Water transparency environment-ecology coupling analysis method

Abstract

The invention provides a water transparency environment-ecological coupling analysis method. The method comprises the following steps: acquiring real-time turbidity data of a target water body; constructing a water turbidity and suspended matter concentration relation model based on the turbidity data, and calculating the suspended matter concentration of the target water body; analyzing the concentration of the suspended solids according to the lab rules of sedimentation and resuspension of the suspended solids to obtain a concentration change result of the suspended solids; fitting a suspended matter concentration change result to obtain a data relation model among the water transparency, the turbidity and the chlorophyll concentration, and obtaining an initial water transparency value; analyzing based on the water transparency value to obtain the algae concentration under the corresponding environmental characteristics; and realizing real-time analysis and effective prediction of the shading effect of the water body by dynamically adjusting the transparency value of the water body based on the influence relationship among the turbidity, the algae growth and the transparency of the water body. The water quality monitoring accuracy and efficiency can be improved, and powerful technical support can be provided for water environment treatment.

Description

Technical Field

[0001] This application belongs to the field of ecological environment monitoring and data analysis technology, and relates to a method for analyzing the environmental-ecological coupling of water body transparency. Background Technology

[0002] The various water bodies in the Taihu Lake basin are characterized by their large size and shallowness, making them highly susceptible to wind influence. Wind and waves play a crucial role in the evolution of the Taihu Lake's aquatic ecosystem. Sediment resuspension and pollutant migration associated with hydrodynamic processes alter water color and transparency, affecting light transmittance, photosynthesis and primary productivity of aquatic plants, and consequently significantly impacting the aquatic ecosystem and overall sensory experience.

[0003] Despite the abundant research findings on the relationship between hydrodynamics and the aquatic environment of Taihu Lake, the impact of turbidity on aquatic sensory perception and the ecosystem still requires further investigation. The coupled influence of phytoplankton ecology and sediment on turbidity needs further clarification. Currently, dynamic turbidity simulations of shallow lakes like Taihu Lake largely rely on empirical statistics or simplified parameters, making it difficult to accurately characterize the coupling relationship between water flow, turbidity, and illumination, leading to biases in the simulation of water shading effects. Summary of the Invention

[0004] This application provides a water transparency environment-ecology coupled analysis method to solve the problem that the mechanism by which changes in turbidity caused by wind and waves in large shallow lakes affect the water body's shading effect and ecological characteristics such as phytoplankton is unclear.

[0005] In a first aspect, this application provides a water body transparency environment-ecological coupling analysis method, the method comprising: acquiring real-time turbidity data of a target water body; constructing a model of the relationship between water body turbidity and suspended solids concentration based on the turbidity data, and calculating the suspended solids concentration of the target water body, thereby solving the technical problem of complex gravimetric measurement process for suspended solids concentration;

[0006] Based on the laboratory laws governing the settling and resuspension of suspended solids, the concentration of suspended solids was analyzed to obtain data on the variation law of suspended solids concentration, thus solving the technical problem that the parameters of the sediment suspension and settling mechanism are complex and difficult to measure.

[0007] Using sediment turbidity factor and initial chlorophyll concentration factor as dependent variables and water transparency as independent variable, a data relationship model between water transparency and turbidity and chlorophyll concentration was obtained, and the initial water transparency value was acquired.

[0008] Based on the water transparency value, the algae growth formula is developed by combining the water surface light element and water nutrient element, with the light and nutrient salts affecting algae growth as the core, and the algae concentration under the corresponding environmental characteristics is solved.

[0009] Under the dynamic ecological (algae) evolution law, based on the influence relationship between turbidity, algae and water transparency, the water transparency value is dynamically adjusted and solved iteratively to achieve real-time analysis and effective prediction of the water shading effect.

[0010] In this application, by comprehensively considering the influence of suspended particulate matter and phytoplankton on light scattering and absorption in the water, a high-precision water shading model was established and an analytical function was constructed. This enabled refined simulation calculation of chlorophyll concentration in the water area. Furthermore, based on changes in algae concentration, coupled calculation of water transparency was achieved, effectively improving the accuracy of water transparency calculation and prediction, and providing a scientific basis for water quality management and ecological environmental protection.

[0011] In one implementation of the first aspect, constructing a model of the relationship between water turbidity and suspended solids concentration based on the turbidity data and calculating the suspended solids concentration of the target water body includes the following steps: acquiring the turbidity value of the target water body at the same time and simultaneously measuring the suspended solids concentration in the target water body; fitting the turbidity value and the suspended solids concentration to obtain a model of the relationship between water turbidity and suspended solids concentration; and calculating the suspended solids concentration of the target water body based on the model of the relationship between water turbidity and suspended solids concentration.

[0012] In one implementation of the first aspect, the sedimentation law includes the static sedimentation and dynamic resuspension laws of suspended solids. Analyzing the suspended solids concentration based on the sedimentation law to obtain the suspended solids change results includes the following steps: uniformly stirring the water body with bottom sediment; after the water body is fully mixed, inserting a dense screen to achieve water stillness; measuring the turbidity of the surface water body at different time periods; and fitting the sediment (turbidity) sedimentation law by observing the turbidity change (decrease) over time; and based on the sedimentation law, measuring the surface turbidity values ​​at different water flow velocities when the water body is in a stable state, and combining this with the static sedimentation law to fit the sediment (turbidity) resuspension law.

[0013] During the calculation, the target water body is vertically stratified to obtain the initial suspended solids concentration of each stratum; based on the initial suspended solids concentration, the contribution rate of the upper and lower water bodies to the current water body is obtained; combining the static sedimentation and dynamic resuspension laws of suspended solids, a dynamic model of the change of suspended solids concentration over time is constructed; based on the static sedimentation and dynamic resuspension laws of suspended solids, an NTU change laboratory empirical formula is constructed, and the data on the change law of suspended solids concentration are calculated.

[0014] In one implementation of the first aspect, the formula for the dynamic model of the suspended solids concentration changing over time is:

[0015]

[0016] Among them, settling n-1This represents the rate of change in the concentration of suspended matter in the (n-1)th layer specified by the user, in g·m³. -3 ·d -1 ;settling n This represents the rate of change in the concentration of suspended matter in the nth layer, as specified by the user, in g·m³. -3 ·d -1 ;dz n dz represents the thickness of the nth layer, in meters (m). n-1 This represents the thickness of the (n-1)th layer, in meters (m).

