Method and device for determining water environment capacity and electronic equipment

Abstract

The invention relates to a water environment capacity determination method and device and electronic equipment, and relates to the technical field of water environment evaluation. Comprising the steps that first satellite data, observed by a first satellite, of a target drainage basin, second satellite data, observed by a second satellite, of the target drainage basin and water environment data, obtained through dynamic drainage basin model simulation, of the target drainage basin are fused, and target water environment data of the target drainage basin are obtained; inputting the drainage basin flow, the target water environment data, the pollution source data, the vegetation coverage and the meteorological data of the target drainage basin into the dynamic drainage basin model to obtain the pollutant concentration and the outlet flow of the target drainage basin; obtaining the water environment capacity of the target drainage basin according to the pollutant concentration of the target drainage basin and the outlet flow; the water environment capacity indicates the capacity for containing pollutants in the target drainage basin. By using the method for determining the water environment capacity provided by the invention, the water environment capacity with high coverage, high resolution and accuracy can be obtained.

Description

Technical Field

[0001] The present disclosure relates to the technical field of water environment assessment, and in particular, to a method, apparatus, and electronic device for determining water environment capacity. Background Art

[0002] The assessment of water environment capacity is an important link in environmental protection and water resources management. With the development of water environment assessment technology, the accuracy of the water environment capacity evaluated by the water environment assessment technology proposed in related technologies is not high. Summary of the Invention

[0003] The purpose of the present disclosure is to provide a method, apparatus, and electronic device for determining water environment capacity to solve the above technical problems.

[0004] To achieve the above object, in a first aspect of the present disclosure, a method for determining water environment capacity is provided, including: fusing first satellite data of a target basin observed by a first satellite, second satellite data of the target basin observed by a second satellite, and water environment data of the target basin simulated by a dynamic basin model to obtain target water environment data of the target basin; inputting the basin flow, target water environment data, pollution source data, vegetation coverage, and meteorological data of the target basin into the dynamic basin model to obtain the pollutant concentration and outlet flow of the target basin; obtaining the water environment capacity of the target basin according to the pollutant concentration and the outlet flow of the target basin; the water environment capacity indicates the capacity of the target basin to accommodate pollutants.

[0005] In a possible implementation manner, the fusing first satellite data of a target basin observed by a first satellite, second satellite data of the target basin observed by a second satellite, and water environment data of the target basin simulated by a dynamic basin model to obtain target water environment data of the target basin includes: fusing the first satellite data and the water environment data to obtain first fused data; fusing the first fused data and the second satellite data to obtain the target water environment data.

[0006] In a possible implementation manner, the fusing the first satellite data and the water environment data to obtain first fused data includes: obtaining a first observation error of the first satellite according to the first satellite data and the water environment data; correcting the first observation error by using a first Kalman gain to obtain a first target observation error; the first Kalman gain is used to measure the uncertainty of the water environment data; obtaining the first fused data according to the water environment data and the first target observation error.

[0007] In a possible implementation, the fusion of the first fusion data and the second satellite data to obtain the target water environment data includes: obtaining a second observation error of the second satellite according to the second satellite data and the second fusion data; the second fusion data is obtained by perturbing a third fusion data with a perturbation term, and the third fusion data is the data corresponding to the first fusion data; correcting the second observation error with a second Kalman gain to obtain a second target observation error; the second Kalman gain is used to balance the uncertainty of the water environment and the second observation error; obtaining the target water environment data according to the first fusion data and the second target observation error.

[0008] In a possible implementation, the input of the basin flow, target water environment data, pollution source data, vegetation coverage, and meteorological data of the target basin into the dynamic basin model to obtain the pollutant concentration and outlet flow of the target basin includes: screening out a plurality of target sub-basins from a plurality of sub-basins of the target basin; inputting the basin flow, target water environment data, vegetation coverage, future meteorological data of the target basin, and pollution source data of the plurality of target sub-basins into the dynamic basin model to obtain the future pollutant concentration and outlet flow of the target basin.

[0009] In a possible implementation, the obtaining of the water environment capacity of the target basin according to the pollutant concentration and the outlet flow of the target basin includes: determining a target time period when the pollutant concentration of the target basin is less than the target pollutant concentration; for each time point in the target time period, obtaining the pollutant flux at the time point according to the pollutant concentration and the outlet flow; obtaining the water environment capacity according to the pollutant fluxes at a plurality of time points in the target time period.

[0010] In a possible implementation, the method further includes: determining a plurality of elements that affect the water environment capacity, treatment resources, and ecological anomaly degree; the treatment resources are the resources consumed for treating pollutants in the target basin, and the ecological anomaly degree indicates the ecological anomaly of the target basin; screening out a plurality of target elements from the plurality of elements with the goal of maximizing the water environment capacity, minimizing the treatment resources, and minimizing the ecological anomaly degree; respectively screening out a plurality of target parameters from the parameters of the plurality of target elements.

[0011] In a possible implementation manner, screening a plurality of target elements from the plurality of elements with the goal of maximizing the water environment capacity, minimizing the treatment resources, and minimizing the ecological abnormality degree includes: determining a penalty value when the compliance rate of the pollutant concentration does not meet a preset compliance rate; using the penalty value to correct the water environment capacity, the treatment resources, and the ecological abnormality degree respectively to obtain a corrected water environment capacity, corrected treatment resources, and a corrected ecological abnormality degree; and screening the plurality of target elements from the plurality of elements with the goal of maximizing the corrected water environment capacity, the corrected treatment resources, and the corrected ecological abnormality degree.

[0012] To achieve the above object, a second aspect of the present disclosure provides a device for determining water environment capacity, including: a fusion module configured to fuse first satellite data of a target basin observed by a first satellite, second satellite data of the target basin observed by a second satellite, and water environment data of the target basin simulated by a dynamic basin model to obtain target water environment data of the target basin; a prediction module configured to input the basin flow, the target water environment data, pollution source data, vegetation coverage, and meteorological data of the target basin into the dynamic basin model to obtain the pollutant concentration and the outlet flow of the target basin; and a water environment capacity module configured to obtain the water environment capacity of the target basin according to the pollutant concentration and the outlet flow of the target basin; the water environment capacity indicates the capacity of the target basin to accommodate pollutants.

[0013] To achieve the above object, a third aspect of the present disclosure provides an electronic device, including: a memory on which a computer program is stored; a processor for executing the computer program in the memory to implement Through the above technical solution, after fusing the first satellite data, the second satellite data, and the water environment data, it is possible to obtain target water environment data with high coverage, high spatial resolution, and relatively high accuracy without relying on too many monitoring stations. Based on this target water environment data, the calculated water environment capacity will be more accurate.

[0014] Other features and advantages of the present disclosure will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific implementation, they are used to explain the present disclosure, but do not constitute a limitation to the present disclosure. In the drawings: Figure 1 is a flowchart of the steps of a method for determining water environment capacity according to an exemplary embodiment.

[0016] Figure 2 It is a schematic diagram in which a target basin is divided into multiple target sub - basins proposed according to an exemplary embodiment.

[0017] Figure 3 It is a flowchart of the steps of a method for determining water environment capacity proposed according to an exemplary embodiment.

[0018] Figure 4 It is a flowchart of the steps of a method for determining water environment capacity proposed according to an exemplary embodiment.

[0019] Figure 5 It is a flowchart of the steps of a method for determining water environment capacity proposed according to an exemplary embodiment.

[0020] Figure 6 It is a flowchart of the steps of a method for determining water environment capacity proposed according to an exemplary embodiment.

[0021] Figure 7 It is a flowchart of the steps of a method for determining water environment capacity proposed according to an exemplary embodiment.

[0022] Figure 8 It is a flowchart of the steps of a method for determining water environment capacity proposed according to an exemplary embodiment.

[0023] Figure 9 It is a flowchart of the steps of a method for determining water environment capacity proposed according to an exemplary embodiment.

[0024] Figure 10 It is a flowchart of the steps of a method for determining water environment capacity proposed according to an exemplary embodiment.

[0025] Figure 11 It is a block diagram of a device for determining water environment capacity proposed according to an exemplary embodiment.

[0026] Figure 12 It is a block diagram of an electronic device proposed according to an exemplary embodiment. Detailed implementation manners

[0027] The following will describe the detailed implementation manners of the present disclosure with reference to the accompanying drawings. It should be understood that the detailed implementation manners described herein are only for explaining and interpreting the present disclosure, and are not used to limit the present disclosure.

[0028] Figure 1 It is a method for determining water environment capacity proposed according to an exemplary embodiment. The method for determining water environment capacity includes the following steps: In step S10, the first satellite data of the target basin observed by the first satellite, the second satellite data of the target basin observed by the second satellite, and the water environment data of the target basin simulated by the dynamic basin model are fused to obtain the target water environment data of the target basin.

[0029] Among them, the first satellite can be a Gravity Recovery and Climate Experiment (GRACE) satellite, and the first satellite data can be the Total Water Storage (TWS) observed by the GRACE satellite. The unit of the total water storage is equivalent water height (cm). The total water storage is the total amount of water stored in the underground aquifer of the target basin, which can reflect the total changes in soil moisture, groundwater, and surface water in the target basin.

