Water quality monitoring system and method based on Internet of Things

Abstract

The invention discloses a water quality monitoring system and method based on Internet of Things, relates to the technical field of water quality monitoring, and aims to solve the problem of water quality reduction caused by influence of high-content dissolved oxygen on aquatic organisms in a water area. According to the organic matter concentration and the plant density in the water area, a polynomial regression method is used for analyzing the water area nutrition degree, the water area temperature is detected, the water area nutrition degree and the water area temperature are integrated, risk classification is conducted on the water area through a logistic regression algorithm, and when the water area classification is high in risk, regional division is conducted on the water area; collecting the area width and the area depth of each divided area to evaluate the water body flow potential of the divided area, detecting the water flow velocity of the divided area, calculating the area dissolved oxygen weight by using an optimal sequence diagram method according to the water flow velocity and the water body flow potential of the divided area, and calculating the total dissolved oxygen amount of the water area by integrating the dissolved oxygen amount of each divided area.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality monitoring, and more specifically, to a water quality monitoring system and method based on the Internet of Things. Background Art

[0002] Water quality monitoring technology is a technology that detects and analyzes various physical, chemical and biological parameters in water bodies. It is used to assess the health and safety of water bodies. The continued integration of water quality monitoring technology based on the Internet of Things can improve the efficiency of water quality monitoring and leave more processing time.

[0003] The prior art has the following deficiencies: In the past, when testing the dissolved oxygen content in water bodies, several points were randomly selected for dissolved oxygen measurement, without taking into account that the conditions at different locations in the water body lead to different dissolved oxygen levels. When the dissolved oxygen content at the measurement point is lower than the overall dissolved oxygen level in the water body, the high dissolved oxygen content in the water body will affect the normal activities of aquatic organisms and thus affect the water quality. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a water quality monitoring method based on the Internet of Things, which classifies the water area risk by analyzing the degree of eutrophication of the water area, selects whether to divide the water area into regions according to the water area risk classification, and analyzes the weights of the divided regions to calculate the overall dissolved oxygen level of the water area to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The water quality monitoring method based on the Internet of Things includes the following steps: Step S1: Detect the concentration of organic matter in the water area, obtain the area blocked by aquatic plants and determine the plant density, and analyze the eutrophication degree of the water area according to the concentration of organic matter and the plant density in the water area; Step S2: Detect the water temperature, comprehensively classify the water risk based on the eutrophication degree and water temperature, and divide the water into regions when the water is classified as a high-risk water area; Step S3: collecting the area width and area depth of each divided area, and evaluating the water flow potential of the divided area according to the area width and area depth of the divided area; Step S4: Detect the water flow velocity of the divided area, calculate the regional dissolved oxygen weight using the priority diagram method according to the water flow velocity of the divided area and the flow potential of the water area, detect the dissolved oxygen content of each divided area, and calculate the overall dissolved oxygen content of the water area by comprehensively considering the regional dissolved oxygen weight and dissolved oxygen content of the divided area.

[0006] In a preferred embodiment, in step S1, the organic matter concentration in the water area is the nitrogen concentration, and the absorbance reaction nitrogen concentration in the water area sample is measured by a spectrophotometer; the aquatic plant blocking area is obtained, and the ratio of the aquatic plant blocking area to the total water area is used as the plant density in the water area.

[0007] In a preferred embodiment, in step S1, the eutrophication degree of the water area is analyzed using a polynomial regression method according to the organic matter concentration and plant density in the water area, and the specific steps are as follows: The nitrogen concentration and plant density in the water area are used to calculate the water nutrient coefficient using the polynomial regression formula: , where D is the eutrophication coefficient of the water area, a is the nitrogen concentration in the water area, b is the plant density in the water area, and c is the error term; The degree of eutrophication of water bodies is quantified by the water body eutrophication coefficient. The larger the water body eutrophication coefficient, the higher the degree of eutrophication of water bodies.

