A method for generalizing boundary conditions of a lake hydrodynamic water quality model

Abstract

The present application relates to a kind of lake hydrodynamic water quality model boundary condition generalization method, by data-driven constraint mechanism to flow, water level and pollutant concentration such as parameter are adaptively grouped, can better reflect the non-constant process of the above-mentioned physical quantity, to more accurately reflect the movement of pollutant in lake.The method has higher precision and efficiency compared with artificial cascade generalization, and each parameter has clear physical meaning, small processing capacity, model calculation speed is fast, provides reliable data support for lake pollution accurate tracing and management decision.

Description

Technical Field

[0001] This invention relates to the field of lake hydrodynamic and water quality prediction technology, specifically to a method for generalizing boundary conditions of a lake hydrodynamic and water quality model. Background Technology

[0002] With the increasing severity of lake eutrophication, water quality prediction technology based on numerical simulation has become an important tool for water environment management. Existing hydrodynamic-water quality coupled models, by solving the Navier-Stokes equations and pollutant transport equations in the σ coordinate system, can effectively simulate the interaction mechanisms between water flow, pollutant migration and diffusion, and ecosystems under normal conditions, providing a scientific basis for long-term water quality assessment and governance planning.

[0003] However, for sudden lake pollution events such as storm runoff impacts and industrial accident emissions, traditional numerical simulation schemes face two technical bottlenecks: First, at the data input level, the use of daily average hydrological monitoring data significantly weakens the dynamic characterization ability of boundary conditions. This smoothing effect on the time scale prevents the model from capturing the transient coupling characteristics between pollutant concentration, water level fluctuations, and flow velocity changes. Especially when the inflow of rivers and pollutant concentrations surge under the drive of storm runoff events, this data smoothing affects the simulation accuracy of pollutant movement. For example, CN115758944A discloses a river hydrodynamic and water quality coupling model and its construction method, using the SWAT model and a one-dimensional / two-dimensional coupling method, but the boundary conditions are still based on conventional monitoring data and do not optimize for high-frequency input issues. Second, at the computational efficiency level, although modern monitoring systems have achieved high-frequency data acquisition at the hourly or even minute level, directly using the original monitoring sequence as boundary conditions will lead to an exponential increase in computation time. When the time resolution of the boundary conditions is 1 hour, the computation of a typical lake model is 40% longer than that of 24 hours, seriously delaying the timeliness of emergency response plan formulation. For example, CN118536431A discloses a method and system for open-boundary simulation of water quality in drainage systems based on the intelway-SWMM model. Although the open-boundary simulation is improved, the problem of high-frequency data calculation load is not solved.

[0004] Existing research largely focuses on improving the efficiency of numerical algorithms for the models themselves, while neglecting the optimization of boundary condition parameterization methods. Therefore, it is essential to develop a boundary condition generalization method that can both reflect typical non-constant hydrodynamic, water quality, and wind speed processes and accelerate computation. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a generalized method for boundary conditions in lake hydrodynamic and water quality models. This method can better reflect the non-constant processes of parameters such as flow rate, water level, and pollutant concentration, while reducing computational complexity and time. It provides a high-precision simulation tool for accurate source tracing and governance decisions in lake pollution.

[0006] To achieve this objective, the present invention adopts the following technical solution: This invention provides a method for generalizing boundary conditions in a lake hydrodynamic and water quality model. The stepwise generalization method for boundary conditions includes the following steps: (1) Real-time collection of multi-source monitoring data of lakes, including hydrodynamic parameters, water quality parameters and meteorological parameters; fusion of multi-source monitoring data based on timestamp alignment technology, and extraction of hourly feature sequences using sliding window algorithm to construct a spatiotemporal correlation database; (2) For the hydrodynamic parameters, water quality parameters and meteorological parameters extracted in step (1), determine the constraints of each parameter and obtain the generalized threshold of the cascade. (3) Based on the constraints determined in step (2), perform cascade generalization on the multi-source monitoring data of the lake; (4) Based on the boundary conditions and cascade wind field conditions obtained by the cascade generalization in step (3), construct a two-dimensional planar or three-dimensional vertical hydrodynamic-water quality mathematical model.

