A method and system for analyzing pollution discharge and pollution bearing of a static water body in coordination

Abstract

This invention discloses a method and system for synergistic analysis of pollution discharge and carrying capacity in static water bodies. Based on monitoring data of static water bodies, the method uses fluid dynamics equations to simulate the transport and diffusion behavior of pollutants in the static water body, obtaining the flow velocity field. The monitoring data of pollutant concentrations are input into a neural network model to predict continuous-time pollutant concentration sequences. Specifically, a convection-diffusion response equation is established based on the flow velocity field to construct the loss function of the neural network model. The maximum carrying capacity sequence of the static water body for the pollutants is calculated to quantify the pollution carrying capacity of the static water body. A water quality dynamic balance coefficient is calculated based on the pollutant concentration sequence and the maximum carrying capacity sequence to quantify the synergistic effect of pollution discharge and carrying capacity. This invention proposes the concept of synergistic discharge and carrying capacity for the first time, realizing the comprehensive ecological performance assessment of static water bodies such as lakes and reservoirs, and providing a new quantitative calculation method for the comprehensive water quality evaluation of static water bodies such as lakes.

Description

Technical Field

[0001] This invention relates to the field of water ecological assessment, and in particular to a method and system for the coordinated analysis of pollution discharge and pollution carrying capacity in static water bodies. Background Technology

[0002] Currently, climate change, urbanization, and industrialization have exacerbated water pollution, posing significant threats to ecosystems such as degradation, toxicity spread, and reduced biodiversity. The protection and restoration of aquatic ecosystem services have become key issues in global policy, and aquatic ecological environment protection is undergoing a paradigm shift from pollution control as the core to taking into account aquatic ecological health.

[0003] Existing work largely focuses on pollution control or local ecological restoration, aiming to address the challenges of data scarcity and accurate assessment. For example, Chinese patent CN116432439A discloses a method and system for planning the pollution carrying capacity of urban rivers based on numerical simulation. This method constructs a one-dimensional hydrodynamic-water quality coupled mathematical model of urban river sections based on pollutant convection and diffusion equations, combines in-situ monitoring data to simulate the hydrodynamic and water quality changes along the river, calculates the comprehensive degradation coefficient of pollutants, and thus realizes the planning of the river's pollution carrying capacity. Chinese patent CN121233956A discloses a method, system, and computer-readable storage medium for predicting lake water quality based on hybrid networks. This method uses LSTM-KAN to predict the total phosphorus concentration in Dianchi Lake, achieving a relatively accurate prediction with an R² higher than 0.75. However, these methods are mostly limited to the evaluation or prediction of a single aspect, failing to form a collaborative analysis framework for the entire process of pollution discharge and carrying capacity from multiple perspectives. They lack a systematic analysis of the inherent collaborative mechanisms driving the system, making it difficult to support the systematic judgment and scientific regulation of the static water body's discharge and carrying capacity balance. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a method and system for the coordinated analysis of pollution discharge and collection in static water bodies, which solves the core problems of the lack of evaluation indicators for pollution discharge and collection capacity in current static water bodies such as lakes and reservoirs, and the shortcomings of traditional evaluation methods in process characterization and mechanism explanation.

[0005] Technical solution: The present invention provides a method for the coordinated analysis of pollution discharge and pollution carrying capacity in static water bodies, comprising the following steps:

[0006] Obtain monitoring data for static water bodies;

[0007] Based on the monitoring data, the transport and diffusion behavior of pollutants in static water bodies is simulated using fluid hydrodynamic equations to obtain the velocity field of the static water body.

[0008] The monitoring data of pollutant concentrations are input into a trained neural network model to predict the continuous time sequence of pollutant concentrations; wherein, a convection-diffusion reaction equation is established based on the flow velocity field to construct the loss function of the neural network model;

[0009] Calculate the maximum carrying capacity sequence of the static water body for the pollutants to quantify the pollution carrying capacity of the static water body;

[0010] The water quality dynamic balance coefficient is calculated based on the normalized values ​​of the pollutant concentration sequence and the maximum carrying capacity sequence, which is used to quantify the synergistic effect of pollution discharge and carrying capacity.

[0011] Furthermore, before inputting the monitoring data of pollutant concentrations into the trained neural network model, outlier removal and missing value linear interpolation completion processing are performed on the monitoring data of pollutant concentrations.