[0008] In a preferred embodiment, in step S2, when the water area temperature is detected, the water area temperature is detected by temperature sensors at multiple detection points in the water area, and the average value of the water area temperature is selected as the average water area temperature, and the average water area temperature is standardized using logarithmic transformation: ,in, The average water temperature, This is the result after standardization of the average water temperature; The result of the standardized average temperature of the water area is used as the water temperature coefficient of the water area. The water temperature coefficient and the eutrophication coefficient of the water area are used to calculate the eutrophication risk coefficient of the water area using the logistic regression algorithm. The water area is classified according to the eutrophication risk coefficient of the water area. The specific steps are as follows: The trophication risk coefficient of the water area is calculated using the logistic regression algorithm formula: , where e is the natural base, Y is the eutrophication risk coefficient of the water area, z is the logistic regression parameter and z is the sum of the water temperature coefficient and the eutrophication coefficient of the water area; The water area is classified into risk categories based on the eutrophication risk coefficient of the water area and the preset eutrophication risk threshold. When the eutrophication risk coefficient of the water area exceeds the eutrophication risk threshold, the water area is classified as a high-risk water area; when the eutrophication risk coefficient of the water area is lower than the eutrophication risk threshold, the water area is classified as a low-risk water area.

[0009] In a preferred embodiment, in step S2, when the water area is classified as a low-risk water area, a region in the water area is randomly selected to detect the dissolved oxygen content of the region, and the dissolved oxygen content of the region is divided by the water area proportion of the region to calculate the overall dissolved oxygen content of the water area; When a water area is classified as a high-risk water area, the water area is divided into N areas of equal area. The pH value of the water body in each area is tested. After obtaining the pH value of the water body in all areas, the average pH value of the water area is calculated. Compare the water pH value of each area with the average pH value of the water area, and geometrically expand the areas where the water pH value is lower than the average pH value of the water area until the water pH value of the corresponding area reaches the average pH value of the water area; geometrically reduce the areas where the water pH value is higher than the average pH value of the water area until the water pH value of the corresponding area reaches the average pH value of the water area.

[0010] In a preferred embodiment, in step S3, the water flow potential index of each divided area is obtained by subtracting the area depth and area width of each divided area: S=kh, S is the water flow potential index of each divided area, k is the area width of each divided area, and h is the area depth of each divided area.

[0011] In a preferred embodiment, in step S4, for each divided area, the water flow velocity of each divided area is detected, and the water flow velocity of each divided area is normalized using a Max-Min normalization algorithm as a water flow velocity index; The water velocity index and water flow potential index of each divided area are marked as v and l respectively, and the score of each divided area is calculated using the geometric mean algorithm: , F is the score of each divided area.

[0012] In a preferred embodiment, in step S4, the regional dissolved oxygen weight of each divided area is calculated using the priority diagram method according to the score of each divided area, the dissolved oxygen content of each divided area is tested, and the dissolved oxygen content test results of each divided area and the regional dissolved oxygen weight of the corresponding divided area are weightedly summed, and the sum result is used as the overall dissolved oxygen content of the water area.

[0013] The water quality monitoring system based on the Internet of Things is used to implement the above-mentioned water quality monitoring method based on the Internet of Things, including a data acquisition module, a risk judgment module, a water flow measurement module and a dissolved oxygen assessment module; The data acquisition module is used to obtain the area of ​​aquatic plant shelter in the water area, detect the organic matter concentration, temperature, water flow velocity and dissolved oxygen content of each divided area in the water area, and collect the area width and area depth of each divided area; The risk assessment module calculates plant density based on the area blocked by aquatic plants in the water area, analyzes the eutrophication degree of the water area through the organic matter concentration and plant density in the water area, and uses the eutrophication degree and temperature of the water area to classify the risk of the water area and divide it into regions; The water flow determination module evaluates the water flow potential of the divided area according to the area width and area depth of the divided area; The dissolved oxygen assessment module calculates the regional dissolved oxygen weight based on the water flow velocity and water flow potential of each divided area, and calculates the overall dissolved oxygen content of the water area based on the regional dissolved oxygen weight and dissolved oxygen content of each divided area.