[0007] The boundary condition generalization method for lake hydrodynamic and water quality models provided by this invention, compared with the time-averaged processing method, which is widely used in current lake hydrodynamic and water quality models, adopts data-driven constraint technology to adaptively group the basic physical quantities involved in the lake flow and pollutant movement process. This method can better reflect the non-constant process of the above physical quantities, thus more accurately reflecting the movement of pollutants in the lake. Compared with manual stepwise generalization, this method has higher accuracy and efficiency, and the physical meaning of each parameter is clear. It also has a small processing volume and fast model calculation speed, which can provide reliable data support for the precise control of pollutant diffusion.

[0008] Preferably, the hydrodynamic parameters in step (1) include at least one of the following: the flow rate of the river flowing into the lake, the water level, or the flow velocity.

[0009] Preferably, the water quality parameters in step (1) include pollutant concentrations, which include at least one of total phosphorus, total nitrogen, or chemical oxygen demand.

[0010] Preferably, the meteorological parameters in step (1) include at least one of wind speed, wind direction, or temperature.

[0011] Preferably, the real-time acquisition of multi-source monitoring data of the lake in step (1) is carried out through Internet of Things (IoT) sensors.

[0012] Preferably, the specific steps for determining the constraints of each parameter in step (2) include: first, setting the threshold for the wind force level, and setting the absolute deviation of wind speed under different wind force levels. Relative deviation of wind speed Then, based on hydrodynamic and water quality data, the boundary thresholds between water flow rate and pollutant concentration levels are set, and the absolute deviation of flow rate under different water flow rate levels is also set. Flow rate relative deviation absolute deviation of water level and relative deviation of water level Set the absolute deviation of pollutant concentration under different levels of pollutant concentration. Relative deviation of pollutant concentration Based on the duration data of wind force and water flow, set the maximum number of hours for a single wind force level. and maximum number of hours for a single water flow level This yields 10 control parameters.

[0013] Preferably, the threshold values ​​for wind force levels include: light wind (level 0-2), wind speed ≤ 3.3 m / s; moderate wind (level 3-5), wind speed 3.4-10.7 m / s; and strong wind (level 6-8), wind speed 10.8-20.7 m / s.

[0014] The light wind is level 0-2, for example, level 0, level 1 or level 2.

[0015] The wind speed is ≤3.3m / s, for example, it can be 3.3m / s, 3m / s, 2.5m / s, 2m / s or 1m / s, but is not limited to the listed values. Other unlisted values ​​within the range are also applicable.

[0016] The stroke level 3-5 can be, for example, level 3, level 4 or level 5.

[0017] The wind speed is 3.4-10.7 m / s, for example, it can be 3.4 m / s, 5 m / s, 6 m / s, 8 m / s or 10.7 m / s, but is not limited to the listed values. Other unlisted values ​​within the range are also applicable.

[0018] The wind force is 6-8, for example, it can be 6, 7 or 8.

[0019] The wind speed is 10.8-20.7 m / s, for example, it can be 10.8 m / s, 12 m / s, 15 m / s, 18 m / s or 20.7 m / s, but is not limited to the listed values. Other unlisted values ​​within the range are also applicable.

[0020] The different levels of wind force refer to high wind speed, medium wind speed, and low wind speed; the different levels of water flow rate refer to high flow rate, medium flow rate, and low flow rate; the different levels of pollutant concentration refer to high pollutant concentration, medium pollutant concentration, and low pollutant concentration.

[0021] Preferably, the absolute deviation of wind speed Relative deviation of wind speed The expressions for are shown in equations (1) and (2) respectively: (1) (2) in: express Wind speed at any given time, in m / s; This represents the average wind speed of the generalized cascade, in m / s.

[0022] Preferably, the absolute deviation of the flow rate Flow rate relative deviation absolute deviation of water level and relative deviation of water level The expressions are shown in equations (3)-(6) respectively: (3) (4) (5) (6) in: express The flow rate at any given moment, in milliseconds (m). 3 / s; This represents the generalized average flow rate of the cascade, in cubic meters per second (m³). 3 / s; express Water level at any given time, in meters (m). This represents the average water level of the cascade after generalization, in meters (m).