[0017] The NTU change laboratory empirical formula is as follows:

[0018] max((ntu a ·NTU+ntu b )·exp(v·ntu m ),0.001)

[0019] Among them, ntu a ntu b ntu m All represent laboratory analytical parameters; v represents the water flow velocity, and max limits the minimum decay rate of ntu.

[0020] In one implementation of the first aspect, the following steps are taken to obtain a data relationship model between water transparency and turbidity and chlorophyll concentration, using sediment turbidity factor and initial chlorophyll concentration factor as dependent variables and water transparency as independent variable: collecting water transparency, chlorophyll concentration and turbidity data from multiple points in different regions; and fitting the water transparency, chlorophyll concentration and turbidity data through regression to obtain a data relationship model between water transparency and turbidity and chlorophyll concentration.

[0021] In one implementation of the first aspect, the formula for the water transparency value is:

[0022] SD=1 / (a+b*NTU-c*NTU*NTU+d*CHL-e*CHL*CHL)

[0023] Where SD is the transparency value; a represents the constant term; b represents the turbidity coefficient; c represents the temperature square term coefficient; d represents the chlorophyll concentration coefficient; e represents the chlorophyll a concentration square term coefficient; CHL represents the chlorophyll a concentration; and NTU is the turbidity value per unit.

[0024] In one implementation of the first aspect, the process of analyzing the initial water transparency value to obtain the influence relationship between algal growth and water transparency includes the following steps: using the Lambert-Beer Law, obtaining the transmittance of each water layer based on the water transparency value; calculating the algal growth influencing factor K for each water layer based on the transmittance of each water layer; obtaining the algal growth rate based on the algal growth influencing factor K for each water layer, combined with other environmental parameters; and inputting the algal growth rate and the water transparency value into a dynamic model to analyze and obtain the influence relationship between algal growth and transparency.

[0025] In one implementation of the first aspect, the formula used for calculating Beer-Lambert's law is as follows:

[0026] LAMBERT Beer1 =light*e eta*∑tcp

[0027] Where light represents the intensity of the incident light; e eta Indicates the absorption coefficient; tcp represents the thickness of the water body;

[0028] The formula for calculating the light transmittance of each water layer is as follows:

[0029]

[0030] Among them, I n I represents the amount of solar radiation used for primary production on the current nth layer; n-1 dz represents the solar radiation at the current (n-1)th layer. n Indicates layer thickness; η n Indicates the extinction coefficient;

[0031] The formula for calculating the influencing factor K on algal growth is as follows:

[0032] K = K max *exp(1.6 / SD*dz n )

[0033] Where K represents the current algal growth rate; K max This indicates the maximum growth rate of algae, expressed in mg / L / day.

[0034] The formula for calculating the algal growth rate is as follows:

[0035]

[0036] Where K represents the current algal growth rate, which can be further adjusted based on the nitrogen and phosphorus concentrations in the water; d CHLdt represents the time component of chlorophyll change; CHL represents chlorophyll concentration; dt represents the time component; t represents time.

[0037] Secondly, this application provides a water transparency environment-ecology coupled analysis system, the system comprising: a data acquisition module for acquiring real-time turbidity data of a target water body; a first model construction module for constructing a model of the relationship between water turbidity and suspended solids concentration based on the turbidity data, and calculating the suspended solids concentration of the target water body; an analysis module for analyzing the suspended solids concentration according to the sedimentation law of suspended solids, and obtaining data on the variation law of suspended solids concentration; a second model construction module for fitting a model of the data relationship between water transparency and turbidity and chlorophyll concentration with sediment turbidity factor and initial chlorophyll concentration factor as dependent variables and water transparency as independent variables, and obtaining an initial water transparency value; an analysis module for analyzing the water transparency value to obtain the influence relationship between algal growth and water transparency; and a dynamic adjustment module for realizing real-time monitoring and prediction of the water body shading effect by dynamically adjusting the water transparency value.

[0038] Thirdly, this application provides an electronic device, which includes: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory, so that the electronic device performs the above-described water transparency environment-ecology coupling analysis method.

[0039] As described above, the water transparency environment-ecology coupled analysis method of this application has the following beneficial effects:

[0040] The environmental-ecological coupled analytical method for water transparency provided in this application comprehensively considers the influence of suspended particulate matter and phytoplankton on light scattering and absorption in water bodies, while also analyzing the impact of turbidity on the sensory perception of water bodies and the ecosystem. This method enables refined simulation calculations of chlorophyll concentration in water areas, providing a scientific basis for water quality management and ecological environmental protection. This application not only improves the accuracy and efficiency of water quality monitoring but also provides strong technical support for water environment governance. Attached Figure Description

[0041] Figure 1 The diagram shows a hardware application scenario of the water transparency environment-ecology coupling analysis method described in this application embodiment.

[0042] Figure 2 The diagram shows the overall flow chart of the water transparency environment-ecology coupling analysis method described in the embodiments of this application.

[0043] Figure 3The diagram shown is a schematic diagram of multi-source data processing for the water transparency environment-ecology coupled analysis method described in the embodiments of this application.

[0044] Figure 4 The diagram shown is a schematic flowchart of step S2 of the water transparency environment-ecology coupling analysis method described in this application embodiment.

[0045] Figure 5 The diagram shows the relationship between annual transparency and suspended solids concentration at various monitoring points of the target water body described in this application embodiment.

[0046] Figure 6 The diagram shown is a flowchart of step S3 of the water transparency environment-ecology coupling analysis method described in the embodiments of this application.

[0047] Figure 7 The diagram shown is a schematic representation of the suspended solids settling process described in an embodiment of this application.

[0048] Figure 8 The diagram shown is a flowchart of step S4 of the water transparency environment-ecology coupling analysis method described in this application embodiment.

[0049] Figure 9 The diagram shown is a schematic representation of the optical attenuation function described in an embodiment of this application.

[0050] Figure 10 The diagram shown is a structural schematic of the water transparency environment-ecology coupled analysis system described in the embodiments of this application.

[0051] Figure 11 The diagram shown is a structural schematic of the electronic device described in an embodiment of this application.