[0014] The technical effects and advantages of the water quality monitoring system and method based on the Internet of Things of the present invention are as follows: The present invention detects the organic matter concentration in the water area, obtains the aquatic plant shielding area and determines the plant density, analyzes the eutrophication degree of the water area according to the organic matter concentration and the plant density in the water area, detects the water area temperature, classifies the water area risk by comprehensively considering the eutrophication degree and the water area temperature, divides the water area into regions when the water area is classified as high risk, performs further processing according to the risk classification of the water area, provides a monitoring method and reduces the monitoring cost, collects the regional width and regional depth of each divided area to evaluate the water flow potential of the divided area, detects the water flow velocity of the divided area, calculates the regional dissolved oxygen weight according to the water flow velocity and the water flow potential of the divided area, calculates the overall dissolved oxygen content of the water area by comprehensively considering the dissolved oxygen content of each divided area, improves the detection accuracy of the dissolved oxygen content, prevents the problem of water quality degradation caused by excessive dissolved oxygen in the water area, and reduces the water quality hidden danger caused by the dissolved oxygen detection error. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of the water quality monitoring method based on the Internet of Things of the present invention.

[0016] Figure 2 This is a flow chart of the water quality monitoring system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] The present invention detects the concentration of organic matter in the water area, obtains the aquatic plant shielding area and determines the plant density, analyzes the eutrophication degree of the water area according to the organic matter concentration and the plant density in the water area, detects the temperature of the water area, classifies the water area according to the comprehensive eutrophication degree and the water area temperature, divides the water area into regions when the water area is classified as high risk, collects the regional width and regional depth of each divided area to evaluate the water flow potential of the divided area, detects the water flow velocity of the divided area, calculates the regional dissolved oxygen weight according to the water flow velocity and the water flow potential of the divided area, calculates the overall dissolved oxygen content of the water area by comprehensively calculating the dissolved oxygen content of each divided area, and reduces the water quality risks caused by the dissolved oxygen detection errors.

[0019] Example 1, a water quality monitoring method based on the Internet of Things, such as Figure 1 As shown, the following steps are included: Step S1: Detect the concentration of organic matter in the water area, obtain the area blocked by aquatic plants and determine the plant density, and analyze the eutrophication degree of the water area according to the concentration of organic matter and the plant density in the water area; Step S2: Detect the water temperature, comprehensively classify the water risk based on the eutrophication degree and water temperature, and divide the water into regions when the water is classified as a high-risk water area; Step S3: collecting the area width and area depth of each divided area, and evaluating the water flow potential of the divided area according to the area width and area depth of the divided area; Step S4: Detect the water flow velocity of the divided area, calculate the regional dissolved oxygen weight using the priority diagram method according to the water flow velocity of the divided area and the flow potential of the water area, detect the dissolved oxygen content of each divided area, and calculate the overall dissolved oxygen content of the water area by comprehensively considering the regional dissolved oxygen weight and dissolved oxygen content of the divided area.

[0020] The specific implementation is as follows: In step S1, nitrogen source, as an important organic matter in the water area, can reflect the concentration of organic matter in the water area. The absorbance in the water area sample is measured by a spectrophotometer. The higher the nitrogen concentration in the water area sample, the higher the absorbance obtained by the measurement. Use remote sensing technology to obtain a full view of the water area and count the area blocked by aquatic plants, and use the ratio of the area blocked by aquatic plants to the total area of ​​the water area as the plant density in the water area; The polynomial regression method is used to analyze the eutrophication degree of water bodies based on the organic matter concentration and plant density in the water bodies. The specific steps are as follows: The nitrogen concentration and plant density in the water area are used to calculate the water nutrient coefficient using the polynomial regression formula: , where D is the eutrophication coefficient of the water area, a is the nitrogen concentration in the water area, b is the plant density in the water area, and c is the error term; The water eutrophication coefficient can reflect the degree of water eutrophication. The larger the water eutrophication coefficient, the higher the degree of water eutrophication.

[0021] It should be noted that a spectrophotometer is an instrument that measures the absorption or transmission characteristics of light in a liquid sample. It is used to measure the absorbance reaction to reflect the concentration of organic matter in the water area. Remote sensing technology is a technology that collects information about objects at a long distance through sensors. It is used to obtain a full view of the water area and obtain the area blocked by aquatic plants. In the polynomial regression formula, the error term is set by professionals in this field and will not be elaborated here.

