Method for improving water quality of water supply reservoir

Abstract

The invention discloses a method for improving water quality of a water supply reservoir, which relates to the technical field of ecological restoration and comprises the following steps: firstly, preprocessing a reservoir image and sludge data, and evaluating water turbidity; if the water body is turbid, the unmanned aerial vehicle puts a curing agent and repeatedly monitors until the water body is clear, and after the water body is clear, water body components are analyzed and the eutrophication degree is evaluated; if eutrophication occurs, putting aquatic plant seeds until the water quality is normal, otherwise, continuously monitoring. The curing agent is put through the unmanned aerial vehicle, solidification of reservoir sludge is facilitated, water body stinking caused by sludge loosening is avoided, and aquatic plant seeds and aquatic plants are put through the unmanned aerial vehicle, so that water quality improvement is facilitated, and water body eutrophication is avoided.

Description

Technical Field

[0001] The invention relates to the technical field of ecological restoration, and in particular to a method for improving water quality in a water supply reservoir. Background Art

[0002] Water supply reservoirs are a key large-scale water conservancy engineering facility. Their main function is to store and regulate water resources. These reservoirs not only ensure the stability and safety of water supply, but also play a vital role in flood prevention and disaster reduction, hydropower generation, and maintaining and improving the ecological environment. They are an indispensable foundation for modern urban water supply systems and agricultural irrigation, and provide solid support for ensuring residents' domestic water use, supporting agricultural production and promoting economic development.

[0003] The bottom sediment of a reservoir is an important component of the water ecosystem. Its composition, ingredients, properties and content have an important impact on the water quality of the reservoir. The bottom sediment generally comes from silt and other substances carried by upstream water, the remains and decomposition products of aquatic organisms in the water body, and pollutants in the water body.

[0004] However, the loose state of the bottom mud easily causes its particulate matter to dissolve and become suspended in the water, which not only makes the water turbid, but may also cause the water to stink; and when the nutrients in the bottom mud are released into the water, it will aggravate the eutrophication of the water, thereby causing serious damage to the aquatic ecosystem. Summary of the invention

[0005] Technical issues solved

[0006] In view of the deficiencies of the prior art, the present invention provides a method for improving the water quality of a water supply reservoir, which solves the problem of water odor caused by loose bottom sludge and water eutrophication caused by loose bottom sludge, thereby destroying the ecosystem.

[0007] Technical Solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for improving the water quality of a water supply reservoir, comprising the following specific steps: Step 1: Acquire reservoir image data and reservoir silt data, pre-process the reservoir image data and reservoir silt data, evaluate the pre-processed reservoir image data and reservoir silt data, and obtain a water turbidity assessment value; Step 2: Make a judgment based on the water turbidity assessment value; Step 3: If the water body is judged to be turbid, enable the drone to release a curing agent and continue to acquire reservoir image data and reservoir silt data until the water body is judged to be clear; if the water body is judged to be clear, execute step 4; Step 4: Acquire water body component data, pre-process the water body component data, evaluate the pre-processed water body component data, and obtain a water quality eutrophication assessment value; Step 5: Make a judgment on the water quality eutrophication assessment value; Step 6: If the water quality is judged to be eutrophic, enable the drone to release aquatic plant seeds until the water quality is judged to be normal; If the water quality is judged to be normal, continue monitoring.