[0052] Component designation explanation

[0053] 1. Water Transparency Environmental-Ecological Coupled Analysis Device

[0054] 11. Data Acquisition Module

[0055] 12 Data Processing Module

[0056] 13. Light-blocking effect analysis module

[0057] 14 Data Interaction Module

[0058] 15 Self-calibration mechanism

[0059] 10. Water Transparency Environmental-Ecological Coupled Analysis System

[0060] 101 Data Acquisition Module

[0061] 102 First Model Construction Module

[0062] 103 Analysis Module

[0063] 104 Second Model Construction Module

[0064] 105 Parsing Module

[0065] 106 Dynamic Adjustment Module

[0066] 110 Electronic Equipment

[0067] 111 Memory

[0068] 112 processor

[0069] 113 Monitor

[0070] Steps S1 to S6 Detailed Implementation

[0071] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0072] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0073] The following embodiments of this application provide a water transparency environment-ecological coupling analysis method, including but not limited to hardware application scenarios such as water quality monitoring buoys and intelligent spectral analyzers. The following description will take the hardware application scenario of the water transparency environment-ecological coupling analysis device as an example.

[0074] like Figure 1As shown, this embodiment provides a hardware application scenario for a water transparency environment-ecological coupling analysis device, specifically including: a multi-source sensing module 11, which integrates a turbidity sensor and a chlorophyll fluorescence probe, with a sampling frequency adjustable from 0.1-10Hz; used to collect relevant data such as sediment content, real-time turbidity value, transparency, and chlorophyll a concentration in the target water body. A data processing module 12 is used to preprocess and fuse the collected data to extract key information, namely: it incorporates an adaptive weighted fusion algorithm to correct the spatiotemporal differences of each sensor in real time and establish a parameter correlation matrix. The shading effect analysis module 13 includes: a dynamic calculation model for the light attenuation coefficient, a vertical light field reconstruction engine, and a shading contribution rate decomposition module (distinguishing between phytoplankton and inorganic suspended matter contributions). It is used to construct a water body shading effect analysis model based on processed data, specifically: analyzing the impact of turbidity on water transparency; quantitatively assessing water transparency by constructing a numerical relationship between transparency and turbidity; evaluating the impact of transparency changes on algal growth; considering transparency as a key limiting factor for algal growth, revealing the interaction mechanism between transparency and algal growth by simulating algal growth under different transparency conditions; and considering the feedback effect of algal growth on transparency, achieving a refined simulation of the mutual influence between algal growth and transparency by dynamically adjusting transparency parameters. The data interaction module 14 supports 4G / BeiDou dual-mode transmission and has edge computing and cloud-based collaborative analysis capabilities; the self-calibration mechanism 15 is equipped with a standard color chart and cleaning brush to achieve automatic weekly calibration.

[0075] In this embodiment, a three-level processing architecture of multi-source data spatiotemporal registration → parameter coupling analysis → shading contribution decoupling is used to achieve centimeter-level vertical resolution analysis of water shading effect, which is applicable to typical scenarios such as eutrophic lakes and nearshore sea areas.

[0076] It should be noted that the device provided in this application is not only applicable to the large and shallow water characteristics of Taihu Lake, but also applicable to large shallow lakes that are "large and shallow" and highly susceptible to the influence of wind.

[0077] The technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0078] In this application, by comprehensively considering the influence of suspended particulate matter and phytoplankton on light scattering and absorption in the water, a high-precision water shading model was established and an analytical function was constructed. This enabled refined simulation calculation of chlorophyll concentration in the water area. Furthermore, based on changes in algae concentration, coupled calculation of water transparency was achieved, effectively improving the accuracy of water transparency calculation and prediction, and providing a scientific basis for water quality management and ecological environmental protection.

[0079] Please see Figure 2 and Figure 3 The figures shown are a schematic diagram of the overall process of the water transparency environment-ecological coupling analysis method described in the embodiments of this application and a schematic diagram of the multi-source data processing of the water transparency environment-ecological coupling analysis method described in the embodiments of this application.

[0080] like Figure 2 As shown, this embodiment provides a method for analyzing water transparency using an environmental-ecological coupling approach. The method includes the following steps:

[0081] S1, acquire real-time turbidity data of the target water body.

[0082] Turbidity refers to the degree of cloudiness in water caused by the presence of suspended particulate matter (such as silt, clay, organic matter, plankton, microorganisms, and inorganic matter). It reflects the extent to which light is scattered or absorbed by these particles when passing through a water sample.

[0083] In this embodiment, the multi-source water environment parameters include: turbidity, transparency, suspended solids concentration, water depth, chlorophyll a concentration, phytoplankton / algae community, and other data.

[0084] Turbidity sensors, chlorophyll fluorescence probes, and other equipment are installed at monitoring points in the target water body area, and real-time data is collected through these devices.

[0085] Specifically, turbidity sensors monitor the ability of suspended particulate matter (such as silt, clay, inorganic particles, phytoplankton, and organic debris) in water to scatter light. The higher the turbidity value, the more turbid the water.

[0086] Chlorophyll a concentration is monitored using a chlorophyll fluorescence probe. This directly reflects the biomass abundance of phytoplankton (e.g., algae) in water bodies and is a key indicator for assessing primary productivity and eutrophication status.

[0087] Therefore, by fusing and analyzing the data of the above-mentioned multi-source water environment parameters, this application can gain a deeper understanding of the optical characteristics, biogeochemical cycle processes, water quality status and its driving factors of water bodies, providing strong support for scientific research, environmental monitoring, fisheries management and water resource protection.

[0088] S2, Based on the turbidity data, construct a model of the relationship between water turbidity and suspended solids concentration, and calculate the suspended solids concentration of the target water body to solve the complex technical problem of the gravimetric method for measuring suspended solids concentration.

[0089] Please see Figure 4 and Figure 5 The diagrams shown are, respectively, a flowchart of step S2 of the water transparency environment-ecology coupled analysis method described in this application embodiment and a schematic diagram of the relationship between annual transparency and suspended solids concentration at each monitoring point of the target water body described in this application embodiment. Figure 4 and Figure 5 As shown, step S2 includes the following steps:

[0090] S21, obtain the turbidity value of the target water body continuously within the same time interval, and measure the concentration of suspended solids in the target water body at the same time.

[0091] S22, Fit the turbidity value and the suspended solids concentration to obtain a model showing the relationship between water turbidity and suspended solids concentration;

[0092] S23, Calculate the suspended solids concentration of the target water body based on the model relating water turbidity and suspended solids concentration.

[0093] Turbidity is closely related to the physicochemical properties of lake water, the composition and content of suspended solids, and meteorological conditions. It is influenced by a variety of environmental factors, with the composition and content of suspended solids being the main factors affecting turbidity. Lake water turbidity represents the ability of suspended particulate matter in a water body to scatter and absorb light. Its size varies depending on the differences in the absorption and scattering of incident light by the lake water and its suspended solids and phytoplankton. Therefore, the composition and content of suspended solids greatly affect the changes in lake water turbidity.

