A method and system for calculating ecological flow rate by controlling river algae blooms with dams and gates

Abstract

The present invention discloses a method and system for deducing ecological flow for regulating water bloom in rivers controlled by dams and gates, including identification of dominant algae species and their key influencing factors, construction of a hydrodynamic-water environment model, construction of an algae dynamic model, determination of water bloom outbreak sections, and deduction of regulated flow. The present invention couples the hydrodynamic-water environment model and the algae dynamic model, and uses a machine learning method to obtain algae characteristic parameters through data such as hydrodynamics, water environment, and algae density / biomass, so as to simulate and predict the dynamic changes of algae along the river section under dam and gate scheduling. Under the premise of identifying the sections with the fastest water bloom outbreak or growth rate, the section controlled flow is deduced based on the hydrodynamic characteristics of the section, and the dam and gate regulated flow upstream and downstream of the river section can be further deduced through the hydrodynamic model. The present invention can guide the ecological scheduling of water bloom prevention and control in rivers controlled by dams and gates, and has important theoretical and practical significance for the protection of water ecological safety in the basin and the planning and allocation of water resources.

Description

Technical Field

[0001] The present invention relates to the field of water conservancy technology, and in particular to a method and system for deriving ecological flow rate through dams and gates to control river algae blooms. Background Art

[0002] Algal bloom refers to the excessive growth of algae in water bodies, resulting in excessive algae density in water bodies and the formation of a thick algae layer on the surface, which endangers the water environment and the health of water ecology. It is an extreme manifestation of river eutrophication. Algal blooms are generally common in lakes, reservoirs and other water bodies with poor mobility and relatively static, and are relatively rare in large rivers. With the development of social economy, in order to meet the needs of power generation, port transportation, water resource utilization, etc., humans have developed a large number of water conservancy projects on rivers. The construction and operation of reservoir dams have greatly changed the original natural connectivity and hydrological situation of rivers, resulting in a slowdown in water flow and an increase in hydraulic retention time; coupled with the rapid development of urbanization, industrialization and agricultural production activities along the river banks, a large amount of nitrogen, phosphorus and other biogenic elements have entered the river, which has increased the nutrient concentration of river water bodies. The combined effect of the two has created suitable conditions for the outbreak of river algae blooms. Since rivers have unidirectional flow, when river algae blooms occur, algae are transported downstream under the influence of hydrodynamic conditions, causing more serious impacts on the aquatic ecosystem at the basin scale; especially for rivers that serve as drinking water sources, algae blooms pose a serious threat to the safety of drinking water along the coast, and therefore have gradually attracted widespread attention.

[0003] The causes and mechanisms of river algae blooms are very complex. The environmental factors that affect the growth of phytoplankton and the outbreak of algae blooms mainly include nutrients, hydrological conditions and meteorological conditions, but the control of river algae blooms is mainly achieved by adjusting hydrodynamic conditions and maintaining a certain ecological flow. At present, the derivation of river ecological flow for controlling algae blooms is mainly based on hydrological methods. The range of ecological flow that suppresses algae blooms is deduced by analyzing the frequency of river flow when algae blooms occur through statistical methods. Such methods can only characterize the relationship between flow and algae density in a certain section, and are limited by the time series of the analyzed data, and there is a large uncertainty; at the same time, such methods are difficult to describe the impact of sluice gate regulation on the growth, transport and accumulation of river algae blooms along the way, and cannot achieve preventive scheduling in the early or early stages of algae blooms, and it is difficult to guide the practice of sluice gate river scheduling. Summary of the invention

[0004] Purpose of the invention: The present invention provides a method and system for deriving ecological flow for regulating algal blooms in rivers controlled by dams and gates, so as to simulate the accumulation process of algal blooms along the river and derive the ecological flow controlled by algal blooms in the corresponding sections, thus providing a basis for emergency ecological dispatching of rivers controlled by dams and gates when algal blooms break out.

[0005] Technical solution: To achieve the above-mentioned purpose, the present invention provides a method for deriving ecological flow rate by controlling river algae blooms with dams, comprising the following steps:

[0006] (1) Identify the dominant species of algae blooms and their key factors affecting water dynamics and water environment;

[0007] (2) Establish a hydrodynamic-water environment model to obtain the distribution characteristics of the hydrodynamic field and water environment factor concentrations in the study river section;

[0008] (3) Establishing an algae dynamic model, obtaining river section hydrodynamic and water environment data based on the results of the hydrodynamic-water environment model, and combining the measured algae biomass data to calibrate the algae characteristic parameters; combining the hydrodynamic-water environment model, the algae dynamic model and the machine learning method to achieve dynamic acquisition of algae characteristic parameters; in the algae dynamic model, the net growth rate of algae is related to the hydrodynamics, water environment factors and the algae's own biomass, and the algae characteristic parameters of different sections are obtained based on the output results of the river section hydrodynamic-water environment model and the measured / simulated algae biomass through the machine learning method; the machine learning method uses the hydrodynamic factors, water environment factors and algae biomass / density as input variables, and the net growth rate of algae as the output variable;

