

[0084] S23, extracting the first two or three eigenvectors of the normalized matrix, and mapping the first two or three eigenvectors into a two-dimensional or three-dimensional space;

[0085] S24. Use the k-means algorithm to perform feature clustering (find the cluster centers of the data, representing different wastewater treatment scenarios), divide the wastewater data into several different categories, and obtain representative combinations of wastewater quality parameters;

[0086] S25. Simplify the wastewater treatment scenario based on representative wastewater quality parameter combinations to obtain a typical wastewater treatment scenario;

[0087] S26. Develop decision rules for intelligent dosing based on typical wastewater treatment scenarios. For example, if the BOD value of a cluster center is high, the dosage of the biological treatment agent needs to be increased.

[0088] Based on the relationship between wastewater data parameters and dosage, a mathematical model or empirical formula is established to calculate the required dosage. When multiple agents need to be added simultaneously, priorities are set for each agent based on treatment urgency and effectiveness. The order and time intervals for adding agents are determined to ensure optimal treatment results.

[0089] In a further embodiment, obtaining a normalized matrix using the similarity matrix and the degree matrix comprises the following steps:

[0090] S221, construct a similarity matrix, and each element W in the similarity matrix ab Indicates the similarity between sample a and sample b;

[0091] S222. Construct a degree matrix, where the elements on the diagonal of the degree matrix are the sum of the elements in any row of the similarity matrix;

[0092] S223, obtaining an unnormalized Laplace matrix by calculating the difference between the degree matrix and the similarity matrix;

[0093] S224. Obtain a normalized matrix using a symmetric normalization calculation formula.

[0094] In a further embodiment, when a normalized matrix is obtained using a symmetric normalization calculation formula, the symmetric normalization calculation formula is:

[0095] ;

[0096] Where, represents a normalized matrix;

[0097] D represents the degree matrix, W represents the similarity matrix;

[0098] represents the inverse square root matrix of the degree matrix, I Represents the identity matrix.

[0099] S3. Grid the wastewater treatment area and ensure that each dosing point is within the grid range.

[0100] S3 specifically includes the following steps:

[0101] The entire wastewater treatment area is divided into several small grid cells according to its size and shape. The grids should cover the entire treatment area as evenly as possible.

[0102] The size of the grid is determined so that it covers a sufficient spatial range while maintaining sufficient resolution for monitoring and control.

[0103] Identify one or more dosing points within each grid. Based on the location of the dosing points, plan the layout of the dosing equipment, including pipelines, pumping stations, and chemical storage facilities.

[0104] S4. Construct a drug dosing demand function, determine several areas with the greatest drug dosing demand, and perform drug dosing in the areas with the greatest drug dosing demand.

[0105] In a further embodiment, constructing a medication dosing demand function, determining several areas with the greatest medication dosing demand, and performing medication dosing in the several areas with the greatest medication dosing demand includes the following steps:

[0106] S41. Define a dosing demand function, where the dosing demand function reflects the degree of dosing demand in each grid area, and obtain a dosing demand function plane;

[0107] S42, obtaining several areas with the greatest dosing demand based on the expansion operation;

[0108] S43, adding medicine in several areas with the greatest demand for medicine addition according to the decision rules of intelligent medicine addition;

[0109] Wherein, when obtaining the dosing requirement function plane, the dosing requirement function value is calculated for each grid point in the wastewater treatment area to form the dosing requirement function plane.

[0110] In a further embodiment, obtaining several areas with the greatest dosing requirements based on the expansion operation includes the following steps:

[0111] S421. Determine the initial point of dosing based on the wastewater data, and determine the size and shape of the neighborhood where the dosing point expands. Specifically, according to the goals and standards of wastewater treatment, set specific standards for selecting dosing points. For example, the initial point is selected at a location where the pollutant concentration exceeds the standard. If multiple pollutants exceed the standard, it may be necessary to comprehensively consider the situation of each pollutant and select one or more initial points.