[0022] In step S2, the temperature increase will promote the photosynthesis of aquatic plants in the water area, so that the eutrophication of the water area is accelerated. When the water area temperature is detected, the water area temperature is detected by temperature sensors at multiple detection points in the water area, and the average value of the water area temperature is selected as the average water area temperature, and the average water area temperature is standardized by logarithmic transformation: ,in, The average water temperature, This is the result after standardization of the average water temperature; The result of the standardized average temperature of the water area is used as the water temperature coefficient of the water area. The water temperature coefficient and the eutrophication coefficient of the water area are used to calculate the eutrophication risk coefficient of the water area using the logistic regression algorithm. The water area is classified according to the eutrophication risk coefficient of the water area. The specific steps are as follows: The trophication risk coefficient of the water area is calculated using the logistic regression algorithm formula: , where e is the natural base, Y is the eutrophication risk coefficient of the water area, z is the logistic regression parameter and z is the sum of the water temperature coefficient and the eutrophication coefficient of the water area; The water area is classified into risk categories based on the eutrophication risk coefficient of the water area and the preset eutrophication risk threshold. When the eutrophication risk coefficient of the water area exceeds the eutrophication risk threshold, the water area is classified as a high-risk water area; when the eutrophication risk coefficient of the water area is lower than the eutrophication risk threshold, the water area is classified as a low-risk water area.

[0023] When a water area is classified as a low-risk water area, an area within the water area is randomly selected to test the regional dissolved oxygen content, and the regional dissolved oxygen content is divided by the water area proportion in the area to calculate the overall dissolved oxygen content of the water area.

[0024] When a water area is classified as a high-risk water area, the water area is divided into N areas of equal area. The pH value of the water body in each area is tested. After obtaining the pH value of the water body in all areas, the average pH value of the water area is calculated. Compare the water pH value of each area with the average pH value of the water area, and geometrically expand the areas where the water pH value is lower than the average pH value of the water area until the water pH value of the corresponding area reaches the average pH value of the water area; geometrically reduce the areas where the water pH value is higher than the average pH value of the water area until the water pH value of the corresponding area reaches the average pH value of the water area.

[0025] It should be noted that geometric expansion is a method of expanding all edges in proportion without changing their directions; geometric reduction is a method of reducing all edges in proportion without changing their directions.

[0026] In step S3, the width and depth of each divided area are measured by a sonar depth sounder and a laser rangefinder. The smaller the area width, the more concentrated the water flow will be in a narrower channel, and the flow rate of the water will be faster; the larger the area depth, the more water is available for flow, and the flow rate of the water will be faster.

[0027] The water flow potential index of each divided area is obtained by subtracting the area depth from the area width: S=kh, where S is the water flow potential index of each divided area, k is the area width of each divided area, and h is the area depth of each divided area; It should be noted that the water flow potential index is the difference between the regional depth and the regional width. The greater the regional depth or the smaller the regional width of the divided area, the greater the water flow potential index of the divided area. The water flow potential index can reflect the degree of change in the speed of water flow in the divided area.

[0028] In step S4, for each divided area, the water flow velocity of each divided area is detected by a flow meter, and the water flow velocity of each divided area is normalized using the Max-Min normalization algorithm. The formula of the Max-Min normalization algorithm is: ,in is the water flow velocity in each divided area, is the maximum value of water flow velocity in all divided areas, is the minimum value of water flow velocity in all divided areas, is the result of normalizing the water velocity in the corresponding divided area. It is used as the water velocity index; The water velocity index and water flow potential index of each divided area are marked as v and l respectively, and the score of each divided area is calculated using the geometric mean algorithm: , F is the score of each divided area; The greater the score of the divided area, the greater the water flow velocity index or water flow potential of the divided area, that is, the greater the water flow velocity of the divided area or the greater the degree of change in the velocity of the water flow; It needs to be explained that water flow helps to increase the contact area between water and air, promotes the dissolution of oxygen from the air into water, and increases the dissolved oxygen content of the water body. The greater the water flow rate or the greater the degree of change in the water flow rate, the higher the oxygen dissolution efficiency and the greater the dissolved oxygen content.

[0029] W Area S area Area A D area TTL indicator Regional dissolved oxygen weight W Area Relative Importance 0.5 0 0 0 0.5 0.0625 S area relative importance 1 0.5 0 0 1.5 0.1875 A. Relative importance of region 1 1 0.5 0 2.5 0.3125 D. Relative importance of region 1 1 1 0.5 3.5 0.4325 Table 1 It should be explained that in Table 1, four areas, W, S, A, and D, are used as examples. W, S, A, and D are four divided areas in the water area, respectively. The four divided areas are sorted from small to large according to the scores of the corresponding divided areas, and then correspond to the W, S, A, and D areas in sequence.