[0094] In large, shallow lakes (such as Taihu Lake), strong winds and waves easily suspend bottom sediments. As wind force increases, the waves become larger, further disturbing the sediments and causing them to resuspend, leading to an increase in suspended matter in the water. This also increases the reflectivity of the lake surface, reducing the intensity of light penetrating the lake and increasing turbidity.

[0095] In this embodiment, the first step is to conduct detailed sampling and analysis of suspended particulate matter in the water. This suspended particulate matter may include silt, organic matter, inorganic salts, and other tiny particles. Through water quality sampling and analysis, key information such as the type, concentration, and particle size distribution of suspended particulate matter can be obtained.

[0096] Based on the state analysis of suspended particulate matter, the specific value of turbidity can be calculated. Turbidity is an important indicator for measuring the content of suspended particulate matter in water bodies, and it is usually expressed as the intensity of scattered light.

[0097] In this application, information such as the concentration and particle size distribution of suspended particulate matter can be converted into turbidity values ​​through a specific algorithm or model.

[0098] Specifically, Taihu Lake is preferred as the target water body for explanation.

[0099] Please see Figure 5The turbidity of Taihu Lake was monitored throughout the year using an NTU online monitoring device. A model showing the relationship between turbidity and suspended solids concentration was obtained by fitting the turbidity values ​​with the collected suspended solids concentration values. The fitted function shows that turbidity increases with increasing suspended solids content, exhibiting a clear positive proportional relationship.

[0100] Furthermore, after determining the ratio between the two through laboratory experiments, the turbidity value can be read through real-time online monitoring equipment, the suspended solids concentration can be calculated, and the suspended solids change process can be calculated based on the suspended solids settling law.

[0101] S3, Analyze the concentration of suspended solids based on laboratory laws governing sedimentation and resuspension to obtain data on the variation patterns of suspended solids concentration. The sedimentation patterns include: static sedimentation and dynamic resuspension patterns of suspended solids.

[0102] Please see Figure 6 and Figure 7 The diagrams shown are, respectively, a flowchart of step S3 of the water transparency environment-ecology coupled analysis method described in this application embodiment and a schematic diagram of the suspended solids sedimentation process described in this application embodiment. Figure 6 and Figure 7 As shown, step S3 includes the following steps:

[0103] S31, Vertically stratify the target water body and obtain the initial suspended solids concentration of each stratum;

[0104] S32, based on the initial suspended solids concentration, obtain the contribution rates of the upper and lower water layers to the current water layer;

[0105] S33, combining the static sedimentation and dynamic resuspension laws of suspended solids, constructs a dynamic model of the change of suspended solids concentration over time;

[0106] S34. Based on the static settling and dynamic resuspension laws of the suspended matter, an NTU change laboratory empirical formula is constructed, and the data on the change law of suspended matter concentration are calculated to solve the technical problem that the parameters of the sediment suspension and settling mechanism are complex and difficult to measure.

[0107] In this embodiment, the water containing bottom sediment is first uniformly stirred. After the water is fully mixed, a dense screen is inserted to achieve water stillness. Then, the turbidity of the surface water is measured at different time periods. By observing the change (decrease) in turbidity over time, the sediment (turbidity) settling pattern is fitted. Based on the settling pattern, the surface turbidity values ​​at stable states are measured under different water flow velocities. Combined with the static sediment settling pattern, the sediment (turbidity) resuspension pattern is fitted.

[0108] Specifically, during the calculation, firstly, the target water body is divided into regions, and then each water body region is further divided into vertical layers (e.g., Figure 7The system acquires the initial suspended solids concentration for each layer using a data acquisition device. Then, in the multi-layer water system, it calculates the contribution of the upper and lower water layers to the suspended solids concentration of the current layer, ignoring the upper layer input if the current layer is the top layer.

[0109] Next, the sedimentation process is expressed by differential equations, and a dynamic model of the change of suspended solids concentration over time is established by combining static sedimentation rate and dynamic resuspension.

[0110] Settling describes the vertical transport of a state variable to the bottom. The settling process requires specifying an expression to describe the change in concentration [mg / l / d] from the current cell to the cell below. To correctly express the property of state variables being transported downwards in a water body, the sign of the settling process is defined as "negative" in the differential equation. In multi-layered systems, the contribution of the upper water layer to this state variable (if not the top layer) also needs to be considered. When the settling process is included,

[0111] The formula for the dynamic model of the change of suspended solids concentration over time is:

[0112]

[0113] Among them, settling n-1 This represents the rate of change of concentration of the (n-1)th layer of suspended matter (state variable) specified by the user, in g·m³. -3 ·d -1 This concentration change is caused by the settling process from layer (n-1) to layer n, and is usually a function of the concentration in layer (n-1). n This represents the rate of change of concentration of the nth layer of suspended matter (state variable) specified by the user, in g·m³. -3 ·d -1 This concentration change is caused by the sedimentation process from layer n to layer (n+1), and is usually a function of the concentration in layer n; dz n dz represents the thickness of the nth layer, in meters (m). n-1 This represents the thickness of the (n-1)th layer, in meters (m).

[0114] Then, based on the sedimentation law of suspended matter, the change process of suspended matter is calculated. The sedimentation law includes the static sedimentation and dynamic resuspension law of suspended matter, which is determined through laboratory experiments. The concentration of suspended matter and its change process are used as state variables for subsequent environmental-ecological coupling analysis of water transparency.

[0115] When determining the relationship between suspended solids and turbidity, it is also necessary to fully consider the static settling and dynamic resuspension patterns of suspended solids. Static settling refers to the process by which suspended particles settle to the bottom of the water body under the action of gravity; while dynamic resuspension refers to the process by which settled particles are resuspended in the water body due to factors such as water flow disturbance and wind and waves.

[0116] Based on static sedimentation and dynamic resuspension experimental data, an empirical formula for the relationship between turbidity (NTU) and suspended solids concentration (i.e., the laboratory empirical formula for NTU change) is constructed and embedded in the differential equation; the model is used to simulate the vertical distribution and time change process of suspended solids concentration, and the sedimentation flux and concentration change results are output.

[0117] The NTU change laboratory empirical formula is as follows:

[0118] max((ntu a ·NTU+ntu b )·exp(v·ntu m ),0.001)

[0119] Among them, ntu a ntu b ntu m All represent laboratory analytical parameters; v represents the water flow velocity, and max limits the minimum decay rate of ntu.

