A water bloom discrimination method and device based on vertical distribution state and biomass of algae
By constructing an algal bloom model that couples the buoyancy characteristics of Microcystis, the vertical distribution and biomass of Microcystis are simulated, solving the problem of inaccurate algal bloom prediction, achieving accurate algal bloom identification and early warning, and improving the stability of freshwater ecosystems.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TIANJIN UNIV
- Filing Date
- 2026-04-13
- Publication Date
- 2026-07-10
AI Technical Summary
Existing technologies cannot accurately capture the vertical migration and aggregation signals of Microcystis algae, leading to inaccurate prediction of algal blooms and making it difficult to prevent the health and stability of freshwater ecosystems.
An algal bloom model coupled with the buoyancy characteristics of Microcystis was constructed to simulate the vertical distribution and biomass of Microcystis. Combined with a hydrodynamic model, the risk of algal bloom was determined by both vertical aggregation state and biomass.
It enables accurate identification and early warning of algal blooms, reduces monitoring costs, and improves the accuracy of algal bloom prediction and the effectiveness of prevention and control.
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Figure CN122365202A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of aquatic environment ecological technology, and in particular to a method and device for identifying algal blooms based on the vertical distribution and biomass of algae. Background Technology
[0002] Algal blooms are an abnormal ecological phenomenon in freshwater bodies, characterized by the massive proliferation and aggregation of algae. Their occurrence leads to decreased dissolved oxygen, aquatic organism mortality, and water quality deterioration, seriously threatening drinking water safety and the stability of aquatic ecosystems. Current research identifies algal blooms solely based on single indicators of algal biomass, including chlorophyll a concentration, algal cell density, and dry weight biomass. When these indicators reach a set threshold, an algal bloom is identified. However, the occurrence of algal blooms is not only regulated by the phytoplankton's own proliferation process but also closely related to their vertical distribution. When underwater algae rapidly proliferate under suitable conditions and migrate quickly to the surface, algal blooms can erupt suddenly within minutes to hours, making it difficult to capture this dynamic process using a single biomass indicator.
[0003] Especially for Microcystis, a common dominant species causing algal blooms in freshwater bodies, its unique pseudo-empty cell structure endows it with the ability to migrate vertically using buoyancy. In environments with weak hydrodynamic conditions, Microcystis can use this buoyancy advantage to migrate towards the water surface and aggregate, eventually forming visible algal blooms. However, current research on algal blooms has not yet incorporated the vertical distribution characteristics of algae into its core discrimination system, making it impossible to accurately capture the vertical migration and aggregation signals of Microcystis, thus making it difficult to accurately predict the risk of algal blooms and posing a potential threat to the health and stability of freshwater ecosystems such as lakes. Summary of the Invention
[0004] The purpose of this invention is to overcome the above-mentioned shortcomings and provide a method and device for identifying algal blooms based on the vertical distribution and biomass of algae. By constructing an algal bloom model coupled with the buoyancy characteristics of Microcystis, the vertical distribution and biomass of Microcystis are simulated, thereby achieving accurate identification and early warning of algal blooms.
[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for identifying algal blooms based on the vertical distribution and biomass of algae includes: S1. Collect basic data; S2. Construct an algal bloom model that couples the buoyancy characteristics of Microcystis aeruginosa. The algal bloom model is associated with the hydrodynamic model and is used to simulate the vertical distribution and biomass of Microcystis aeruginosa. S3. Set initial conditions, boundary conditions and model parameters. Vertical stratification is divided according to water depth, covering the water surface to the bottom. Run the model to obtain Microcystis cell density data at each time point and depth layer. S4. Based on the Microcystis cell density data at each depth layer, calculate the vertical average biomass of Microcystis, as well as the vertical distribution index and determine the aggregation state. S5. Based on the vertical average biomass and vertical aggregation state of Microcystis, determine whether an algal bloom will occur and the risk of an outbreak.