[0030] A portable dissolved oxygen meter is used to detect the dissolved oxygen content in each divided area. The dissolved oxygen detection results of each divided area are weighted and summed with the regional dissolved oxygen weights of the corresponding divided areas. The sum result is used as the overall dissolved oxygen content of the water area, which improves the detection accuracy of the dissolved oxygen content, prevents water quality deterioration caused by excessive dissolved oxygen in the water area, and reduces water quality risks caused by dissolved oxygen detection errors.

[0031] It should be noted that the portable dissolved oxygen meter is an instrument for quickly measuring the dissolved oxygen concentration in water. In this example, it is used to measure the dissolved oxygen content in each divided area in the water area in real time. The above-mentioned priority diagram method takes four divided areas as an example. In actual monitoring, the divided areas can be any number of designated areas. After the regional dissolved oxygen weights of each divided area are determined by the priority diagram method, the dissolved oxygen in the water area is calculated and monitored, which will not be elaborated here.

[0032] Example 2, a water quality monitoring system based on the Internet of Things, such as Figure 2 As shown, a water quality monitoring method based on the Internet of Things is implemented, including a data acquisition module, a risk judgment module, a water flow measurement module and a dissolved oxygen assessment module; The data acquisition module is used to obtain the area of ​​aquatic plant shelter in the water area, detect the organic matter concentration, temperature, water flow velocity and dissolved oxygen content of each divided area in the water area, and collect the area width and area depth of each divided area; The risk assessment module calculates plant density based on the area blocked by aquatic plants in the water area, analyzes the eutrophication degree of the water area through the organic matter concentration and plant density in the water area, and uses the eutrophication degree and temperature of the water area to classify the risk of the water area and divide it into regions; The water flow determination module evaluates the water flow potential of the divided area according to the area width and area depth of the divided area; The dissolved oxygen assessment module calculates the regional dissolved oxygen weight based on the water flow velocity and water flow potential of each divided area, and calculates the overall dissolved oxygen content of the water area based on the regional dissolved oxygen weight and dissolved oxygen content of each divided area.

[0033] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0034] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application of the technical solution and the invention constraints. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0035] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0036] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0037] Finally: 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 protection scope of the present invention.

Claims

1. A water quality monitoring method based on the Internet of Things, characterized in that: The following steps are included: Step S1: Detect the concentration of organic matter in the water area, obtain the area blocked by aquatic plants and determine the plant density, and analyze the eutrophication degree of the water area according to the concentration of organic matter and the plant density in the water area; Step S2: Detect the water temperature, comprehensively classify the water risk based on the eutrophication degree and water temperature, and divide the water into regions when the water is classified as a high-risk water area; Step S3: collecting the area width and area depth of each divided area, and evaluating the water flow potential of the divided area according to the area width and area depth of the divided area; Step S4: Detect the water flow velocity of the divided area, calculate the regional dissolved oxygen weight using the priority diagram method according to the water flow velocity of the divided area and the flow potential of the water area, detect the dissolved oxygen content of each divided area, and calculate the overall dissolved oxygen content of the water area by comprehensively considering the regional dissolved oxygen weight and dissolved oxygen content of the divided area.

2. The water quality monitoring method based on the Internet of Things according to claim 1 is characterized in that: In step S1, the organic matter concentration in the water area is the nitrogen concentration, and the absorbance in the water sample is measured by a spectrophotometer to reflect the nitrogen concentration; the aquatic plant blocking area is obtained, and the ratio of the aquatic plant blocking area to the total water area is used as the plant density in the water area.

3. The water quality monitoring method based on the Internet of Things according to claim 2 is characterized in that: In step S1, the eutrophication degree of the water area is analyzed using the polynomial regression method according to the organic matter concentration and plant density in the water area. The specific steps are as follows: The nitrogen concentration and plant density in the water area are used to calculate the water nutrient coefficient using the polynomial regression formula: , where D is the eutrophication coefficient of the water area, a is the nitrogen concentration in the water area, b is the plant density in the water area, and c is the error term; The degree of eutrophication of water bodies is quantified by the eutrophication coefficient of water bodies. The larger the eutrophication coefficient of water bodies, the higher the degree of eutrophication of water bodies.