[0012] When there are many missing values ​​in the monitoring data of pollutant concentration that cannot be filled by linear interpolation, the concentration of pollutants is simulated using fluid hydrodynamic equations to obtain simulated values ​​of pollutant concentration. The monitoring data and simulated values ​​of pollutant concentration are then input into the trained neural network model.

[0013] Furthermore, the neural network model is a physical information neural network model, employing a multi-layer deep neural network architecture.

[0014] Furthermore, a convection-diffusion reaction equation is established based on the velocity field to construct the physical loss function of the neural network model. for:

[0015] ;

[0016] in, For physical loss; For pollutant concentration, For the velocity field; For the diffusion tensor, For pollution source items, This shows how the concentration changes over time.

[0017] Furthermore, the total loss function of the neural network model for:

[0018] ;

[0019] in, The mean squared error constitutes the data loss function. These are the weights of the data loss function. These are the weight values ​​for the physical loss function.

[0020] Furthermore, the pollutants include non-eutrophic pollutants and eutrophic pollutants;

[0021] The maximum carrying capacity sequence of the static water body for the non-eutrophic pollutants was calculated using a non-uniform mixing model, and the maximum carrying capacity sequence of the static water body for the eutrophic pollutants was calculated using a eutrophication model.

[0022] Furthermore, the water quality dynamic balance coefficient for:

[0023] ;

[0024] in, These are the normalized values ​​of the pollutant concentration series. This is the normalized value of the maximum capacity sequence. It is a water ecological sensitivity factor, with a value of [0,1].

[0025] Then the static water body is in an unbalanced state; Then the static water body is in a state of equilibrium; If so, the static water body is in a steady state.

[0026] Furthermore, the values ​​of the ecological sensitivity factors are determined based on the ecological vulnerability of the static water body.

[0027] The present invention provides a static water body wastewater discharge and pollution collection collaborative analysis system, comprising:

[0028] The data acquisition module is used to acquire monitoring data of static water bodies;

[0029] The wastewater discharge quantification module is used to simulate the transport and diffusion behavior of pollutants in static water bodies using fluid hydrodynamic equations based on the monitoring data, so as to obtain the flow velocity field and pollutant concentration of the static water body.

[0030] The pollutant concentrations are input into a trained neural network model to predict a continuous time sequence of pollutant concentrations; wherein, a convection-diffusion reaction equation is established based on the flow velocity field to construct the loss function of the neural network model;

[0031] The pollution carrying capacity quantification module is used to calculate the maximum carrying capacity sequence of the pollutants in the static water body, and to quantify the pollution carrying capacity level of the static water body;

[0032] The discharge and carrying capacity synergy analysis module is used to calculate the water quality dynamic balance coefficient based on the normalized values ​​of the pollutant concentration sequence and the maximum carrying capacity sequence, and is used to quantify the synergistic effect of discharge and carrying capacity.

[0033] The computer program product of the present invention includes a computer program that, when executed by a processor, implements the method for coordinated analysis of pollution discharge and pollution carrying in static water bodies.

[0034] Beneficial effects: Compared with the prior art, the advantages of the present invention are: (1) The present invention relies on the basic data of static water body monitoring stations, uses EFDC and PINN joint modeling to simulate and predict the concentration distribution of key pollutants, proposes the concept of discharge and collection coordination for the first time, incorporates the maximum carrying capacity LAC to independently propose the balance coefficient ECWQ, and constructs a water quality comprehensive discharge and collection coordination evaluation method driven by mechanism and data, which solves the black box problem of traditional machine learning water quality prediction that strongly depends on single data analysis, and provides scientific and accurate decision support for water ecological environment management. (2) The PINN model of the present invention embeds physical mechanisms to realize more theoretically driven data output function and reduce data dependence; (3) The present invention completes the measured data through the output of the EFDC simulation field, without relying on traditional data completion schemes (such as mean / KNN, etc.), and realizes accurate completion of missing data. Attached Figure Description

[0035] Figure 1 This is a flowchart of the wastewater discharge and wastewater collection collaborative analysis method according to an embodiment of the present invention.

[0036] Figure 2 This is a schematic diagram of the PINN structure according to an embodiment of the present invention.