[0030] The GRACE satellite can use microwave ranging technology to measure the distance change between two satellites. When the satellite flies over areas with different gravitational field intensities on the Earth's surface, due to the different gravitational accelerations, the satellite's orbit will undergo small perturbations, resulting in changes in the distance between the two satellites. By analyzing these distance change data and combining information such as the satellite's orbit parameters, the Earth's gravitational field can be calculated. And the total water storage in the target basin will affect the change of the Earth's gravitational field. Therefore, the total water storage in the target basin can be inverted through the observed Earth's gravitational field.

[0031] Among them, the second satellite can be a Soil Moisture Active Passive (SMAP) satellite, and the second satellite data can be the soil moisture observed by the SMAP satellite.

[0032] The SMAP satellite is equipped with a passive radiometer and an active radar. Both instruments operate in the L-band. The passive radiometer provides soil moisture with a wider coverage and higher accuracy, and the active radar provides soil moisture with a higher spatial resolution. Then, the soil moisture provided by the passive radiometer and the soil moisture provided by the active radar are fused to obtain soil moisture with a higher coverage and accuracy.

[0033] Among them, the Land Surface Processes Model with Coupled Hydrology and Energy fluxes (LSPC) will consider parameters such as the basin flow, water environment data, pollution source data, vegetation coverage, and meteorological data of the target basin to simulate the water environment data. This water environment data is the water environment data of the target basin, which includes soil moisture and groundwater level in the target basin.

[0034] The target basin can be divided into multiple sub - basins, and monitoring stations are set up on some of the target sub - basins among the multiple sub - basins. The monitoring stations are used to monitor the pollution source data of each target sub - basin. Then, the initial basin flow, initial water environment data, meteorological data of the target basin and the pollution source data of each target sub - basin monitored by the monitoring stations are input into the dynamic basin model to obtain the water environment data of the entire target basin. The initial basin flow is the initially set default basin flow. The initial water environment data includes the initial soil moisture and the initial groundwater level, which are also the initially set default values. The meteorological data are parameters such as rainfall, temperature, wind speed and solar radiation of the target area predicted by the meteorological model. This meteorological data can be obtained through meteorological model prediction. The pollution source data includes the pollutant emissions of various pollutants in the target basin, etc.

[0035] Among them, since the spatial resolution of the water environment data is higher than that of the first satellite data and the second satellite data, and the spatial resolution of the second satellite data is higher than that of the first satellite data, the spatial resolutions of the water environment data, the second satellite data and the first satellite data gradually decrease. The spatial resolution of the water environment data can compensate for the spatial resolutions of the first satellite data and the second satellite data; and the accuracies and coverages of the first satellite data and the second satellite data are higher than those of the water environment data. Therefore, the first satellite data and the second satellite data can compensate for the accuracies and coverages of the water environment data.

[0036] In step S20, the basin flow, target water environment data, pollution source data, vegetation coverage and meteorological data of the target basin are input into the dynamic basin model to obtain the pollutant concentration and outlet flow of the target basin.

[0037] Among them, when the basin flow of the target basin increases, the dilution ability of the water body in the target basin is enhanced, and the pollutant concentration will decrease. Therefore, the basin flow will affect the pollutant concentration; the basin flow will also directly affect the outlet flow of the basin. If the basin flow in the upper reaches of the target basin increases, then the outlet flow in the lower reaches will also increase. It can be seen that the basin flow will affect the outlet flow.

[0038] Among them, the target water environment data includes the target soil humidity and the target groundwater level. Regarding the target soil humidity, the target soil humidity affects the migration speed of pollutants in the soil. When the target soil humidity is high, pollutants are likely to enter surface water or groundwater through soil pores, thus affecting the pollutant concentration in the target basin; when the target soil humidity is high, the soil's permeability is stronger, and more water will flow into the target basin through underground runoff, thus increasing the outlet flow. Regarding the target groundwater level, changes in the target groundwater level affect the exchange between groundwater and surface water. If the target groundwater level rises, pollutants in the groundwater will seep into the surface water, thus increasing the pollutant concentration; and when the groundwater level rises, the amount of groundwater replenishing the surface water increases, and the outlet flow will also increase correspondingly. It can be seen that the target soil humidity and the target groundwater level will affect the pollutant concentration and the outlet flow.

[0039] Among them, the pollution source data includes the number of pollution sources, the pollutant emissions from pollution sources, etc. If the number of pollution sources and the pollutant emissions increase, it will directly affect the pollutant concentration in the target basin. For example, an increase in pollutant emissions will lead to an increase in the pollutant concentration in the target basin.

[0040] Among them, when the vegetation coverage of the target basin increases, the vegetation can absorb and fix some pollutants, thus reducing the amount of pollutants entering the water body of the target basin. Therefore, in the target basin with high vegetation coverage, the pollutant concentration may be appropriately reduced; and the vegetation can reduce surface runoff through transpiration and enhance the soil permeability. In areas with high vegetation coverage, the surface runoff decreases, and the outlet flow will decrease correspondingly.

[0041] Among them, the meteorological data includes parameters such as rainfall, temperature, wind speed, and solar radiation. For example, regarding rainfall, rainfall will increase surface runoff and wash pollutants in the soil into the target basin, thus increasing the pollutant concentration; and rainfall is a key factor for the outlet flow. When rainfall increases, both surface runoff and underground runoff will increase, and then the outlet flow will also increase.

[0042] It can be seen that the dynamic basin model will consider the comprehensive factors such as the basin flow of the target basin, the target water environment data, the pollution source data, the vegetation coverage, and the meteorological data on the pollutant concentration and the outlet flow of the target basin, so as to predict relatively accurate pollutant concentration and outlet flow.

[0043] In step S30, according to the pollutant concentration and the outlet flow of the target basin, the water environment capacity of the target basin is obtained.

[0044] Among them, the water environment capacity of the target basin refers to the maximum load of water volume that the target basin can accommodate pollutants when the pollutant concentration in the target basin is less than the preset pollutant concentration. The target basin is also understood as the maximum pollutant load that the water body of the target basin can accommodate when meeting the water quality target. If the water environment capacity of the target water body exceeds the standard, it indicates that there are more pollutants in the target basin and the pollutants exceed the standard.

[0045] Among them, the pollutant concentration is the pollutant emission contained in the water body per unit volume in the target basin, and its unit is mg / L or μg / L. If the pollutant concentration exceeds the self-purification capacity of the water body in the target basin, the water body will not be able to meet the water quality target. Therefore, the pollutant concentration is an important factor in determining the water environment capacity.

[0046] Among them, the outlet flow is the amount of water discharged from the target basin per unit time, and its unit is m³ / s or m³ / h. The outlet flow determines the dilution capacity and transfer capacity of the water body in the target basin. A larger outlet flow can increase the dilution effect of the water body, thereby reducing the pollutant concentration and improving the water environment capacity. Therefore, the outlet flow is also an important factor in determining the water environment capacity.

[0047] Based on this, it is possible to consider using the pollutant concentration and outlet flow that affect the water environment capacity to calculate the water environment capacity of the target basin.

[0048] In the process of studying the water environment capacity, it is considered that the following multiple schemes can be used to obtain the water environment capacity of the target basin, but each scheme has its own advantages and disadvantages: The first scheme is to use a dynamic basin model to simulate the water environment data of the target basin and use the simulated water environment data to obtain the water environment capacity of the target basin.

[0049] The advantages of the dynamic basin model simulation include: the data such as the pollution source data input into the dynamic basin model are high-spatial-resolution data monitored by monitoring stations, so the spatial resolution of the water environment data output by the dynamic basin model is also relatively high. For example, please refer to Figure 2 As shown, taking the target basin as a lake as an example, the lake is divided into 10 regions, and there are monitoring stations in 5 sub-basins among the 10 regions ( Figure 2 the black dots in represent the monitoring stations), the monitoring stations in the 5 sub-basins monitor the pollution source data in these 5 sub-basins, and input the pollution source data of these 5 sub-basins, the overall basin flow of the target basin, the overall vegetation coverage of the target basin, and the meteorological data of the target basin as a whole into the dynamic basin model to obtain the water environment data of each sub-basin in the target basin. It can be seen that the simulation based on the dynamic basin model can obtain the water environment data of areas with relatively small areas, and the spatial resolution of the obtained water environment data is relatively high.

[0050] The defects of the dynamic watershed model simulation include: First, the input data of the dynamic watershed model includes the pollution source data monitored by monitoring stations. Therefore, the water environment data obtained by the dynamic watershed model simulation depends on the monitoring of the monitoring stations. Second, due to the layout cost, monitoring stations are not deployed in every area of the entire target watershed. The coverage of the distribution of monitoring stations is low. Therefore, the pollution source data monitored by the monitoring stations cannot represent the pollution source data of the entire target watershed. It represents the pollution source data of a local area in the target watershed. The water environment data obtained by inputting the pollution source data of the local area into the dynamic watershed model cannot represent the overall target watershed, and its overall representativeness and coverage are poor. Third, the dynamic watershed model obtains the current water environment data based on historical pollution source data, watershed flow, etc. Since there are certain errors in the dynamic watershed model no matter how it is trained, there is still a certain error between the water environment data simulated based on the dynamic watershed model and the real water environment data, and the accuracy of the simulated water environment data is not high.