[0009] (4) Couple the hydrodynamic-water environment model and the algae dynamic model to obtain the distribution characteristics of algae along the river, and identify the sections with the maximum algae growth rate or algae bloom outbreak sections based on the algae transport and accumulation characteristics caused by the control of river sluices and dams in the same period;

[0010] (5) For situations where algal blooms have already occurred, the controlled flow is deduced based on the section where algal blooms have occurred, according to the rules of algal bloom transport and accumulation, and the corresponding regulated flow of upstream and downstream dams is obtained through the hydrodynamic model. For situations where algal blooms have not yet occurred, the algal characteristic parameters are controlled by adjusting key hydrodynamic and water environment factors based on the section with the maximum algal growth rate, and the corresponding section flow and the regulated flow of upstream and downstream dams are obtained through the hydrodynamic model.

[0011] Preferably, in step (2), an open source model EFDC model is used to establish a hydrodynamic-water environment model; wherein the hydrodynamic model adopts a continuity equation and a momentum equation based on an orthogonal curvilinear coordinate system, and the water environment model adopts a convection-diffusion equation including source-sink terms and reaction terms.

[0012] Preferably, the algae dynamic model in step (3) is expressed as:

[0013]

[0014] Among them, B x is the biomass or density of algae; t is time; NP xis the net growth rate of algae, v is the flow rate output by the hydrodynamic model, c represents the key water environment factor identified in step (1) and obtained from the water environment model; WS x is the positive sedimentation rate of algae, which is related to the flow velocity v and is calibrated by the measured algae biomass or density at the cross section and the output of the hydrodynamic model in the same period; Z is the river depth, obtained through the hydrodynamic model; WB x is the external load of algae, which is calculated based on the measured tributary inflow density and flow; V is the volume of the simulation unit; NP x (v,c) Using machine learning methods, we constructed v, c, B based on the output of the river section hydrodynamic-water environment model and the measured / simulated algae biomass. x The quantitative relationship between parameters is determined.

[0015] As a preferred method, the algae characteristic parameters of different sections are obtained by: using the measured data to determine the algae characteristic parameter NP of the section x , combined with the output results of the hydrodynamic-water environment model, the hydrodynamic factors, water environment factors, and algae biomass / density obtained by the model are used as input variables, NP x The hydrodynamic factors, water environment factors, algae biomass / density and algae characteristic parameters NP were established through the support vector machine model as output variables. x The numerical relationship between them; the hydrodynamic-water environment model and algae dynamic model results are used to further obtain the NP along the section x ; The penalty factor and kernel function parameters of the support vector machine model are optimized by particle swarm optimization algorithm.

[0016] Preferably, in step (4), the distribution characteristics of algae density / biomass along the river section controlled by the dam are obtained based on the algae dynamic model, the net accumulation of algae in the section is the sum of the net growth of algae in the section and the transfer between the upstream and downstream of the section, minus the sedimentation; the growth of algae in the section is the average value of the algae growth in the section simulated by the algae dynamic model; the algae transfer in the section is the average of the difference between the input of the upstream section and the output of the downstream section; the sedimentation is the cross-sectional average value of the sedimentation simulated by the algae dynamic model.

[0017] As a preferred method, the method for identifying the maximum section of water bloom growth rate in step (4) is: dividing the simulation period t into N moments {t 0 ,t 1 ,t 2 ,…,t N}, t 0 Net algae growth rate at each moment The results of calibration based on the measured algae density / biomass data and the hydrodynamic-water environment model are introduced into the algae dynamic model to obtain t 0 The distribution characteristics of algae density / biomass along the river section at each moment;0 The results of the algae dynamic model and the hydrodynamic-water environment model are input into the support vector machine model to obtain the net growth rate of algae in each section. As t 1 The dynamic model parameters of algae at each moment; and so on, to obtain the NP at each moment x,t , take NP in time period t x,t The largest section is taken as the section with the maximum growth rate of algal bloom.

[0018] Preferably, the method for identifying the initial section of the algal bloom in step (4) is as follows: the algae density on a certain day is used as the initial field for simulation, and the algae density variation along the river section separated by the dam is simulated from upstream to downstream by the algae dynamic model, and the water level / discharge flow of the upstream and downstream dams is adjusted to obtain the algae density / biomass transfer accumulation characteristics of each section, and the river section position with the maximum algae density / biomass greater than the set threshold during the simulation period is determined, which is the initial section of the algal bloom; during the simulation process, the algae characteristic parameters are dynamically updated based on the changes of hydrodynamics and water environment factors along the river section combined with the algae dynamic model.