[0006] Preferably, in step S1, the basic data includes: Simulate regional morphological features data, including regional shape, elevation, and water depth; Hydrological and hydrodynamic data, including water level, water temperature, and flow velocity in the simulated area; Water quality data, including nitrogen and phosphorus concentrations in the simulated area; Microcystis data include cell density and population diameter; Meteorological data, including wind speed, wind direction, temperature, air pressure, relative humidity, solar radiation, precipitation, and evaporation; Boundary condition data, including daily inflow / outflow rates, water temperature, and Microcystis cell density.
[0007] Preferably, in S2, the algal bloom model consists of three parts: material transport, advection diffusion, and dynamic processes, represented as follows: ; in, C Microcystis concentration, cells / mL; u , v , w They are respectively x , y and z The velocity component in the direction, in m / s; w s The vertical migration velocity of Microcystis aeruginosa is given in m / s. w s >0 indicates that the Microcystis colony has settled. w s <0 indicates an increase in the Microcystis population; Sc t It is the turbulent Schmidt number; A H and A V These are the horizontal and vertical turbulent diffusion coefficients, respectively, m 2 / s; P d represents the growth rate of algae. -1 ; BM The basal metabolic rate of algae, d -1 ; SC For turbulence-related mortality, d -1 .
[0008] Preferably, in S2, the buoyancy characteristics of Microcystis are represented by the Stokes equation: ; in, g The acceleration due to gravity is m / s². 2 ; r w The density of water is kg / m³. 3 ; r col Microcystis colony density, kg / m³ 3 ; D is the equivalent diameter of the Microcystis colony, in meters; n ρ is the viscosity coefficient of water, kg / (m·s); In S2, the hydrodynamic model is established using the open-source EFDC model, the fluid dynamics model adopts the continuity equation and momentum equation based on the orthogonal curvilinear coordinate system, and the turbulence calculation adopts the 2.5th order turbulence closure mode developed by Mellor-Yamada.
[0009] Preferably, the continuity equation is: ; ; The momentum equation is: ; ; ; in, Indicates the total water depth, in meters (m). h The average water depth is in meters (m). g For free surface ripples, m; x and y These are the horizontal curve coordinates, in meters (m). z for s Coordinates, dimensionless; u , v , w They represent x , y , z Velocity components in three directions, m / s; m x , m y The horizontal coordinate transformation factor. m It measures the square root of the determinant of a tensor. m=m x m y ; f Let s be the Coriolis coefficient. -1; A v Let m be the vertical turbulent viscosity coefficient. 2 / s; Q u and Q v For the source and sink terms of momentum, N·s; Q H This represents source and sink terms for other point or non-point sources such as precipitation, evaporation, and groundwater exchange, m. 3 / s; p The relative hydrostatic pressure is expressed in Pa. r This indicates the mixed density, expressed in kg / m³. r 0 is the reference density, kg / m³; b The relative buoyancy is N; The relevant equations for the 2.5th order turbulent closed-mode are: ; ; ; in, A v Let m be the vertical turbulent viscosity coefficient. 2 / s; A b Let m be the vertical turbulent diffusion coefficient. 2 / s; q Turbulence intensity; l It is a long-scale turbulent mixture; R q It is a Richardson number; f v and f b It is a stability function used to determine the increase or decrease in vertical mixing or transport of water in stable and unstable vertical density stratification environments, respectively.
[0010] Preferably, in step S4, the vertically average biomass of Microcystis is calculated using a vertically weighted average, expressed as follows: ; in, C i The cell density of each layer of Microcystis, d i For the first i The depth of each vertical layer d Water depth; the threshold for algal bloom detection is 5.0 × 10⁻⁶. 4 cells / mL.
[0011] Preferably, in step S4, the vertical distribution index is the Morisita index, which is a dispersion index for single samples used to characterize the dispersion pattern of Microcystis in water. The calculation formula is as follows: ; in, x i The cell density of Microcystis in each layer, n MI represents the vertical layer number. MI>1 indicates that Microcystis aeruginosa is aggregated in the vertical direction, MI=1 indicates random distribution, and MI<1 indicates uniform distribution.