4. The water quality monitoring method based on the Internet of Things according to claim 1, characterized in that: In step S2, when the water area temperature is detected, the water area temperature is detected by temperature sensors at multiple detection points in the water area, and the average value of the water area temperature is selected as the average water area temperature, and the average water area temperature is standardized by logarithmic transformation: ,in, The average water temperature, This is the result after standardization of the average water temperature; The result of the standardized average temperature of the water area is used as the water temperature coefficient of the water area. The water temperature coefficient and the eutrophication coefficient of the water area are used to calculate the eutrophication risk coefficient of the water area using the logistic regression algorithm. The water area is classified according to the eutrophication risk coefficient of the water area. The specific steps are as follows: The trophication risk coefficient of the water area is calculated using the logistic regression algorithm formula: , where e is the natural base, Y is the eutrophication risk coefficient of the water area, z is the logistic regression parameter and z is the sum of the water temperature coefficient and the eutrophication coefficient of the water area; The water area is classified into risk categories based on the eutrophication risk coefficient of the water area and the preset eutrophication risk threshold. When the eutrophication risk coefficient of the water area exceeds the eutrophication risk threshold, the water area is classified as a high-risk water area; when the eutrophication risk coefficient of the water area is lower than the eutrophication risk threshold, the water area is classified as a low-risk water area.

5. The water quality monitoring method based on the Internet of Things according to claim 4 is characterized in that: In step S2, when the water area is classified as a low-risk water area, a region within the water area is randomly selected to detect the dissolved oxygen content of the region, and the dissolved oxygen content of the region is divided by the water area proportion of the region to calculate the overall dissolved oxygen content of the water area; When a water area is classified as a high-risk water area, the water area is divided into N areas of equal area. The pH value of the water body in each area is tested. After obtaining the pH value of the water body in all areas, the average pH value of the water area is calculated. Compare the water pH value of each area with the average pH value of the water area, and geometrically expand the areas where the water pH value is lower than the average pH value of the water area until the water pH value of the corresponding area reaches the average pH value of the water area; geometrically reduce the areas where the water pH value is higher than the average pH value of the water area until the water pH value of the corresponding area reaches the average pH value of the water area.

6. The water quality monitoring method based on the Internet of Things according to claim 5 is characterized in that: In step S3, the water flow potential index of each divided area is obtained by subtracting the area depth and area width of each divided area: S=kh, S is the water flow potential index of each divided area, k is the area width of each divided area, and h is the area depth of each divided area.

7. The water quality monitoring method based on the Internet of Things according to any one of claims 1 or 5, characterized in that: In step S4, for each divided area, the water flow velocity of each divided area is detected, and the water flow velocity of each divided area is normalized by using the Max-Min normalization algorithm as the water flow velocity index; The water velocity index and water flow potential index of each divided area are marked as v and l respectively, and the score of each divided area is calculated using the geometric mean algorithm: , F is the score of each divided area.

8. The water quality monitoring method based on the Internet of Things according to claim 7 is characterized in that: In step S4, the regional dissolved oxygen weight of each divided area is calculated using the priority diagram method according to the score of each divided area, the dissolved oxygen content of each divided area is tested, and the dissolved oxygen content test results of each divided area and the regional dissolved oxygen weight of the corresponding divided area are weighted summed, and the sum result is used as the overall dissolved oxygen content of the water area.

9. A water quality monitoring system based on the Internet of Things, based on the water quality monitoring method based on the Internet of Things according to any one of claims 1 to 8, characterized in that: It includes data acquisition module, risk judgment module, water flow measurement module and dissolved oxygen assessment module; The data acquisition module is used to obtain the area of ​​aquatic plant shelter in the water area, detect the organic matter concentration, temperature, water flow velocity and dissolved oxygen content of each divided area in the water area, and collect the area width and area depth of each divided area; The risk assessment module calculates plant density based on the area blocked by aquatic plants in the water area, analyzes the eutrophication degree of the water area through the organic matter concentration and plant density in the water area, and uses the eutrophication degree and temperature of the water area to classify the risk of the water area and divide it into regions; The water flow determination module evaluates the water flow potential of the divided area according to the area width and area depth of the divided area; The dissolved oxygen assessment module calculates the regional dissolved oxygen weight based on the water flow velocity and water flow potential of each divided area, and calculates the overall dissolved oxygen content of the water area based on the regional dissolved oxygen weight and dissolved oxygen content of each divided area.

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