A method for calibrating hydraulic performance parameters of a small-scale surface flow constructed wetland

Abstract

This invention provides a method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland, relating to the field of constructed wetland data calibration and processing technology. The method includes: in a numerical model, determining experimental schemes for multiple parameter levels using the hydraulic performance parameters to be calibrated as variables; simulating the process using the numerical model; outputting the model simulation results; analyzing the correlation between the model simulation results and the hydraulic performance parameters; and screening out characteristic result indicators that are significantly sensitive to the hydraulic performance parameters. Based on a pre-established physical model of the surface flow constructed wetland, a tracer-containing solution is continuously injected into the physical model until the system stabilizes, forming a tracer loading build-up period. Subsequently, the injected solution is switched to clean water and continuously injected at the same flow rate, forming a tracer release monitoring period. During the tracer release monitoring period, measured data corresponding to the characteristic result indicators are collected. This invention enables accurate calibration of hydraulic performance parameters.

Description

Technical Field

[0001] This invention relates to the field of constructed wetland data calibration and processing technology, and more specifically, to a method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland. Background Technology

[0002] Surface flow constructed wetlands are widely used in decentralized wastewater treatment due to their simple technology, low investment, convenient operation and maintenance, and eco-friendliness. The effective removal of pollutants in surface flow constructed wetlands, whether through physical sedimentation, plant uptake, or microbial degradation, relies fundamentally on hydrodynamic processes. Hydrodynamic characteristics such as flow path, velocity distribution, hydraulic retention time, and mixing degree directly determine the contact opportunities between pollutants and the substrate, as well as the interaction time with plant roots and microbial communities, thus profoundly affecting the overall purification efficiency of the system.

[0003] In related technologies, the study of the hydrodynamic characteristics of surface flow constructed wetlands can achieve certain results by establishing large-scale physical models for experimental observation, or by combining traditional tracer experiments with numerical simulation techniques such as computational fluid dynamics for simulation analysis. However, for small-scale surface flow constructed wetlands, if attempts are made to establish miniaturized physical models to reduce costs and improve experimental controllability, tracer experiments (such as salt or dye pulse injection) often fail in such small systems when using numerical simulations for parameter calibration, leading to inaccurate and unreliable calibration of hydraulic performance parameters. Summary of the Invention

[0004] The problem that this invention aims to solve is that tracer experiments of related technologies (such as pulse injection of salt or dye) often fail in such small systems, resulting in inaccurate and unreliable calibration of hydraulic performance parameters.

[0005] To address the aforementioned problems, in a first aspect, the present invention provides a method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland, comprising: In the numerical model of surface flow constructed wetlands, experimental schemes with multiple parameter levels are determined using hydraulic performance parameters to be calibrated as variables. The numerical model is used for simulation, the simulation results are output, the correlation between the simulation results and the hydraulic performance parameters is analyzed, and characteristic result indicators that are significantly sensitive to the hydraulic performance parameters are screened out. Based on the pre-established physical model of the surface flow constructed wetland, a solution containing tracer is continuously injected into the physical model until the system stabilizes, forming the tracer load build-up period; then the injected solution is switched to clean water and continuously injected at the same flow rate, forming the tracer release monitoring period; during the tracer release monitoring period, measured data corresponding to the characteristic result indicators are collected; Based on the structural parameters of the physical model, a corresponding numerical model is constructed. The hydraulic performance parameters to be calibrated are input into the numerical model with preset initial values ​​and simulated to obtain numerical simulation results. The numerical simulation results are compared and iterated with the measured data to calibrate the optimal values ​​of the hydraulic performance parameters in reverse.

[0006] The present invention provides a method for calibrating the hydraulic performance parameters of small-scale surface flow constructed wetlands. This method involves determining an experimental scheme with multiple parameter levels in a numerical model of the surface flow constructed wetland, using the hydraulic performance parameters to be calibrated as variables. By simulating the output results and analyzing their correlation with the parameters, highly sensitive characteristic indicators are selected. This method clarifies the correspondence between each hydraulic performance parameter and specific monitoring indicators through numerical pre-analysis, enabling subsequent physical experiments to collect measured data highly correlated with the hydraulic performance parameters to be calibrated. This provides an accurate comparison benchmark for reverse calibration of the parameters, ensuring the reliability and accuracy of the calibration results. Based on a pre-established physical model... The model uses a tracer-containing solution as the base solution, continuously injected until the system stabilizes to form a tracer load build-up period. Then, it switches to injecting clean water at the same flow rate to form a tracer release monitoring period. During this monitoring period, measured data corresponding to characteristic results are collected. This reverse tracer operation overcomes the shortcomings of traditional pulse tracer and other methods in small wetlands, which cannot form obvious concentration peaks. A corresponding numerical model is constructed based on the structural parameters of the physical model. The hydraulic performance parameters to be calibrated are input with preset initial values ​​and simulated. The simulation results are then compared and iterated with the measured data to reverse-calibrate the optimal values ​​of the parameters. Accurate calibration of hydraulic performance parameters is achieved through closed-loop comparison and parameter adjustment.

[0007] In a second aspect, the present invention provides an electronic device, including a memory and a processor; The memory is used to store computer programs; The processor is configured to, when executing the computer program, implement the hydraulic performance parameter calibration method for small-scale surface flow constructed wetlands as described in the first aspect.

[0008] Thirdly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for calibrating hydraulic performance parameters of a small surface flow constructed wetland as described in the first aspect.

[0009] The electronic device and computer-readable storage medium provided by this invention have the same beneficial effects compared to the prior art as the hydraulic performance parameter calibration method for small surface flow constructed wetlands has compared to the prior art, and will not be repeated here. Attached Figure Description

[0010] Figure 1 A flowchart illustrating a method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland according to an embodiment of the present invention is shown. Figure 2 The changes in NaCl concentration at the outlet positions of Experiments 1 through 4 in this embodiment of the invention are shown. Figure 1 ; Figure 3 The changes in NaCl concentration at the outlet positions of Experiments 5 through 9 in this embodiment of the invention are shown. Figure 2 ; Figure 4 This invention illustrates a trend diagram showing the change in pollutant (NaCl) concentration in the area affected by the wake behind the plant in the direction of water flow, as measured in an embodiment of the invention. Figure 5 A schematic diagram of the physical model in an embodiment of the present invention is shown; Figure 6 An overall technical roadmap diagram of an embodiment of the present invention is shown; Figure 7 This diagram illustrates the parameter interface configuration for the numerical simulation during the salt solution influent stage (salt load establishment period) in an embodiment of the present invention. Figure 8 This diagram illustrates the parameter interface configuration for the numerical simulation during the clean water replacement influent stage (salt release monitoring period) in an embodiment of the present invention. Figure 9 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention is shown.

[0011] Explanation of reference numerals in the attached figures: 1. Water inlet pipe; 2. Water storage chamber; 3. Water inlet baffle; 4. Emergent plants; 5. Water outlet area. Detailed Implementation

[0012] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0013] It should be noted that relational terms such as "first" and "second" in this invention are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In the description of this specification, references to terms such as "embodiment," "an embodiment," and "an implementation" indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or implementation is included in at least one embodiment or illustrative embodiment of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or implementation. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or implementations.

[0014] Reference Figure 1 As shown in the figure, this invention proposes a method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland, including: S100: In the numerical model of surface flow constructed wetland, an experimental scheme for determining multiple parameter levels is established using the hydraulic performance parameters to be calibrated as variables. The simulation is performed through the numerical model, and the simulation results are output. The correlation between the simulation results and the hydraulic performance parameters is analyzed, and characteristic result indicators that are significantly sensitive to the hydraulic performance parameters are selected.