[0019] As a preferred method, the method for determining the threshold value of the ecological flow rate for controlling the water bloom outbreak in the section in step (5) is as follows: based on the water bloom outbreak section, by calculating the transfer / accumulation characteristics of the algae density / biomass from the upstream dam to the outbreak section, the flow rate with a negative cumulative amount in the outbreak section is the river flow rate for controlling the water bloom in the outbreak section, which is recorded as Q 1 Considering the transfer / accumulation characteristics of algae density / biomass from the outbreak section to the downstream dam, the river flow at the downstream section with an accumulation volume less than the set threshold during the regulation period is recorded as Q 2 , take Q 1 , Q 2 The outer envelope of is the ecological flow threshold for controlling algal blooms in the outbreak section, and the hydrodynamic model is used to obtain the corresponding regulation flow thresholds for the upstream and downstream dam sections.

[0020] As a preferred method, the method for determining the threshold value of the ecological flow rate for controlling the maximum algal bloom growth rate in step (5) is as follows: take the section with the maximum net algal growth, calculate the average flow velocity of the section when the net algal growth is 0, derive the average flow velocity of the section corresponding to the algal bloom outbreak according to the continuity of the hydrodynamic model, and calculate the flow rate of the corresponding section, and obtain the section flow rate Q for inhibiting algal growth 3 ; From the section with the maximum net growth of algae bloom to the section with the most downstream dam in the river section, calculate the flow rate corresponding to the cross-sectional flow that makes the accumulation of algae in this interval less than 0, which is the cross-sectional flow rate Q for controlling the accumulation of algae bloom 4 , take Q 3 , Q 4 The outer envelope of is the ecological flow threshold for the maximum algal bloom growth rate in the control section, and the hydrodynamic model is used to obtain the corresponding regulation flow threshold for the upstream and downstream dam sections.

[0021] The present invention also provides a computer system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, the steps of the method for deriving the ecological flow rate by controlling river algae blooms with dams are implemented.

[0022] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0023] (1) In the past, ecological flow for river algae bloom prevention and control was mostly obtained based on hydrological methods, which has a strong dependence on data sequences and can only describe the relationship between the flow of a single river section and characteristic indicators such as algae density / biomass. It is impossible to obtain the dynamic characteristics of algae growth, transport, and accumulation along the river when the river bloom breaks out, and it is difficult to achieve targeted scheduling of sections with different characteristics of the bloom. The present invention combines the hydrodynamic-water environment model, the algae dynamic model and the machine learning method, which can not only simulate the change process of the biomass / density of the river bloom along the river, but also realize the simulation of the change characteristics of the algae characteristic parameter net growth rate along the river, and can fully realize the dynamic process simulation of the growth, transport and accumulation of the river bloom when the bloom breaks out.

[0024] (2) The present invention combines the hydrodynamic-water environment model, the algae dynamic model and the machine learning method to determine the section where the algal bloom breaks out and the section with the maximum algal bloom growth rate, and deduce the corresponding flow control demand. Based on the hydrodynamic model, the ecological flow control threshold of the dam section is obtained, which can achieve targeted scheduling for the two situations where the algal bloom has already occurred and the situation where the algal bloom is suppressed, and provide accurate flow control information for the ecological scheduling of the dam to control the river algal bloom. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flow chart of an embodiment of the present invention;

[0026] Figure 2 It is a schematic diagram of redundant analysis of factors such as meteorology, hydrology, hydrodynamics, and water environment in a river section;

[0027] Figure 3 It is a schematic diagram of the verification of the hydrodynamic-water environment model of a certain river section;

[0028] Figure 4 This is a schematic diagram of the algae density model results for a certain river section;

[0029] Figure 5 It is a flow chart of a method for deducing characteristic parameters of algae in different cross sections according to an implementation example of the present invention;

[0030] Figure 6 This is a schematic diagram of the ecological flow threshold and sluice-dam flow threshold for controlling algal bloom outbreaks and inhibiting algal bloom growth in a certain river section. DETAILED DESCRIPTION

[0031] The technical solution of the invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] like Figure 1 As shown, a method for deriving ecological flow rate regulation by controlling river algae blooms by dams and gates disclosed in an embodiment of the present invention includes the following steps:

[0033] (1) Identification of dominant algae species and their key influencing factors: Collect and organize basic data on algae, hydrology, and water environment in the study area, and use principal component analysis and correlation analysis to identify the dominant species of algae blooms in the study area and their key factors affecting hydrodynamics and water environment.