[0023] Preferably, the absolute deviation of the pollutant concentration Relative deviation of pollutant concentration The expressions are shown in equations (7) and (8) respectively: (7) (8) in: express The pollutant concentration at any given time, expressed in g / mL; This represents the generalized average pollutant concentration for that cascade, expressed in g / mL.

[0024] It should be noted that the 10 control parameters described in this invention can be freely adjusted according to the actual conditions of lake hydrodynamics, water quality and meteorology, and are not fixed values.

[0025] Preferably, the specific steps of the tiered generalization in step (3) include: (a) Starting from the multi-source monitoring data of the lake in the first hour, determine whether the measured data at the current moment meets the 10 control parameters; if not, set the moment as an independent cascade; if it meets, add the wind speed, flow rate, water level and pollutant concentration data of the next moment to the cascade, and determine whether the wind speed, flow rate, water level and pollutant concentration of the cascade meet the 10 control parameters after the addition of data. (b) When the newly added time data does not meet the constraints of the 10 control parameters, terminate the current cascade generalization and calculate the total duration, average wind speed, average flow rate, average water level and average pollutant concentration of the cascade. (c) Repeat steps (a) to (b) until all timestamp data has been processed.

[0026] Preferably, the conditions for generating the independent tier in step (a) include: a sudden change in wind speed caused by gusts or thunderstorms, or a sudden increase in flow exceeding the numerical constraints of the parameters.

[0027] Preferably, step (b) outputs the average wind speed, average flow rate, average water level, and average pollutant concentration of each cascade, thus obtaining the generalized non-steady flow and pollutant coupling process.

[0028] Preferably, when the total number of ladder levels in step (3) exceeds a preset threshold or the result does not meet the actual needs, the numerical constraints of the 10 control parameters are dynamically adjusted.

[0029] Preferably, the calculation method for the tiered generalization in step (3) is as follows: First, a non-steady flow and pollutant concentration monitoring data are set. M Hours, generalized as N The formula for calculating a constant ladder level is as follows: ; ; ; ; .

[0030] in: This represents the zeroth stage of a non-constant process; Indicates the first tier to the tier 1 j The total number of hours included in the tier. … ; The duration of the ladder runout in hours must be constrained using the aforementioned 10 control parameters; … .

[0031] Then, the average pollutant concentration of each stage is calculated according to the principle of mass conservation, and the formula is as follows: .

[0032] The above calculation formula means that for a non-constant process after tiered generalization, based on the average data of the previous tier, within the constraint of 10 parameters, the hourly data that is closest to the previous tier is found and assigned to the previous tier.

[0033] Preferably, the governing equations for constructing the planar two-dimensional or vertical three-dimensional hydrodynamic-water quality mathematical model in step (4) are shown in equations (9)-(12): (9) (10) (11) (12) in: Indicates time; , , They represent the horizontal direction respectively. Horizontal direction and the vertical velocity component; c Indicates the volume concentration of pollutants; h Indicates water depth; ; , , These represent the diffusion terms corresponding to the horizontal motion equation and the concentration transport equation, respectively. Indicates vertical Coordinate transformation; This indicates the riverbed elevation relative to a reference elevation.

[0034] The Specifically, it refers to the riverbed elevation with a certain reference height as the zero point. For example, the riverbed elevation obtained by surveying in China often uses the Wusong Elevation or Yellow Sea Elevation as the reference point for height calculation.

[0035] Compared with the prior art, the present invention has the following beneficial effects: (1) The boundary condition generalization method of the lake hydrodynamic and water quality model provided by the present invention, compared with the boundary treatment method used more often in the current lake hydrodynamic-water quality model - the time-average treatment method, adopts data-driven constraint technology to adaptively group the basic physical quantities involved in the lake water flow and pollutant movement process, which can better reflect the non-constant process of the above physical quantities, and thus more accurately reflect the movement of pollutants in the lake.