[0009] Furthermore, in step 1, a monitoring time period is set and divided into n sub-time periods; the reservoir image data contains the water body pixel value in each sub-time period; the water body pixel value in each sub-time period is recorded as the pixel value; the pixel value includes the water body color value in each sub-time period and the water body brightness value in each sub-time period; the water body color value in each sub-time period is recorded as the color value; the water body brightness value in each sub-time period is recorded as the brightness value; the reservoir silt data includes the silt thickness in each sub-time period, the silt tightness in each sub-time period and the granular mud in each sub-time period; each sub-time period The silt thickness in the section is recorded as silt thickness; the silt tightness in each sub-time period is recorded as silt tightness; the granular mud in each sub-time period is recorded as granular mud; the reservoir image data and the reservoir silt data are filtered and denoised; a comprehensive analysis is performed on the reservoir image data and the reservoir silt data after filtering and denoising to obtain the water body color anomaly coefficient and the bottom silt loose coefficient; the water body color anomaly coefficient and the bottom silt loose coefficient are normalized; a comprehensive analysis is performed on the water body color anomaly coefficient and the bottom silt loose coefficient after normalization to obtain the water body turbidity assessment value; Among them, HP represents the water turbidity assessment value, SY represents the water color abnormality coefficient, and DS represents the bottom silt looseness coefficient.

[0010] Furthermore, the specific method for obtaining the water color anomaly coefficient is as follows: summing up n color values ​​to obtain the total color value; averaging the total color value to obtain the average color value; calculating the variance between the color value and the average color value to obtain the water color difference value; performing contour detection algorithm and contour motion tracking algorithm on the reservoir image data to obtain the water turbidity anomaly value in each sub-time period; the water turbidity difference value in each sub-time period is recorded as the water turbidity difference value; and performing a comprehensive analysis based on the water color difference value and the water turbidity anomaly value to obtain the water color anomaly coefficient.

[0011] Furthermore, the specific method for obtaining the water turbidity difference value is as follows: perform contour detection algorithm and contour motion tracking algorithm calculation on the reservoir image data to obtain the total contour of the reservoir water body within the monitoring time period and the contour of the turbid water in the reservoir; the total contour of the reservoir water body within the monitoring time period includes the total number of pixel points and the total pixel point coordinates; the total number of pixel points and the total pixel point coordinates are calculated to obtain the total contour area of ​​the reservoir water body; the contour of the turbid water in the reservoir includes the number of dark pixel point coordinates and the coordinates of the dark pixel point coordinates; the contour area of ​​the turbid water in the reservoir is calculated based on the number of dark pixel point coordinates and the coordinates of the dark pixel point coordinates; the quotient of the total contour area of ​​the reservoir water body and the contour area of ​​the turbid water in the reservoir is calculated to obtain the water turbidity difference value.

[0012] Furthermore, the specific method for obtaining the bottom mud looseness coefficient is as follows: perform difference calculation between the previous mud thickness and the next mud thickness, and so on, to obtain several mud thickness reduction values; perform sum calculation on several mud thickness reduction values ​​to obtain a mud thickness difference value; perform difference calculation between the previous mud tightness and the next mud tightness, and so on, to obtain several mud tightness reduction values; perform sum calculation on several mud tightness reduction values ​​to obtain a mud tightness difference value; perform contour detection algorithm and contour motion tracking algorithm on the granular mud to obtain the mud looseness value in each sub-time period; the mud looseness value in each sub-time period is recorded as the mud looseness value; perform comprehensive analysis based on the mud thickness difference value, the mud tightness difference value and the mud looseness value to obtain the bottom mud looseness coefficient.

[0013] Furthermore, the specific method for obtaining the sludge looseness value is as follows: perform contour detection algorithm and contour motion tracking algorithm on the granular mud to obtain the number of contours of the granular mud in each sub-time period; count the number of contours of the granular mud in each sub-time period to obtain the number of granular mud in each sub-time period; the number of granular mud in each sub-time period is recorded as the number of granular mud; sum the number of granular mud to obtain the sludge looseness value.

[0014] Furthermore, in step 2, a water turbidity threshold is set and compared with the water turbidity assessment value; if the water turbidity assessment value is greater than or equal to the water turbidity threshold, the water is judged to be turbid; if the water turbidity assessment value is less than the water turbidity threshold, the water is judged to be clear.