[0012] This invention also discloses an algal bloom discrimination device based on the vertical distribution state and biomass of algae, applied to the above-mentioned algal bloom discrimination method based on the vertical distribution state and biomass of algae, comprising: The data acquisition module is used to collect the basic data required to construct a simulation of Microcystis biomass and vertical distribution; The model building module is used to construct an algal bloom model that couples the buoyancy characteristics of Microcystis aeruginosa, and the algal bloom model is associated with the hydrodynamic model. The model calculation module is used to set initial conditions, boundary conditions and model parameters. The vertical stratification is divided according to the water depth, covering the water surface to the bottom. Running the model obtains Microcystis cell density data at each time point and at each depth layer. The index calculation module is used to calculate the vertical average biomass of Microcystis and the vertical distribution index and determine the aggregation state based on the Microcystis cell density data of each depth layer. The algal bloom discrimination module is used to determine whether an algal bloom has occurred and the risk of an outbreak based on the vertical average biomass and vertical aggregation state of Microcystis.
[0013] The present invention also discloses a computer device, comprising: a memory and a processor, wherein the memory and the processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform the above-mentioned method for identifying algal blooms based on the vertical distribution state and biomass of algae.
[0014] The present invention also discloses a computer-readable storage medium storing computer instructions for causing a computer to execute the above-described method for identifying algal blooms based on the vertical distribution state and biomass of algae.
[0015] Beneficial effects of this invention: The algal bloom discrimination method based on the vertical distribution state and biomass of algae provided by this invention constructs an algal bloom model coupled with the buoyancy characteristics of Microcystis aeruginosa to simulate the biomass and vertical distribution state. It eliminates the need for complex on-site stratified sampling and monitoring, solving the problem of difficult acquisition of vertical distribution data, significantly reducing monitoring costs, and improving the engineering applicability of the method. By adopting a dual-dimensional discrimination of vertical aggregation state and biomass, it overcomes the shortcomings of single biomass discrimination, enabling more accurate prediction of algal bloom risk and improving the effectiveness of prevention and control. Attached Figure Description
[0016] Figure 1 This is a flowchart of a method for identifying algal blooms based on the vertical distribution status and biomass of algae, provided in an embodiment of the present invention.
[0017] Figure 2 This is a schematic diagram of the algal bloom discrimination results under different wind speeds and durations provided in the embodiments of the present invention.
[0018] Figure 3 This is a flowchart of an algal bloom discrimination device based on the vertical distribution state and biomass of algae provided in an embodiment of the present invention. Detailed Implementation
[0019] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.
[0020] Example 1: A method for identifying algal blooms based on the vertical distribution and biomass of algae; such as Figure 1 As shown, the method includes the following steps: S1: Collect basic data; Specifically, the collected data includes morphological characteristics of the simulated area, such as shape, elevation, and water depth; hydrological and hydrodynamic data, such as water level, temperature, and flow velocity; water quality data, such as nitrogen and phosphorus concentrations; Microcystis data, such as cell density and canopy diameter; meteorological data, such as wind speed, wind direction, air temperature, air pressure, relative humidity, solar radiation, precipitation, and evaporation; and boundary condition data, such as daily inflow / outflow, water temperature, and Microcystis cell density. S2: Construct an algal bloom model coupled with the buoyancy characteristics of Microcystis aeruginosa. This algal bloom model is associated with a hydrodynamic model and is used to simulate the vertical distribution and biomass of Microcystis aeruginosa. This includes the following sub-steps: S21: In this embodiment of the disclosure, the algal bloom model consists of three parts: material transport, advection diffusion, and dynamic processes, which can be represented as follows: ; in, C Microcystis concentration, cells / mL; u , v ,w They are respectively x , y and z The velocity component in the direction, in m / s; w s The vertical migration rate of Microcystis ( w s >0 indicates that the Microcystis colony has settled; w s <0 indicates that the Microcystis population is rising (m / s); Sc t It is the turbulent Schmidt number; A H and A V These are the horizontal and vertical turbulent diffusion coefficients, m, respectively. 2 / s; P d represents the growth rate of algae. -1 ; BM The basal metabolic rate of algae, d -1 ; SC For turbulence-related mortality, d -1 .