[0036] (2) The method provided by the present invention has higher accuracy and efficiency than manual stepwise generalization, and the physical meaning of each parameter is clear, the amount of processing is small, the model calculation speed is fast, and it can provide reliable data support for the precise control of pollutant diffusion.

[0037] (3) The boundary condition generalization method provided by this invention has universality. It can be used not only for the Navier-Stokes equation and pollutant transport equation in the σ coordinate system, but also for the two-dimensional shallow water equation in the plane. In addition, the boundary condition generalization method provided by this invention can also be used in shallow water areas such as canals and estuaries. Attached Figure Description

[0038] Figure 1 This is a comparison chart of the generalized wind field time series and the unprocessed wind field time series provided in Embodiment 1 of the present invention; Figure 2 This is a comparison chart of the generalized inflow river time series and the unprocessed inflow river time series provided in Embodiment 1 of the present invention; Figure 3 This is a comparison chart of the generalized time series of pollutant concentrations (total phosphorus) in rivers flowing into the lake and the untreated time series of pollutant concentrations (total phosphorus) in rivers flowing into the lake, provided in Embodiment 1 of the present invention. Figure 4 This is a comparison chart of pollutant concentrations at monitoring points provided in Embodiment 1 of the present invention. Detailed Implementation

[0039] The technical solution of the present invention will be further illustrated below through specific embodiments. Those skilled in the art should understand that the embodiments described are merely illustrative of the present invention and should not be construed as limiting the invention in any way.

[0040] This invention provides a method for generalizing boundary conditions in a lake hydrodynamic and water quality model. The stepwise generalization method for boundary conditions includes the following steps: (1) Collect multi-source monitoring data of lakes in real time through IoT sensors, including the flow rate, water level, flow velocity, total phosphorus, total nitrogen, chemical oxygen demand and wind speed of rivers flowing into the lake; integrate multi-source monitoring data based on timestamp alignment technology, and extract hourly feature sequences using sliding window algorithm to construct a spatiotemporal correlation database.

[0041] (2) For the hydrodynamic parameters, water quality parameters and meteorological parameters extracted in step (1), determine the constraints of each parameter and obtain the generalized threshold values ​​for the tiered classification. The specific steps include: first, setting the threshold values ​​for wind force levels, namely: light wind level 0-2, wind speed ≤3.3m / s; moderate wind level 3-5, wind speed 3.4-10.7m / s; strong wind level 6-8, wind speed 10.8-20.7m / s; and setting the absolute deviation of wind speed under different wind force levels. Relative deviation of wind speed Then, based on hydrodynamic and water quality data, the boundary thresholds between water flow rate and pollutant concentration levels are set, and the absolute deviation of flow rate under different water flow rate levels is also set. Flow rate relative deviation absolute deviation of water level and relative deviation of water level Set the absolute deviation of pollutant concentration under different levels of pollutant concentration. Relative deviation of pollutant concentration Based on the duration data of wind force and water flow, set the maximum number of hours for a single wind force level. and maximum number of hours for a single water flow level This yields 10 control parameters.

[0042] The absolute deviation of wind speed Relative deviation of wind speed The expressions for are shown in equations (1) and (2) respectively: (1) (2) in: express Wind speed at any given time, in m / s; This represents the average wind speed of the generalized cascade, in m / s.

[0043] The absolute deviation of the flow rate Flow rate relative deviation absolute deviation of water level and relative deviation of water level The expressions are shown in equations (3)-(6) respectively: (3) (4) (5) (6) in: express The flow rate at any given moment, in milliseconds (m). 3 / s; This represents the generalized average flow rate of the cascade, in cubic meters per second (m³). 3 / s; express Water level at any given time, in meters (m). This represents the average water level of the cascade after generalization, in meters (m).

[0044] The absolute deviation of pollutant concentration Relative deviation of pollutant concentration The expressions are shown in equations (7) and (8) respectively: (7) (8) in: express The pollutant concentration at any given time, expressed in g / mL; This represents the generalized average pollutant concentration for that cascade, expressed in g / mL.