[0015] Further, in step 4, the water composition data includes the total phosphorus within the monitoring time period, the total nitrogen within the monitoring time period, and the pH value within the monitoring time period; the total phosphorus within the monitoring time period is recorded as the total phosphorus; the total nitrogen within the monitoring time period is recorded as the total nitrogen; the pH value within the monitoring time period is recorded as the pH value; the water composition data is filtered and denoised; the water composition data after filtering and denoising is comprehensively analyzed to obtain the water pollution coefficient; the water pollution coefficient and the bottom silt loose coefficient are normalized; the normalized water pollution coefficient and the bottom silt loose coefficient are comprehensively analyzed to obtain the water quality eutrophication assessment value; Among them, FY represents the water eutrophication assessment value, DS represents the bottom silt looseness coefficient, and SW represents the water pollution coefficient.

[0016] Furthermore, the specific method for obtaining the water pollution coefficient is as follows: set a standard value for total phosphorus; set a standard value for total nitrogen; set a standard value for pH value; conduct a comprehensive analysis of total phosphorus, total nitrogen, pH value, total phosphorus standard value, total nitrogen standard value and pH value standard value to obtain the water pollution coefficient.

[0017] Furthermore, in step five, a water quality eutrophication threshold is set and compared with the water quality eutrophication assessment value; if the water quality eutrophication assessment value is greater than or equal to the water quality eutrophication threshold, the water quality is judged to be eutrophic; if the water quality eutrophication assessment value is less than the water quality eutrophication threshold, the water quality is judged to be normal.

[0018] Beneficial Effects

[0019] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0020] 1. By setting up a first camera, a silt thickness sensor, a pore water pressure sensor and a second camera, the first camera is used to obtain reservoir image data, and the silt thickness sensor, the pore water pressure sensor and the second camera are used to obtain reservoir silt data for evaluation, which is helpful to judge the turbidity of the water body;

[0021] 2. By setting up total phosphorus sensors, total nitrogen sensors and pH sensors, water composition data can be obtained for evaluation, which helps to judge the eutrophication of water quality;

[0022] 3. By using drones to drop curing agents, it helps to solidify the reservoir silt and prevent the loose silt from causing the water to stink.

[0023] 4. By using drones to drop aquatic plant seeds, aquatic plants can help improve water quality and avoid eutrophication of water bodies.

[0024] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The present invention is a flow chart of a method for improving water quality in a water supply reservoir. DETAILED DESCRIPTION

[0026] 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.

[0027] It should be noted that, in this article, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0028] like Figure 1 As shown, the embodiment of the present invention provides a method for improving water quality in a water supply reservoir, which includes the following specific steps:

[0029] Step 1: Set a first camera, a silt thickness sensor, a pore water pressure sensor and a second camera, obtain reservoir image data through the first camera, and obtain reservoir silt data through the silt thickness sensor, the pore water pressure sensor and the second camera;

[0030] Set the monitoring time period and divide it into n sub-time periods;

[0031] The reservoir image data contains the water pixel values ​​in each sub-time period;

[0032] The pixel value of the water body in each sub-time period is recorded as the pixel value;

[0033] The pixel value includes the water color value in each sub-time period and the water brightness value in each sub-time period;

[0034] The water color value in each sub-time period is recorded as the color value;

[0035] The brightness value of the water body in each sub-time period is recorded as the brightness value;

[0036] The reservoir silt data includes the silt thickness in each sub-time period obtained by the silt thickness sensor, the silt tightness in each sub-time period obtained by the pore water pressure sensor, and the granular mud in each sub-time period obtained by the second camera;

[0037] The silt thickness in each sub-time period is recorded as silt thickness;

[0038] The silt tightness in each sub-time period is recorded as silt tightness;

[0039] The granular mud in each sub-time period is recorded as granular mud;

[0040] Granular mud refers to the mud particles that float up from the surface of the silt;

[0041] Preprocessing the reservoir image data and the reservoir silt data, evaluating the preprocessed reservoir image data and the reservoir silt data, and obtaining a water turbidity evaluation value;

[0042] Filtering and denoising the reservoir image data and reservoir silt data helps improve data quality;

[0043] Comprehensive analysis is performed on the reservoir image data and reservoir silt data after filtering and noise reduction to obtain the water color anomaly coefficient and the bottom silt looseness coefficient.