[0021] S22: Construct the vertical migration equation in the Microcystis bloom model to characterize the buoyancy characteristics of Microcystis under the action of hydrodynamic field; In this embodiment of the disclosure, the vertical migration equation of Microcystis can be calculated using the Stokes equation: ; in, g This is the acceleration due to gravity, typically taken as 9.8 m / s². 2 ; r w The density of water is kg / m³. 3 ; r col Microcystis colony density, kg / m³ 3 ; D is the equivalent diameter of the Microcystis colony, in meters; n ρ is the viscosity coefficient of water, kg / (m·s).
[0022] Since each Microcystis colony consists of numerous single cells containing pseudoempty cells surrounded by mucus, the population density of Microcystis can be calculated using the following formula: ; in, r cell Microcystis cell tissue density, kg / m³ 3 ; n cellThe percentage of cells in a Microcystis colony, % n gas The percentage of pseudo-empty cells in a single cell, % r muc The density of the mucus within the genus Microcystis, kg / m³ 3 .
[0023] The cell density of *Microcystis* species changes with light intensity according to the following pattern: During the day, cell density increases with varying rates of change in light intensity. At night, however, cell density gradually decreases, which can be expressed as: ; in, I ref To compensate for light intensity, μmol photons / (m 2 / s), the depth at which this threshold irradiance is reached is defined as the illumination depth; N 0 is the standard factor, kg / (m·μmol photons); I z The light intensity at different depths below the water surface is expressed in μmol photons / (m). 2 / s); I dmax The light intensity at which the population density is maximum, μmol photons / (m 2 / s); A for I z Density change rate at 0, kg / (m³) 3 ·s); B s represents the rate of density change in darkness. -1 ; r 1 represents the initial density, kg / m³ 3 ; D 0 represents the theoretical density change rate of Microcystis when stored without carbohydrates, in kg / (m³). 3 ·s).
[0024] Taking into account the time lag between the rate of increase in carbohydrates and density and the rate of increase in light intensity, it can be expressed as: ; in, t r The response time is in seconds (s).
[0025] S23: In this embodiment of the disclosure, the hydrodynamic model is established using the open-source EFDC model. The governing equations adopt the continuity equation and momentum equation based on the orthogonal curvilinear coordinate system. The turbulence calculation adopts the 2.5th order turbulence closed mode developed by Mellor-Yamada. The continuity equation is: ; ; The momentum equation is: ; ; ; in, Indicates the total water depth, in meters (m). h The average water depth is in meters (m). g For free surface undulations, m. x and y These are the horizontal curve coordinates, in meters (m). z for s Coordinates, dimensionless; u , v , w They represent x , y , z Velocity components in three directions, m / s. m x , m y The horizontal coordinate transformation factor. m It measures the square root of the determinant of a tensor. m=m x m y . f Let s be the Coriolis coefficient. -1 ; A v Let m be the vertical turbulent viscosity coefficient. 2 / s; Q u and Q v For the source and sink terms of momentum, N·s; Q H This represents source and sink terms for other point or non-point sources such as precipitation, evaporation, and groundwater exchange, m. 3 / s; p The relative hydrostatic pressure is expressed in Pa. r This indicates the mixed density, expressed in kg / m³. r 0 is the reference density, kg / m³; b The relative buoyancy is N.
[0026] The relevant equations for the 2.5th order turbulent closed-mode are: ; ; ; in,A v Let m be the vertical turbulent viscosity coefficient. 2 / s; A b Let m be the vertical turbulent diffusion coefficient. 2 / s; q Turbulence intensity; l It is a long-scale turbulent mixture; R q It is a Richardson number; f v and f b It is a stability function used to determine the increase or decrease in vertical mixing or transport of water in stable and unstable vertical density stratification environments, respectively.