[0045] (3) Based on the constraints determined in step (2), the multi-source monitoring data of the lake are generalized in a tiered manner. The specific steps include: (a) Starting from the multi-source monitoring data of the lake in the first hour, determine whether the measured data at the current moment meets the 10 control parameters; if not, set the moment as an independent level; if it meets the requirements, add the wind speed, flow rate, water level and pollutant concentration data of the next moment to the level, and determine whether the wind speed, flow rate, water level and pollutant concentration of the level after adding the data meet the 10 control parameters; the conditions for generating the independent level include: sudden change in wind speed caused by gusts or thunderstorms, or a sudden increase in flow rate exceeding the numerical constraints of the parameters.

[0046] (b) When the newly added time data does not meet the constraints of the 10 control parameters, terminate the current cascade generalization, calculate the total duration, average wind speed, average flow rate, average water level and average pollutant concentration of the cascade, and output the average wind speed, average flow rate, average water level and average pollutant concentration of the cascade to obtain the non-steady flow and pollutant coupling process after cascade generalization.

[0047] (c) Repeat steps (a) to (b) until all timestamp data has been processed.

[0048] The calculation method for the tiered generalization is as follows: First, a non-steady flow and pollutant concentration monitoring data are set. M Hours, generalized as N The formula for calculating a constant ladder level is as follows: ; ; ; ; .

[0049] in: This represents the zeroth stage of a non-constant process; Indicates the first tier to the tier 1 j The total number of hours included in the tier. … ; The duration of the ladder runout in hours must be constrained using the aforementioned 10 control parameters; … .

[0050] Then, the average pollutant concentration of each stage is calculated according to the principle of mass conservation, and the formula is as follows: .

[0051] The above calculation formula means that for a non-constant process after tiered generalization, based on the average data of the previous tier, within the constraint of 10 parameters, the hourly data that is closest to the previous tier is found and assigned to the previous tier.

[0052] When the total number of generalized ladder levels exceeds a preset threshold or the result does not meet actual requirements, the numerical constraints of the 10 control parameters are dynamically adjusted.

[0053] (4) Based on the boundary conditions and cascade wind field conditions obtained by the cascade generalization in step (3), construct a two-dimensional planar or three-dimensional vertical hydrodynamic-water quality mathematical model, and the governing equations are shown in equations (9)-(12): (9) (10) (11) (12) in: Indicates time; , , They represent the horizontal direction respectively. Horizontal direction The velocity component in the vertical direction; c represents the volume concentration of pollutants; h represents the water depth; ; , , These represent the diffusion terms corresponding to the horizontal motion equation and the concentration transport equation, respectively. Indicates vertical Coordinate transformation; This indicates the riverbed elevation relative to a reference elevation.

[0054] Example 1 This example uses a sudden pollution incident in a lake as an example.

[0055] We have a two-month monitoring dataset for a deep lake, including data on inflow river flow, water level, total phosphorus, total nitrogen, chemical oxygen demand (COD), and wind speed, with hourly intervals, totaling 1440 data sets. During the monitoring period, the deep lake experienced a pollution incident, primarily caused by excessive total phosphorus levels in the inflow river. To accurately simulate the coupled hydrodynamic and water quality response process of the lake during this period, systematic preprocessing of the raw monitoring data is necessary.

[0056] The boundary condition generalization method for the lake hydrodynamic and water quality model provided in this embodiment includes the following steps: (1) Collect multi-source monitoring data of lakes in real time, extract water level and flow data from hydrodynamic data, extract chemical oxygen demand and total phosphorus data from water quality data, and extract wind speed data from meteorological data.

[0057] (2) Determine the applicable range and control threshold of the control parameters. The specific parameters are shown in Table 1. It should be noted that the control conditions of the parameters need to be fine-tuned according to the actual situation of the lake.