[0034] This step specifically includes:

[0035] (1-1) Collection of hydrological and hydrodynamic, water environment and water ecological data of river sections with algal blooms: Collect data on phytoplankton and related hydrological and hydrodynamic, water environment and meteorological data of the study river sections. Phytoplankton data include species composition, density, biomass, etc.; hydrological and hydrodynamic data include flow, flow velocity, water level, water temperature, etc. of the study river section; water environment data include total nitrogen, total phosphorus, ammonia nitrogen, phosphate, permanganate index, etc., which mainly affect the growth of algae; meteorological data mainly include precipitation, temperature, sunshine hours, etc.

[0036] (1-2) Identification of major algae and key influencing factors: The dominant algae species during the algal bloom period in the study section are selected based on the dominance index; the hydrological, hydrodynamic, water environment, and meteorological factors are screened using principal component analysis, and the main factors affecting algae growth and migration along the river are selected based on the principal component contribution rate; and the key factors affecting the algae density or biomass in the river section are further screened through redundancy analysis.

[0037] For example, a typical section of a dam-controlled river in my country where algal blooms frequently occur was selected to collect relevant phytoplankton data and related hydrological and hydrodynamic, water environment, and meteorological data. Through data analysis, it was found that the frequency of algal blooms in spring in this section of the river was the highest, and the main algae species was the genus Cyclotella of the diatom family. Fifteen environmental factors, including total nitrogen, total phosphorus, chlorophyll a, ammonia nitrogen, water temperature, dissolved oxygen, flow rate, and pH, were selected for principal component analysis, and the results showed that the first two principal components explained 41.55% and 29.08% of the data carrying capacity, respectively. Environmental factors with two main axis change scores greater than 0.6 were selected. Flow rate, water temperature, total phosphorus, and permanganate index were the main driving factors for algal blooms.

[0038] Table 1 Principal component analysis of environmental factors

[0039]

[0040]

[0041] like Figure 2 As shown, further redundancy analysis showed that the environmental factors affecting the dominant algal communities were flow velocity and total phosphorus, respectively. The first and second ordination axes cumulatively explained 68.8% of the species data, and flow velocity (F=1.8, p=0.001) explained 40.4% of the distribution of dominant algal populations, reaching a significant level.

[0042] (2) Construction of hydrodynamic-water environment model: Collect and organize basic data on the hydrodynamics, water environment, topography, and meteorology of the study section, use the open source EFDC model to build a hydrodynamic-water environment model, and verify it with measured data to obtain the distribution characteristics of the hydrodynamic field and water environment factor concentrations in the study section.

[0043] Step (2) specifically includes:

[0044] (2-1) Collect basic data on hydrology, water environment, topography and meteorology in the study area, and use the open source model EFDC to establish a hydrodynamic-water environment model based on the basic data, and verify it with measured data. The constructed hydrodynamic model uses the continuity equation and momentum equation based on the orthogonal coordinate system of the curve, and the vertical direction is processed by coordinate system transformation. Continuity equation:

[0045]

[0046] Where: ζ is the water level above the reference plane z = 0 (m); t is the time (s); d is the water depth below the reference plane (m); u and v are the horizontal flow velocities along the ξ and η directions, respectively (m / s); G ξξ ,G ηη It is the conversion coefficient between the horizontal rectangular coordinate system and the orthogonal curvilinear coordinate system. The two coefficients are used to convert the flow velocity parallel to the reference plane to the vertical direction; Q is the change in water volume per unit area due to precipitation, evaporation, drainage, water diversion and other factors (m / s). The specific expression can be seen in the following formula:

[0047]

[0048] Where: H is the total water depth (m); q in ,q out They are the amount of water flowing in and out of the local source and sink per unit volume (1 / s); P is the precipitation (m / s); and E is the evaporation (m / s).

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] Where: u, v, ω are the horizontal flow velocities in the ξ, η, σ directions respectively (m / s); ζ is the water level above the reference plane z = 0 (m); d is the water depth below the reference plane (m); f is the Coriolis force coefficient (1 / s); ρ 0 is the water density (kg / m 3 );P ξ ,P η are the hydrostatic pressure gradients in the ξ and η directions (kg / m 2 s 2 );F ξ ,F η are the turbulent momentum fluxes in the ξ and η directions (m / s 2 );M ξ ,M η are the source or sink of momentum in the ξ and η directions respectively (m / s 2 );v V is the turbulent viscosity coefficient (m 2 / s).

[0056] (2-2) The constructed water environment model adopts the convection-diffusion equation including source-sink term and reaction term:

[0057]

[0058] Where c is the substance concentration (kg / m 3 ); t is time (s); Dx, Dy are diffusion coefficients in x and y directions (m 2 / s; S is the source and sink term (kg / m 3 / s);f R is the reaction term (kg / m 3 / s).