[0120] For example, by conducting sedimentation and suspension experiments at three locations—inside and outside the dikes, and in the lake basin—a typical lake in the Yangtze River Delta, the following empirical formula can be obtained:

[0121] max(ntu a ·NTU 2 +ntu b *NTU+ntu m *ntu n NTU ,0.001)

[0122] The following are the analytical parameters for laboratories in different regions:

[0123] Table 1 Laboratory analytical parameters

[0124] area <![CDATA[ntu a ]]> <![CDATA[ntu b ]]> <![CDATA[ntu m ]]> <![CDATA[ntu n ]]> Lakes 0.0201 -0.01699 0 0 outside the dike 0.1696 -3.887 0 0 Inside the dike 0 0 0.05636 1.834

[0125] S4 involves fitting a data model of the relationship between water transparency and turbidity / chlorophyll concentration using sediment turbidity factor and initial chlorophyll concentration factor as dependent variables and water transparency as independent variable, and obtaining the initial water transparency value. S4 includes the following steps:

[0126] S41, collect water quality transparency, chlorophyll concentration and turbidity at multiple points in different areas;

[0127] S42, by fitting the water transparency, chlorophyll concentration, and turbidity using regression, a data relationship model between water transparency and turbidity, and between chlorophyll concentration, is obtained.

[0128] In this embodiment, water transparency, chlorophyll concentration, and turbidity were collected from multiple locations in different areas. A regression analysis was used to construct the numerical relationship between transparency and NTU (Natural Temperature Unit) and chlorophyll content. Each parameter was determined based on water quality sampling and analysis for different water bodies. (This formula still uses Taihu Lake as the target water body for calculation.)

[0129] The formula for the water transparency value is:

[0130] SD=1 / (a+b*NTU-c*NTU*NTU+d*CHL-e*CHL*CHL)

[0131] Where SD represents transparency value; a represents constant term; b represents turbidity coefficient; c represents temperature square term coefficient; d represents chlorophyll concentration coefficient; e represents chlorophyll a concentration square term coefficient; CHL represents chlorophyll a concentration; and NTU is turbidity value / unit.

[0132] Specifically, in the target water body, water quality transparency (Seyrsch disk method), turbidity (NTU instrument spectrophotometry), and CHL (chlorophyll fluorescence method) are collected at several points inside and outside the dike. Then, the transparency is fitted by the latter two according to the aforementioned relationship.

[0133] It should be noted that this formula is applicable to ordinary freshwater environments with NTU < 30 and CHL < 0.03; extreme parameter values ​​may lead to distorted calculation results; in practical applications, calibration with on-site measured data is required.

[0134] For example, suppose the following parameter values ​​are measured in a certain body of water: a = 0.005104, b = 0.0008458, c = 0.00000663, d = 50.53, e = 393400:

[0135] SD=1 / (0.005104+0.0008458*NTU-0.00000663*NTU*NTU+50.53*CHL

[0136] -393400*CHL*CHL)

[0137] When turbidity (NTU) = 15 NTU and CHL = 0.002 mg / L,

[0138] Substitute the values ​​into the formula SD = 1 / (a + b*NTU - c*NTU*NTU + d*CHL - e*CHL*CHL) for calculation. That is:

[0139] SD=1 / (0.005104+0.0008458×15-0.00000663×15 2 +50.53×0.002-393400×0.002 2 )

[0140] = 1 / (0.005104+0.012687-0.001492+0.10106-1.5736)

[0141] = 1 / (-1.4562)

[0142] ≈-0.6867 meters

[0143] A negative result indicates that the combination of parameters exceeds the applicable boundary of the formula, and attention should be paid to the parameter range in practical applications.

[0144] It should be noted that in routine water quality monitoring, the SD value should be positive, representing the depth of water that light can penetrate. For example, classified by trophic status: when SD > 3.7 meters, the water is extremely clear with very little suspended matter, indicating an oligotrophic water body; when SD is between 2.0 and 3.7 meters, the water is slightly turbid with moderate algae or suspended matter, indicating a mesotrophic water body; when SD is between 1.0 and 2.0 meters, algae proliferation is significant and transparency is significantly reduced, indicating a eutrophic water body; when SD < 1.0 meters, the water is severely turbid or experiencing algal blooms, such as cyanobacterial blooms where SD can drop below 0.5 meters, indicating a severely eutrophic water body. Similarly, classified by water type: when SD reaches 5–10 meters (such as some lakes on the Qinghai-Tibet Plateau), the water body is a clean plateau lake; when SD is typically between 0.5 and 2.0 meters, the water body is an urban lake, etc. The specific determination and classification methods need to be determined based on the user's analysis needs.

[0145] S5. Based on the initial water transparency value, analyze the data to obtain the algae concentration under the corresponding environmental characteristics, as well as the influence relationship between algae growth and water transparency.

[0146] Please see Figure 8 and Figure 9 The diagrams shown are, respectively, a flowchart of step S5 of the water transparency environment-ecology coupled analysis method described in this application embodiment and a schematic diagram of the light attenuation function described in this application embodiment. Figure 8 and Figure 9 As shown, step S5 includes the following steps:

[0147] S51, using Beer-Lambert law, the light transmittance of each water layer is obtained based on the initial water transparency value;

[0148] S52, calculate the algae growth influencing factor K of each water layer based on the light transmittance of each water layer;

[0149] S53, based on the algae growth influencing factor K of each water layer, and in combination with other environmental parameters, obtain the algae growth rate;

[0150] S54, based on the algae growth rate and the water transparency value, the dynamic model is input to analyze the influence relationship between algae growth and transparency.

[0151] In this embodiment, based on the water transparency value, the algae growth formula is developed by combining the water surface light element and water nutrient element, with the light and nutrients affecting algae growth as the core, and the algae concentration under the corresponding environmental characteristics is solved.

[0152] Specifically, firstly, according to the steps in S4 mentioned above, transparency data at different depths can be obtained through multi-point data collection in the field.

[0153] Then, the factors affecting algal growth are calculated, namely: the transmittance of each water layer is calculated using the Lambert-Beer law; and the factors affecting algal growth, K, are calculated based on the transmittance.

[0154] The Lambert-Beer law quantitatively describes the mathematical relationship between the absorbance of a beam of monochromatic light passing through a homogeneous solution and the concentration of the light-absorbing substance in the solution as well as the length of the light passing through the solution (optical path).