[0058] Table 1 (3) Based on the control conditions determined in step (2), the multi-source monitoring data of the lake are generalized in stages. The comparison between the generalized wind field time series and the unprocessed wind field time series is shown in the figure below. Figure 1 As shown in the figure, the comparison between the generalized inflow river flow time series and the unprocessed inflow river flow time series is as follows. Figure 2 As shown in the figure, the comparison between the generalized time series of pollutant concentrations (total phosphorus) in the rivers flowing into the lake and the untreated time series of pollutant concentrations (total phosphorus) in the rivers flowing into the lake is as follows. Figure 3 As shown. By Figure 1 As can be seen, when wind speeds are within the allowable range of the speed gradient, multiple wind speeds are grouped into the same gradient; when wind speeds change abruptly, that wind speed is listed as a separate gradient. This generalization method can reflect both the impact of extreme wind speeds on the model and the constant effects when wind speed fluctuations are small. Figure 2 and Figure 3 It is evident that the flow rate and pollutant concentration gradients are nearly identical, indicating that both flow rate and pollutant concentration jointly constrain the gradient division. It is worth noting that the flow rate and pollutant concentration gradients are not necessarily strictly uniform, as human activities cause pollutant concentrations to vary in time and space. Furthermore, Figure 2 and Figure 3 At certain times, the flow rate and pollutant concentration in the rivers flowing into the lake are zero. This is because the local government built a sluice gate at the river's inlet to prevent pollutants from contaminating the lake.

[0059] (4) Based on the boundary conditions and cascade wind field conditions obtained in step (3), construct a two-dimensional planar or three-dimensional elevation hydrodynamic-water quality mathematical model. A comparison of pollutant concentrations at monitoring points is shown in the figure below. Figure 4 As shown in the figure, the method used in this embodiment has high calculation accuracy. Compared with the hydrodynamic water quality model that does not use the method provided in this embodiment to generalize the boundary conditions, the method used in this embodiment can save 36% of the calculation time.

[0060] In summary, the boundary condition generalization method for the lake hydrodynamic and water quality model provided by this invention, compared with the time-averaged processing method, which is more commonly used in current lake hydrodynamic and water quality models, adopts data-driven constraint technology to adaptively group the basic physical quantities involved in the lake flow and pollutant movement process. This method can better reflect the non-constant process of the above physical quantities, and thus more accurately reflect the movement of pollutants in the lake.

[0061] Compared with manual tiered generalization, the method provided by this invention has higher accuracy and efficiency, and the physical meaning of each parameter is clear. It requires less processing and has a fast model calculation speed, which can provide reliable data support for the precise control of pollutant diffusion.

[0062] The boundary condition generalization method provided by this invention has universality, applicable not only to the Navier-Stokes equations and pollutant transport equations in the σ coordinate system, but also to two-dimensional planar shallow water equations. Furthermore, the boundary condition generalization method provided by this invention can also be applied to shallow water areas such as canals and estuaries.

[0063] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method for generalizing boundary conditions of a lake hydrodynamic water quality model, characterized in that, The boundary condition generalization method includes the following steps: (1) Real-time collection of multi-source monitoring data of lakes, including hydrodynamic parameters, water quality parameters and meteorological parameters; fusion of multi-source monitoring data based on timestamp alignment technology, and extraction of hourly feature sequences using sliding window algorithm to construct a spatiotemporal correlation database; (2) For the hydrodynamic parameters, water quality parameters and meteorological parameters extracted in step (1), determine the constraints of each parameter and obtain the generalized threshold of the cascade. The specific steps for determining the constraints of the parameters include: firstly, setting the threshold of wind grade and the absolute deviation of wind speed under different grades of wind and the relative deviation of wind speed ; then, setting the threshold of water flow and pollutant concentration grade and the absolute deviation of flow under different grades of water flow , the relative deviation of flow , the absolute deviation of water level and the relative deviation of water level , the absolute deviation of pollutant concentration under different grades of pollutant concentration and the relative deviation of pollutant concentration based on the water power and water quality data; setting the maximum duration of single wind grade and the maximum duration of single water flow grade based on the duration data of wind and water flow, obtaining 10 control parameters; (3) Based on the constraints determined in step (2), the multi-source monitoring data of the lake are generalized in a cascade manner; The specific steps of the tiered generalization include: (a) Starting from the multi-source monitoring data of the lake in the first hour, determine whether the measured data at the current moment meets the 10 control parameters; if not, set the moment as an independent level; if it meets the requirements, add the wind speed, flow rate, water level and pollutant concentration data of the next moment to the level, and determine whether the wind speed, flow rate, water level and pollutant concentration of the level after adding the data meet the 10 control parameters; the conditions for generating the independent level include: sudden change in wind speed caused by gusts or thunderstorms, or a sudden increase in flow rate exceeding the numerical constraints of the parameters; (b) When the newly added time data does not meet the constraints of 10 control parameters, terminate the current cascade generalization, calculate the total duration, average wind speed, average flow rate, average water level and average pollutant concentration of the cascade, and output the average wind speed, average flow rate, average water level and average pollutant concentration of the cascade to obtain the non-steady flow and pollutant coupling process after cascade generalization. (c) Repeat steps (a) to (b) until all timestamp data has been processed; (4) Based on the boundary conditions and cascade wind field conditions obtained by the cascade generalization in step (3), construct a two-dimensional planar or three-dimensional vertical hydrodynamic-water quality mathematical model.