[0059] (2-3) Calibration and verification of the hydrodynamic-water environment model: The initial value range of the relevant parameters of the hydrodynamic model is set through relevant literature ("Water Quality Ecological Model and Numerical Simulation of the Lower Reaches of the Han River" (Peng Hong, Guo Shenglian, 2002)), and the hydrodynamic model parameters are calibrated and verified according to the measured water depth and flow rate of the study section. The initial value range of the relevant parameters of the water environment model is set through the literature, and the water environment model parameters are further calibrated and verified according to the measured water environment indicators of the study section. This embodiment calibrates the hydrodynamic-water environment model according to the measured data. Figure 3 shown.

[0060] (3) Construction of algae dynamic model: Establish an algae dynamic model, obtain the river section hydrodynamic and water environment data based on the results of the hydrodynamic-water environment model, and calibrate the algae characteristic parameters in combination with the measured algae biomass data; combine the hydrodynamic-water environment model, algae dynamic model and machine learning methods to achieve dynamic acquisition of algae characteristic parameters.

[0061] Step (3) specifically includes:

[0062] (3-1) Construction of algae dynamic model: Collect data on algae species, density, and biomass in the study area, calculate the dominance index, and determine that diatoms are the dominant algae species in the river section during the study period based on a comprehensive consideration of density, biomass, and dominance.

[0063] The basic kinetic equation describing the dynamic process of algae growth is:

[0064]

[0065] Among them, B x is the biomass or density of algae (g / L or cell / L); t is the time (days); NP x is the net growth rate of algae (1 / day), which is related to the hydrodynamics and key water environment factors that affect the dominant algae species, v is the flow rate (m / s) output by the hydrodynamic model, c represents the key water environment factor identified in step (1), obtained by the water environment model; WS x is the positive sedimentation rate of algae (m / day), which is related to the flow velocity v and is calibrated by the measured algae biomass or density at the cross section and the output of the hydrodynamic model in the same period; Z is the river depth, obtained through the hydrodynamic model (m); WB x is the external load of algae (g / day or cell / day), estimated based on the measured tributary inflow density and flow rate; V is the simulation unit volume (L). NP x (v,c) Using machine learning methods, we constructed v, c, B based on the output of the river section hydrodynamic-water environment model and the measured / simulated algae biomass. x The quantitative relationship between parameters is determined.

[0066] (3-2) Calibration of algae dynamic model: The net growth rate in the algae dynamic model is related to hydrodynamics and water environment factors; the sedimentation rate is related to hydrodynamic factors. Based on the reference "Response of algae growth in river-type reservoirs to reduction of watershed pollution load based on EFDC model - a case study of Changtan Reservoir in Guangdong" (Li Yiping et al., 2015), the parameter NP was initially obtained. x (v,c),WS x (v) The initial values ​​range from 1.76 to 1.8 and 0 to 0.1, respectively.

[0067] (3-3) Acquisition of algae characteristic parameters at different sections: The river section selected in the example has 60 groups of measured data at 3 sections, 50 of which are selected as training sets and 10 as test sets.

[0068] Determining the algae characteristic parameter NP of the cross section using measured data x , the algae density distribution characteristics in the river section are obtained as follows Figure 4 Combined with the output results of the hydrodynamic-water environment model, the hydrodynamic factors, water environment factors, and algae biomass / density obtained by the model are used as input variables, NP x The hydrodynamic factors, water environment factors, algae biomass / density and algae characteristic parameters NP were established through the support vector machine model as output variables. x The numerical relationship between them is used to further obtain the NP along the section by applying the hydrodynamic-water environment model and the algae dynamic model results. x The support vector machine model is implemented using the Python Libsvm software extension package, and the kernel function is the Gaussian kernel function (RBF kernel function), and its mathematical formula is:

[0069]

[0070] Among them, K(x i , x) is called the kernel function; x i , x is the sample set and its mathematical expectation respectively; σ is the width of Gaussian distribution, i.e., the kernel parameter. The particle swarm optimization algorithm in Python swarm software extension package is used to optimize the penalty factor C and kernel function parameter σ of the SVM model.

[0071] The example uses mean square error (MSE) as the fitness function of the PSO algorithm, the penalty factor is initially set to C=100, σ=10, the target accuracy is 0.01, and the final penalty factor of the optimization result is C=132.4, σ=40.6, and MES=0.009.

[0072] (4) Determination of algal bloom sections: Couple the hydrodynamic-water environment model and the algae dynamic model to obtain the distribution characteristics of algae along the river. Based on the algae transport and accumulation characteristics caused by the control of river sluices and dams in the same period, identify the sections with the maximum algae growth rate or the algal bloom sections.