[0044] Normalizing the water color anomaly coefficient and the bottom mud looseness coefficient helps improve the calculation efficiency;

[0045] The water turbidity assessment value is obtained by comprehensive analysis based on the normalized water color anomaly coefficient and bottom silt looseness coefficient;

[0046]

[0047] Among them, HP represents the water turbidity assessment value, SY represents the water color abnormality coefficient, and DS represents the bottom silt looseness coefficient.

[0048] The specific method of obtaining the water color anomaly coefficient is as follows:

[0049] Sum the n color values ​​to get the total color value;

[0050] The sum of the color values ​​is averaged to obtain the average color value;

[0051] Calculate the variance between the color value and the average color value to obtain the water color difference value;

[0052] The contour detection algorithm and contour motion tracking algorithm are used to calculate the reservoir image data to obtain the abnormal value of water turbidity in each sub-time period;

[0053] The difference in water turbidity within each sub-time period is recorded as the water turbidity difference value;

[0054] Contour detection algorithms, such as the Sobel algorithm, perform convolution of the reservoir image data in the horizontal and vertical directions to obtain a 3x3 matrix, then calculate the gradient value of each pixel in the reservoir image data through pixel value calculation, and finally determine the total contour of the water in the reservoir and the contour of the turbid water based on the gradient value;

[0055] Contour motion tracking algorithms, such as the optical flow method, infer the motion trajectory of the contour of turbid water by analyzing the changes in brightness values ​​over time in consecutive frames of reservoir image data based on the principle that brightness values ​​remain constant;

[0056] The water color anomaly coefficient is obtained by comprehensive analysis based on the water color difference value and the water turbidity anomaly value;

[0057]

[0058] Among them, SY represents the water color anomaly coefficient, YC represents the water color difference value, and TY represents the water turbidity anomaly value.

[0059] The specific method of obtaining the water turbidity difference value is as follows:

[0060] The contour detection algorithm and contour motion tracking algorithm are used to calculate the reservoir image data to obtain the total contour of the reservoir water body and the contour of the turbid water in the reservoir during the monitoring period;

[0061] The total outline of the reservoir water body during the monitoring period includes the total number of pixels and the total pixel coordinates;

[0062] The total number of pixels and the total pixel coordinates are calculated to obtain the total contour area of ​​the reservoir water body;

[0063] The outline of turbid water in the reservoir includes the number of dark pixel coordinates and the coordinates of dark pixel coordinates;

[0064] The contour area of ​​turbid water in the reservoir is obtained by calculating the number of dark pixel coordinates and the coordinates of the dark pixel coordinates;

[0065] The turbidity difference value of the water body is obtained by taking the quotient of the total contour area of ​​the reservoir water body and the contour area of ​​the turbid water in the reservoir.

[0066] The specific method of obtaining the loose coefficient of bottom silt is as follows:

[0067] The difference between the previous silt thickness and the next silt thickness is calculated, and so on, to obtain several silt thickness reduction values;

[0068] The silt thickness reduction values ​​are summed up to obtain the silt thickness difference value;

[0069] The difference between the previous silt tightness and the next silt tightness is calculated, and so on, to obtain several silt tightness reduction values;

[0070] The silt tightness difference value is obtained by summing up several silt tightness reduction values;

[0071] The contour detection algorithm and the contour motion tracking algorithm are used to calculate the granular mud to obtain the silt looseness value in each sub-time period;

[0072] The silt looseness value in each sub-time period is recorded as the silt looseness value;

[0073] The bottom silt looseness coefficient is obtained by comprehensive analysis based on the difference in silt thickness, silt tightness and silt looseness.

[0074]

[0075] Among them, DS represents the loose coefficient of bottom silt, HC represents the difference value of silt thickness, JC represents the difference value of silt tightness, and SC represents the loose value of silt.