[0112] S422. Perform an expansion operation on the dosing demand function plane, compare the dosing demand function value of each grid point with the dosing demand function values of the grid points in its neighborhood, and take the maximum dosing demand function value in the neighborhood to replace the dosing demand function value of the current grid point.

[0113] S423. Compare the expanded dosing demand function plane with the original unexpanded dosing demand function plane, extract several areas with the largest dosing demand, and determine them as dosing points.

[0114] In a further embodiment, the calculation formula of the dosing requirement function value is:

[0115] ;

[0116] Where, f ( x ) represents the value of the dosing demand function, which represents the overall degree of dosing demand;

[0117] n Indicates the number of samples of wastewater data;

[0118] w i Indicates the i The weight of each sample indicates the importance of the parameter in the dosing decision;

[0119] P i Indicates the i The actual measured value of the samples, T i Indicates the i The target value of each sample;

[0120] g i Represents the deviation calculation function, which is used to calculate the i The deviation between the actual value of a sample and the target value.

[0121] In a further embodiment, the weights of the samples in the calculation formula of the dosing requirement function value are preliminarily set by expert scoring and adjusted by decision analysis.

[0122] In a further embodiment, the weights of samples in the calculation formula of the dosing requirement function value are initially set by expert scoring and adjusted by decision analysis method, including the following steps:

[0123] By collecting various parameter data in wastewater treatment scenarios, a decision matrix is constructed, where the rows represent different typical,wastewater treatment scenarios and the columns represent different sample parameters;

[0124] The initial weight of each sample is determined by the expert scoring method, and the decision matrix is multiplied by the initial weight to obtain the weighted decision matrix;

[0125] Determine the ideal solution and the negative ideal solution, and use the Euclidean distance to calculate the distance between the sample parameter value of each typical wastewater treatment scenario and the ideal solution and the negative ideal solution;

[0126] For each typical wastewater treatment scenario, the relative proximity is obtained by calculating the ratio of the distance between each sample parameter value of the typical wastewater treatment scenario and the negative ideal solution to the sum of the distances between each sample parameter value of the typical wastewater treatment scenario and the ideal solution and the negative ideal solution;

[0127] The weight of each sample is adjusted according to the relative proximity. The higher the proximity of the sample parameters, the greater the weight.

[0128] The ideal solution is the solution consisting of the optimal value for each evaluation indicator (i.e., each column in the decision matrix). For benefit-based indicators (higher is better), the ideal solution is the maximum value of that indicator; for cost-based indicators (lower is better), the ideal solution is the minimum value of that indicator.

[0129] The negative ideal solution is a solution consisting of the worst value of each evaluation indicator. For benefit-based indicators, the negative ideal solution is the minimum value of the indicator; for cost-based indicators, the negative ideal solution is the maximum value of the indicator.

[0130] like Figure 2 As shown, according to another embodiment of the present invention, an intelligent dosing system for wastewater treatment based on the Internet of Things technology is provided, and the intelligent dosing system for wastewater treatment based on the Internet of Things technology includes a data collection module 1, a scene simplification module 2, a grid processing module 3 and a region determination module 4; wherein, the data collection module 1 is connected to the scene simplification module 2 and the grid processing module 3, and the scene simplification module 2 and the grid processing module 3 are both connected to the region determination module 4.

[0131] The data collection module 1 is used to collect wastewater data from various sensors in the wastewater treatment area based on the Internet of Things technology and clean the collected wastewater data.

[0132] The scenario simplification module 2 is used to simplify the wastewater treatment scenario based on the clustering algorithm and formulate decision rules for intelligent dosing based on the simplified wastewater treatment scenario.

[0133] The grid processing module 3 is used to perform grid processing on the wastewater treatment area and ensure that each dosing point is located within the grid range.

[0134] The region determination module 4 is used to construct a drug addition demand function, determine several regions with the greatest drug addition demand, and perform drug addition in the several regions with the greatest drug addition demand.