[0051] The second solution is to use the first satellite data observed by the first satellite in the target watershed to obtain the water environment capacity of the target watershed.

[0052] The advantages of the first satellite observation are as follows: The first satellite inverses the first satellite data (such as groundwater storage) of the target watershed based on the change of the earth's gravity field. Therefore, the first satellite data is based on the data obtained from actual observation, and its accuracy is higher than that of the dynamic watershed model simulation.

[0053] The disadvantages of the first satellite observation are as follows: The change of the gravity field of the first satellite is the comprehensive result of a large-scale area. Therefore, the first satellite data observed by the first satellite is the data of a large area. For example, the first satellite data observed by the first satellite is the data of an area of more than 200,000 square kilometers. The first satellite cannot observe the satellite data of an area of 1 km * 1 km.

[0054] The third solution is to use the second satellite data observed by the second satellite in the target watershed to obtain the water environment capacity of the target watershed.

[0055] The advantages of the second satellite observation are as follows: The second satellite fuses the data collected by the passive radiometer and the active radar. Since the passive radiometer provides the second satellite data with a wider coverage and higher accuracy, and the active radar provides the second satellite data with a higher spatial resolution, after fusing the two, the second satellite data with a higher spatial resolution than the first satellite and a higher accuracy and coverage than the dynamic watershed model can be obtained.

[0056] The disadvantage of the second satellite observation is that since the second satellite is also a satellite at a relatively far distance from the Earth's surface, when using the second satellite for observation, although the spatial resolution of the second satellite data observed is higher than that of the first satellite, the spatial resolution of the second satellite data is still lower than that of the water environment data of the dynamic watershed model.

[0057] Through the above technical solutions, fusing the first satellite data, the second satellite data, and the water environment data has the following multiple advantages: First, since the accuracy of the first satellite data and the second satellite data is higher than that of the water environment data, after compensating the water environment data with the first satellite data and the second satellite data, more accurate target water environment data can be obtained.

[0058] Second, since the coverage of the first satellite data and the second satellite data is higher than that of the water environment data, after compensating the water environment data with the first satellite data and the second satellite data, target water environment data with higher coverage can also be obtained.

[0059] Third, since the first satellite and the second satellite actually observe the water environment data of the target watershed and do not rely on the pollution source data monitored by the monitoring stations, even if the amount of pollution source data of the monitoring stations input into the dynamic watershed model is small and the number of monitoring stations deployed is small, the satellite data observed by the first satellite and the second satellite can still be used to compensate the water environment data output by the dynamic watershed model, so as to obtain accurate target water environment data and reduce the dependence on the monitoring stations.

[0060] Fourth, since the spatial resolution of the water environment data output by the dynamic watershed model is higher than that of the first satellite data and the second satellite data, after compensating the first satellite data and the second satellite data with the water environment data, target water environment data with higher spatial resolution can be obtained.

[0061] In summary of the above four points, after fusing the first satellite data, the second satellite data, and the water environment data, high-coverage, high-spatial-resolution, and relatively accurate target water environment data can be obtained without relying on too many monitoring stations. Based on this target water environment data to calculate the water environment capacity, the obtained water environment capacity will also be more accurate.

[0062] Figure 3 This is an exemplary embodiment related to the present disclosure, and its exemplary solution for aligning remote sensing data and watershed flow in time includes the following steps: In step S40, for any one of the remote sensing data in the vegetation data and the second satellite data, align the remote sensing data and the watershed flow at the time point.

[0063] Among them, the vegetation data is used to determine the vegetation coverage input into the dynamic watershed model, which indicates the vegetation coverage area in the target watershed.

[0064] Among them, after the remote sensing data and the watershed flow are aligned in time, there is a set of remote sensing data and watershed flow at any time point.

[0065] It can be understood that the time resolution of the remote sensing data is 30 minutes, which means that the remote sensing data is collected every 30 minutes, while the time resolution of the watershed flow is 1 day, which means that the watershed flow is collected every 1 day. Therefore, the cubic spline interpolation method can be used to encrypt the time resolution of the watershed flow to the hourly level, so that the time points of the watershed flow can be aligned with the time points of the remote sensing data as much as possible.

[0066] Through the above technical solution, the remote sensing data and the watershed flow can be aligned in time. Then, the second satellite data in the remote sensing data and the watershed flow are time-aligned, and the vegetation coverage obtained based on the vegetation data in the remote sensing data and the watershed flow are also time-aligned. Then, the watershed flow, vegetation coverage, and second satellite data input into the dynamic watershed model are time-aligned with each other, so as to obtain the pollutant concentration and outlet flow at different time points with higher time resolution.

[0067] Figure 4 This is an exemplary embodiment involved in the present disclosure, which is used to interpret an exemplary scheme for converting vegetation data into vegetation coverage, including the following steps: In step S50, the vegetation data is normalized to obtain the normalized vegetation data.

[0068] Among them, the vegetation data includes the collected near-infrared band reflectance and red light band reflectance. The normalized vegetation data can be obtained through the following formula: (1) In formula (1), is the near-infrared band reflectance, is the red light band reflectance.

[0069] In step S60, the normalized vegetation data is processed to obtain the vegetation coverage.

[0070] Among them, the calculation formula for obtaining the vegetation coverage by processing the normalized vegetation data is as follows: FCC = 0.72 ⋅ NDVI + 0.15 * R 2 (2) In formula (2), FCC is the vegetation coverage, 0.72 represents that for each unit increase in NDVI, FCC increases by an average of 0.2 units, 0.15 is the intercept, indicating that when NDVI is 0, the baseline value of FCC is 0.15, and 0.15 is used to reflect the influence of the non-vegetation-covered part. NDVI is the normalized vegetation data, and R 2 is the coefficient of determination, representing the extent to which NDVI can explain FCC. When R² = 0.89, it means that NDVI can explain 89% of the FCC changes, and the fitting effect is good.

[0071] In related technologies, manual measurement of vegetation coverage is adopted, and the method of manual measurement of vegetation coverage is cumbersome and inefficient.

[0072] Through the above technical solution, the vegetation coverage can be calculated in real time through the vegetation data in the remote sensing data, thus solving the problems of cumbersome and low efficiency brought by manual measurement of vegetation coverage, and eliminating the non-linear deviation between the normalized vegetation data and the vegetation coverage through the normalization method, ensuring the credibility of the obtained vegetation coverage.

[0073] Figure 5 This is an exemplary embodiment related to the above step S10, which is used to interpret an exemplary scheme for fusing the first satellite data, water environment data, and second satellite data, including the following steps: In step S11, the first satellite data and the water environment data are fused to obtain first fused data.

[0074] In a possible implementation manner, fusing the first satellite data and the water environment data to obtain first fused data includes the following sub-steps: Sub-step A1, obtaining the first observation error of the first satellite according to the first satellite data and the water environment data.

[0075] Among them, the water environment data includes first water environment data and second water environment data. The first water environment data is the water environment data in the first state, and the first state indicates that the first prediction noise is not 0; the second water environment data is the water environment data in the second state, and the second state indicates that the first prediction noise is 0, that is, there is no first prediction noise.

[0076] For the first water environment data, the first water environment data at the current moment can be obtained according to the first water environment data predicted by the dynamic watershed model at the previous moment, the state transition matrix, and the first prediction noise.

[0077] Among them, the calculation formula for obtaining the first water environment data at the current moment according to the first water environment data predicted by the dynamic watershed model at the previous moment, the state transition matrix, and the first prediction noise is as follows: (3) In formula (3), x k is the first water environment data at the current moment, A is the state transition matrix, is the first water environment data at the previous moment, w k is the first prediction noise.

[0078] The first water environment data at the current moment includes the soil moisture and the groundwater level at the current moment. The first water environment data at the current moment is the data obtained by simulating the dynamic watershed model, and there may be errors due to the structural error of the dynamic watershed model or the parameter uncertainty of the dynamic watershed model.

[0079] The form of the state transition matrix is a 2X2 matrix, which respectively corresponds to the state transitions of the soil moisture and the groundwater level. Through the state transition matrix, the physical relationship between the soil moisture and the groundwater level can be quantitatively described, driving the forward movement of the first water environment data.

[0080] The first prediction noise is used to describe the prediction uncertainty of the dynamic watershed model, which is represented by the covariance matrix Q. The covariance matrix Q is used to describe the prediction uncertainty of the dynamic watershed model. The covariance matrix Q can be determined by the error statistics during the verification period of the dynamic watershed model. This error indicates the error (RMSE) between the first water environment data obtained by simulating the dynamic watershed model and the real water environment data. Covariance matrix Q = error RMSE 2 .