[0037] Figure 3 This is a lake grid diagram according to an embodiment of the present invention.

[0038] Figure 4 This is a dynamic balance coefficient distribution diagram of ammonia nitrogen in water quality according to an embodiment of the invention.

[0039] Figure 5 This is a distribution diagram of the total phosphorus water quality dynamic balance coefficient in an embodiment of the present invention. Detailed Implementation

[0040] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0041] like Figure 1 As shown, the method for coordinated analysis of pollution discharge and pollution carrying capacity in static water bodies includes the following steps.

[0042] S1. Obtain key data such as water quality, hydrology, water depth, and climate from various monitoring stations in static water bodies such as lakes and reservoirs.

[0043] Specifically, the monitoring stations included in the data scope are located at or near the inlet and outlet points of static water bodies, and the collected data includes key data such as water quality, hydrology, water depth, and climate.

[0044] Specifically, the actual concentration detection values ​​often have gaps in the time scale. Therefore, this embodiment performs outlier removal and missing value completion on the collected concentration detection values, including:

[0045] For the basic data of the river cross-section at the lake inlet, outliers were identified and removed using the 3σ principle, as follows:

[0046] ;

[0047] ;

[0048] in, For the first Each concentration observation value, The total number of observations. S is the mean of the concentration observation series, and S is the standard deviation of the concentration observation series, representing the degree of data fluctuation.

[0049] Linear interpolation is performed on the missing values ​​as follows:

[0050] ;

[0051] in, These are estimated values ​​for the time points to be interpolated. for Point observations for Point observations For time, The time is the left endpoint of the interpolation interval; The time is the right endpoint of the interpolation interval.

[0052] S2. The transport and diffusion behavior of key pollutants in static water bodies is simulated by the fluid hydrodynamic equations in the EFDC model. The output data is used as the training and testing dataset for the Physical Information Neural Network (PINN) to obtain a continuous concentration prediction sequence of water ecological indicators in the future.

[0053] Specifically, the EFDC simulation of pollutant transport includes lake and reservoir grid generation, input of initial and boundary conditions, and acquisition and calibration of model parameters. Before performing EFDC calculations, the static water surface is divided using a grid generation tool, and then hydrodynamic and water quality parameters are calibrated through continuous experiments. The simulation results of EFDC include the velocity field, which is used to construct the physical loss function of PINN.

[0054] Furthermore, if there are a large number of missing values ​​in the actual concentration detection values ​​collected in step S1, such as missing values ​​for several consecutive days or weeks, then the concentration simulation values ​​for a certain time range are obtained by simulating the hydrodynamic equations in the EFDC model to fill in the missing values. Together with the actual concentration detection values ​​collected, they constitute the emission dataset, which serves as the training set and prediction set for PINN.

[0055] Specifically, the PINN in this embodiment adopts a multi-layered cascaded deep neural network architecture, which is a nonlinear mapping model based on a multilayer perceptron. The PINN embeds convection-diffusion-reaction equations as physical information terms, enabling the neural network to not rely entirely on data to generate mapping relationships. It can also achieve more accurate predictions guided by the physical mechanisms of pollutant migration and transport in lakes and reservoirs. After outputting the predicted sequence, the emission dataset is periodically aligned.

[0056] like Figure 2 As shown, the structural design of the PINN model is as follows:

[0057] Input layer: Includes ammonia nitrogen emission concentrations and time series obtained from the cross-section of the river flowing into Chaohu Lake, hydrodynamic field obtained from EFDC simulation (including geographic latitude and longitude coordinates), and ammonia nitrogen concentrations generated by the EFDC concentration field as labels.

[0058] Hidden layers: Construct a three-layer fully connected neural network with 100 neurons in each layer, using the Tanh activation function.

[0059] Output layer: A single neuron structure, such as the predicted concentration of ammonia nitrogen, a pollutant.

[0060] In the process of training the PINN model, the total loss function of the PINN model is composed of a weighted sum of the data fitting loss function and the physical constraint loss function. The data fitting loss function is used to measure the difference between the predicted concentration of pollutant factors and the actual concentration; the physical constraint function is used to measure the difference between the prediction of the neural network and the physical constraints.

[0061] (a) The data fitting loss function is established based on the mean squared error:

[0062] ;

[0063] in, Let n be the loss function for fitting the data, and n be the number of training samples. and The predicted concentrations of the corresponding pollutants predicted by the network are compared with the actual concentrations in the dataset.