[0015] Specifically, surface flow constructed wetlands are a type of wastewater treatment system where water flows over the wetland surface, removing pollutants through plant absorption, microbial degradation, and physical sedimentation. Small-scale surface flow constructed wetlands refer to laboratory-scale wetland models, typically no longer than 2m, with a water depth of no more than 0.5m and a theoretical hydraulic retention time of no more than 3 hours. In these models, molecular diffusion effects outweigh convection-dominated hydrodynamic forces, making it difficult to directly calibrate hydraulic performance parameters using traditional tracer experiments (such as pulsed injection) that produce tracer curves with distinct concentration peaks and tails. Numerical models, based on computational fluid dynamics, simulate the hydrodynamic and tracer transport processes within the wetland. They typically include hydrodynamic modules (such as the Hydrodynamic module in MIKE21) and convection-diffusion modules (such as the Transport module in MIKE21). Hydraulic performance parameters to be calibrated may include, for example, the Manning coefficient (reflecting the resistance characteristics of the bed and plants to water flow), eddy viscosity (characterizing the mixing efficiency of turbulent eddies), and diffusion coefficient (controlling the tracer transport rate). Using these parameters as variables, multiple levels (e.g., three levels) are set for each variable, and orthogonal or full factorial experimental designs are employed to form multiple parameter combinations. Numerical model simulations are run for each group, and the simulation results are output. Using methods such as analysis of variance or range analysis, the variation patterns of the simulation results for each parameter at different levels are calculated, and output results significantly correlated with each parameter (e.g., tracer concentration fluctuation characteristics at the outlet location, equilibrium flow velocity in the plant backflow zone, concentration change characteristics in the plant wake zone, etc.) are identified and used as characteristic result indicators for calibrating the corresponding parameters. The core of this step lies in objectively determining the sensitive monitoring areas and characteristic result indicators for each parameter through data-driven statistical analysis methods, rather than subjectively pre-setting correspondences.

[0016] S200: Based on the pre-established physical model of the surface flow constructed wetland, a solution containing tracer is continuously injected into the physical model until the system stabilizes, forming a tracer load build-up period; then the injected solution is switched to clean water and continuously injected at the same flow rate, forming a tracer release monitoring period; during the tracer release monitoring period, measured data corresponding to the characteristic result indicators are collected.

[0017] Specifically, the physical model is a scaled-up model of a real wetland used to simulate the hydrodynamic and pollutant transport characteristics of a real wetland. The physical model in this invention is not limited to a specific structure; it only needs to possess the basic components of an influent distribution structure, a wetland body, and an effluent structure. The influent distribution structure ensures uniform influent flow into the wetland body, avoiding jet interference; the wetland body accommodates water and emergent plants, providing a reaction site for hydrodynamic processes and tracer transport; the effluent structure maintains the designed water depth within the wetland and discharges the water. This generalized physical model can cover various types of small-scale surface flow constructed wetlands, ensuring the broad applicability of the calibration method of this invention.

[0018] The tracer can be, for example, a NaCl solution, with its mass concentration configured within a range where conductivity and concentration are linearly related (e.g., 25~12000 mg / L). The tracer-containing solution is continuously injected at a designed flow rate for a duration not less than a preset multiple (e.g., 3 times) of the theoretical hydraulic residence time, until the tracer achieves a uniform and stable distribution within the physical model, forming the tracer load build-up period. Subsequently, the injected solution is switched to clean water, continuously injected at the same flow rate as during the tracer load build-up period, maintaining a consistent flow field state for a duration not less than a preset multiple (e.g., 4 times) of the theoretical hydraulic residence time, forming the tracer release monitoring period. During the tracer release monitoring period, based on the monitoring point locations corresponding to the characteristic result indicators screened in S100 (e.g., outlet location, plant backflow zone, plant wake influence area), corresponding monitoring equipment is deployed in the physical model (e.g., water quality analyzers are deployed at the outlet location to monitor the tracer concentration time series, flow velocity monitors are installed in the plant backflow zone to monitor the equilibrium flow velocity, and concentration monitoring points are deployed in the plant wake zone to monitor the concentration change curve), and measured data are collected.

[0019] It should be noted that the traditional pulse tracer method involves instantaneously injecting a small amount of tracer into flowing clean water. Due to the extremely rapid molecular diffusion in small wetlands, the tracer is already mixed before reaching the outlet, resulting in no significant change in the outlet concentration and making it impossible to form a breakthrough curve that can be used for parameter calibration. In contrast, the reverse tracer method of this invention first fills the entire wetland with tracer solution to form a uniform salt load, and then continuously injects clean water at the same flow rate. At this time, the removal of tracer mainly relies on macroscopic convection rather than molecular diffusion, thereby forming a clear concentration decay curve at the outlet, providing an effective data basis for the calibration of hydraulic performance parameters such as diffusion coefficient.

[0020] S300: Based on the structural parameters of the physical model, construct the corresponding numerical model, input the hydraulic performance parameters to be calibrated into the numerical model with preset initial values ​​and perform simulation to obtain numerical simulation results, compare and iterate the numerical simulation results with the measured data, and reverse-calibrate the optimal values ​​of the hydraulic performance parameters.

[0021] Specifically, calibration refers to the process of determining unknown parameters in a measuring instrument, computational model, or system through experiments or calibration, so that its output results achieve the best fit with the true or standard values. In this step, the geometric dimensions, terrain elevation, boundary contours, and internal vegetation distribution of the physical model are extracted as structural parameters. For example, an adaptive triangular mesh is generated and boundary conditions are set (e.g., setting the vegetation generalization model boundary as an open boundary and the shoreline as a land boundary). Terrain elevation data is imported to generate a numerical computation domain, which is divided into a hydrodynamic module and a convection-diffusion module to establish a numerical model. Preset initial values ​​are determined based on the structural parameters of the physical model and engineering experience (e.g., Manning coefficient is taken from empirical values ​​in hydraulics manuals, eddy viscosity coefficient is between 0.1 and 0.5, and diffusion coefficient is between 1 and 5 m² / s) and input into the numerical model. The numerical model is run to obtain simulation results, which are then compared with the measured data collected in the S200 to calculate the deviation between the two (e.g., root mean square error or relative deviation). Adjust the values ​​of the hydraulic performance parameters to be calibrated based on the deviation (e.g., decrease the parameter value if the simulated value is too large, and increase the parameter value if it is too small), and repeat the numerical simulation until the deviation meets the preset accuracy requirements (e.g., the relative deviation is less than 5%). Then determine the parameter values ​​at this point as the optimal values, and complete the reverse calibration of the hydraulic performance parameters.

[0022] In practical application, this embodiment uses a numerical model of a surface flow constructed wetland to determine an experimental scheme with multiple parameter levels, using the hydraulic performance parameters to be calibrated as variables. By simulating the output results and analyzing their correlation with the parameters, highly sensitive characteristic indicators are selected. This numerical pre-analysis clarifies the correspondence between each hydraulic performance parameter and specific monitoring indicators, enabling subsequent physical experiments to collect measured data highly correlated with the hydraulic performance parameters to be calibrated. This provides an accurate comparison benchmark for reverse calibration of the parameters, ensuring the reliability and accuracy of the calibration results. Based on the pre-established physical model, with tracers... The tracer solution is continuously injected as a base solution until the system stabilizes to form the tracer load build-up period. Then, clean water is continuously injected at the same flow rate to form the tracer release monitoring period. During this monitoring period, the measured data corresponding to the characteristic result indicators are collected. This reverse tracer operation overcomes the shortcomings of traditional pulse tracer and other methods in small wetlands that cannot form obvious concentration peaks. A corresponding numerical model is constructed based on the structural parameters of the physical model. The hydraulic performance parameters to be calibrated are input with preset initial values ​​and simulated. The simulation results are then compared and iterated with the measured data to reverse-calibrate the optimal values ​​of the parameters. The accurate calibration of the hydraulic performance parameters is achieved through closed-loop comparison and parameter adjustment.

[0023] This invention establishes a complete system of calibration methods for hydraulic performance parameters of small-scale surface flow constructed wetlands, which is particularly suitable for small-scale surface flow constructed wetlands. Through the technical path of "numerical analysis and prediction - physical experiment data collection - reverse iterative calibration", it can achieve accurate calibration of key parameters such as diffusion coefficient at the scale of small wetlands where traditional tracing methods fail. It breaks through the technical bottleneck of molecular diffusion dominance and weak convection signal in small wetlands, and provides reliable technical support and parameter guarantee for numerical simulation, engineering design and operation optimization of small-scale surface flow constructed wetlands.

[0024] like Figure 2 and Figure 3 As shown, as an optional embodiment of the present invention, the hydraulic performance parameters to be calibrated include Manning coefficient, eddy viscosity coefficient and diffusion coefficient; Specifically, the Manning coefficient reflects the roughness characteristics of the wetland bed and vegetation, affecting water flow resistance and velocity distribution. Its value range is determined based on the type of wetland bottom substrate (such as mud, gravel) and vegetation density with reference to hydraulics handbooks, for example, between 0.03 and 0.2, with the corresponding reciprocal of the Manning coefficient between 5 and 33. The eddy viscosity coefficient characterizes the equivalent mixing efficiency of turbulent eddies for pollutant transport. Its value range is estimated using empirical formulas based on influent flow rate, water depth, and flow velocity, for example, between 0.1 and 0.5. The diffusion coefficient controls the intensity of molecular diffusion and turbulent diffusion, affecting the overall transport and mixing process of the tracer. Its value range is initially set based on empirical values ​​of molecular diffusion, for example, between 1 and 5 m² / s. The above three parameters together constitute the core hydraulic performance parameters to be calibrated in the numerical model of surface flow constructed wetland hydrodynamics and convection diffusion. The Manning coefficient and eddy viscosity coefficient are calibrated through the hydrodynamic module, and the diffusion coefficient is calibrated through the convection diffusion module, as shown in Tables 3 (including Tables 3-1 and 3-2) and 4 (including Tables 4-1 and 4-2). Different parameters correspond to different sensitive monitoring areas and characteristic result indicators.