[0155] The formula used for Beer-Lambert Law is as follows:

[0156] LAMBERT Beer1 =light*e eta*Σtcp

[0157] Where light represents the intensity of the incident light; e eta tcp represents the light absorption coefficient; tcp represents the thickness of the water body.

[0158] The formula for calculating the light transmittance of each water layer is as follows:

[0159]

[0160] Among them, I n I represents the amount of solar radiation used for primary production on the current nth layer; n-1 dz represents the solar radiation at the current (n-1)th layer. n η represents the thickness of the nth layer of the target water body. nThis represents the extinction coefficient.

[0161] The formula for calculating the influencing factor K on algal growth is as follows:

[0162] K = K max *exp(1.6 / SD*dz n )

[0163] Where K represents the current algal growth rate; K max This indicates the maximum growth rate of algae, expressed in mg / L / day.

[0164] Next, based on the factors influencing algal growth and in conjunction with other environmental parameters (such as light intensity, water temperature, and nutrients), the algal growth rate is calculated. The formula for calculating the algal growth rate can be adjusted based on specific experimental data and models.

[0165] The formula for calculating the algal growth rate is as follows:

[0166]

[0167] Where K represents the current algal growth rate; d CHL dt represents the time component of chlorophyll change; CHL represents chlorophyll concentration; dt represents the time component; t represents time.

[0168] Then, the calculated algal growth rate and transparency data are input into the dynamic model to analyze the interaction between algal growth and transparency. Through layer-by-layer calculations in a multi-layer system, the transparency and algal growth rate of each layer are updated.

[0169] Specifically, algae growth affects water transparency, and at the same time, water transparency also affects algae growth; there is a mutually influential relationship between the two.

[0170] The Lambert Beer function can be used to solve for the light transmittance of water. In multilayer systems, the extinction coefficient varies vertically, requiring calculation for each layer using the Lambert Beer formula. Therefore, the Lambert Beer expression needs to use the calculation results from the previous layer.

[0171] By analyzing changes in light intensity, we can estimate data on factors influencing algal growth (such as the current algal growth rate and the algal growth velocity).

[0172] For example, firstly, the target water body is divided into multiple discrete layers in the vertical direction (e.g., n = 1, 2, 3, ..., N), each layer having a specific thickness dz. n .

[0173] Then, initialize parameters such as incident light intensity at the water surface, initial extinction coefficient of each layer, maximum potential algal growth rate, thickness of each water layer, water transparency, and state variables for algal biomass concentration of each layer.

[0174] Next, the Lambert Beer function is used to calculate the light transmittance of the water body layer by layer. This includes: obtaining the light intensity at the upper boundary of the first layer; calculating the light intensity at the lower boundary of the first layer and the upper boundary of the second layer based on the light intensity at the upper boundary of the first layer; and so on, calculating layer by layer downwards until the bottom layer N. Moreover, the light intensity calculation of each layer strictly depends on the result I of the previous layer. n-1 This reflects the coupling relationship between different layers of the target water body.

[0175] The formula for calculating the light transmittance of each water layer is as follows:

[0176]

[0177] Among them, I n I represents the amount of solar radiation used for primary production on the current nth layer; n-1 dz represents the solar radiation at the current (n-1)th layer. n η represents the thickness of the nth layer of the target water body. n This represents the extinction coefficient.

[0178] Then, based on the above, the algal growth rate of each layer of the target water body is calculated, that is: the calculation formula for the algal growth influencing factor K is:

[0179] K = K max *exp(1.6 / SD*dz n )

[0180] Where K represents the current algal growth rate; K max This indicates the maximum growth rate of algae, expressed in mg / L / day.

[0181] The formula for calculating the algal growth rate is as follows:

[0182]

[0183] Where K represents the current algal growth rate, which can be further adjusted based on the nitrogen and phosphorus concentrations in the water; d CHL dt represents the time component of chlorophyll change; CHL represents chlorophyll concentration; dt represents the time component; t represents time.

[0184] This formula reflects the transparency (SD) and depth (dz). n Limiting effect on algal growth. A smaller SD (lower transparency) or dz nThe larger the exponent (the deeper the water body and the thicker the layer), the greater the exponent term exp(1.6 / SD*dz). n The smaller the value of ), the lower the actual growth rate of that layer will be compared to K. max .

[0185] As can be seen from the above, the light intensity environment information of this layer calculated in the above steps (although I is not explicitly stated in the formula of this step) can be used. n However, SD itself is a comprehensive characterization of the overall optical properties of water bodies, strongly correlated with the extinction coefficient (implying illumination limitations), combined with a given K max SD and the thickness of the layer dz n The growth rate of algae in each layer can be calculated directly.

[0186] S6. Under the dynamic ecological (algae) evolution law, based on the influence relationship between turbidity, algae and water transparency, the water transparency value is dynamically adjusted and solved iteratively to achieve real-time analysis and effective prediction of the water shading effect.

[0187] In this embodiment, the water environment parameter data is dynamically updated based on real-time acquisition to achieve real-time monitoring and prediction of the shading effect of the water body.

[0188] Therefore, in this application, the diffuse attenuation coefficient of dynamic transparency is calculated in real time through a dynamic adjustment mechanism, and the shading rate of the water mixing layer is calculated based on the value of the real-time diffuse attenuation coefficient to monitor the shading effect in real time; then, the shading effect of the target water body is predicted by correlating temperature and the real-time diffuse attenuation coefficient and making a dynamic response.

[0189] In other words, based on the aforementioned points, the algal growth rate alters the algal biomass concentration in that layer, and the algal biomass concentration is a major component of the extinction coefficient. Therefore, the extinction coefficient of each layer is updated based on the calculated new chlorophyll concentration value. Then, in the next preset time period, the aforementioned steps are repeated to perform time-progression calculations using the updated algal growth rate, chlorophyll concentration value, and incident light intensity in the water, thus forming a dynamic feedback loop simulating algal growth – transparency change – light change – algal growth restriction / promotion.

[0190] It should be noted that NTU was used for processing in this application, but the concentration of suspended solids (SS) can also be used as an alternative.

[0191] This application constructs a turbidity-shading analytical function to quantify the impact of turbidity changes caused by wind and waves on the light conditions of water bodies, revealing the law of its effect on the ecological characteristics of phytoplankton; based on the analytical function, a model can be established to simulate the turbidity distribution and shading effect under different wind and wave conditions, providing data support for ecological assessment and management; at the same time, it improves the accuracy of the water environment of large shallow lakes.