[0081] It can be seen from formula (3) that this formula (3) describes the dynamic process of water environment data such as soil moisture and groundwater level over time, and it has the following multiple functions: First, time continuity, which can predict the first water environment data at the current moment based on the first water environment data at the previous moment, reflecting the dynamic evolution law of the target watershed system. Second, physical constraint, the state transition matrix is embedded in the water environment data to ensure that the changes in the water environment data conform to the natural physical laws. Third, it provides a basis for generating subsequent first observation errors.

[0082] For the second water environment data, the second water environment data at the current moment is the data when the first prediction noise w k in the first water environment data at the current moment is 0. According to the second water environment data predicted by the dynamic watershed model at the previous moment and the state transition matrix, the calculation formula for the second water environment data at the current moment is as follows: (4) In formula (4), is the second water environment data at the current moment, is the state transition matrix, is the second water environment data at the previous moment.

[0083] Among them, obtaining the first observation error of the first satellite according to the first satellite data and the water environment data includes: mapping the second water environment data at the current moment by using a first mapping matrix to obtain the mapped second water environment data; then subtracting the mapped second water environment data from the first satellite data to obtain the first observation error, and its calculation formula is as follows: vk GRACE = (5) In formula (5), vk GRACE is the first observation error, is the first satellite data observed by the first satellite, is the first mapping matrix, is the second water environment data at the current moment.

[0084] The first observation error indicates the measurement error of the first satellite itself (such as the inversion error of the grace gravity field) and the atmospheric correction error of the first satellite (such as the interference caused by air pressure changes to the observation of the first satellite). The first observation error has the characteristics of Gaussian white noise and is used to quantify the reliability of the first satellite data. If the gap between the first satellite data and the mapped second water environment data is larger, it means that the first satellite data is less reliable.

[0085] The first satellite data is the satellite data observed by the first satellite, which can be the terrestrial water storage.

[0086] The first mapping matrix is used to map the two-dimensional second water environment data (soil moisture and groundwater level) simulated by the dynamic watershed model into one dimension, so as to be in the same dimension as the one-dimensional terrestrial water storage. The first mapping matrix performs weighted summation on the soil moisture and groundwater level simulated by the dynamic watershed model and converts them into observation data in the same dimension as the terrestrial water storage, realizing the coupling of the high-spatial-resolution soil moisture and groundwater level and the low-spatial-resolution terrestrial water storage. Essentially, it calculates the contribution of the soil moisture and groundwater level simulated by the dynamic watershed model to the terrestrial water storage observed by the first satellite.

[0087] As can be seen from the above formula (5), formula (5) maps the first satellite data retrieved from the first satellite and the second water environment data simulated by the dynamic watershed model to the same dimension, achieving the following functions: First, spatial constraint, which correlates and assimilates the two types of second water environment data, soil moisture and groundwater level, that cannot be observed, with the first satellite data, terrestrial water storage, that can be observed; Second, error transfer, which converts the error between the second water environment data predicted by the dynamic watershed model and the real second water environment data into an error in the satellite observation space through the first mapping matrix; Third, different first mapping matrices can be designed to adapt to different types of first satellite data.

[0088] Sub-step A2, use the first Kalman gain to correct the first observation error to obtain the first target observation error.

[0089] Among them, the first Kalman gain is used to measure the uncertainty of the second water environment data. The first Kalman gain matrix can be obtained based on the error between the second water environment data predicted by the dynamic watershed model and the real second water environment data, the first mapping matrix, and the first observation noise. Its calculation formula is as follows: (6) In formula (6), is the first Kalman gain, is the error between the second water environment data predicted by the dynamic watershed model and the real second water environment data, is the first mapping matrix, is the first observation noise.

[0090] The first Kalman gain is used to correct the first observation error and measure the importance between the first observation error and the second water environment data simulated by the dynamic watershed model.

[0091] The error between the second water environment data predicted by the dynamic watershed model and the real water environment data reflects the inherent error of the dynamic watershed model. For example, the simulation error caused by inaccurate parameters of the dynamic watershed model itself or the simplification of the simulation process.

[0092] The first observation noise is used to indicate the uncertainty of the first satellite data retrieved by the first satellite. This uncertainty comes from the measurement error of the first satellite, atmospheric correction error, etc.

[0093] Sub-step A3, obtain the first fusion data according to the water environment data and the first target observation error.

[0094] Obtaining the first fusion data based on the water environment data and the first target observation error includes: The second fusion data can be obtained according to the second water environment data at the current moment and the first target observation error.

[0095] Among them, the product of the first Kalman gain and the first observation error can be used as the first target observation error, and the first target observation error is superimposed on the basis of the second water environment data to obtain the first fusion data. The calculation formula is as follows: (7) In formula (7), is the first fusion data, is the second water environment data at the current moment, is the first Kalman gain, is the first observation error.

[0096] The first fusion data represents the fusion data at a coarse scale or low resolution. It is obtained by fusing the first satellite data with low resolution but high accuracy and the second water environment data with high resolution but low accuracy, so as to obtain the first fusion data with high accuracy and low resolution. It can be understood that the low resolution of the first fusion data means that the resolution of the first fusion data is lower than that of the target water environment data finally fused, but objectively the resolution of the first fusion data is not low.

[0097] The first Kalman gain is used to measure the weight between the second water environment data simulated by the dynamic watershed model and the first satellite data simulated by the first satellite. Combining formula (6) and formula (7), it can be seen that when the first observation noise of the first satellite data becomes larger, the first Kalman gain becomes smaller, thereby reducing the influence of the first observation error of the first satellite data on the first fusion data, and increasing the influence of the second water environment data simulated by the dynamic watershed model on the first fusion data. The weight of the second water environment data is greater than the weight of the first observation error , indicating that more trust is placed in the prediction result of the dynamic watershed model at this time. When the error between the second water environment data simulated by the dynamic watershed model and the real water environment data is large, the first Kalman gain becomes larger, thereby increasing the influence of the first observation error of the first satellite data on the first fusion data, and reducing the influence of the second water environment data simulated by the dynamic watershed model on the first fusion data. The weight of the first observation error is greater than the weight of the second water environment data The weight indicates that at this time, more trust is placed in the observation results of the first satellite.

[0098] As can be seen from the above formula (7), the influence degree of the first satellite data and the second water environment data obtained by simulating the dynamic watershed model on the first fusion data can be measured by the first Kalman gain. If the accuracy of the first satellite data is higher than that of the second water environment data, the weight of the first satellite data will be higher than that of the second water environment data through the first Kalman gain, so that the obtained first fusion data is mainly affected by the first satellite data; on the contrary, if the accuracy of the first satellite data is lower than that of the second water environment data, the weight of the first satellite data will be lower than that of the second water environment data through the first Kalman gain, so that the obtained first fusion data is mainly affected by the second water environment data, ultimately ensuring the accuracy of the obtained first fusion data.

[0099] In step S12, the first fusion data and the second satellite data are fused to obtain the target water environment data.

[0100] In a possible implementation manner, fusing the first fusion data and the second satellite data to obtain the target water environment data includes the following sub-steps B1 to B3: Sub-step B1, obtaining the second observation error of the second satellite according to the second satellite data and the second fusion data.

[0101] Among them, the second fusion data is the data obtained by perturbing the third fusion data with a perturbation term. The third fusion data is the data corresponding to the first fusion data. For example, the third fusion data is the data obtained based on the first water environment data when the first observation noise is not 0, and the first fusion data is the data obtained based on the second water environment data when the first observation noise is 0.

[0102] Among them, the second fusion data is obtained through the following sub-steps C1 and C2.

[0103] Sub-step C1, obtaining the third fusion data according to the first satellite data, the first water environment data and the first Kalman gain.

[0104] Exemplarily, the second observation error can be obtained according to the first satellite data and the first water environment data, and then the first Kalman gain is used to correct the second observation error to obtain the second target observation error; then the third fusion data is obtained according to the first water environment data and the second target observation error, and its calculation formula is as follows: (8) In formula (8), is the third fusion data, is the first water environment data at the current moment, is the first Kalman gain, is the first observation error, which is essentially the same as the first observation error in the above formula (5) and represents the observation error of the first satellite, is the first satellite data, is the first mapping matrix. is the first mapping matrix.

[0105] It can be seen from the above formulas (8), (5) and (7) that after substituting formula (5) into formula (7), the obtained first fusion data is essentially the same as the third fusion data obtained based on formula (8). However, the first fusion data obtained from formula (7) is based on the second water environment data when w k is 0, and the third fusion data in formula (8) is based on the first water environment data when w k is not 0.

[0106] Sub-step C2: Obtain the second fusion data according to the perturbation term and the third fusion data.

[0107] Exemplarily, the perturbation term can be used to perturb the third fusion data at the current moment to obtain the second fusion data, and its calculation formula is as follows: (9) In formula (9), is the second fusion data, is the third fusion data, is the perturbation term.

[0108] It can be seen from formula (9) that the perturbation term can be superimposed on the basis of the third fusion data to obtain the second fusion data.