[0025] In the numerical model of the surface flow constructed wetland, the experimental scheme for determining multiple parameter levels is based on the hydraulic performance parameters to be calibrated as variables. Simulation is performed using the numerical model, and the simulation results are output. The correlation between the simulation results and the hydraulic performance parameters is analyzed, and characteristic indicators that are significantly sensitive to the hydraulic performance parameters are selected, including: Based on the eddy viscosity coefficient, the Manning coefficient, and the diffusion coefficient, multiple variables are determined, and a multi-level orthogonal experimental scheme or a full-factor experimental scheme based on the multiple variables is established. The numerical model is used for simulation, and the model simulation results are output. The model simulation results include the changes in tracer concentration at the wetland outlet during the tracer release monitoring period, the changes in the equilibrium flow velocity in the plant backflow zone, and the changes in the tracer concentration in the plant wake influence area. Specifically, the eddy viscosity coefficient (A), the reciprocal of the Manning coefficient (B), and the diffusion coefficient (C) are used as three variables, each with three levels: eddy viscosity coefficient at 0.24, 0.30, and 0.36; reciprocal of the Manning coefficient at 25, 30, and 35; and diffusion coefficient at 2, 3, and 4. Nine parameter combinations are designed using an L9 (9 groups) orthogonal experimental table, with a blank column allowed to reduce experimental error; as shown in Tables 1 and 2 below.

[0026] Table 1 Variable Settings

[0027] Table 2 Orthogonal Experiment Setup

[0028] Other settings for each parameter combination (such as boundary conditions, initial conditions, simulation duration, etc.) remain consistent. Nine simulations are run using the numerical model, and the simulation results are output. These results include the time series of tracer concentrations at the wetland outlet during the tracer release monitoring period (e.g.,...). Figure 2 and Figure 3 As shown in the sub-figures), the equilibrium velocity in the plant backflow zone (as shown in Table 4-1 below) and the time series of tracer concentrations in the plant wake influence area (as shown in the sub-figures below) Figure 4 (As shown).

[0029] Through range analysis and variance analysis, the significant correlation between each hydraulic performance parameter to be calibrated and each item in the model simulation results was determined. Specifically, based on the nine orthogonal experiments corresponding to Tables 1 and 2, simulations were performed using the numerical model to obtain the simulation results. Taking the change in NaCl concentration as an example, a combination of range analysis and variance analysis was used to determine the impact. Range analysis calculated the mean (k1, k2, k3) and range (R) of the experimental results for each parameter at each level, and compared the magnitude of the range of each parameter to determine the degree of influence, as shown in Tables 3-1 and 3-2 below: Table 3-1 Correlation analysis between NaCl concentration changes and calibration parameters

[0030] Table 3-2 Significance Analysis

[0031] Note: "***" indicates significant correlation at the 0.01 level (two-tailed); "**" indicates significant correlation at the 0.05 level (two-tailed); "*" indicates significant correlation at the 0.1 level (two-tailed). In the range analysis of orthogonal experiments, K1, K2, and K3 represent the sum of experimental results for each factor (e.g., eddy viscosity coefficient) at the same level (e.g., levels 1, 2, and 3). k1, k2, and k3 are the corresponding average values ​​(i.e., K value divided by the number of experiments at that level). R represents the range, which is the difference between the maximum and minimum values ​​among k1, k2, and k3. The magnitude of the R value reflects the degree of influence of the factor on the experimental results: the larger the R value, the more significant the influence of the factor on the results.

[0032] Range analysis results show that, as shown in Table 3-1, the range of the diffusion coefficient R = 0.30098 is much larger than the range of the eddy viscosity coefficient R = 0.00987 and the Manning coefficient R = 0.00844, indicating that the diffusion coefficient has the greatest impact on the fluctuation characteristics of the outlet tracer concentration; as shown in Table 4-1, the range of the Manning coefficient R = 1.707E-07 is larger than the range of the eddy viscosity coefficient and the diffusion coefficient (2.15667E-08), indicating that the Manning coefficient has the greatest impact on the equilibrium velocity in the plant backflow zone; Analysis of variance results show that, as shown in Table 3-2, the diffusion coefficient P=0.001 (<0.01), indicating a highly significant correlation; as shown in Table 4-2, the Manning coefficient P=0.0193 (<0.05), indicating a significant correlation; the eddy viscosity coefficient at the outlet P=0.568 (>0.05), indicating no significant correlation, but through... Figure 4 Control experiments confirmed a significant correlation between it and the area affected by plant wake. Table 4-1 Correlation analysis of equilibrium flow velocity and calibration parameters in the vegetation backflow zone of the central wetland (vegetation blocking flow)

[0033] Table 4-2 Significance Analysis

[0034] Note: "***" indicates a significant correlation at the 0.01 level (two-tailed); "**" indicates a significant correlation at the 0.05 level (two-tailed); "*" indicates a significant correlation at the 0.1 level (two-tailed).

[0035] The corresponding terms in the model simulation results that have significant correlation are used as the characteristic result indicators for calibrating the corresponding hydraulic performance parameters.

[0036] Specifically, based on the conclusions of the above range analysis and variance analysis, the corresponding terms in the model simulation results with significant correlation are used as characteristic result indicators for calibrating the corresponding hydraulic performance parameters: the fluctuation characteristics (first-order difference fluctuation rate) of the tracer concentration time series at the wetland outlet location during the tracer release monitoring period are used as characteristic result indicators for calibrating the diffusion coefficient, because Tables 3-1 and 3-2 show that it is highly significantly correlated with the diffusion coefficient (P<0.01, R=0.30098); the equilibrium velocity in the plant backflow zone is used as a characteristic result indicator for calibrating the Manning coefficient, because Tables 4-1 and 4-2 show that it is significantly correlated with the Manning coefficient (P<0.05, R=1.707E-07); the tracer concentration variation characteristics (such as...) in the plant wake influence area are used as... Figure 2 The difference in the shape of the curve shown is used as a characteristic result index for calibrating the eddy viscosity coefficient, because the comparative experiment confirmed that the concentration curve in this region showed significant changes under different eddy viscosity coefficient values.

[0037] In practical application, this embodiment establishes a three-variable, three-level L9 orthogonal experimental scheme and runs a numerical model under nine parameter combinations. The system analyzes the correlation between three hydraulic performance parameters (Manning coefficient, eddy viscosity coefficient, and diffusion coefficient) and the simulation results of three monitoring areas (outlet location, vegetation backflow zone, and vegetation wake zone). Range analysis (R-value method) quickly ranked the influence of each parameter, while variance analysis (P-value method) precisely quantified the significance level of each correlation. The two methods mutually validate and complement each other. The analysis results show that the diffusion coefficient is highly significantly correlated with the outlet fluctuation characteristics (P=0.001), the Manning coefficient is significantly correlated with the equilibrium velocity in the backflow zone (P=0.019), and the eddy viscosity coefficient is significantly correlated with the concentration change characteristics in the wake zone, as confirmed by a control experiment.

[0038] Based on the above, a correspondence was established between "outlet fluctuation characteristics → diffusion coefficient, equilibrium velocity → Manning coefficient, wake region concentration change → eddy viscosity coefficient," providing a clear theoretical basis for the layout of monitoring points in subsequent physical experiments and parameter calibration in numerical simulations. This method avoids subjectively pre-setting correspondences and objectively determines the sensitive monitoring areas and characteristic result indicators of each parameter through data-driven statistical analysis, significantly improving the scientificity and accuracy of parameter calibration.