[0192] The scope of protection of the water transparency environment-ecology coupling analysis method described in this application is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.

[0193] This application also provides a water transparency environment-ecological coupling analysis system, which can implement the water transparency environment-ecological coupling analysis method described in this application. However, the implementation device of the water transparency environment-ecological coupling analysis method described in this application includes, but is not limited to, the structure of the water transparency environment-ecological coupling analysis system listed in this embodiment. All structural modifications and substitutions of the prior art made based on the principles of this application are included within the protection scope of this application.

[0194] like Figure 10 As shown, this embodiment provides a water transparency environment-ecology coupled analysis system, including: a data acquisition module 101, a first model construction module 102, an analysis module 103, a second model construction module 104, an analysis module 105, and a dynamic adjustment module 106.

[0195] The data acquisition module 101 is used to acquire real-time turbidity data of the target water body.

[0196] In this embodiment, the multi-source water environment parameters include: turbidity, transparency, suspended solids concentration, water depth, chlorophyll a concentration, phytoplankton / algal community, etc. Turbidity sensors, chlorophyll fluorescence probes, CDOM detection units, underwater light intensity arrays, etc., are installed at monitoring points in the target water body area, and real-time data is collected through these devices.

[0197] The first model construction module 102 is used to construct a model of the relationship between water turbidity and suspended solids concentration based on the turbidity data, and to calculate the suspended solids concentration of the target water body.

[0198] In this embodiment, the turbidity value of the target water body is continuously monitored within the same time interval, and the concentration of suspended solids in the target water body is measured simultaneously; the turbidity value and the suspended solids concentration are fitted to obtain a model of the relationship between water turbidity and suspended solids concentration; and the suspended solids concentration of the target water body is obtained by calculation based on the model of the relationship between water turbidity and suspended solids concentration.

[0199] The analysis module 103 is used to analyze the concentration of suspended solids according to the sedimentation law of suspended solids, and obtain data on the change law of suspended solids concentration.

[0200] In this embodiment, the target water body is vertically stratified to obtain the initial suspended solids concentration of each stratum; based on the initial suspended solids concentration, the contribution rate of the upper and lower water layers to the current water layer is obtained; combining the static settling and dynamic resuspension laws of suspended solids, a dynamic model of the change of suspended solids concentration over time is constructed; based on the static settling and dynamic resuspension laws of suspended solids, an NTU change laboratory empirical formula is constructed, and the data on the change law of suspended solids concentration are calculated.

[0201] The second model construction module 104 is used to fit the data relationship model between water transparency and turbidity and chlorophyll concentration with sediment turbidity factor and initial chlorophyll concentration factor as dependent variables and water transparency as independent variable, and to obtain the initial water transparency value.

[0202] In this embodiment, water transparency, chlorophyll concentration, and turbidity are collected from multiple points in different regions. The water transparency, chlorophyll concentration, and turbidity are fitted by regression to obtain a data relationship model between water transparency and turbidity and chlorophyll concentration.

[0203] The analysis module 105 is used to analyze the water transparency value to obtain the influence relationship between algae growth and water transparency.

[0204] In this embodiment, the Lambert-Beer law is adopted to obtain the light transmittance of each water layer based on the water transparency value; the algae growth influencing factor K of each water layer is calculated based on the light transmittance of each water layer; the algae growth rate is obtained by combining the algae growth influencing factor K of each water layer with other environmental parameters; the algae growth rate and the water transparency value are input into the dynamic model to analyze the influence relationship between algae growth and transparency.

[0205] The dynamic adjustment module 106 is used to dynamically adjust the water transparency value and iteratively solve the problem based on the influence relationship between turbidity, algae and water transparency under the dynamic ecological (algae) evolution law, so as to realize the real-time analysis and effective prediction of the water shading effect.

[0206] The water transparency environment-ecological coupling analysis model can improve the accuracy and efficiency of water quality monitoring and provide strong technical support for water environment management.

[0207] It should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways, given the several embodiments provided in this application. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules or units may be electrical, mechanical, or other forms.

[0208] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0209] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0210] Please see Figure 11 The diagram shows a structural schematic of the electronic device described in an embodiment of this application. Figure 11 As shown, this embodiment provides an electronic device, the electronic device 110 including a memory 111 and a processor 112.

[0211] The memory 111 is used to store computer programs; preferably, the memory 111 includes various media that can store program code, such as ROM, RAM, magnetic disk, USB flash drive, memory card or optical disk.

[0212] Specifically, memory 111 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. Electronic device 110 may further include other removable / non-removable, volatile / non-volatile computer system storage media. Memory 111 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application. It is understood that memory 111 may be volatile memory or non-volatile memory, or both. Non-volatile memory may be read-only memory (ROM) or programmable read-only memory (PROM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0213] The processor 112 is connected to the memory 111 and is used to execute the computer program stored in the memory 111 so that the electronic device 110 executes the water transparency environment-ecology coupling analysis method described in any embodiment of this application.

[0214] Optionally, the processor 112 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0215] Optionally, in this embodiment, the electronic device 110 may further include a display 113. The display 113 is communicatively connected to the memory 111 and the processor 112, and is used to display the relevant graphical user interface (GUI) of the water transparency environment-ecological coupling analysis method described in the embodiments of this application and / or the water transparency environment-ecological coupling analysis method described in other embodiments of this application.

[0216] This application also provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the program implements the water transparency environment-ecological coupling analysis method described in any embodiment of this application and / or the water transparency environment-ecological coupling analysis method described in other embodiments of this application.

[0217] As used in this specification, the terms "component," "module," "system," etc., are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0218] In summary, the water transparency environment-ecological coupling analysis method provided in this application has the following beneficial effects:

[0219] The environmental-ecological coupled analytical method for water transparency provided in this application comprehensively considers the influence of suspended particulate matter and phytoplankton on light scattering and absorption in water bodies, while also analyzing the impact of turbidity on the sensory perception of water bodies and the ecosystem. This method enables refined simulation calculations of chlorophyll concentration in water areas, providing a scientific basis for water quality management and ecological environmental protection. This application not only improves the accuracy and efficiency of water quality monitoring but also provides strong technical support for water environment governance; furthermore, it has broad applicability.