[0076] The specific method of obtaining the silt looseness value is as follows:

[0077] The particle mud is subjected to contour detection algorithm and contour motion tracking algorithm calculation to obtain the number of particle mud contours in each sub-time period;

[0078] The number of contours of the granular mud in each sub-time period is counted to obtain the number of granular mud in each sub-time period;

[0079] The number of granular mud in each sub-time period is recorded as the number of granular mud;

[0080] The amount of granular mud is summed up to obtain the sludge looseness value.

[0081] Step 2: Make a judgment based on the water turbidity assessment value;

[0082] Step 3: If the water is judged to be turbid, the drone is used to release a curing agent and continue to obtain reservoir image data and reservoir silt data until the water is judged to be clear.

[0083] If the water is judged to be clear, proceed to step 4;

[0084] Set water turbidity threshold and compare it with water turbidity assessment value;

[0085] Obtain water turbidity experimental data through reservoir image data and reservoir silt data;

[0086] The water turbidity experimental data is saved and stored in the water turbidity database;

[0087] The water turbidity threshold calls the water turbidity database;

[0088] If the water turbidity assessment value is greater than or equal to the water turbidity threshold, the water is judged to be turbid;

[0089] If the water turbidity assessment value is less than the water turbidity threshold, the water is judged to be clear.

[0090] Step 4: Set up total phosphorus sensor, total nitrogen sensor and pH sensor to obtain water composition data;

[0091] The water composition data include total phosphorus during the monitoring period, total nitrogen during the monitoring period, and pH value during the monitoring period;

[0092] The total phosphorus during the monitoring period was recorded as total phosphorus;

[0093] The total nitrogen during the monitoring period was recorded as total nitrogen;

[0094] The pH value during the monitoring period was recorded as pH value;

[0095] When water bodies are eutrophic, total phosphorus, total nitrogen and pH values ​​will be higher than normal;

[0096] Preprocess the water body composition data, evaluate the preprocessed water body composition data, and obtain the water quality eutrophication evaluation value;

[0097] Filtering and denoising water composition data helps improve data quality;

[0098] Comprehensively analyze the water composition data after filtering and noise reduction to obtain the water pollution coefficient;

[0099] Normalizing the water pollution coefficient and the bottom mud loose coefficient helps improve the calculation efficiency;

[0100] Comprehensively analyze the normalized water pollution coefficient and bottom mud loose coefficient to obtain the water eutrophication assessment value;

[0101]

[0102] Among them, FY represents the water eutrophication assessment value, DS represents the bottom silt looseness coefficient, and SW represents the water pollution coefficient.

[0103] The specific method of obtaining the water pollution coefficient is as follows:

[0104] Set standard values ​​for total phosphorus;

[0105] Obtain total phosphorus experimental data through water composition data;

[0106] Save the total phosphorus experimental data and store them in the total phosphorus database;

[0107] The total phosphorus standard value is used to call the total phosphorus database;

[0108] Set the standard value of total nitrogen;

[0109] Obtain total nitrogen experimental data through water composition data;

[0110] Save the total nitrogen experimental data and store them in the total nitrogen database;

[0111] The total nitrogen standard value calls the total nitrogen database;

[0112] Set the pH standard value;

[0113] Obtain pH value experimental data through water composition data;

[0114] Save the pH value experimental data and store them in the pH value database;

[0115] The pH value standard value calls the pH value database;

[0116] Comprehensively analyze total phosphorus, total nitrogen, pH value, standard value of total phosphorus, standard value of total nitrogen and standard value of pH value to obtain water pollution coefficient;

[0117]

[0118] Among them, SW represents the water pollution coefficient, ZL represents total phosphorus, LB represents the standard value of total phosphorus, ZD represents total nitrogen, DB represents the standard value of total nitrogen, PJ represents pH value, and JB represents the standard value of pH value.