[0039] As an optional embodiment of the present invention, the method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland further includes: For hydraulic performance parameters that are not significantly correlated with the model simulation results, a set of benchmark parameter combinations is selected as the control experimental group. The hydraulic performance parameters are changed as single variables for numerical simulation to obtain model simulation results under different parameter values. The hydraulic performance parameters are at least one of Manning coefficient, eddy viscosity coefficient, or diffusion coefficient. Specifically, the "insignificant correlation" was determined using the significance p-value in the analysis of variance. As shown in Table 3-2, the p-value for the eddy viscosity coefficient was 0.568 (>0.05), and the p-value for the Manning coefficient was 0.672 (>0.05). Both were greater than 0.05, indicating that the correlation between the eddy viscosity coefficient and the Manning coefficient and the fluctuation characteristics of the tracer concentration at the outlet location was insignificant, and effective calibration could not be performed using outlet monitoring data. However, the p-value for the diffusion coefficient was 0.001 (<0.01), showing a highly significant correlation, and it could be directly calibrated using the outlet fluctuation characteristics.

[0040] For hydraulic performance parameters with insignificant correlation (taking eddy viscosity coefficient as an example), a set of baseline parameter combinations is selected from the orthogonal experimental scheme as the control experimental group. For example, Experiment 6 in Table 2 (eddy viscosity coefficient A2=0.30, reciprocal of Manning coefficient B3=35, diffusion coefficient C1=2) is selected. Keeping other parameters in this baseline parameter combination unchanged, only the target parameter to be calibrated (i.e., eddy viscosity coefficient) is changed as a single variable for numerical simulation. For example, the eddy viscosity coefficient is set to 0.30 and 0.36 respectively (as shown in Table 1, A2 and A3), while other parameters (reciprocal of Manning coefficient 35, diffusion coefficient 2) remain unchanged. By controlling for a single variable, the interference of other hydraulic performance parameters on the simulation results is eliminated, ensuring that the changes in the simulation results are solely attributed to the change in the hydraulic performance parameter to be calibrated. This accurately verifies the correlation between this parameter and the simulation results of the sensitive area model.

[0041] Within the sensitive region corresponding to the hydraulic performance parameters, the differences between the model simulation results obtained under different parameter values ​​are compared. Specifically, for parameters with insignificant correlation (such as eddy viscosity), the sensitive region is not the outlet location, but rather the region where turbulent eddy effects are significant. According to fluid mechanics principles, eddy viscosity characterizes the equivalent mixing efficiency of turbulent eddies for pollutant transport. In the wake influence region formed on the back of plant stems, the turbulent eddy effect is significant, and changes in eddy viscosity directly affect the tracer concentration distribution in this region. This invention selects the NaCl concentration in the wake influence region formed on the back of plant stems along the downstream direction of water flow as the observation index. Therefore, the sensitive region corresponding to the eddy viscosity is the plant wake influence region. Tracer concentration monitoring points are set up within this region to continuously monitor changes in NaCl concentration over time. Experiment 6 is selected as the control group (baseline parameter combination: eddy viscosity 0.30, Manning's reciprocal 35, diffusion coefficient 2), with only the eddy viscosity changed as a single variable (e.g., taking values ​​of 30 and 36 respectively), comparing the differences in the time series of tracer concentrations within the plant wake influence region under different eddy viscosity values. Figure 4As shown, when the eddy viscosity coefficients are 30 and 36, the tracer concentration time series in the wake-affected region shows significant changes (the curve shape, decay rate, etc. are significantly different), indicating that the model simulation results in this sensitive region have a significant response to changes in the eddy viscosity coefficient.

[0042] If the difference shows a significant change, the model simulation results obtained in the sensitive area will be used as the characteristic result index for calibrating the hydraulic performance parameters.

[0043] Specifically, when the model simulation results (such as the curve shape, peak time, and decay rate of tracer concentration time series) within the sensitive area show significant changes under different parameter values, it proves that there is a significant correlation between the hydraulic performance parameter and the model simulation results within the sensitive area. Therefore, the model simulation results within the sensitive area (e.g., the tracer concentration time series within the area affected by plant wake) are used as characteristic result indicators for calibrating the hydraulic performance parameter. Figure 2 As shown in the figure, the control experiment confirmed that there is a significant correlation between the tracer concentration change characteristics and the eddy viscosity coefficient in the area affected by plant wake. Therefore, the concentration change characteristics in this area are used as the characteristic result index for calibrating the eddy viscosity coefficient.

[0044] In practical application, this embodiment addresses hydraulic performance parameters that are not significantly correlated with the outlet monitoring point in orthogonal experiments (such as the eddy viscosity coefficient P=0.568 in Table 3-2). It selects a set of baseline parameter combinations as a control group, changes only the target parameter as a single variable, and sets its monitoring points in the corresponding sensitive areas (such as the area affected by vegetation wakes corresponding to the eddy viscosity coefficient). The differences in model simulation results within the sensitive areas under different parameter values ​​are then compared (e.g., ...). Figure 2 The concentration curve shown changes significantly, and the significant changes in this sensitive area are used as characteristic results indicators for calibrating the parameter. This effectively solves the problem that conventional monitoring points cannot be calibrated, and can achieve as much coverage and accurate calibration of hydraulic performance parameters as possible.

[0045] As an optional embodiment of the present invention, based on the pre-established physical model of the surface flow constructed wetland, a tracer-containing solution is continuously injected into the physical model until the system stabilizes, forming a tracer load build-up period; subsequently, the injected solution is switched to clean water and continuously injected at the same flow rate, forming a tracer release monitoring period; during the tracer release monitoring period, the measured data corresponding to the characteristic result indicators are collected, including: Prepare a solution containing a tracer, and adjust the mass concentration of the tracer-containing solution to be within a concentration range where conductivity and concentration are linearly related; Specifically, for example, such as Figure 7As shown, the pre-established physical model of the surface flow constructed wetland includes: an inlet pipe 1, a water storage chamber 2, an inlet baffle 3, emergent plants 4, and an outlet zone 5 (which has an outlet). The inlet pipe 1 is located on the inlet side of the model and connects to the water storage chamber 2, used to introduce water into the water storage chamber 2. The water storage chamber 2 is located at the front end of the wetland body and works with the inlet baffle 3 to buffer the inlet water flow and ensure even distribution before it enters the wetland body. The interior of the wetland body is covered with a bottom substrate and planted with emergent plants 4 to simulate the effects of plant interception, root absorption, and microbial attachment in real wetlands. The outlet zone 5 is located below the top of the end of the wetland body to maintain the designed water depth inside the wetland and to discharge treated water from the model. Water flows into the storage chamber 2 through the inlet pipe 1, and after being evenly distributed by the inlet baffle 3, it enters the main body of the wetland. Under the action of emergent plants 4 and bottom substrate, hydrodynamic processes and tracer convection and diffusion processes occur, and finally the water is discharged through the outlet of the outlet area 5, thereby realizing the physical simulation of the hydraulic characteristics and pollutant transport characteristics of small surface flow artificial wetlands.

[0046] The tracer is a NaCl (sodium chloride) solution with a mass concentration ranging from 25 to 12000 mg / L. Within this concentration range, the conductivity of the NaCl solution exhibits a linear relationship with its concentration (conductivity ∝ concentration). Therefore, conductivity can be monitored in real-time using an online conductivity meter and converted to concentration values, eliminating the need for frequent sampling and analysis. Furthermore, within this concentration range, the recovery rate of NaCl can reach over 90% (i.e., tracer mass is conserved, and attenuation is negligible). Therefore, the attenuation coefficient of the tracer can be set to 0 in the convection-diffusion module, simplifying the numerical model calculations.

[0047] The tracer-containing solution is continuously injected until the tracer inside the physical model reaches a stable distribution state, forming the tracer load build-up period; Specifically, a prepared NaCl solution is continuously injected into the wetland physical model at a design flow rate Q (e.g., 0.1 m³ / h), initially with no water remaining inside the wetland. The injection duration is no less than three times the theoretical hydraulic retention time (HRT). For example, if the theoretical HRT is 3 hours, the injection duration is ≥9 hours, ensuring that the NaCl solution fills the entire wetland and reaches a stable distribution state (i.e., the concentration at each point inside the wetland tends to be uniform, and the outlet concentration is basically consistent with the inlet concentration). This stage only involves hydrodynamic processes and does not involve the convection-diffusion attenuation of the tracer.

[0048] The injection solution is switched to water and continuously injected at the same flow rate as the tracer load build-up period to maintain the flow field state inside the physical model and form the tracer release monitoring period. Specifically, immediately after the tracer loading build-up period, the influent should be switched from NaCl solution to clean water (without tracer). The switching process should be completed quickly to avoid flow field disturbance. The influent flow rate should be maintained at the same design flow rate Q (e.g., 0.1 m³ / h) as during the tracer loading build-up period to ensure that the flow field state (water level, velocity distribution) within the wetland does not change due to the influent switching. The clean water should be continuously injected for at least four times the theoretical hydraulic residence time (e.g., if the theoretical HRT is 3 hours, then the clean water injection time should be ≥12 hours) to obtain the complete tracer concentration decay characteristics (the complete process of gradually decaying from an initial high concentration to near zero).