[0073] Step (4) specifically includes:

[0074] According to the control of dams in the studied river section, based on the established hydrodynamic-water environment model, the distribution of hydrodynamic and water environment indicators of each river section separated by dams was simulated in sections; combined with the algae dynamic model, the dynamic changes of algae biomass / density in different river sections under certain hydrodynamic and water environment conditions were further simulated, and the changes of algae characteristic parameters along the river section were obtained through the support vector machine model results to clarify the growth, transport and accumulation process of algae in each river section.

[0075] (4-1) Determination of algae growth in a river within a certain period of time: Based on the results of the algae dynamic model, the results in step (3-1) are integrated within the period of time to obtain the net algae growth G in a certain section of the river. x :

[0076]

[0077] In the formula, G x Indicates that x algae species are present in t 1 to 2 Net growth during the period (cell / L).

[0078] (4-2) Determination of algae transport in a river within a certain period of time: Based on the model results, the algae transport caused by the river's effect on material migration is considered. x It is the average of the difference between the input at the upstream section and the output at the downstream section of the river:

[0079]

[0080] Where, T x Indicates that x algae species are present in t 1 to 2 Net transport in a river section during a period (cell / L), T x To indicate that algae are transported to this river section and accumulate, T x A negative value indicates that algae do not accumulate in this river section; v 1 Obtain the average flow velocity (m / s) of the upstream section of the river for the model, v 2 Obtain the average flow velocity (m / s) of the downstream section of the river for the model; B x1 To obtain the simulated algae density (cell / L) in the upstream river section for the model, B x2 The simulated algae density (cell / L) of the downstream river section is obtained for the model; L is the average length of the river section (m).

[0081] (4-3) Determination of the accumulation of algae in a river within a certain period of time: For a certain river section, at t 1 to 2 The accumulation of algae during the period A x It can be expressed as:

[0082]

[0083] Where WS x is the algae settling rate in step (2-3) (m / day); Z is the average water depth of the river section (m); t 1 to 2 Average algae density in the river section during the period (cell / L). x ≥0, indicating that algae in the river have accumulated during the period; when A x <0, indicating that the algae accumulated in the river channel were diluted during the period.

[0084] (4-4) Identification of algal bloom sections: Based on the hydrodynamic-water environment model and algae dynamic model, the A values ​​of different sections in the river at different times are calculated. x，t , when A x，t When it is greater than the threshold of algal bloom outbreak, that is:

[0085] MAX[A x ] t ≥10 7 cell / L

[0086] The above formula shows that if the algae density in the cross section of the study section is greater than 10 7 cell / L, it is determined that algal bloom occurs in this section, and this section is the algal bloom outbreak section.

[0087] (4-5) Identification of the maximum section of algal bloom growth rate: The simulation period t is divided into N moments, {t 0 , t 1 , t 2 , ..., t N}, t 0 Algae characteristic parameters in step (3-1) The results of calibration based on the measured algae density / biomass data and the hydrodynamic-water environment model are introduced into the algae dynamic model to obtain t 0 The distribution characteristics of algae density / biomass along the river section; using t 0 The results of the algae dynamic model and the hydrodynamic-water environment model are input into the support vector machine model to obtain the characteristic parameters of algae in each section. As t 1 The algae dynamic model parameters at each moment. Similarly, the NP at each moment is obtained. x，t (like Figure 5 As shown), take NP in time period t x，t The largest section is taken as the section with the maximum growth rate of algal bloom.

[0088] The final judgment result of the example is as follows Figure 5 As shown, the simulation results at section 1-1 have A1-1 ≥10 7 cell / L, it is determined as the algal bloom section; NP x The maximum value appears at section 2-2, which is determined to be the section with the maximum water bloom growth rate.

[0089] (5) Derivation of characteristic flow for controlling algal blooms: For situations where algal blooms have already occurred, the control flow is deduced based on the section where algal blooms occurred and the rules of algal bloom transport and accumulation, and the corresponding control flow of upstream and downstream dams is obtained through the hydrodynamic model. For situations where algal blooms have not yet occurred, the algal characteristic parameters are controlled by adjusting key hydrodynamic and water environment factors based on the section with the maximum algal growth rate, and the corresponding section flow and the control flow of upstream and downstream dams are obtained through the hydrodynamic model.

[0090] Step (5) specifically includes:

[0091] (5-1) Determination of ecological flow regulation at the algae bloom section: For the algae bloom section determined in step (4-4), based on the value obtained in step (3-1), x ≈0 and calculate the corresponding cross-sectional flow rate. According to the continuity of the hydrodynamic model, the dispatching flow rate at the upstream and downstream dams is deduced. In the example, section 1-1 at A x ≈0, the average flow velocity in the section is 0.52m / s, corresponding to the flow rate below the dam upstream of the river section of 890m 3 / s, corresponding to the flow rate on the downstream dam of the river section is 904m 3 / s.