[0135] In summary, the present invention provides an intelligent dosing method and system for wastewater treatment based on Internet of Things technology, which effectively reduces the complexity of data processing during intelligent dosing while retaining typical wastewater treatment scenarios and improving data processing efficiency. The constructed dosing demand function can dynamically adjust the dosing strategy based on real-time data, thereby improving the flexibility and adaptability of wastewater treatment. Collecting wastewater data through Internet of Things technology provides an accurate basis for intelligent dosing; by simplifying the wastewater treatment scenario and formulating intelligent dosing decision rules based on the simplified wastewater treatment scenario, and by retaining representative wastewater treatment scenarios, it helps to simplify the decision-making process, reduce unnecessary complexity, improve the accuracy and efficiency of dosing decisions, reduce operating costs, and ensure wastewater treatment results. The present invention defines a dosing demand function and performs an expansion operation on the dosing demand function plane, so that the dosing demand value of each grid point is compared with the dosing demand value of the grid points in its neighborhood, and the maximum value in the neighborhood is taken to replace the value of the current point. The deviation between the wastewater data of several areas with the greatest dosing demand and the target value is large, thereby quantifying the dosing demand, ensuring that dosing is carried out in the areas with the greatest demand, optimizing resource allocation, and locating areas with high dosing demand through spatial operations to ensure maximum dosing effect. At the same time, by combining the use of expert scoring and decision analysis methods, and by reasonably setting the weight of each sample, the calculation of the dosing demand function value is made more accurate.

[0136] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional charging modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0137] The terms "first," "second," "third," "fourth," and the like (if any) in the specification of the present application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in orders other than those illustrated or described herein. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus.

[0138] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent dosing method for wastewater treatment based on Internet of Things technology, characterized in that: The intelligent dosing method for wastewater treatment based on Internet of Things technology includes the following steps: S1. Collect wastewater data from various sensors in the wastewater treatment area based on IoT technology and clean the collected wastewater data; S2. Simplify the wastewater treatment scenario based on the clustering algorithm and formulate intelligent dosing decision rules based on the simplified wastewater treatment scenario; S3. Grid the wastewater treatment area and ensure that each dosing point is within the grid range; S4. defining a dosing demand function, wherein the dosing demand function reflects the degree of dosing demand of each grid area, and obtaining a dosing demand function plane; Perform an expansion operation on the dosing demand function plane, and compare the dosing demand function value of each grid point with the dosing demand function value of the grid points in its neighborhood, and take the maximum dosing demand function value in the neighborhood to replace the dosing demand function value of the current grid point; compare the expanded dosing demand function plane with the original unexpanded dosing demand function plane, extract several areas with the largest dosing demand, and determine them as dosing points; Dosing is carried out in several areas with the greatest dosing demand according to the decision rules of intelligent dosing.

2. The intelligent dosing method for wastewater treatment based on Internet of Things technology according to claim 1, characterized in that: The method of simplifying the wastewater treatment scenario based on the clustering algorithm and formulating a decision rule for intelligent dosing based on the simplified wastewater treatment scenario includes the following steps: S21. Normalize the wastewater data, and form the rows of the sample space matrix with the samples of the normalized wastewater data, and form the columns of the sample space matrix with the features of the wastewater data; S22. Obtain a normalized matrix using the similarity matrix and the degree matrix; S23, extracting the first two or three eigenvectors of the normalized matrix, and mapping the first two or three eigenvectors into a two-dimensional or three-dimensional space; S24. Use the k-means algorithm to perform feature clustering, divide the wastewater data into several different categories, and obtain representative wastewater quality parameter combinations; S25. Simplify the wastewater treatment scenario based on representative wastewater quality parameter combinations to obtain a typical wastewater treatment scenario; S26. Develop decision rules for intelligent dosing based on typical wastewater treatment scenarios.