[0049] Based on the second monitoring point corresponding to the characteristic result index, corresponding monitoring equipment is arranged in the physical model to collect the measured data corresponding to the characteristic result index; the second monitoring point covers the monitoring point corresponding to the characteristic result index; at least one of the following is obtained based on the measured data: the outlet tracer concentration time series, the flow velocity time series in the plant backflow zone, and the tracer concentration time series in the wake influence zone.

[0050] Specifically, for example, the second monitoring points include the wetland outlet location, the plant backflow zone, and the plant wake influence area. The specific locations of each monitoring point are set according to the sensitive areas corresponding to the characteristic result indicators determined through orthogonal experiments and variance analysis. A water quality analyzer is deployed at the wetland outlet location to continuously monitor the tracer concentration changes at the outlet during the clear water replacement stage, obtaining the outlet tracer concentration time series. A flow velocity monitor is installed in the plant backflow zone to continuously monitor the flow velocity changes in the plant backflow zone. After the flow velocity stabilizes, the equilibrium flow velocity is extracted to obtain the flow velocity time series of the plant backflow zone. Tracer concentration monitoring points are deployed in the plant wake influence area to continuously monitor the tracer concentration changes in this area, obtaining the tracer concentration time series of the wake influence area.

[0051] In practical application, each monitoring point corresponds one-to-one with the sensitive monitoring points corresponding to the selected characteristic indicators mentioned above. This ensures that the measured data collected from the physical experiment and the comparison results from the numerical simulation have the same spatial reference, providing reliable measured data support for reverse calibration. This monitoring scheme can effectively acquire characteristic data that can be used for parameter calibration in small-scale surface flow constructed wetlands where traditional tracer experiments have failed.

[0052] like Figure 2 and Figure 3 As shown, in an optional embodiment of the present invention, the actual measured data corresponding to the feature result index collected includes: Water quality monitoring instruments are deployed at the outlet of the wetland to obtain the time series of the outlet tracer concentration, and its fluctuation characteristics are extracted as characteristic result indicators for calibrating the diffusion coefficient. Specifically, the water quality analyzer can be an online conductivity meter or an ion concentration monitor to continuously monitor the concentration changes of the tracer (NaCl solution) at the outlet in real time. Since the conductivity of the NaCl solution is linearly related to its concentration in the range of 25–12000 mg / L, the real-time concentration value can be calculated by monitoring the conductivity. The sampling frequency of the outlet tracer concentration time series should be sufficiently high (e.g., once every 30 seconds) to capture subtle fluctuations in concentration over time. These fluctuation characteristics are quantified by calculating the first-order difference volatility, using the following formula: ; d represents the first-order difference rate. k represents the total number of samples; in this case, samples within 3 hours were selected, with a time step of 30 seconds, therefore k = 360; x i The concentration of NaCl in the i-th data group (e.g.) Figure 2 and Figure 3 (as shown) This fluctuation characteristic reflects the severity of the concentration curve at different time points and is highly significantly correlated with the diffusion coefficient (P<0.01 in Table 3-2).

[0053] A flow velocity monitor is installed on the back of the plants at the end point of the wetland in the direction of water flow to obtain the flow velocity time series of the back flow zone of the plants, and the equilibrium flow velocity is extracted as a characteristic result index for calibrating the Manning coefficient. Specifically, the flow velocity monitor can be an acoustic Doppler current meter (ADV) or a miniature spiral current meter, installed on the back of the plant stem at the endpoint of the water flow direction in the wetland (i.e., the back-flow side of the plant). The back-flow zone formed by the plant obstruction creates a low-velocity backflow area, where the flow velocity is significantly affected by the bed surface roughness (Manning coefficient). The flow velocity monitor continuously monitors the change in flow velocity in this area over time. Once the flow velocity stabilizes (i.e., the hydrodynamics reaches steady state), the average flow velocity during this stable phase is extracted as the equilibrium flow velocity. Table 4-2 shows that the equilibrium flow velocity in the back-flow zone is significantly correlated with the Manning coefficient (reciprocal) at the P < 0.05 level; therefore, the equilibrium flow velocity can be used as a characteristic indicator for calibrating the Manning coefficient.

[0054] Tracer concentration monitoring points are set up in the wake influence area formed on the back of the plant stem in the direction of water flow to obtain the time series of tracer concentration in the wake influence area, and the concentration change characteristics are extracted as characteristic result indicators for calibrating the eddy viscosity coefficient.

[0055] Specifically, the wake influence region refers to the turbulent eddy zone formed on the back of plant stems in the direction of water flow. Within this region, the molecular diffusion effect of the water flow itself is relatively weakened, and the turbulent eddies significantly affect the mixing efficiency (i.e., eddy viscosity coefficient) of pollutant transport. Miniature conductivity probes or microelectrodes are deployed in this region to continuously monitor the change in tracer concentration over time. By setting up a control experiment (e.g., selecting a set of baseline parameter combinations and changing only the eddy viscosity coefficient as a single variable), the time series of tracer concentrations in the wake influence region under different eddy viscosity coefficient values ​​are compared. Figure 2 As shown, when the eddy viscosity coefficients are 30 and 36, the tracer concentration curves in the wake region exhibit significant differences (different curve shapes, decay rates, etc.). These concentration variation characteristics (such as curve shape, peak time, decay slope, etc.) can be used as characteristic indicators for calibrating the eddy viscosity coefficient.

[0056] In practical application, this embodiment achieves independent observation of the diffusion coefficient, Manning coefficient, and eddy viscosity coefficient by deploying different types of monitoring equipment in three distinct characteristic regions (outlet location, vegetation backflow zone, and vegetation wake influence zone). The water quality analyzer at the outlet location acquires concentration fluctuation characteristics, which are sensitive to the diffusion coefficient; the velocity monitor in the vegetation backflow zone acquires the equilibrium velocity, which is sensitive to the Manning coefficient; and the concentration monitoring point in the vegetation wake zone acquires the concentration change curve, which is sensitive to the eddy viscosity coefficient. The three monitoring regions do not interfere with each other, allowing each hydraulic performance parameter to respond independently within its respective sensitive spatial window, providing a basis of measured data for subsequent parameter calibration.

[0057] like Figure 6 As shown, in an optional embodiment of the present invention, the step of constructing a corresponding numerical model based on the structural parameters of the physical model, inputting the hydraulic performance parameters to be calibrated into the numerical model with preset initial values, and performing simulation to obtain numerical simulation results includes: The geometric dimensions, terrain elevation, boundary contour, and internal vegetation distribution of the physical model are extracted as the structural parameters. Specifically, the geometric dimensions include the length, width, water depth, and the location and size of the inlet and outlet of the physical model; the topographic elevation includes elevation data at different locations on the bottom of the wetland, which can be obtained through leveling or 3D scanning; the boundary contour includes the planar coordinates and orientation of the wetland shoreline; and the internal vegetation distribution includes the species, plant height, stem diameter, planting density, and planar arrangement of plants. These structural parameters collectively constitute the geometric and physical skeleton of the numerical model, serving as the foundational data for subsequent mesh generation and module configuration.

[0058] An adaptive triangular mesh is generated based on the boundary contour and the internal plant distribution, and the open boundary and land boundary are determined. Specifically, a mesh generation tool (such as the SMS mesh generation tool) is used to transform the wetland boundary outline and the generalized plant model boundary into an adaptive triangular mesh (the mesh density can be automatically adjusted according to the geometric characteristics and physical field changes of the computational region). The adaptive triangular mesh can flexibly adapt to the irregular boundary shape of the wetland and the point-like or cluster-like distribution of plants. Regarding boundary type settings: the inlet / outlet boundaries and the generalized plant model boundary are defined as open boundaries (Ocean boundaries), allowing water flow and tracers to pass freely; the remaining shoreline boundary is defined as a mainland boundary, set to zero flow passage. For areas with drastic flow field changes, such as the plant backflow zone, local mesh refinement can be set (e.g., the plant model mesh node spacing is 0.01m, smaller than the outer boundary node spacing of 0.025m) to improve the simulation accuracy of this area.