[0109] The third fusion data is obtained based on the first water environment data when the first observation noise wk is not 0, and it is the coarse-scale and low-resolution data obtained by combining the first water environment data simulated by the dynamic watershed model with the first observation error of the first satellite.

[0110] The perturbation term is a local fine-scale and high-resolution perturbation term, which represents the high-resolution spatial details not captured in the coarse-scale and low-resolution background field. For example, it can be characterized by the water holding capacity differences of high-resolution sandy soil and clay, etc., such as the local vegetation coverage changes and micro-topography effects such as slope runoff convergence caused by differences in evapotranspiration of farmland crops.

[0111] The second fusion data is the fine-scale and higher-resolution data obtained by considering the fine-scale perturbation term on the basis of the second fusion data.

[0112] The above formulas (8) and (9) have the following effects: The first observation error between the first water environment data simulated by the dynamic watershed model and the first satellite data is fused, and the obtained third fused data with coarse scale and low resolution is used as the initial background of the disturbance term. Then, the disturbance term is used to capture the influence of terrain and vegetation coverage differences that cannot be resolved in the coarse scale environment on soil moisture, which can consider the individual differences of each region, making the obtained second fused data have higher resolution and higher accuracy.

[0113] Among them, obtaining the second observation error based on the second fused data and the second satellite data includes: mapping the second fused data using the second mapping matrix to obtain the mapped second fused data; then subtracting the mapped second fused data from the second satellite data to obtain the second observation error, and its calculation formula is as follows: (10) In formula (10), is the second observation error, is the second satellite data, is the second mapping matrix, is the second fused data.

[0114] The second observation error indicates the observation error of the second satellite itself.

[0115] The second satellite data is the satellite data observed by the second satellite, which can be soil moisture with high spatial resolution.

[0116] The second mapping matrix is a local observation operator, which is used to map the second fused data with fine scale and high resolution to the observation space of the second satellite, so that the mapped second fused data can have the same physical dimension and spatial resolution as the second satellite data retrieved by the second satellite.

[0117] It can be seen from formula (10) that formula (10) maps the second satellite data observed by the second satellite and the second fused data to the same dimension, achieving the following effects: First, the second satellite data is the data in the observation space of the second satellite data, and the second fused data is the data in the prediction space of the dynamic watershed model, solving the scale and unit differences between the observation space of the second satellite and the prediction space simulated by the dynamic watershed model; Second, the high-resolution observation of the second satellite is used to correct the local deviation existing in the second fused data at fine scale and high resolution.

[0118] Sub-step B2, correcting the second observation error using the second Kalman gain to obtain the second target observation error.

[0119] Among them, the second Kalman gain is used to measure the uncertainty between the first fusion data and the second satellite data. The second Kalman gain can be obtained based on the error between the first water environment data predicted by the dynamic watershed model and the real water environment data, the second mapping matrix, and the second observation noise. The calculation formula is as follows: (11) In formula (11), is the second Kalman gain, is the error between the second water environment data predicted by the dynamic watershed model and the real second water environment data, is the second mapping matrix, is the second observation noise.

[0120] The second Kalman gain is a weight matrix used to balance the uncertainty between the error between the second water environment data predicted by the dynamic watershed model and the real second water environment data and the second observation noise. The error between the second water environment data predicted by the dynamic watershed model and the real second water environment data can also be the error between the first water environment data predicted by the dynamic watershed model and the real first water environment data.

[0121] The error between the second water environment data predicted by the dynamic watershed model and the real second water environment data reflects the inherent error of the dynamic watershed model. For example, the parameters of the dynamic watershed model itself are inaccurate or the simulation error caused by the simplification of the simulation process. This error is the uncertainty of the dynamic watershed model for the output second water environment data / first water environment data, which is caused by the changes in water environment data such as soil moisture and groundwater level due to terrain influence and human activities, resulting in the uncertainty of the second water environment data / first water environment data output by the dynamic watershed model.

[0122] The second observation noise is used to indicate the uncertainty of the second satellite data retrieved by the second satellite.

[0123] Among them, the error between the second water environment data predicted by the dynamic watershed model and the real second water environment data can be calculated by the following formula: (12) In formula (12), is the error between the second water environment data and the real second water environment data, is the i-th second water environment data, is the average value of multiple second water environment data.

[0124] Sub-step B3: Obtain the target water environment data based on the first fusion data and the second target observation error.

[0125] Among them, the product of the second Kalman gain and the second observation error can be used as the second target observation error, and the second target observation error is superimposed on the basis of the first fusion data to obtain the target water environment data. The calculation formula is as follows: (13) In formula (13), is the target water environment data, is the first fusion data, is the second Kalman gain, is the second observation error.

[0126] The target water environment data represents the fused water environment data at a fine scale and high resolution. It is the target water environment data with higher accuracy and higher resolution obtained after the secondary fusion of the first fusion data with high accuracy and high resolution and the second satellite data with high accuracy and high resolution.

[0127] The first fusion data represents the fused data at a coarse scale or low resolution (the coarse scale and low resolution are in comparison with the target water environment data). It is the first fusion data with high accuracy and low resolution obtained by fusing the first satellite data with low resolution but high accuracy and the second water environment data with high resolution but low accuracy.

[0128] The second Kalman gain is used to measure the weight between the first fusion data after the fusion of the second water environment data simulated by the dynamic watershed model and the first satellite data observed by the first satellite and the second satellite data. Combining formula (11) and formula (13), it can be seen that when the second observation noise of the second satellite data increases, the second Kalman gain becomes smaller, thereby reducing the influence of the second observation error of the second satellite data on the target water environment data, and increasing the influence of the first fusion data on the target water environment data. The weight of the first fusion data is greater than the weight of the second observation error, indicating that more trust is placed on the first fusion data of the dynamic watershed model and the first satellite at this time. When the error between the second water environment data simulated by the dynamic watershed model and the real second water environment data increases, the second Kalman gain becomes larger, thereby increasing the influence of the second observation error of the second satellite data on the target water environment data. The weight of the second observation error of the second satellite data is greater than that of the first fusion data, indicating that more trust is placed on the observation results of the second satellite at this time.

[0129] As can be seen from formula (13), the influence degrees of the second satellite data and the first fusion data on the target water environment data can be measured by the second Kalman gain. If the accuracy of the second satellite data is higher than that of the first fusion data, the weight of the second satellite data will be higher than that of the first fusion data through the second Kalman gain, so that the obtained target water environment data is mainly affected by the second satellite data; conversely, if the accuracy of the second satellite data is lower than that of the first fusion data, the weight of the second satellite data will be lower than that of the first fusion data through the second Kalman gain, so that the obtained target water environment data is mainly affected by the first fusion data.

[0130] Through the above technical solution, the first satellite data with high accuracy observed by the first satellite is first fused with the water environment data with low accuracy simulated by the dynamic watershed model, and the first fusion data with low spatial resolution and high accuracy can be obtained; then the second satellite data with high resolution and high accuracy observed by the second satellite is used to perform secondary fusion on the first fusion data, so as to further improve the resolution and accuracy of the first fusion data.

[0131] Figure 6 This is an exemplary embodiment involved in the present disclosure, which is used to interpret an exemplary solution for training a dynamic watershed model, including the following steps: In step S70, the initial model is trained with historical training samples and the labels corresponding to the historical training samples. During multiple training processes, the network parameters of the initial model are updated until the error between the predicted water environment data output by the trained initial model and the real water environment data is less than a preset value, and the trained dynamic watershed model is obtained.

[0132] Among them, the historical training samples include historical watershed flow, water environment data, pollution source data, vegetation coverage and meteorological data, and the labels corresponding to the historical training samples include historical pollutant concentration and outlet flow.

[0133] Among them, the network parameters of the initial model include roughness coefficient and permeability coefficient. The roughness coefficient refers to the channel roughness of the target watershed. The higher the channel roughness of the target watershed, the lower the channel fluidity, thus affecting the outlet flow; the permeability coefficient includes the permeability coefficient of the soil in the target watershed and the permeability coefficient of the soil around the target watershed. The higher the permeability coefficient of the soil in the target watershed, the more water quality is lost, and then the outlet flow will decrease. The higher the permeability coefficient of the soil around the target watershed, the more water quality penetrates into the target watershed, and the outlet flow will increase.

[0134] During the process of training the initial model, an online particle optimization algorithm can be used to obtain the target network parameters of the initial model.

[0135] For example, network parameters such as roughness coefficient and permeability coefficient can be regarded as particles. Each particle has its own position and velocity. The position represents the position of the particle in the search space, and the velocity represents the flying speed and flying direction of the particle in the search space. The optimal solution found by each particle is called the individual optimal solution, and each particle knows the optimal solution in the entire search space, which is called the global optimal solution. Each particle will adjust its own flying speed and flying direction. If a particle finds that other particles have found a better individual optimal solution, it will adjust its own flying speed and flying direction and move closer to the other individual optimal solutions. Repeat the above steps until all particles in the search space are close to the global optimal solution, so as to obtain network parameters such as roughness coefficient and permeability coefficient in the global optimal solution.