[0064] (b) The physical constraint loss function is established based on the convection-diffusion reaction equation:

[0065] ;

[0066] in, For physical loss; For pollutant concentration, For the velocity field; For the diffusion tensor, For pollution source items, This shows how the concentration changes over time.

[0067] (c) The total loss function is:

[0068] ;

[0069] in, These are the weights of the data loss function. These are the weight values ​​for the physical loss function.

[0070] S3. Determine the water quality function of the water body and calculate the maximum carrying capacity sequence (LAC) of the water body according to the relative functional zoning, which is used to quantify the pollution carrying capacity of the water body.

[0071] Specifically, the water quality functional zoning is based on the "Surface Water Environmental Quality Standard" (GB 3838-2002).

[0072] Specifically, the pollution factor indicators are divided into non-eutrophic and eutrophic categories. A non-uniform mixing model is used to calculate the maximum carrying capacity sequence of the static water body for the non-eutrophic pollutants, and a eutrophication model is used to calculate the maximum carrying capacity sequence of the static water body for the eutrophic pollutants. The calculation formula is as follows:

[0073] ;

[0074] ;

[0075] in, COD Cr The water body's capacity to carry pollutants, including non-eutrophic water indicators such as ammonia nitrogen. The water body's capacity to hold pollutants, as measured by eutrophication indicators such as total nitrogen and total phosphorus. The target water quality value is mg / L. The initial pollutant concentration at the cross-section is in mg / L. The overall pollutant attenuation coefficient is 1 / s; The diffusion angle is determined by the topography near the sewage outlet, and is usually taken as π (vertical discharge on open shore) and 2π (discharge in lakes and reservoirs). The average water depth of the lake (reservoir) in the diffusion area is in meters (m). The distance from the outer boundary of the water area to the sewage outlet into the river, in meters (m). For wastewater discharge, m 3 / s; The average water depth of a static water body is given in meters (m). The outflow rate of the static water body, in m 3 / a; The volume of the static water body is given in m. 3 ; , where 1 / d represents the settlement coefficient (per day); The area of ​​the calculated water body corresponding to the annual average water level, in km² 2 .

[0076] S4. Calculate the dynamic water quality balance coefficient (ECWQ) to measure the synergistic effect of water ecosystem's sewage discharge / sewage carrying capacity.

[0077] Specifically, the concentration prediction values ​​and corresponding maximum capacity sequences obtained in steps S2 and S3 are first normalized:

[0078] ;

[0079] ;

[0080] in, represent or , and These are the normalized predicted concentration and the maximum capacity, respectively. , This indicates the minimum / maximum value in the sequence.

[0081] Normalized predicted ammonia nitrogen concentration and corresponding maximum capacity , Together with the introduced water body sensitivity factors, they constitute the water quality dynamic balance coefficient ECWQ:

[0082] ;

[0083] in, The water ecology sensitivity factor is determined based on the ecological vulnerability of the target water body, with a value range of 0-1. The corresponding ecological vulnerability is shown in Table 1 below.

[0084] Table 1 Value Reference Table

[0085]

[0086] Furthermore, the synergy between wastewater discharge and wastewater reception in the receiving water body is assessed using the calculated ECWQ value:

[0087] .

[0088] The method described in this invention will be verified through a specific example below. Chaohu Lake was selected as the specific implementation target for this experiment.

[0089] Step 1: Collect initial data for the main rivers flowing into Chaohu Lake—Hangbu River, Nanfei River, Fengle River, Pai River—and its only outlet, Yuxi River, from January 1, 2021 to December 31, 2022. This data will be used for EFDC and PINN training, including:

[0090] Initial conditions: The model was started under static conditions, assuming that the computational domain was initially a fixed still water surface and zero flow component, and the initial water level, ammonia nitrogen concentration, total phosphorus concentration, and geographic latitude and longitude coordinates were obtained.

[0091] Boundary conditions: Inflow boundary conditions include the inflow rates and water temperatures of the Hangbu River, Nanfei River, Fengle River, and Pai River; water quality boundary conditions include the time series of ammonia nitrogen concentration and total phosphorus concentration of the Hangbu River, Nanfei River, Fengle River, and Pai River.