[0059] Import terrain elevation data and interpolate it vertically to generate a numerical computation domain containing spatial topology. Specifically, the measured terrain elevation data from the physical model is processed into an xyz format elevation data file (containing the plane coordinates x, y and elevation value z of each measuring point). This elevation data file is then imported into the generated planar mesh, and interpolation is performed in the vertical direction of the mesh nodes to ensure that each planar mesh node obtains a corresponding elevation value. After interpolation, a numerical computation domain with spatial topology (e.g., a .grd or .mesh file) is generated. This computation domain is an adaptive triangular mesh in the plane and represents single-layer or layered water depth variations in the vertical direction, fully characterizing the three-dimensional terrain features of the physical model.

[0060] The numerical computation domain is divided into a hydrodynamic module and a convection-diffusion module, and the numerical model is established accordingly. Specifically, the generated mesh file (.mesh file) is imported into the MIKE21 software (MIKE21 is a two-dimensional water simulation software package developed by the Danish Hydraulic Institute (DHI) for simulating water flow, waves, sediment, and water quality environmental issues in rivers, lakes, estuaries, coasts, and oceans). A hydrodynamic module and a convection-diffusion module are then set up. The hydrodynamic module is responsible for calculating water level changes and velocity distribution under boundary and initial conditions, and solving the shallow water equations. The convection-diffusion module is responsible for calculating the convection-diffusion process of the tracer (NaCl solution) under the influence of the flow field, and solving the convection-diffusion equations. The two modules share the same computational mesh, terrain elevation, and boundary conditions, and exchange data through a file interface to jointly form a complete numerical model.

[0061] The preset initial values ​​are input into the numerical model as the hydraulic performance parameters to obtain the numerical simulation results. The preset initial values ​​are determined based on the structural parameters and engineering experience parameters of the physical model.

[0062] Specifically, and exemplarily, the preset initial values ​​include initial estimates of the Manning coefficient, eddy viscosity coefficient, and diffusion coefficient. The Manning coefficient can be obtained from hydraulics handbooks by consulting empirical values ​​based on the wetland bottom substrate type (e.g., mud, gravel) and vegetation density, for example, between 0.03 and 0.2. The eddy viscosity coefficient can be estimated using empirical formulas based on influent flow rate, water depth, and flow velocity, for example, between 0.1 and 0.5. The diffusion coefficient can be initially set based on empirical values ​​of molecular diffusion, for example, between 1 and 5 m² / s. The selection of initial values ​​should ensure that the numerical model can run stably and produce reasonable physical trends, but it is not required to be close to the true values; subsequent accuracy calibration will be performed through comparative iterations. The preset initial values ​​are input into the hydrodynamic module (Manning coefficient, eddy viscosity coefficient) and the convection-diffusion module (diffusion coefficient), respectively, and the numerical model is run to obtain initial numerical simulation results.

[0063] In practical application, this embodiment achieves a precise correspondence between the numerical model and the physical model in terms of geometric shape and internal structure by extracting the structural parameters of the physical model and generating an adaptive triangular mesh. The reasonable setting of open and land boundaries ensures the physical correctness of the boundary conditions (inlets / outlets and vegetation boundaries allow water flow, while shorelines do not). The import and vertical interpolation of topographic elevation data enable the numerical computation domain to accurately reflect the topographic undulation characteristics of the physical model. The division into hydrodynamic and convection-diffusion modules provides a modular foundation for subsequent phased simulations and parameter calibration. The selection of preset initial values ​​follows the principle of physical rationality, ensuring that while the initial output of the numerical model may be inaccurate, the trend is correct, providing a reasonable iterative starting point for subsequent reverse calibration.

[0064] like Figure 7 and Figure 8 As shown, in an optional embodiment of the present invention, after dividing the numerical computation domain into a hydrodynamic module and a convection-diffusion module, the method further includes: The numerical model is divided into two phases according to the time sequence. In the first phase, only the hydrodynamic module is run to simulate the hydrodynamic steady-state establishment process during the tracer load establishment period. In the second phase, the convection-diffusion module is coupled to the hydrodynamic module to simulate the tracer convection-diffusion process during the tracer release monitoring period. Specifically, such as Figure 7As shown, for the first stage: the hydrodynamic module is used to solve the shallow water equations of the surface flow constructed wetland, calculating the spatiotemporal distribution of water level and velocity under given boundary and initial conditions. At the initial moment of the first stage, both the water level and velocity inside the wetland are zero. The inlet uses a flow rate boundary (design inlet flow rate), and the outlet uses a water level boundary (design water depth). The runtime is no less than three times the theoretical hydraulic residence time to ensure that the hydrodynamics inside the wetland reaches a stable steady state. After the run is completed, the water depth value and velocity vector (including velocity components in the x and y directions) of each grid cell in the numerical computation domain are output, as shown below. Figure 7 As shown in the “Salt solution inlet stage (salt load establishment period)”, only the hydrodynamic module is selected in this stage.

[0065] like Figure 8 As shown, the second stage couples the convection-diffusion module to the hydrodynamic module to solve the convection-diffusion equations of the tracer under the influence of the flow field. The initial conditions are directly imported from the water depth and velocity field at the end of the first stage. The tracer concentration boundary at the inlet is set to zero to simulate the process of clear water replacing the tracer solution. The runtime is no less than four times the theoretical hydraulic residence time to obtain complete tracer concentration decay characteristics. After completion, the concentration distribution in the numerical computation domain is output, such as... Figure 8 As shown in the “Clear Water Replacement Intake Stage (Salt Release Monitoring Period)”, the Hydrodynamic module and the Transport module are selected for this stage.

[0066] The simulation results of the first stage are used as the initial conditions for the second stage to achieve data transfer between the two stages.

[0067] Specifically, data transfer is achieved through a file interface: after the first phase of operation is completed, the water depth and flow velocity results are saved to a results file (e.g., ...). Figure 7 The output of the previous numerical simulation), import the result file when starting the second stage, and interpolate it to the nodes of the current computational grid as initial conditions (e.g., Figure 8 (As shown in the "Initial Conditions" section). Both stages use the same computational grid and terrain elevation data to ensure spatial consistency of data transfer.

[0068] In practical application, this embodiment first runs only the Hydrodynamic module to establish a stable hydrodynamic background. The second stage couples the Transport module to simulate the convective diffusion process of the tracer based on the stable flow field. This phased approach avoids errors introduced by the tracer calculation when the hydrodynamics are unstable, ensuring the simulation accuracy of the tracer concentration decay characteristics. The stable water depth and velocity field in the first stage serve as the initial conditions for the second stage, ensuring the physical continuity and data integration of the flow field state between the two stages. This phased coupling strategy is particularly suitable for small-scale surface flow constructed wetlands, accurately simulating the convective diffusion process of tracer replacement by clean water under conditions of strong molecular diffusion effects, providing a reliable numerical simulation basis for the accurate calibration of the diffusion coefficient.

[0069] As an optional embodiment of the present invention, based on the previous embodiment, the numerical model operation is further divided into two stages according to the time sequence, and the model simulation results of the first stage are used as the initial conditions for the second stage. That is, this step is further defined, and it can be determined that the hydraulic performance parameter calibration method for small surface flow constructed wetlands further includes: The initial conditions for the first stage are set as zero water level and zero flow velocity inside the wetland. The boundary conditions are set as flow inlet boundary and water level outlet boundary. The system is run until the hydrodynamic stability is reached, and the water depth value and flow velocity vector that vary with space are output. Specifically, the first stage corresponds to the tracer load establishment period, during which only the hydrodynamic module is run. Initially, the wetland has zero water, zero water level, and zero flow velocity. The inlet uses a flow rate boundary condition, setting the flow rate based on the design inlet flow rate; the outlet uses a water level boundary condition, setting the water level to the wetland's design water depth to ensure a stable water level and flow velocity distribution gradually establishes within the wetland during the inlet process. The runtime must ensure that the hydrodynamics within the wetland reaches a stable steady state; for example, the runtime should not be less than three times the theoretical hydraulic residence time. After completion, the water depth value and flow velocity vector (including velocity components in the x and y directions) of each grid cell in the numerical computation domain are output to characterize the steady-state hydrodynamic distribution characteristics within the wetland and to provide initial conditions for the second stage.

[0070] The initial conditions for the second stage are set as the spatially varying water depth and velocity fields at the end of the first stage. The boundary conditions are set as the inlet tracer concentration is zero. The model simulation results are output, including the water depth, velocity, and concentration distribution in the numerical computation domain.