[0092] (5-2) Determination of ecological flow regulation at the maximum section of algae bloom growth rate: For the maximum section of algae bloom growth rate determined in step (4-5), based on the section in step (3-3) where NP x The corresponding cross-section hydrodynamic factor and water environment factor when ≈0 are finally determined based on the hydrodynamic-water environment model to ensure that the cross-section hydrodynamic / water environment conditions meet the standard average flow rate and calculate the corresponding cross-section flow. According to the continuity of the hydrodynamic model, the dispatching flow at the upstream and downstream dams is derived. In the example, NP at section 2-2 x ≈0, the average flow velocity in the section is 0.46m / s, corresponding to the flow rate under the dam upstream of the river section of 718m 3 / s, corresponding to the flow rate on the downstream dam of the river section is 741m 3 / s.

[0093] The final recommended threshold value of ecological flow for river algae bloom regulation in the example is as follows: Figure 6 As shown in Figure 2, the final river section dispatching flow threshold is the outer envelope of the flow calculated by the above two methods.

[0094] An embodiment of the present invention also discloses a computer system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, the steps of the method for deriving the ecological flow rate regulation method of controlling river algae blooms by dams and gates are implemented.

[0095] The above disclosure is only a preferred embodiment of the present invention, which cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for calculating ecological flow rate by controlling river algal blooms with dams and sluice gates. It is characterized in that The steps include: (1) Identify the dominant species of algae blooms and their key factors affecting water dynamics and water environment; (2) Establish a hydrodynamic-water environment model to obtain the distribution characteristics of the hydrodynamic field and water environment factor concentrations in the study river section; (3) Establishing an algae dynamic model, obtaining river section hydrodynamic and water environment data based on the results of the hydrodynamic-water environment model, and combining the measured algae biomass data to calibrate the algae characteristic parameters; combining the hydrodynamic-water environment model, the algae dynamic model and the machine learning method to achieve dynamic acquisition of algae characteristic parameters; in the algae dynamic model, the net growth rate of algae is related to the hydrodynamics, water environment factors and the algae's own biomass, and the algae characteristic parameters of different sections are obtained based on the output results of the river section hydrodynamic-water environment model and the measured / simulated algae biomass through the machine learning method; the machine learning method uses the hydrodynamic factors, water environment factors and algae biomass / density as input variables, and the net growth rate of algae as the output variable; (4) Couple the hydrodynamic-water environment model and the algae dynamic model to obtain the distribution characteristics of algae along the river, and identify the sections with the maximum algae growth rate or algae bloom outbreak sections based on the algae transport and accumulation characteristics caused by the control of river sluices and dams in the same period; (5) For situations where algal blooms have already occurred, the controlled flow is deduced based on the bloom-occurring cross-section of the river section according to the transport and accumulation rules of the algal blooms, and the corresponding regulated flow of the upstream and downstream dams is obtained through the hydrodynamic model; for situations where algal blooms have not yet occurred, the algal characteristic parameters are controlled by adjusting key hydrodynamic and water environment factors based on the cross-section with the maximum algal growth rate of the river section, and the corresponding cross-section flow and the regulated flow of the upstream and downstream dams are obtained through the hydrodynamic model; The method for identifying the section with the maximum bloom growth rate in step (4) is as follows: divide the simulation period t into N moments {t 0 ,t 1 ,t 2 ,…,t N }, t 0 Net algae growth rate at each moment The results of calibration based on the measured algae density / biomass data and the hydrodynamic-water environment model are introduced into the algae dynamic model to obtain t 0 Distribution characteristics of algae density / biomass along the river section at each moment; Using t 0 The results of the algae dynamic model and the hydrodynamic-water environment model are input into the support vector machine model to obtain the net growth rate of algae in each section. As t 1 The dynamic model parameters of algae at each moment; and so on, to obtain the NP at each moment x,t , take NP in time period t x,t The largest section is used as the section with the maximum bloom growth rate; The method for identifying the initial section of the algal bloom in step (4) is as follows: the algae density on a certain day is used as the initial field for simulation, and the algae density variation along the river section separated by the sluice dam is simulated from upstream to downstream by means of the algae dynamic model, and the water level / discharge flow of the upstream and downstream sluice dams is adjusted to obtain the algae density / biomass transfer accumulation characteristics of each section, and the river section position with the maximum algae density / biomass greater than the set threshold during the simulation period is determined, which is the initial section of the algae bloom; during the simulation process, the algae characteristic parameters are dynamically updated based on the variation of the hydrodynamic and water environment factors along the river section combined with the algae dynamic model; The method for determining the threshold value of the ecological flow rate for controlling the algal bloom outbreak in the section in step (5) is as follows: based on the algal bloom outbreak section, by calculating the transfer / accumulation characteristics of the algae density / biomass from the upstream dam to the outbreak section, the flow rate with a negative accumulation in the outbreak section is the river flow for controlling the algal bloom in the outbreak section, which is recorded as Q 1 Considering the transfer / accumulation characteristics of algae density / biomass from the outbreak section to the downstream dam, the river flow at the downstream section with an accumulation volume less than the set threshold during the regulation period is recorded as Q 2 , take Q 1 , Q 2 The outer envelope of is the ecological flow threshold for controlling the bloom in the section, and the corresponding control flow threshold for the upstream and downstream dam sections is obtained using the hydrodynamic model; Step (5) The method for determining the threshold value of the ecological flow rate for controlling the maximum algal bloom growth rate in the control section is as follows: take the section with the maximum algal net growth, calculate the average flow velocity of the section when the algal net growth is 0, derive the average flow velocity of the corresponding algal bloom section according to the continuity of the hydrodynamic model, and calculate the corresponding section flow, and obtain the section flow Q that inhibits algal growth. 3 ; From the section with the maximum net growth of algae bloom to the section with the most downstream dam in the river section, calculate the flow rate corresponding to the cross-sectional flow that makes the accumulation of algae in this interval less than 0, which is the cross-sectional flow rate Q for controlling the accumulation of algae bloom 4 , take Q 3 , Q 4 The outer envelope of is the ecological flow threshold for the maximum algal bloom growth rate in the control section, and the hydrodynamic model is used to obtain the corresponding regulation flow threshold for the upstream and downstream dam sections.