[0027] S3: Set initial conditions, boundary conditions, and model parameters. Divide the vertical layers according to water depth, covering the water surface to the bottom. Run the model to obtain Microcystis cell density data at each time point and depth layer, including the following sub-steps: S31: Initial Conditions Setting. In this embodiment, the simulated area is the enclosure of Meiliang Bay in Taihu Lake, where there is no phytoplankton exchange between the inside and outside of the enclosure. The initial conditions mainly include water level, water temperature, and algae density. Specifically, the initial water level is 2m, the initial flow velocity is 0 m / s, the initial water temperature is set to 29℃, and the initial algae density for the first vertical layer is 2.5 × 10⁻⁶. 6 The initial value for cells / mL was 0 for all other layers, and the initial vertical average biomass was 5 × 10⁻⁶. 4 cells / mL; S32: Boundary Condition Settings. The hydrodynamics within the modeling area is primarily driven by wind. The boundary conditions acting on the model surface include wind speed, wind direction, air temperature, air pressure, relative humidity, solar radiation, and evaporation. Wind events are randomly generated with wind speeds ranging from 0-14 m / s and durations from 0-7 days to explore the outbreak risk of algal blooms under different wind events. S33: Vertical Stratification. The modeling area has a water depth of 2 m and is divided into 20 non-uniform layers vertically. The weights of each layer are 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.04, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.08, 0.08, 0.08, 0.08, 0.08, with a total weight of 1. S34: Based on the vertical stratification, preset initial conditions and boundary conditions, the parameters of the Microcystis bloom model are calibrated, the control equations are solved, and the Microcystis cell density data at each time point and each depth layer are obtained. S4: Based on the Microcystis cell density data at each depth layer, calculate the vertical average biomass of Microcystis, as well as the vertical distribution index and determine the aggregation state. S41: Calculation of vertically averaged biomass. In this embodiment of the disclosure, the vertically averaged biomass is a vertically weighted average, which can be expressed as: ; in, C i The cell density of each layer of Microcystis, d i For the first i The depth of each vertical layer d The water is deep.
[0028] In this embodiment, the algal bloom detection threshold is 5.0 × 10⁻⁶. 4 When the vertical average biomass exceeds the algal bloom threshold, the risk of algal bloom is considered to exist. S42: Calculation of Vertical Distribution Index. In this embodiment of the disclosure, the Morisita index (MI) is used as the vertical distribution index. MI, as a dispersion single-sample index, is used to characterize the dispersion pattern of Microcystis in water, thereby effectively describing its vertical distribution status. The MI is calculated as follows: ; in, x i The cell density of Microcystis in each layer, n MI represents the vertical layer number. MI>1 indicates that Microcystis aeruginosa are aggregated in the vertical direction, MI=1 indicates random distribution, and MI<1 indicates uniform distribution.
[0029] S5: Based on the vertical average biomass and vertical aggregation state of Microcystis, determine whether an algal bloom will occur and the risk of an outbreak.
[0030] In this embodiment, the vertical average biomass and MI index of algae under different wind speeds and durations were calculated. Figure 2 The results showed that when the wind speed was below 0.8 m / s and the wind lasted for more than one day, the average vertical biomass of algae was below the algal bloom threshold, but they were still aggregated vertically, and there was still a risk of algal bloom. When the wind speed was in the range of 0.8 to 2.5 m / s, the average vertical biomass of algae was above the algal bloom threshold, and they were aggregated vertically, which could be identified as an algal bloom. When the wind speed was in the range of 2.5 to 4.5 m / s, although the average vertical biomass of algae was above the algal bloom threshold, they were dispersed vertically. If the wind speed subsequently decreased, Microcystis aeruginosa would easily aggregate in the water surface, so there was still a risk of algal bloom. When the wind speed was above 4.5 m / s, the average vertical biomass of algae was below the algal bloom threshold, and they were dispersed vertically, which could be identified as no risk of algal bloom.