[0071] Specifically, the second stage corresponds to the tracer release monitoring period, and a convection-diffusion module is coupled to the hydrodynamic module. The initial conditions directly import the water depth and velocity vector at the end of the first stage, ensuring that the flow field state inside the wetland at the start of the second stage is completely consistent with the steady state at the end of the first stage, guaranteeing flow field continuity between the two stages. The tracer concentration boundary condition at the inlet is set to zero, indicating that the clean water entering the wetland does not contain tracer, thus simulating the convection-diffusion process of clean water replacing the tracer solution. After completion, the numerical computation domain outputs water depth, velocity vector, and tracer concentration distribution. The tracer concentration distribution includes at least the concentration time series at the wetland outlet and the concentration time series in the plant wake influence area, and the velocity distribution includes at least the velocity time series in the plant backflow area, for comparison and calibration with measured data from physical experiments.

[0072] In practical application, this embodiment divides the numerical simulation into two phases according to time sequence. The first phase runs only the hydrodynamic module, while the second phase couples the convection-diffusion module, achieving decoupled simulation of the hydrodynamic process and the tracer transport process. The stable water depth and velocity field at the end of the first phase serve as the initial conditions for the second phase, ensuring physical continuity between the two phases. This allows the second phase to simulate the tracer's convection-diffusion process based on an accurate flow field. The reasonable setting of boundary conditions (flow inlet and water level outlet are used to maintain a stable flow field, and the tracer concentration at the inlet is zero to simulate clean water replacement) enables the numerical model to accurately reproduce the entire process of tracer load establishment and release monitoring in the physical experiment, providing a reliable numerical simulation basis for subsequent parameter calibration. This phased simulation method is particularly suitable for small-scale surface flow constructed wetlands, accurately capturing the tracer concentration decay characteristics under conditions of strong molecular diffusion effects, providing an effective means for accurate calibration of the diffusion coefficient.

[0073] As an optional embodiment of the present invention, the step of reverse calibration of the optimal values ​​of the hydraulic performance parameters by comparing and iterating the numerical simulation results with the measured data includes: Determine the deviation between the numerical simulation results and the measured data; Specifically, for example, for the diffusion coefficient, the time series of tracer concentration at the wetland outlet output by numerical simulation is compared hourly with the time series of tracer concentration at the outlet measured by physical experiments, and the root mean square error or mean absolute error between the two is calculated, or the difference in the first-order difference fluctuation rate between the two is calculated; for the Manning coefficient, the equilibrium velocity of the plant backflow zone output by numerical simulation is compared with the equilibrium velocity measured by physical experiments, and the absolute deviation or relative deviation is calculated; for the eddy viscosity coefficient, the tracer concentration change curve of the plant wake influence area output by numerical simulation is compared with the concentration change curve measured by physical experiments, and the morphological differences between the curves (including but not limited to the deviation of characteristic parameters such as concentration peak difference, concentration decay rate difference, and curve slope difference) are calculated.

[0074] Adjust the values ​​of the corresponding hydraulic performance parameters in the numerical model according to the deviation, and repeat the numerical simulation. Specifically, when the deviation exceeds a preset allowable range, the direction and degree of deviation of the current parameter value from the optimal solution are determined, and the values ​​of the hydraulic performance parameters are adjusted accordingly: if the simulated value is too large, the parameter value is decreased; if the simulated value is too small, the parameter value is increased. The adjustment step size can be set according to the magnitude of the deviation (a large step size is used for rapid approximation when the deviation is large, and a small step size is used for fine adjustment when the deviation is small). After the adjustment is completed, the new parameter values ​​are re-input into the numerical model for simulation, and the numerical simulation results of the corresponding monitoring points are output again, forming a new round of comparison and iteration.

[0075] Determine whether the deviation meets the preset accuracy requirements. If it does, determine the current hydraulic performance parameter value as the optimal value. If it does not meet the requirements, continue to adjust and repeat the numerical simulation.

[0076] Specifically, the preset accuracy requirement can be set according to actual engineering needs. For example, when the absolute value of the deviation is less than or equal to 5%, it is determined that the accuracy requirement is met, indicating that the numerical model is very close to the actual physical model. When the accuracy requirement is met, the current hydraulic performance parameter values ​​are used as the optimal values ​​after calibration and verification for subsequent numerical simulation calculations. When the accuracy requirement is not met, the iterative cycle of parameter adjustment and numerical simulation continues until the preset accuracy requirement is met or the preset maximum number of iterations is reached.

[0077] In practical application, this embodiment achieves reverse calibration of hydraulic performance parameters by establishing a closed-loop iterative mechanism of numerical simulation output → measured data → deviation calculation → parameter adjustment. This method does not rely on the complete penetration curve in traditional tracer experiments, but instead performs local comparisons on measurable characteristic indicators in small-scale surface flow constructed wetlands. This ensures that the optimal values ​​of the final hydraulic performance parameters have clear reliability, providing an accurate foundation of hydraulic performance parameters for the numerical simulation and engineering design of surface flow constructed wetlands.

[0078] As an optional embodiment of the present invention, the method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland further includes: During the tracer load setup period, the Manning coefficient and the eddy viscosity coefficient are calibrated, and during the tracer release monitoring period, the diffusion coefficient is calibrated. After fixing the optimal values ​​of the calibrated hydraulic performance parameters, the calibration of the next hydraulic performance parameter to be calibrated is then performed.

[0079] Specifically, during the tracer load setup period, only the hydrodynamic module operates. The output of this phase is steady-state hydrodynamic information (such as velocity distribution and water level changes), without involving the tracer's convection and diffusion processes. Therefore, it is suitable for calibrating the Manning coefficient related to hydrodynamic drag characteristics and the eddy viscosity coefficient related to turbulent mixing characteristics. During the tracer release monitoring period, the hydrodynamic module and the convection and diffusion module are coupled. The output of this phase is the tracer concentration decay process under clean water replacement, making it suitable for calibrating the diffusion coefficient controlling pollutant transport rates. In terms of calibration sequence, the optimal values ​​of the Manning coefficient and eddy viscosity coefficient obtained during the tracer load setup period are fixed first, and then the diffusion coefficient calibration during the tracer release monitoring period is performed to avoid the calibration non-uniqueness problem caused by multi-parameter coupling.

[0080] In practical applications, this embodiment decouples hydrodynamic parameters (Manning coefficient, eddy viscosity coefficient) from convection-diffusion parameters (diffusion coefficient) by calibrating them independently at different time intervals. This effectively solves the problem of multiple parameters being coupled and difficult to converge simultaneously in traditional methods. The strategy of fixing the calibrated hydraulic performance parameters before calibrating the next parameter significantly improves the stability and convergence efficiency of the calibration process, making the parameter calibration results of the numerical model more unique and reliable. This method is particularly suitable for systems such as small-scale surface flow constructed wetlands where molecular diffusion effects are stronger than convection-dominant effects, providing accurate hydraulic performance parameters for numerical simulation.

[0081] like Figure 9 As shown, an electronic device 300 provided in this embodiment of the invention includes a memory 310 and a processor 320; the memory 310 is used to store a computer program; the processor 320 is used to implement the hydraulic performance parameter calibration method for a small surface flow constructed wetland as described above when the computer program is executed.

[0082] Alternatively, an electronic device 300 includes a memory 310 and a processor 320 coupled to the memory 310; the memory 310 is configured to store a computer program; and the processor 320 is configured to perform the following operations when the computer program is executed: In the numerical model of surface flow constructed wetlands, experimental schemes with multiple parameter levels are determined using hydraulic performance parameters to be calibrated as variables. The numerical model is used for simulation, the simulation results are output, the correlation between the simulation results and the hydraulic performance parameters is analyzed, and characteristic result indicators that are significantly sensitive to the hydraulic performance parameters are screened out. Based on the pre-established physical model of the surface flow constructed wetland, a solution containing tracer is continuously injected into the physical model until the system stabilizes, forming the tracer load build-up period; then the injected solution is switched to clean water and continuously injected at the same flow rate, forming the tracer release monitoring period; during the tracer release monitoring period, measured data corresponding to the characteristic result indicators are collected; Based on the structural parameters of the physical model, a corresponding numerical model is constructed. The hydraulic performance parameters to be calibrated are input into the numerical model with preset initial values ​​and simulated to obtain numerical simulation results. The numerical simulation results are compared and iterated with the measured data to calibrate the optimal values ​​of the hydraulic performance parameters in reverse.

[0083] This invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the hydraulic performance parameter calibration method for a small surface flow constructed wetland as described above.