[0119] Step 5: Determine the water eutrophication assessment value;

[0120] Set water quality eutrophication thresholds and compare them with water quality eutrophication assessment values;

[0121] Obtain water eutrophication experimental data through water composition data;

[0122] Save the water eutrophication experimental data and store them in the water eutrophication database;

[0123] The water quality eutrophication threshold is used to call the water quality eutrophication database;

[0124] If the water quality eutrophication assessment value is greater than or equal to the water quality eutrophication threshold, the water quality is judged to be eutrophic;

[0125] If the water quality eutrophication assessment value is less than the water quality eutrophication threshold, the water quality is judged to be normal.

[0126] Step 6: If the water quality is judged to be eutrophic, the drone will be used to drop aquatic plant seeds until the water quality is judged to be normal;

[0127] If the water quality is judged to be normal, continue monitoring.

[0128] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for improving water quality in a water supply reservoir, characterized in that: The specific steps include: Step 1: Acquire reservoir image data and reservoir silt data, pre-process the reservoir image data and reservoir silt data, evaluate the pre-processed reservoir image data and reservoir silt data, and obtain a water turbidity evaluation value; Step 2: Make a judgment based on the water turbidity assessment value; Step 3: If the water is judged to be turbid, the drone is used to release a curing agent and continue to obtain reservoir image data and reservoir silt data until the water is judged to be clear. If the water is judged to be clear, proceed to step 4; Step 4: Obtain water body composition data, pre-process the water body composition data, evaluate the pre-processed water body composition data, and obtain the water quality eutrophication assessment value; Step 5: Determine the water eutrophication assessment value; Step 6: If the water quality is judged to be eutrophic, the drone will be used to drop aquatic plant seeds until the water quality is judged to be normal; If the water quality is judged to be normal, continue monitoring.

2. A method for improving water quality in a water supply reservoir according to claim 1, characterized in that: In step 1, a monitoring time period is set and divided into n sub-time periods; The reservoir image data contains the water pixel values ​​in each sub-time period; The pixel value of the water body in each sub-time period is recorded as the pixel value; The pixel value includes the water color value in each sub-time period and the water brightness value in each sub-time period; The water color value in each sub-time period is recorded as the color value; The brightness value of the water body in each sub-time period is recorded as the brightness value; The reservoir silt data include silt thickness in each sub-time period, silt compactness in each sub-time period, and granular mud in each sub-time period; The silt thickness in each sub-time period is recorded as silt thickness; The silt tightness in each sub-time period is recorded as silt tightness; The granular mud in each sub-time period is recorded as granular mud; Perform filtering and noise reduction on reservoir image data and reservoir silt data; Comprehensive analysis is performed on the reservoir image data and reservoir silt data after filtering and noise reduction to obtain the water color anomaly coefficient and the bottom silt looseness coefficient. Normalize the water color anomaly coefficient and the bottom mud looseness coefficient; The water turbidity assessment value is obtained by comprehensive analysis based on the normalized water color anomaly coefficient and bottom silt looseness coefficient; Among them, HP represents the water turbidity assessment value, SY represents the water color abnormality coefficient, and DS represents the bottom silt looseness coefficient.

3. A method for improving water quality in a water supply reservoir according to claim 2, characterized in that: The specific method for obtaining the water color anomaly coefficient is as follows: Sum the n color values ​​to get the total color value; The sum of the color values ​​is averaged to obtain the average color value; Calculate the variance between the color value and the average color value to obtain the water color difference value; The contour detection algorithm and contour motion tracking algorithm are used to calculate the reservoir image data to obtain the abnormal value of water turbidity in each sub-time period; The difference in water turbidity within each sub-time period is recorded as the water turbidity difference value; The water color anomaly coefficient is obtained by comprehensive analysis of the water color difference value and the water turbidity anomaly value.