[0031] Example 2: This example provides a device for identifying algal blooms based on the vertical distribution and biomass of algae. This device is used to implement the above examples, such as... Figure 3 As shown, the device includes: Data acquisition module 1 is used to collect basic data; Model building module 2 is used to build an algal bloom model that couples the buoyancy characteristics of Microcystis aeruginosa, and the algal bloom model is associated with the hydrodynamic model; Model calculation module 3 is used to set initial conditions, boundary conditions and model parameters. Vertical stratification is divided according to water depth, covering the water surface to the bottom. Running the model obtains Microcystis cell density data at each time point and at each depth layer. The index calculation module 4 is used to calculate the vertical average biomass of Microcystis and the vertical distribution index and determine the aggregation state based on the Microcystis cell density data of each depth layer. The algal bloom discrimination module 5 is used to determine whether an algal bloom has occurred and the risk of an outbreak based on the vertical average biomass and vertical aggregation state of Microcystis.
[0032] Example 3: The present invention provides a computer device, including: a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the above-mentioned method for identifying algal blooms based on the vertical distribution state and biomass of algae.
[0033] Example 4: The present invention provides a computer-readable storage medium storing computer instructions thereon, which are used to cause a computer to execute the above-described method for identifying algal blooms based on the vertical distribution state and biomass of algae.
[0034] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.
Claims
1. A method for identifying algal blooms based on the vertical distribution and biomass of algae, characterized in that, include: S1. Collect basic data; S2. Construct an algal bloom model that couples the buoyancy characteristics of Microcystis aeruginosa. The algal bloom model is associated with the hydrodynamic model and is used to simulate the vertical distribution and biomass of Microcystis aeruginosa. S3. Set initial conditions, boundary conditions and model parameters. Vertical stratification is divided according to water depth, covering the water surface to the bottom. Run the model to obtain Microcystis cell density data at each time point and depth layer. S4. Based on the Microcystis cell density data at each depth layer, calculate the vertical average biomass of Microcystis, as well as the vertical distribution index and determine the aggregation state. S5. Based on the vertical average biomass and vertical aggregation state of Microcystis, determine whether an algal bloom will occur and the risk of an outbreak.
2. The method for identifying algal blooms based on the vertical distribution and biomass of algae according to claim 1, characterized in that, In S1, the basic data includes: Simulate regional morphological features data, including regional shape, elevation, and water depth; Hydrological and hydrodynamic data, including water level, water temperature, and flow velocity in the simulated area; Water quality data, including nitrogen and phosphorus concentrations in the simulated area; Microcystis data include cell density and population diameter; Meteorological data, including wind speed, wind direction, temperature, air pressure, relative humidity, solar radiation, precipitation, and evaporation; Boundary condition data, including daily inflow / outflow rates, water temperature, and Microcystis cell density.
3. The method for identifying algal blooms based on the vertical distribution and biomass of algae according to claim 1, characterized in that, In S2, the algal bloom model consists of three parts: mass transport, advection diffusion, and dynamic processes, represented as follows: ； in, C Microcystis concentration, cells / mL; u , v , w They are respectively x , y and z The velocity component in the direction, in m / s; w s The vertical migration velocity of Microcystis aeruginosa is given in m / s. w s >0 indicates that the Microcystis colony has settled. w s <0 indicates an increase in the Microcystis population; Sc t It is the turbulent Schmidt number; A H and A V These are the horizontal and vertical turbulent diffusion coefficients, respectively, m 2 / s; P d represents the growth rate of algae. -1 ; BM The basal metabolic rate of algae, d -1 ; SC For turbulence-related mortality, d -1 .
4. The method for identifying algal blooms based on the vertical distribution and biomass of algae according to claim 1, characterized in that, In S2, the buoyancy characteristics of Microcystis are represented by the Stokes equation: ； in, g The acceleration due to gravity is m / s². 2 ; ρ w The density of water is kg / m³. 3 ; ρ col Microcystis colony density, kg / m³ 3 ; D is the equivalent diameter of the Microcystis colony, in meters; ν ρ is the viscosity coefficient of water, kg / (m·s); In S2, the hydrodynamic model is established using the open-source EFDC model, the fluid dynamics model adopts the continuity equation and momentum equation based on the orthogonal curvilinear coordinate system, and the turbulence calculation adopts the 2.5th order turbulence closure mode developed by Mellor-Yamada.