[0220] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0221] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A water body transparency environment-ecology coupling analysis method, characterized in that, The method comprises: acquiring real-time turbidity data of a target water body; constructing a water body turbidity and suspended matter concentration relationship model based on the turbidity data, and calculating the suspended matter concentration of the target water body; analyzing the suspended matter concentration according to the laboratory rules of the settlement and resuspension of suspended matter to obtain suspended matter concentration change rule data; fitting the sediment turbidity factor and the initial chlorophyll concentration factor as dependent variables and the water body transparency as an independent variable to obtain a data relationship model between the water body transparency and the turbidity and chlorophyll concentration, and acquiring an initial water body transparency value; based on the initial water body transparency value, analyzing the algae concentration under the corresponding environmental characteristics; based on the influence relationship among the turbidity, algae and water body transparency, dynamically adjusting the water body transparency value to realize real-time analysis and prediction of the light shielding effect of the water body.

2. The water clarity environment-ecology coupled resolving method according to claim 1, characterized by, The method comprises the following steps: acquiring the turbidity value of the target water body in the same time interval, and simultaneously measuring the suspended matter concentration in the target water body; fitting the turbidity value and the suspended matter concentration to obtain a water body turbidity and suspended matter concentration relationship model; based on the water body turbidity and suspended matter concentration relationship model, calculating the suspended matter concentration of the target water body.

3. The water clarity environment-ecology coupled resolving method according to claim 1, characterized by, The settlement rules include the static settlement and dynamic resuspension rules of suspended matter; the method comprises the following steps: vertically stratifying the target water body to obtain the initial suspended matter concentration of each stratification; based on the initial suspended matter concentration, acquiring the contribution rate of the upper and lower water bodies to the current layer water body; combining the static settlement and dynamic resuspension rules of suspended matter to construct a dynamic model of the change of the suspended matter concentration with time; based on the static settlement and dynamic resuspension rules of suspended matter, constructing an NTU change laboratory empirical formula, and calculating the suspended matter concentration change rule data.

4. The water body transparency environment-ecological coupling analysis method according to claim 3, wherein the formula of the dynamic model of the change of the suspended matter concentration with time is: wherein settling n-1 represents the concentration change rate of the n-1th layer of the user-specified suspended matter, unit: g·m -3 ·d -1 ; settling n represents the concentration change rate of the n-1th layer of the user-specified suspended matter, unit: g·m -3 ·d -1 ; dz n represents the thickness of the n-1th layer, unit: m; dz n-1 represents the thickness of the n-1th layer, unit: m; the NTU change laboratory empirical formula is: max((ntu a · NTU + ntu b ) · exp(v · ntu m ), 0.001) where ntu a , ntu b , ntu m all represent laboratory resolved parameters; v represents the water flow rate, limiting the minimum decay rate of ntu by max.

5. The water clarity environment-ecology coupled resolving method according to claim 1, characterized by, The method comprises the following steps: collecting the water quality transparency, chlorophyll concentration and turbidity of multiple points in different regions; fitting the water quality transparency, chlorophyll concentration and turbidity by regression to obtain a data relationship model between the water body transparency and the turbidity and chlorophyll concentration.

6. The water body transparency environment-ecological coupling analysis method according to claim 5, wherein the formula of the water body transparency value is: SD = 1 / (a + b * NTU - c * NTU * NTU + d * CHL - e * CHL * CHL) Wherein, SD represents the transparency value; a represents the constant term; b represents the turbidity coefficient; c represents the temperature square term coefficient; d represents the chlorophyll concentration coefficient; e represents the chlorophyll a concentration square term coefficient; CHL represents the chlorophyll a concentration, and NTU is the turbidity value / unit.

7. The water clarity environment-ecology coupled resolving method according to claim 1, characterized by, The influence relationship between the algal growth and the water transparency is obtained based on the analysis of the water transparency value. The light transmittance of each layer of water is obtained based on the water transparency value by using the Lambert-Beer law. The algal growth influence factor K of each layer of water is obtained according to the light transmittance of each layer of water. The algal growth rate is obtained according to the algal growth influence factor K of each layer of water and in combination with other environmental parameters. The influence relationship between the algal growth and the transparency is obtained by inputting the algal growth rate and the water transparency value into a dynamic model.

8. The water transparency environmental-ecological coupling analysis method according to claim 7, characterized in that, the calculation formula used by the Lambert-Beer law is: LAMBERT Beer1 = light * e eta*∑tcp wherein, the calculation formula of the light transmittance of each layer of water is: where I n represents the amount of solar radiation for primary production on the current n-th layer; I n-1 represents the amount of solar radiation of the current n-1-th layer; dz n represents the layer thickness; η n represents the extinction coefficient; the calculation formula of the algal growth influence factor K is: K = K max * exp(1.6 / SD * dz n ) where K represents the current algal growth rate; K max represents the maximum algal growth rate, in mg / L / day; the calculation formula of the algal growth rate is: where K represents the current algal growth rate; d CHL represents the differential of chlorophyll change; CHL represents the chlorophyll concentration; dt represents the differential of time; and t represents the time.

9. A water body transparency environment-ecology coupling analysis system, characterized in that, The system comprises: a data acquisition module configured to acquire real-time turbidity data of a target water body; a first model construction module configured to construct a water turbidity and suspended matter concentration relationship model based on the turbidity data, and to calculate the suspended matter concentration of the target water body; an analysis module configured to analyze the suspended matter concentration according to the settling law of the suspended matter, and to obtain suspended matter concentration variation law data; a second model construction module configured to take the sediment turbidity factor and the initial chlorophyll concentration factor as dependent variables, and take the water transparency as an independent variable to perform fitting, so as to obtain a data relationship model between the water transparency and the turbidity and the chlorophyll concentration, and to obtain an initial water transparency value; an analysis module configured to analyze the initial water transparency value, and to obtain the algal concentration under corresponding environmental characteristics; a dynamic adjustment module configured to realize real-time monitoring analysis and prediction of the light-shielding effect of the water body by dynamically adjusting the water transparency value based on the influence relationship among the turbidity, the algae and the water transparency.

10. An electronic device, comprising: The electronic device comprises a processor and a memory. The memory is configured to store a computer program. The processor is configured to execute the computer program stored in the memory, so that the electronic device executes the water transparency environmental-ecological coupling analysis method according to any one of claims 1 to 8. The electronic device comprises a processor and a memory. The memory is configured to store a computer program. The processor is configured to execute the computer program stored in the memory, so that the electronic device executes the water transparency environmental-ecological coupling analysis method according to any one of claims 1 to 8.

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