[0084] Alternatively, a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the following operations: In the numerical model of surface flow constructed wetlands, experimental schemes with multiple parameter levels are determined using hydraulic performance parameters to be calibrated as variables. The numerical model is used for simulation, the simulation results are output, the correlation between the simulation results and the hydraulic performance parameters is analyzed, and characteristic result indicators that are significantly sensitive to the hydraulic performance parameters are screened out. Based on the pre-established physical model of the surface flow constructed wetland, a solution containing tracer is continuously injected into the physical model until the system stabilizes, forming the tracer load build-up period; then the injected solution is switched to clean water and continuously injected at the same flow rate, forming the tracer release monitoring period; during the tracer release monitoring period, measured data corresponding to the characteristic result indicators are collected; Based on the structural parameters of the physical model, a corresponding numerical model is constructed. The hydraulic performance parameters to be calibrated are input into the numerical model with preset initial values ​​and simulated to obtain numerical simulation results. The numerical simulation results are compared and iterated with the measured data to calibrate the optimal values ​​of the hydraulic performance parameters in reverse.

[0085] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0086] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

[0087] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland, characterized in that, include: The hydraulic performance parameters to be calibrated include Manning coefficient, eddy viscosity coefficient, and diffusion coefficient; In the numerical model of surface flow constructed wetlands, experimental schemes with multiple parameter levels are determined using hydraulic performance parameters to be calibrated as variables. Simulations are performed using the numerical model, and the simulation results are output. The correlation between the simulation results and the hydraulic performance parameters is analyzed, and characteristic indicators that are significantly sensitive to the hydraulic performance parameters are selected, including: Based on the eddy viscosity coefficient, the Manning coefficient, and the diffusion coefficient, multiple variables are determined, and a multi-level orthogonal experimental scheme or a full-factor experimental scheme based on the multiple variables is established. The numerical model is used for simulation, and the simulation results are output. The simulation results include the changes in tracer concentration at the wetland outlet during the tracer release monitoring period, the changes in equilibrium flow velocity in the plant backflow zone, and the changes in tracer concentration in the plant wake influence area. Range analysis and variance analysis are used to determine the significant correlation between each hydraulic performance parameter to be calibrated and each item in the simulation results. The corresponding items in the simulation results with significant correlation are used as the characteristic result indicators for calibrating the corresponding hydraulic performance parameters. Based on the pre-established physical model of the surface flow constructed wetland, a solution containing tracer is continuously injected into the physical model until the system stabilizes, forming the tracer load build-up period; then the injected solution is switched to clean water and continuously injected at the same flow rate, forming the tracer release monitoring period; during the tracer release monitoring period, measured data corresponding to the characteristic result indicators are collected; Based on the structural parameters of the physical model, a corresponding numerical model is constructed. The hydraulic performance parameters to be calibrated are input into the numerical model with preset initial values ​​and simulated to obtain numerical simulation results. The numerical simulation results are compared and iterated with the measured data to calibrate the optimal values ​​of the hydraulic performance parameters in reverse.

2. The method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland according to claim 1, characterized in that, Also includes: For hydraulic performance parameters that are not significantly correlated with the model simulation results, a set of benchmark parameter combinations is selected as the control experimental group. The hydraulic performance parameters are changed as single variables for numerical simulation to obtain model simulation results under different parameter values. Within the sensitive region corresponding to the hydraulic performance parameters, the differences between the model simulation results obtained under different parameter values ​​are compared. If the difference shows a significant change, the model simulation results obtained in the sensitive area will be used as the characteristic result index for calibrating the hydraulic performance parameters.

3. The method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland according to claim 1 or 2, characterized in that, The physical model of the pre-established surface flow constructed wetland is used as a base solution. The solution containing tracer is continuously injected into the physical model until the system stabilizes, forming the tracer load build-up period. Then the injected solution is switched to clean water and injected continuously at the same flow rate, forming the tracer release monitoring period. During the tracer release monitoring period, the measured data corresponding to the characteristic result indicators collected include: Prepare a solution containing a tracer, and adjust the mass concentration of the tracer-containing solution to be within a concentration range where conductivity and concentration are linearly related; The tracer-containing solution is continuously injected until the tracer inside the physical model reaches a stable distribution state, forming the tracer load build-up period; The injection solution is switched to water and continuously injected at the same flow rate as the tracer load build-up period to maintain the flow field state inside the physical model and form the tracer release monitoring period. During the tracer release monitoring period, based on the second monitoring point corresponding to the characteristic result index, corresponding monitoring equipment is arranged in the physical model to collect the measured data corresponding to the characteristic result index; the second monitoring point covers the monitoring point corresponding to the characteristic result index; at least one of the following is obtained based on the measured data: the outlet tracer concentration time series, the flow velocity time series in the plant backflow zone, and the tracer concentration time series in the wake influence zone.

4. The method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland according to claim 3, characterized in that, The measured data corresponding to the collected feature result indicators include: Water quality monitoring instruments are deployed at the outlet of the wetland to obtain the time series of the outlet tracer concentration, and its fluctuation characteristics are extracted as characteristic result indicators for calibrating the diffusion coefficient. A flow velocity monitor is installed on the back of the plants at the end point of the wetland in the direction of water flow to obtain the flow velocity time series of the back flow zone of the plants, and the equilibrium flow velocity is extracted as a characteristic result index for calibrating the Manning coefficient. Tracer concentration monitoring points are set up in the wake influence area formed on the back of the plant stem in the direction of water flow to obtain the time series of tracer concentration in the wake influence area, and the concentration change characteristics are extracted as characteristic result indicators for calibrating the eddy viscosity coefficient.

5. The method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland according to claim 1 or 2, characterized in that, The process of constructing a corresponding numerical model based on the structural parameters of the physical model, inputting the hydraulic performance parameters to be calibrated into the numerical model with preset initial values, and performing simulation to obtain numerical simulation results includes: The geometric dimensions, terrain elevation, boundary contour, and internal vegetation distribution of the physical model are extracted as the structural parameters. An adaptive triangular mesh is generated based on the boundary contour and the internal plant distribution, and the open boundary and land boundary are determined. Import terrain elevation data and interpolate it vertically to generate a numerical computation domain containing spatial topology. The numerical computation domain is divided into a hydrodynamic module and a convection-diffusion module, and the numerical model is established accordingly. The preset initial values ​​are input into the numerical model as the hydraulic performance parameters to obtain the numerical simulation results. The preset initial values ​​are determined based on the structural parameters and engineering experience parameters of the physical model.

6. The method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland according to claim 5, characterized in that, After dividing the numerical computation domain into a hydrodynamic module and a convection-diffusion module, the method further includes: The numerical model is divided into two phases according to the time sequence. In the first phase, only the hydrodynamic module is run to simulate the hydrodynamic steady-state establishment process during the tracer load establishment period. In the second phase, the convection-diffusion module is coupled to the hydrodynamic module to simulate the tracer convection-diffusion process during the tracer release monitoring period. The simulation results of the first stage are used as the initial conditions for the second stage to achieve data transfer between the two stages.

7. The method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland according to claim 6, characterized in that, Also includes: The initial conditions for the first stage are set as zero water level and zero flow velocity inside the wetland. The boundary conditions are set as flow inlet boundary and water level outlet boundary. The system is run until the hydrodynamic stability is reached, and the water depth value and flow velocity vector that vary with space are output. The initial conditions for the second stage are set as the spatially varying water depth and velocity fields at the end of the first stage. The boundary conditions are set as the inlet tracer concentration is zero. The model simulation results are output, including the water depth, velocity, and concentration distribution in the numerical computation domain.

8. The method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland according to claim 1 or 2, characterized in that, The process of comparing and iterating the numerical simulation results with the measured data to determine the optimal values ​​of the hydraulic performance parameters includes: Determine the deviation between the numerical simulation results and the measured data; Adjust the values ​​of the corresponding hydraulic performance parameters in the numerical model according to the deviation, and repeat the numerical simulation. Determine whether the deviation meets the preset accuracy requirements. If it does, determine the current hydraulic performance parameter value as the optimal value. If it does not meet the requirements, continue to adjust and repeat the numerical simulation.

9. The method for calibrating the hydraulic performance parameters of a small-scale surface flow constructed wetland according to claim 8, characterized in that, Also includes: During the tracer load setup period, the Manning coefficient and the eddy viscosity coefficient are calibrated, and during the tracer release monitoring period, the diffusion coefficient is calibrated. After fixing the optimal values ​​of the calibrated hydraulic performance parameters, the calibration of the next hydraulic performance parameter to be calibrated is then performed.

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