[0092] It should be noted that the actual ammonia nitrogen concentration detected at the outlet of the Yuxi River has significant time-scale gaps. The datasets generated by traditional methods (such as mean / KNN imputation) do not consider the physical mechanisms in the actual emission process. Therefore, this example uses the ammonia nitrogen and total phosphorus concentrations generated by EFDC simulation as supplements to construct complete ammonia nitrogen emission sets and total phosphorus emission sets as detection values ​​for comparison with the prediction results of the PINN model.

[0093] Step 2: Before performing the EFDC calculation, the surface of Chaohu Lake is divided using a mesh generation tool. The division result is as follows: Figure 3 As shown in Table 2, the EFDC model parameters are as follows:

[0094] Table 2 EFDC Model Parameters

[0095]

[0096] The ammonia nitrogen emission dataset and the total phosphorus emission dataset were divided into training and test sets, respectively. 80% was used as the training set for PINN training, and 20% was used as the test set to validate the prediction results of PINN. The evaluation metrics included the coefficient of determination R², mean absolute error (MAE), mean absolute percentage error (MAE), and root mean square error (RMSE).

[0097] ;

[0098] ;

[0099] ;

[0100] For ammonia nitrogen concentration, the performance achieved by the PINN model on the test set is as follows: , , For total phosphorus concentration, the performance of the PINN model on the test set is as follows: , , .

[0101] Step 3: Calculate the maximum carrying capacity of ammonia nitrogen (a non-eutrophic indicator) in Chaohu Lake using a non-uniform mixing model. The water carrying capacity of total phosphorus, an eutrophication index, in Chaohu Lake was calculated using an eutrophication model (Hetian Ken model). Based on observations from January 2021 to December 2024, most areas of Chaohu Lake are classified as Class IV water bodies. According to the "Surface Water Environmental Quality Standard" (GB3838-2002), the target value for ammonia nitrogen in Class IV water bodies is 1.5 mg / L, and the target value for total phosphorus in Class IV water bodies is 0.3 mg / L. The comprehensive pollutant attenuation coefficient is taken as an empirical value of 0.2. π is taken as the parameter, and all other parameters are taken from the data acquisition in step S1. The final output is aligned with the time series of step S2 to form a complete dataset of Chaohu Lake's ammonia nitrogen pollution carrying capacity and a complete dataset of Chaohu Lake's total phosphorus pollution carrying capacity.

[0102] Step 4: Based on the observation results from January 2021 to December 2022, the area of ​​ecologically fragile areas in the Chaohu region is small, so a sensitivity value is set for it. The coordinated situation of ammonia nitrogen discharge in Chaohu Lake is as follows: Figure 4 As shown, the synergistic effect of Chaohu Lake on total phosphorus discharge is as follows: Figure 5 As shown in the figure, the bars represent the frequency of the calculated balance coefficient values, measured in days, from January 2021 to December 2022.

[0103] The present invention provides a static water body wastewater discharge and pollution collection collaborative analysis system, comprising:

[0104] The data acquisition module is used to acquire monitoring data of static water bodies;

[0105] The wastewater discharge quantification module is used to simulate the transport and diffusion behavior of pollutants in static water bodies using fluid hydrodynamic equations based on the monitoring data, so as to obtain the flow velocity field and pollutant concentration of the static water body.

[0106] The pollutant concentrations are input into a trained neural network model to predict a continuous time sequence of pollutant concentrations; wherein, a convection-diffusion reaction equation is established based on the flow velocity field to construct the loss function of the neural network model;

[0107] The pollution carrying capacity quantification module is used to calculate the maximum carrying capacity sequence of the pollutants in the static water body, and to quantify the pollution carrying capacity level of the static water body;

[0108] The discharge and carrying capacity synergy analysis module is used to calculate the water quality dynamic balance coefficient based on the normalized values ​​of the pollutant concentration sequence and the maximum carrying capacity sequence, and is used to quantify the synergistic effect of discharge and carrying capacity.