2. The method for calculating the ecological flow rate of river algae bloom regulation by dams and gates according to claim 1, It is characterized in that In step (2), the open source model EFDC model is used to establish a hydrodynamic-water environment model; the hydrodynamic model adopts the continuity equation and momentum equation based on the orthogonal curvilinear coordinate system, and the water environment model adopts the convection-diffusion equation including source-sink terms and reaction terms.

3. The method for calculating the ecological flow rate of river algae bloom regulation by dams and gates according to claim 1, It is characterized in that The algae dynamic model in step (3) is expressed as: Among them, B x is the biomass or density of algae; t is time; NP x is the net growth rate of algae, v is the flow rate output by the hydrodynamic model, c represents the key water environment factor identified in step (1) and obtained from the water environment model; WS x is the positive sedimentation rate of algae, which is related to the flow velocity v and is calibrated by the measured algae biomass or density at the cross section and the output of the hydrodynamic model in the same period; Z is the river depth, obtained through the hydrodynamic model; WB x is the external load of algae, which is calculated based on the measured tributary inflow density and flow; V is the volume of the simulation unit; NP x (v,c) Using machine learning methods, we constructed v, c, B based on the output of the river section hydrodynamic-water environment model and the measured / simulated algae biomass. x The quantitative relationship between parameters is determined.

4. The method for calculating the ecological flow rate of river algae bloom regulation by dams and gates according to claim 3, It is characterized in that The method of obtaining the characteristic parameters of algae in different sections is as follows: the algae characteristic parameter NP of the section is determined by using the measured data x , combined with the output results of the hydrodynamic-water environment model, the hydrodynamic factors, water environment factors, and algae biomass / density obtained by the model are used as input variables, NP x The hydrodynamic factors, water environment factors, algae biomass / density and algae characteristic parameters NP were established through the support vector machine model as output variables. x The numerical relationship between them; the hydrodynamic-water environment model and algae dynamic model results are used to further obtain the NP along the section x ; The penalty factor and kernel function parameters of the support vector machine model are optimized by particle swarm optimization algorithm.

5. The method for calculating ecological flow rate of dam-controlled river algae bloom regulation according to claim 1, It is characterized in that In step (4), the distribution characteristics of algae density / biomass along the river section controlled by the dam are obtained based on the algae dynamic model. The net accumulation of algae in the section is the sum of the net growth of algae in the section and the transfer between the upstream and downstream sections, minus the sedimentation; the growth of algae in the section is the average value of the algae growth in the section simulated by the algae dynamic model; the algae transfer in the section is the average of the difference between the input of the upstream section and the output of the downstream section; and the sedimentation is the average cross-sectional sedimentation simulated by the algae dynamic model.

6. A computer system comprising a memory, a processor and a computer program stored in the memory and executable on the processor, It is characterized in that When the computer program is loaded into a processor, the steps of the method for deriving ecological flow rate regulation by controlling river algae blooms with dams and gates are implemented according to any one of claims 1 to 5.

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