5. The method for identifying algal blooms based on the vertical distribution and biomass of algae according to claim 4, characterized in that, The continuity equation is: ； ； The momentum equation is: ； ； ； in, Indicates the total water depth, in meters (m). h The average water depth is in meters (m). ζ For free surface ripples, m; x and y These are the horizontal curve coordinates, in meters (m). z for σ Coordinates, dimensionless; u , v , w They represent x , y , z Velocity components in three directions, m / s; m x , m y The horizontal coordinate transformation factor. m It measures the square root of the determinant of a tensor. m=m x m y ; f Let s be the Coriolis coefficient. -1 ; A v Let m be the vertical turbulent viscosity coefficient. 2 / s; Q u and Q v For the source and sink terms of momentum, N·s; Q H This represents source and sink terms for other point or non-point sources such as precipitation, evaporation, and groundwater exchange, m. 3 / s; p The relative hydrostatic pressure is expressed in Pa. ρ This indicates the mixed density, expressed in kg / m³. ρ 0 is the reference density, kg / m³; b The relative buoyancy is N; The relevant equations for the 2.5th order turbulent closed-mode are: ； ； ； in, A v Let m be the vertical turbulent viscosity coefficient. 2 / s; A b Let m be the vertical turbulent diffusion coefficient. 2 / s; q Turbulence intensity; l It is a long-scale turbulent mixture; R q It is a Richardson number; φ v and φ b It is a stability function used to determine the increase or decrease in vertical mixing or transport of water in stable and unstable vertical density stratification environments, respectively.
6. The method for identifying algal blooms based on the vertical distribution and biomass of algae according to claim 1, characterized in that, In S4, the vertical average biomass of Microcystis is calculated using a vertically weighted average, expressed as follows: ； in, C i The cell density of each layer of Microcystis, d i For the first i The depth of each vertical layer d Water depth; the threshold for algal bloom detection is 5.0 × 10⁻⁶. 4 cells / mL.
7. The method for identifying algal blooms based on the vertical distribution and biomass of algae according to claim 1, characterized in that, In S4, the vertical distribution index uses the Morisita index, which is a dispersion index for single samples used to characterize the dispersion pattern of Microcystis in water. The calculation formula is as follows: ； in, x i The cell density of Microcystis in each layer, n MI represents the vertical layer number. MI>1 indicates that Microcystis aeruginosa is aggregated in the vertical direction, MI=1 indicates random distribution, and MI<1 indicates uniform distribution.
8. An algal bloom discrimination device based on the vertical distribution state and biomass of algae, applied to the algal bloom discrimination method based on the vertical distribution state and biomass of algae as described in any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to collect the basic data required to construct a simulation of Microcystis biomass and vertical distribution; The model building module is used to construct an algal bloom model that couples the buoyancy characteristics of Microcystis aeruginosa, and the algal bloom model is associated with the hydrodynamic model. The model calculation module is used to set initial conditions, boundary conditions and model parameters. The vertical stratification is divided according to the water depth, covering the water surface to the bottom. Running the model obtains Microcystis cell density data at each time point and at each depth layer. The index calculation module is used to calculate the vertical average biomass of Microcystis and the vertical distribution index and determine the aggregation state based on the Microcystis cell density data of each depth layer. The algal bloom discrimination module is used to determine whether an algal bloom has occurred and the risk of an outbreak based on the vertical average biomass and vertical aggregation state of Microcystis.
9. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected and communicate with each other. The memory stores computer instructions, and the processor executes these computer instructions to perform the algal bloom discrimination method based on the vertical distribution state and biomass of algae, as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, It stores computer instructions that enable the computer to execute the algal bloom discrimination method based on the vertical distribution state and biomass of algae as described in any one of claims 1-7.