3. The intelligent dosing method for wastewater treatment based on Internet of Things technology according to claim 2, characterized in that: The method of obtaining a normalized matrix by using a similarity matrix and a degree matrix comprises the following steps: S221, construct a similarity matrix, and each element W in the similarity matrix ij Represents the similarity between sample i and sample j; S222. Construct a degree matrix, where the elements on the diagonal of the degree matrix are the sum of the elements in any row of the similarity matrix; S223, obtaining an unnormalized Laplace matrix by calculating the difference between the degree matrix and the similarity matrix; S224. Obtain a normalized matrix using a symmetric normalization calculation formula.

4. The intelligent dosing method for wastewater treatment based on Internet of Things technology according to claim 3 is characterized in that: When the normalized matrix is obtained by using the symmetric normalization calculation formula, the symmetric normalization calculation formula is: ； Where, represents a normalized matrix; D represents the degree matrix, W represents the similarity matrix; represents the inverse square root matrix of the degree matrix, I Represents the identity matrix.

5. The intelligent dosing method for wastewater treatment based on Internet of Things technology according to claim 4 is characterized in that: When obtaining the dosing requirement function plane, the dosing requirement function value is calculated for each grid point in the wastewater treatment area to form the dosing requirement function plane.

6. The intelligent dosing method for wastewater treatment based on Internet of Things technology according to claim 5, characterized in that: The step of expanding the dosing requirement function plane also includes the following steps: The initial point of dosing is determined based on the wastewater data, and the size and shape of the neighborhood where the dosing point expands are determined.

7. The intelligent dosing method for wastewater treatment based on Internet of Things technology according to claim 6, characterized in that: The calculation formula of the dosing demand function value is: ； Where, f ( x ) represents the value of the dosing demand function; n Indicates the number of samples of wastewater data; w i Indicates the i The weight of the samples; P i Indicates the i The actual measured value of the samples, T i Indicates the i The target value of each sample; g i Represents the deviation calculation function.

8. The intelligent dosing method for wastewater treatment based on Internet of Things technology according to claim 7, characterized in that: The weights of the samples in the calculation formula of the dosing requirement function value are initially set through expert scoring and adjusted through decision analysis.

9. The intelligent dosing method for wastewater treatment based on Internet of Things technology according to claim 8, characterized in that: The weights of samples in the calculation formula of the dosing requirement function value are preliminarily set by expert scoring and adjusted by decision analysis method, including the following steps: Construct a decision matrix, where the rows represent different typical wastewater treatment scenarios and the columns represent different sample parameters; The initial weight of each sample is determined by the expert scoring method, and the decision matrix is multiplied by the initial weight to obtain the weighted decision matrix; Determine the ideal solution and the negative ideal solution, and use the Euclidean distance to calculate the distance between the sample parameter value of each typical wastewater treatment scenario and the ideal solution and the negative ideal solution; For each typical wastewater treatment scenario, the relative proximity is obtained by calculating the ratio of the distance between each sample parameter value of the typical wastewater treatment scenario and the negative ideal solution to the sum of the distances between each sample parameter value of the typical wastewater treatment scenario and the ideal solution and the negative ideal solution; The weight of each sample is adjusted according to the relative proximity.

10. An intelligent dosing system for wastewater treatment based on Internet of Things technology, used to implement the intelligent dosing method for wastewater treatment based on Internet of Things technology according to any one of claims 1 to 9, characterized in that: The intelligent dosing system for wastewater treatment based on Internet of Things technology includes a data collection module, a scene simplification module, a grid processing module and a region determination module; The data collection module is connected to the scene simplification module and the grid processing module, and both the scene simplification module and the grid processing module are connected to the region determination module; The data collection module is used to collect wastewater data from various sensors in the wastewater treatment area based on the Internet of Things technology and clean the collected wastewater data; The scenario simplification module is used to simplify the wastewater treatment scenario based on a clustering algorithm and formulate decision rules for intelligent dosing based on the simplified wastewater treatment scenario; The grid processing module is used to perform grid processing on the wastewater treatment area and ensure that each dosing point is located within the grid range; The region determination module is used to construct a drug addition demand function, determine several regions with the greatest drug addition demand, and perform drug addition in the several regions with the greatest drug addition demand.

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