4. A method for improving water quality in a water supply reservoir according to claim 3, characterized in that: The specific method for obtaining the water turbidity difference value is as follows: The contour detection algorithm and contour motion tracking algorithm are used to calculate the reservoir image data to obtain the total contour of the reservoir water body and the contour of the turbid water in the reservoir during the monitoring period; The total outline of the reservoir water body during the monitoring period includes the total number of pixels and the total pixel coordinates; The total number of pixels and the total pixel coordinates are calculated to obtain the total contour area of ​​the reservoir water body; The outline of turbid water in the reservoir includes the number of dark pixel coordinates and the coordinates of dark pixel coordinates; The contour area of ​​turbid water in the reservoir is obtained by calculating the number of dark pixel coordinates and the coordinates of the dark pixel coordinates; The turbidity difference value of the water body is obtained by taking the quotient of the total contour area of ​​the reservoir water body and the contour area of ​​the turbid water in the reservoir.

5. A method for improving water quality in a water supply reservoir according to claim 2, characterized in that: The specific method for obtaining the bottom sludge looseness coefficient is as follows: The difference between the previous silt thickness and the next silt thickness is calculated, and so on, to obtain several silt thickness reduction values; The silt thickness reduction values ​​are summed up to obtain the silt thickness difference value; The difference between the previous silt tightness and the next silt tightness is calculated, and so on, to obtain several silt tightness reduction values; The silt tightness difference value is obtained by summing up several silt tightness reduction values; The contour detection algorithm and the contour motion tracking algorithm are used to calculate the granular mud to obtain the silt looseness value in each sub-time period; The silt looseness value in each sub-time period is recorded as the silt looseness value; The bottom silt looseness coefficient is obtained by comprehensive analysis based on the difference in silt thickness, silt tightness and silt looseness.

6. A method for improving water quality in a water supply reservoir according to claim 5, characterized in that: The specific method for obtaining the sludge looseness value is as follows: The particle mud is subjected to contour detection algorithm and contour motion tracking algorithm calculation to obtain the number of particle mud contours in each sub-time period; The number of contours of the granular mud in each sub-time period is counted to obtain the number of granular mud in each sub-time period; The number of granular mud in each sub-time period is recorded as the number of granular mud; The amount of granular mud is summed up to obtain the sludge looseness value.

7. A method for improving water quality in a water supply reservoir according to claim 1, characterized in that: In step 2, a water turbidity threshold is set and compared with the water turbidity assessment value; If the water turbidity assessment value is greater than or equal to the water turbidity threshold, the water is judged to be turbid; If the water turbidity assessment value is less than the water turbidity threshold, the water is judged to be clear.

8. A method for improving water quality in a water supply reservoir according to claim 1, characterized in that: In step 4, the water composition data includes total phosphorus within the monitoring period, total nitrogen within the monitoring period, and pH value within the monitoring period; The total phosphorus during the monitoring period was recorded as total phosphorus; The total nitrogen during the monitoring period was recorded as total nitrogen; The pH value during the monitoring period was recorded as pH value; Filter and reduce noise on water composition data; Comprehensively analyze the water composition data after filtering and noise reduction to obtain the water pollution coefficient; Normalize the water pollution coefficient and the bottom mud looseness coefficient; Comprehensively analyze the normalized water pollution coefficient and bottom mud loose coefficient to obtain the water eutrophication assessment value; Among them, FY represents the water eutrophication assessment value, DS represents the bottom silt looseness coefficient, and SW represents the water pollution coefficient.

9. A method for improving water quality in a water supply reservoir according to claim 8, characterized in that: The specific method for obtaining the water pollution coefficient is as follows: Set standard values ​​for total phosphorus; Set the standard value of total nitrogen; Set the pH standard value; A comprehensive analysis is conducted on total phosphorus, total nitrogen, pH value, standard value of total phosphorus, standard value of total nitrogen and standard value of pH value to obtain the water pollution coefficient.

10. A method for improving water quality in a water supply reservoir according to claim 1, characterized in that: In step five, the water quality eutrophication threshold is set and compared with the water quality eutrophication assessment value; If the water quality eutrophication assessment value is greater than or equal to the water quality eutrophication threshold, the water quality is judged to be eutrophic; If the water quality eutrophication assessment value is less than the water quality eutrophication threshold, the water quality is judged to be normal.

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