[0136] Through the above technical solution, a dynamic watershed model can be trained, and the dynamic watershed model can be used to simulate high-resolution water environment data. And through the particle optimization algorithm, it can help the dynamic watershed model quickly find its own network parameters, so as to realize the rapid training of the dynamic watershed model.

[0137] Figure 7 It is an exemplary embodiment involved in the above step S20, which is used to interpret an exemplary solution for obtaining the pollutant concentration and outlet flow of the target watershed based on the watershed flow, target water environment data, pollution source data, vegetation coverage and meteorological data of the target watershed, including the following steps: In step S21, multiple target sub-watersheds are selected from multiple sub-watersheds of the target watershed.

[0138] Among them, the multiple target sub-watersheds are the sub-watersheds with monitoring stations in the target watershed.

[0139] For example, please refer to Figure 2 As shown, the target watershed is divided into 10 sub-watersheds, and 5 of them are equipped with monitoring stations. Then, the sub-watersheds where these 5 monitoring stations are located can be used as target sub-watersheds.

[0140] In step S22, the watershed flow, target water environment data, vegetation coverage, future meteorological data of the target watershed and the pollution source data of the multiple target sub-watersheds are input into the dynamic watershed model to obtain the future pollutant concentration and outlet flow of the target watershed.

[0141] For example, the watershed flow, target water environment data, vegetation coverage, future meteorological data of the entire target watershed and the pollution source data of the sub-watersheds where 5 monitoring stations are located can be input into the dynamic watershed model, so as to simulate the future pollutant concentration and outlet flow of each sub-watershed in the target watershed. Of course, it can also simulate the future pollutant concentration and outlet flow of the entire target watershed as a whole.

[0142] Through the above technical solution, it is possible to obtain the pollutant concentrations and outlet flows of each sub-basin in the entire target basin by using the pollutant source data measured by a relatively small number of monitoring stations and the target water environment data obtained by the above fusion. When the number of monitoring stations deployed in the target basin is small, the data monitored by the monitoring stations cannot reflect the entire target basin. Therefore, the data observed by the second satellite data and the first satellite data can be used to perform fusion compensation on the data monitored by the monitoring stations. Then, the data input into the dynamic basin model is comprehensive and accurate input data for the target basin. Based on these data, the dynamic basin model can predict more comprehensive and accurate pollutant concentrations and outlet flows, thereby reducing the dependence on monitoring stations.

[0143] Figure 8 This is an exemplary embodiment related to step S30, which is used to interpret an exemplary solution for obtaining the water environment capacity of the target basin based on the pollutant concentrations and outlet flows of the target basin, and includes the following steps: In step S31, determine the target time period during which the pollutant concentration in the target basin is less than the target pollutant concentration.

[0144] Among them, the dynamic basin model outputs the pollutant concentrations and outlet flows of the target basin in a future period of time. Therefore, the target time period during which the pollutant concentration is less than the target pollutant concentration can be screened out from the pollutant concentrations in the future period of time of the target basin, which is also the compliance time period when the water quality of the target basin meets the standards.

[0145] For example, taking the pollutant concentration of the target basin as C sim(t) , and the target pollutant concentration as C target as an example, the target time period during which C sim(t) is less than or equal to C target can be screened out from the future time period of the target basin.

[0146] In step S32, for each time point in the target time period, obtain the pollutant flux at the time point according to the pollutant concentration and the outlet flow.

[0147] Among them, the product of the pollutant concentration and the outlet flow can be used as the pollutant flux. The pollutant flux is the amount of pollutant emissions flowing out of the outlet of the target basin per unit time, and its calculation formula is as follows: F(t)=C sim(t) ×Q(t) (14) In formula (14), F(t) is the pollutant flux at the current time point, C sim(t) is the pollutant concentration at the current time point, and Q(t) is the outlet flow at the current time point.

[0148] In step S33, the water environment capacity is obtained according to the pollutant fluxes at multiple time points in the target time period.

[0149] The pollutant fluxes at multiple time points in the target time period can be integrated to obtain the water environment capacity, and its calculation formula is as follows: (15) In formula (15), W is the water environment capacity within the target time period when the water quality meets the standard, and F(t) is the pollutant flux at the current time point.

[0150] It can be understood that after obtaining the water environment capacity, the water capacity threshold corresponding to the current pollutant concentration can be compared with the water environment capacity. If the water environment capacity is greater than the water capacity threshold, it means that the water environment capacity exceeds the standard, and there are more pollutants in the target basin. Of course, when the water environment capacity is less than or equal to the water capacity threshold and the water environment capacity does not exceed the standard, the larger the water environment capacity, the more water bodies can accommodate pollutants.

[0151] Through the above technical solution, the water environment capacity during the compliance period when the pollutant concentration in the target basin is less than the target pollutant concentration can be determined. If the water environment capacity is greater than the water environment capacity threshold, it means that the water environment capacity during the compliance period of the water quality in the target basin still exceeds the standard, further indicating that the water environment capacity during the non-compliance period of the target basin will also exceed the standard. It is not necessary to measure the water environment capacity of the remaining non-compliance periods of the target basin, reducing the additional cost brought by measuring the water environment capacity of the target basin.

[0152] Figure 9 This is an exemplary embodiment involved in the present disclosure, which is used to interpret and screen out the target parameters affecting the water environment capacity, treatment resources, and ecological flow after obtaining the water environment capacity, including the following steps: In step S80, multiple elements affecting the water environment capacity, treatment resources, and ecological anomaly are determined.

[0153] Among them, the water environment capacity refers to the maximum load water volume that the target basin can accommodate pollutants when the pollutant concentration in the target basin is less than the preset pollutant concentration. The elements affecting the water environment capacity include Chemical Oxygen Demand (COD), ammonia nitrogen emission threshold, sewage treatment equipment cost, and ecological flow threshold.

[0154] Chemical Oxygen Demand is the main indicator to measure the organic matter content in water bodies. It represents the amount of oxygen consumed when oxidizing organic matter in water bodies with strong oxidants under certain conditions. The higher the Chemical Oxygen Demand, the more serious the organic matter pollution in the water bodies.

[0155] The ammonia nitrogen emission threshold is the maximum amount of pollutants allowed to be discharged into water bodies.

[0156] The cost of sewage treatment equipment indicates the cost of the hardware equipment required to treat pollutants in water bodies.

[0157] The ecological flow threshold is the minimum downstream flow to ensure the ecological health of rivers. If the outlet flow of the target basin is less than or equal to the ecological flow threshold, it indicates that the river ecology of the target basin is unhealthy.

[0158] Among them, the treatment resources refer to the resource costs required to treat pollutants. The elements affecting the treatment resources include the equipment investment cost and the equipment operation and maintenance cost.

[0159] The equipment investment cost is determined by the initial investment cost, equipment scale and scale effect. The expression of the equipment investment cost is as follows: (16) In formula (16), is the equipment investment cost, a and b are parameters related to the equipment type, a is the proportionality coefficient, representing the initial investment cost under the unit equipment scale, and b is the exponent, indicating the scale effect, x represents the equipment scale, such as the number of equipment.

[0160] It can be seen from the above formula (16) that if b is less than 1, it represents the existence of scale effect. As the equipment scale increases, the equipment investment cost per unit equipment will decrease, and the total equipment investment cost will decrease; if b is equal to 1, it represents a linear relationship between the equipment investment cost and the equipment scale; if b is greater than 1, it represents diseconomies of scale. As the equipment scale increases, the equipment investment cost per unit equipment will increase, and the total equipment investment cost will increase.

[0161] The equipment operation and maintenance cost is the operation and maintenance cost related to time, which is usually related to factors such as equipment load, equipment energy consumption, equipment scale, and equipment aging. For example, as time goes by, equipment aging will lead to an increase in the equipment operation and maintenance cost. Similarly, the larger the equipment scale, the greater the equipment operation and maintenance cost.

[0162] Among them, the ecological anomaly degree refers to the degree of ecological anomaly in the target basin, which can also be ecological risk. The elements affecting ecological instability include the target duration that the outlet flow of the target basin is less than or equal to the outlet flow threshold within a specified period. The larger this target duration is, the longer the duration that the outlet flow is less than or equal to the outlet flow threshold, that is, the longer the duration that the outlet flow does not meet the standard, and then the more abnormal the ecological environment of the target basin is.

[0163] In step S90, a plurality of target elements are selected from the plurality of elements with the objectives of maximizing the water environment capacity, minimizing the treatment resources, and minimizing the ecological abnormality degree.

[0164] Among them, maximizing the water environment capacity means maximizing the amount of water body carrying pollutants, so as to reduce the pollutant concentration.

[0165] Exemplarily, the expression for maximizing the water environment capacity is as follows: (17) In formula (17), is the objective function for maximizing the water environment capacity, is the water environment capacity of the i-th pollutant calculated in the above step S30, is the true and accurate water environment capacity of the i-th pollutant.