2. The boundary condition generalization method according to claim 1, characterized in that, The hydrodynamic parameters mentioned in step (1) include at least one of the following: the flow rate of the river flowing into the lake, the water level, or the flow velocity.

3. The boundary condition generalization method according to claim 1, characterized in that, The water quality parameters in step (1) include pollutant concentrations, which include at least one of total phosphorus, total nitrogen, or chemical oxygen demand.

4. The boundary condition generalization method according to claim 1, characterized in that, The meteorological parameters mentioned in step (1) include at least one of wind speed, wind direction, or temperature.

5. The boundary condition generalization method according to claim 1, characterized in that, The threshold values ​​for wind force levels include: light wind (level 0-2, wind speed ≤ 3.3 m / s); moderate wind (level 3-5, wind speed 3.4-10.7 m / s); and strong wind (level 6-8, wind speed 10.8-20.7 m / s).

6. The boundary condition generalization method according to claim 1, characterized in that, The absolute deviation of wind speed relative deviation of wind speed The expressions for are shown in equations (1) and (2) respectively: ；(1) ； (2) in: express Wind speed at any given time, in m / s; This represents the generalized average wind speed of the cascade, expressed in m / s.

7. The boundary condition generalization method according to claim 1, characterized in that, The absolute deviation of the flow rate Flow rate relative deviation absolute deviation of water level and relative deviation of water level The expressions are shown in equations (3)-(6) respectively: ；(3) ；(4) ；(5) ；(6) in: express The flow rate at any given moment, in milliseconds (m). 3 / s; This represents the generalized average flow rate of the cascade, in cubic meters per second (m³). 3 / s; express Water level at any given time, in meters (m). This represents the average water level of the cascade after generalization, in meters (m).

8. The boundary condition generalization method according to claim 1, characterized in that, The absolute deviation of pollutant concentration and relative deviation of pollutant concentration The expressions are shown in equations (7) and (8) respectively: ；(7) ； (8) in: express The pollutant concentration at any given time, expressed in g / mL; This represents the generalized average pollutant concentration for that cascade, expressed in g / mL.

9. The boundary condition generalization method according to claim 1, characterized in that, When the total number of ladder levels in step (3) exceeds the preset threshold or the result does not meet the actual needs, the numerical constraints of the 10 control parameters are dynamically adjusted.

10. The boundary condition generalization method according to claim 1, characterized in that, The governing equations for constructing the planar two-dimensional or vertical three-dimensional hydrodynamic-water quality mathematical model in step (4) are shown in equations (9) to (12): ；(9) ；(10) ；(11) ；(12) in: Indicates time; , , They represent the horizontal direction respectively. Horizontal direction and the vertical velocity component; c Indicates the volume concentration of pollutants; h Indicates water depth; ; Indicates vertical Coordinate transformation; This indicates the riverbed elevation relative to a reference elevation.

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