Claims

1. A method for synergistic analysis of pollution discharge and pollution carrying capacity in static water bodies, characterized in that, Includes the following steps: Obtain monitoring data for static water bodies; Based on the monitoring data, the transport and diffusion behavior of pollutants in static water bodies is simulated using fluid hydrodynamic equations to obtain the velocity field of the static water body. The monitoring data of pollutant concentrations are input into a trained neural network model to predict the continuous time sequence of pollutant concentrations; wherein, a convection-diffusion reaction equation is established based on the flow velocity field to construct the loss function of the neural network model; Calculate the maximum carrying capacity sequence of the static water body for the pollutants to quantify the pollution carrying capacity of the static water body; The water quality dynamic balance coefficient is calculated based on the normalized values ​​of the pollutant concentration sequence and the maximum carrying capacity sequence, which is used to quantify the synergistic effect of pollution discharge and carrying capacity.

2. The method for coordinated analysis of pollution discharge and pollution carrying capacity in static water bodies according to claim 1, characterized in that, Before inputting the monitoring data of pollutant concentrations into the trained neural network model, the process also includes outlier removal and missing value linear interpolation to complete the monitoring data of pollutant concentrations. When there are many missing values ​​in the monitoring data of pollutant concentration that cannot be filled by linear interpolation, the concentration of pollutants is simulated using fluid hydrodynamic equations to obtain simulated values ​​of pollutant concentration. The monitoring data and simulated values ​​of pollutant concentration are then input into the trained neural network model.

3. The method for coordinated analysis of pollution discharge and pollution carrying capacity in static water bodies according to claim 1, characterized in that, The neural network model is a physical information neural network model, which adopts a multi-layer deep neural network architecture.

4. The method for coordinated analysis of pollution discharge and pollution carrying capacity in static water bodies according to claim 1, characterized in that, The physical loss function of the neural network model is constructed by establishing the convection-diffusion reaction equation based on the velocity field. for: ； in, For physical loss; For pollutant concentration, For the velocity field; For the diffusion tensor, For pollution source items, This shows how the concentration changes over time.

5. The method for coordinated analysis of pollution discharge and pollution carrying capacity in static water bodies according to claim 4, characterized in that, The total loss function of the neural network model for: ； in, The mean squared error constitutes the data loss function. These are the weights of the data loss function. These are the weight values ​​for the physical loss function.

6. The method for coordinated analysis of pollution discharge and pollution carrying capacity in static water bodies according to claim 1, characterized in that, The pollutants include non-eutrophic pollutants and eutrophic pollutants. The maximum carrying capacity sequence of the static water body for the non-eutrophic pollutants was calculated using a non-uniform mixing model, and the maximum carrying capacity sequence of the static water body for the eutrophic pollutants was calculated using a eutrophication model.

7. The method for coordinated analysis of pollution discharge and pollution carrying capacity in static water bodies according to claim 1, characterized in that, The water quality dynamic balance coefficient for: ； in, These are the normalized values ​​of the pollutant concentration sequence. This is the normalized value of the maximum capacity sequence. It is a water ecological sensitivity factor, with a value of [0,1]. Then the static water body is in an unbalanced state; Then the static water body is in a state of equilibrium; If so, the static water body is in a steady state.

8. The method for coordinated analysis of sewage discharge and pollution carrying capacity in static water bodies according to claim 7, characterized in that, The values ​​of the ecological sensitivity factors are determined based on the ecological vulnerability of the static water body.

9. A collaborative analysis system for wastewater discharge and collection in static water bodies, characterized in that, include: The data acquisition module is used to acquire monitoring data of static water bodies; The wastewater discharge quantification module is used to simulate the transport and diffusion behavior of pollutants in static water bodies using fluid hydrodynamic equations based on the monitoring data, so as to obtain the flow velocity field and pollutant concentration of the static water body. The pollutant concentrations are input into a trained neural network model to predict a continuous time sequence of pollutant concentrations; wherein, a convection-diffusion reaction equation is established based on the flow velocity field to construct the loss function of the neural network model; The pollution carrying capacity quantification module is used to calculate the maximum carrying capacity sequence of the pollutants in the static water body, and to quantify the pollution carrying capacity level of the static water body; The discharge and carrying capacity synergy analysis module is used to calculate the water quality dynamic balance coefficient based on the normalized values ​​of the pollutant concentration sequence and the maximum carrying capacity sequence, and is used to quantify the synergistic effect of discharge and carrying capacity.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for coordinated analysis of sewage discharge and pollution carrying capacity of static water bodies according to any one of claims 1-7.

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