[0166] It can be seen from formula (17) that the water environment capacity of the i-th pollutant obtained by simulation can be divided by the true water environment of the i-th pollutant to obtain a water capacity ratio; then the water capacity ratios of various pollutants are added up to obtain a total water capacity, so as to select, with the objective of maximizing the total water capacity, the target elements from the four elements of chemical oxygen demand, ammonia nitrogen emission threshold, sewage treatment equipment cost, and ecological flow threshold that can maximize the water environment capacity.

[0167] Among them, minimizing the treatment resources means minimizing the cost of treating pollutants, so as to reduce the cost.

[0168] Exemplarily, the expression for minimizing the treatment resources is as follows: (18) In formula (18), is the objective function for minimizing the treatment resources, is the equipment input cost, is the equipment operation and maintenance cost.

[0169] It can be seen from formula (18) that the sum of the equipment input cost and the equipment operation and maintenance cost can be used as the treatment resources, and the target elements that can minimize the treatment resources can be selected from the two elements of the equipment input cost and the equipment operation and maintenance cost.

[0170] Among them, minimizing the ecological abnormality degree means minimizing the abnormality degree of the ecological environment, so as to improve the health degree of the ecological environment.

[0171] Exemplarily, the expression for minimizing the ecological abnormality degree is as follows: (19) In formula (19), is the objective function for minimizing the ecological anomaly degree, is the outlet flow of the target basin calculated in real time, is the outlet flow threshold, indicates the target duration determined that the outlet flow is less than or equal to the outlet flow threshold; T is the total observation duration.

[0172] It can be seen from the above formula (19) that the proportion of the target duration when the outlet flow does not meet the standard in the total duration can be calculated, and this proportion is used as the ecological anomaly degree. If the proportion is larger, it means the ecological anomaly degree is higher. Furthermore, the proportion of the duration when the outlet flow does not meet the standard or the target duration is used as the target element that can minimize the ecological anomaly degree.

[0173] In some scenarios, three target elements can be selected from the elements affecting the water environment capacity, treatment resources, and ecological anomaly degree for optimization. For example, chemical oxygen demand can be selected from the elements affecting the water environment capacity, the equipment input cost can be selected from the elements affecting the treatment resources, and the target duration when the outlet flow of the target basin is less than the outlet flow threshold can be selected from the elements affecting the ecological anomaly degree. The chemical oxygen demand, equipment input cost, and target duration are used as the target elements.

[0174] In step S100, multiple target parameters are respectively selected from the parameters of the multiple target elements.

[0175] Among them, each target element has multiple parameters. For example, multiple chemical oxygen demands can be used to measure the target basin, there can be multiple levels of equipment input costs for treating pollutants in the target basin, and there can be multiple target durations when the outlet flow of the target basin does not meet the standard. Therefore, multiple target parameters can be selected from multiple chemical oxygen demands, multiple equipment input costs, and multiple target durations, and then the target basin is measured and the target basin is treated with multiple target parameters, so as to maximize the water environment capacity of the target basin, minimize the treatment resources, and minimize the ecological anomaly degree.

[0176] For example, the chemical oxygen demand has 5 gm / L, 10 mg / L, 15 mg / L, the equipment input cost has 100,000 yuan, 200,000 yuan, and 300,000 yuan, and the target duration when the outlet flow does not meet the standard has 1 h, 2 h, 3 h. Then, from these three target elements, the chemical oxygen demand of 5 gm / L, the equipment input cost of 100,000 yuan, and the target duration of 1 h can be respectively selected as the target parameters for operation. Then ultimately, the water environment capacity of the target basin can be relatively large, the treatment resources can be less, and the ecological anomaly degree can also be less, achieving better ecological environment of the target basin with less treatment cost.

[0177] In a possible implementation, for multiple objective functions such as maximizing the water environment capacity, minimizing the treatment resources, and minimizing the ecological abnormality degree, non-dominated sorting can be used to perform priority sorting. The higher the priority of the objective function, the higher the weight it occupies in the optimization process, and the objective function with a higher optimization priority will be considered first.

[0178] For the non-dominated relationship, in the process of multi-objective optimization with the three objectives of maximizing the water environment capacity, minimizing the treatment resources, and minimizing the ecological abnormality degree, the solution (such as an element) of one objective function may not be superior to another solution in all objective functions, but may be superior to another solution in some objective functions and not inferior to another solution in other objective functions. This relationship is called the non-dominated relationship. The three objective functions can be divided into levels such as F1, F2, F3, etc. The levels of F1, F2, F3 gradually decrease. The level of F1 has the highest non-dominated level, and all influencing elements of the objective function at the F1 level are not dominated by other elements.

[0179] The parallel bubble sort can be used to sort the levels of multiple objective functions. Taking 3241 as the level, the bubble sort compares adjacent two numbers from left to right. If the left number is larger than the right number, they are swapped. For example, since 3 is larger than 2, they are swapped to become 23. Then compare 41, since 4 is larger than 1, they are swapped to become 14, so it becomes 2314. Then use 2314 to repeat the sorting until all data becomes 1234, completing the sorting of the levels. The parallel bubble sort is to divide 21 and 41 into two groups in each round of comparison and perform parallel sorting and comparison, so as to quickly sort the order into 1234.

[0180] In a possible implementation, the approximate calculation of the crowding degree of the target elements can be used to retain the target elements and remove the non-target elements. For multiple target elements under the same objective function, the sum of the distances between the elements in the objective function and other elements can be calculated, and this distance is used to determine the retention of the target elements.

[0181] For example, the crowding degree is used to measure the distribution density of elements in the population of the objective function. For an element, the sum of the distances between this element and adjacent elements in the population can be calculated. The farther the distance, the fewer elements around this element, and the better the diversity of this individual.

[0182] Finally, the three objective functions can be used as three populations, and three objective elements are respectively selected from the three populations. For example, the chemical oxygen demand is selected from the population corresponding to the water environment capacity, the equipment input cost is selected from the treatment resources, and the target duration is selected from the ecological abnormality degree, so that the objective element with the highest non-dominated level and the largest crowding degree in the current population can be obtained. The highest non-dominated level indicates that the element is not dominated by other elements in the population, and the element performs relatively small in each objective function. The largest crowding degree indicates that the diversity of the objective element is good, and the diversity of the population is good.

[0183] Figure 10 This is an exemplary embodiment related to step S90, which is used to interpret an exemplary solution for screening multiple objective elements in the case of introducing a penalty function, including the following steps: In step S91, determine the penalty value when the compliance rate of the pollutant concentration does not meet the preset compliance rate.

[0184] Among them, the compliance rate of the pollutant concentration refers to the ratio of the duration when the pollutant concentration is less than the preset pollutant concentration to the preset duration. For example, taking a 10-day cycle, if within 10 days, the number of days when the pollutant concentration is less than the preset pollutant concentration is 8 days, then the compliance rate is 80%.

[0185] Among them, the penalty value can be obtained according to the difference between the compliance rate and the preset compliance rate and the number of iterations. Its calculation formula is as follows: (20) In formula (20), is the penalty value; is the penalty coefficient brought by the number of iterations, and it is also the penalty coefficient brought by the number of times when the compliance rate does not meet the preset compliance rate. The more times the compliance rate does not meet the preset compliance rate, the higher the penalty coefficient; is the difference between the preset compliance rate and the compliance rate; p is the penalty intensity coefficient, for example, p is greater than or equal to 2.

[0186] It can be seen from the above formula (20) that the greater the difference between the preset compliance rate and the compliance rate, the greater the penalty value. The greater the difference between the preset compliance rate and the compliance rate, it indicates that the gap between the current compliance rate and the expected compliance rate is larger, so the penalty value will be higher; the more times the compliance rate does not meet the preset compliance rate in the iterations, the greater the penalty coefficient, and the corresponding penalty value is also greater.

[0187] Among them, the penalty coefficient is obtained through the following formula: (21) In formula (21), is the penalty coefficient, is the initial penalty factor, is the increasing rate, which is used to determine the speed of constraint tightening, and t is the number of iterations.

[0188] It can be seen from the above formula (21) that the larger the number of iterations, the larger the penalty coefficient.

[0189] In step S92, the penalty value is used to correct the water environment capacity, the treatment resources, and the ecological flow respectively, to obtain the corrected water environment capacity, the corrected treatment resources, and the corrected ecological flow.

[0190] Among them, the penalty value can be subtracted from the water environment capacity to obtain the corrected water environment capacity, and its calculation formula is as follows: (22) In formula (22), is the corrected water environment capacity, is the water environment capacity before correction, is the weight of the penalty value, is the penalty value.

[0191] It can be seen from formula (22) that the penalty value is inversely proportional to the water environment capacity. When the penalty value is higher, the corrected water environment capacity is smaller. The reason is that when the gap between the compliance rate of the pollutant concentration in the target basin and the preset compliance rate is large, the situation of the pollutant concentration in the target basin violating the constraint amount is more serious. Therefore, the increase of the penalty value can be controlled. When the penalty value increases, the corrected water environment capacity becomes smaller. Then, in the process of selecting the target parameters with the maximization of the corrected water environment capacity as the goal, the water environment capacity before correction can be maximized while the penalty value is minimized, so as to obtain the maximized corrected water environment capacity. While maximizing the water environment capacity, the penalty value can also be made smaller, so that the compliance rate of the pollutant concentration in the target basin reaches the preset compliance rate.

[0192] Among them, the penalty value can be added to the treatment resources to obtain the corrected treatment resources, and its calculation formula is as follows: (23) In formula (23), is the corrected treatment resources, is the treatment resources before correction, is the weight of the penalty value, is the penalty value.

[0193] As can be seen from formula (23), the penalty value is directly proportional to the governance resources. When the penalty value is higher, the corrected governance resources are larger. The reason is that when the gap between the pollutant concentration compliance rate of the target basin and the preset compliance rate is large, more governance resources are required to treat the target basin in order to make the pollutant concentration compliance rate of the target basin reach the preset compliance rate. Therefore, the increase of the penalty value can be controlled. When the penalty value increases, the corrected governance resources become larger. Then, in the process of selecting target parameters with the goal of minimizing governance resources, the governance resources before correction and the penalty value can be minimized simultaneously to obtain the minimized corrected governance resources. While minimizing the governance resources, the penalty value can also be reduced, thereby reducing the pollutant concentration of the target basin and increasing the pollutant concentration compliance rate of the target basin.

[0194] Among them, the penalty value can be added to the ecological anomaly degree to obtain the corrected ecological anomaly degree. Its calculation formula is as follows: (24) In formula (24), is the corrected ecological anomaly degree, is the ecological anomaly degree before correction, is the weight of the penalty value, is the penalty value.

[0195] As can be seen from formula (24), the penalty value is directly proportional to the ecological anomaly degree. When the penalty value is higher, the corrected ecological anomaly degree is larger. The reason is that when the gap between the pollutant concentration compliance rate of the target basin and the preset compliance rate is large, it will lead to a more abnormal ecology in the target basin. Therefore, the corrected ecological anomaly degree is larger. Therefore, the increase of the penalty value can be controlled. When the penalty value increases, the corrected ecological anomaly degree becomes larger. Then, in the process of selecting target parameters with the goal of minimizing the ecological anomaly degree, the ecological anomaly degree before correction and the penalty value can be minimized simultaneously to obtain the minimized corrected ecological anomaly degree. While minimizing the corrected ecological anomaly degree, the penalty value will also be reduced, thereby reducing the pollutant concentration of the target basin and increasing the pollutant concentration compliance rate of the target basin.

[0196] In step S93, with the goal of maximizing the corrected water environment capacity, the corrected governance resources, and the corrected ecological flow rate, the multiple target elements are screened out from the multiple elements.

[0197] Through the above technical solution, while maximizing the corrected water environment capacity, the corrected governance resources, and the corrected ecological flow rate, the penalty value can be reduced, thereby increasing the pollutant concentration compliance rate of the target basin and reducing the pollution severity of the target basin.

[0198] Figure 11 is a device for determining the water environment capacity proposed by the present disclosure. The water environment capacity determination device 1100 includes a fusion module 1110, a prediction module 1120, and a water environment capacity module 1130.

[0199] The fusion module 1110 is configured to fuse the first satellite data of the target basin observed by the first satellite, the second satellite data of the target basin observed by the second satellite, and the water environment data of the target basin simulated by the dynamic basin model to obtain the target water environment data of the target basin. The prediction module 1120 is configured to input the basin flow, target water environment data, pollution source data, vegetation coverage, and meteorological data of the target basin into the dynamic basin model to obtain the pollutant concentration and outlet flow of the target basin. The water environment capacity module 1130 is configured to obtain the water environment capacity of the target basin according to the pollutant concentration and the outlet flow of the target basin; the water environment capacity indicates the capacity of the target basin to accommodate pollutants.

[0200] In a possible implementation manner, the fusion module 1110 is further configured to fuse the first satellite data and the water environment data to obtain first fusion data; and fuse the first fusion data and the second satellite data to obtain the target water environment data.

[0201] In a possible implementation manner, the fusion module 1110 is further configured to obtain a first observation error of the first satellite according to the first satellite data and the water environment data; correct the first observation error by using a first Kalman gain to obtain a first target observation error; the first Kalman gain is used to measure the uncertainty of the water environment data; and obtain the first fusion data according to the water environment data and the first target observation error.

[0202] In a possible implementation manner, the fusion module 1110 is further configured to obtain a second observation error of the second satellite according to the second satellite data and second fusion data; the second fusion data is obtained by disturbing a third fusion data with a disturbance term, and the third fusion data corresponds to the first fusion data; correct the second observation error by using a second Kalman gain to obtain a second target observation error; the second Kalman gain is used to balance the uncertainty of the water environment and the second observation error; and obtain the target water environment data according to the first fusion data and the second target observation error.

[0203] In a possible implementation, the prediction module 1120 is further configured to screen out a plurality of target sub-watersheds from the plurality of sub-watersheds of the target watershed; input the watershed flow, target water environment data, vegetation coverage, future meteorological data of the target watershed, and pollution source data of the plurality of target sub-watersheds into the dynamic watershed model to obtain the future pollutant concentration and outlet flow of the target watershed.

[0204] In a possible implementation, the water environment capacity module 1130 is further configured to determine a target time period during which the pollutant concentration of the target watershed is less than the target pollutant concentration; for each time point in the target time period, obtain the pollutant flux at the time point according to the pollutant concentration and the outlet flow; and obtain the water environment capacity according to the pollutant fluxes at the plurality of time points in the target time period.

[0205] In a possible implementation, the apparatus 1100 for determining the water environment capacity further includes: An element determination module, configured to determine a plurality of elements that affect the water environment capacity, treatment resources, and ecological abnormality degree; the treatment resources are the resources consumed for treating pollutants in the target watershed, and the ecological abnormality degree indicates the ecological abnormality of the target watershed; An element screening module, configured to screen out a plurality of target elements from the plurality of elements with the goal of maximizing the water environment capacity, minimizing the treatment resources, and minimizing the ecological abnormality degree; A parameter screening module, configured to respectively screen out a plurality of target parameters from the parameters of the plurality of target elements.

[0206] In a possible implementation, the element screening module is further configured to determine a penalty value when the compliance rate of the pollutant concentration does not meet the preset compliance rate; use the penalty value to respectively correct the water environment capacity, the treatment resources, and the ecological abnormality degree to obtain a corrected water environment capacity, corrected treatment resources, and corrected ecological abnormality degree; and screen out the plurality of target elements from the plurality of elements with the goal of maximizing the corrected water environment capacity, the corrected treatment resources, and the corrected ecological abnormality degree.

[0207] Regarding the apparatus in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0208] Figure 12 is a block diagram of an electronic device 1200 shown according to an exemplary embodiment. As Figure 12As shown, the electronic device 1200 may include: a processor 1201 and a memory 1202. The electronic device 1200 may also include one or more of a multimedia component 1203, an input / output (I / O) interface 1204, and a communication component 1205.

[0209] Among them, the processor 1201 is used to control the overall operation of the electronic device 1200 to complete all or part of the steps in the above-mentioned method for determining the water environment capacity. The memory 1202 is used to store various types of data to support the operation of the electronic device 1200. These data may include, for example, instructions for any application or method operating on the electronic device 1200, as well as application-related data, such as contact data, sent and received messages, pictures, audio, video, and so on. The memory 1202 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc. The multimedia component 1203 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 1202 or sent through the communication component 1205. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 1204 provides an interface between the processor 1201 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 1205 is used for wired or wireless communication between the electronic device 1200 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them. Accordingly, the communication component 1205 may include: a Wi-Fi module, a Bluetooth module, and an NFC module.

[0210] In an exemplary embodiment, the electronic device 1200 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used to execute the above method for determining the water environment capacity.

[0211] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When the program instructions are executed by a processor, the steps of the above method for determining the water environment capacity are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 1202 including program instructions, and the above program instructions can be executed by the processor 1201 of the electronic device 1200 to complete the above method for determining the water environment capacity.

[0212] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program that can be executed by a processor. When the computer program is executed by the processor, the steps of the above method for determining the water environment capacity are implemented.

[0213] The preferred embodiments of the present disclosure have been described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0214] In addition, it should be noted that, in the above specific embodiments, the various specific technical features described can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present disclosure will not separately describe various possible combination methods.

[0215] Furthermore, any combination can be made between various different embodiments of the present disclosure as long as it does not violate the idea of the present disclosure, and it should also be regarded as the content disclosed by the present disclosure.

Claims

1. A method for determining the water environment capacity, characterized in that, Including: Fusing first satellite data of a target basin observed by a first satellite, second satellite data of the target basin observed by a second satellite, and water environment data of the target basin simulated by a dynamic basin model to obtain target water environment data of the target basin; Inputting the basin flow, target water environment data, pollution source data, vegetation coverage, and meteorological data of the target basin into the dynamic basin model to obtain the pollutant concentration and outlet flow of the target basin; Obtaining the water environment capacity of the target basin according to the pollutant concentration and the outlet flow of the target basin; the water environment capacity indicates the capacity of the target basin to accommodate pollutants.

2. The method according to claim 1, wherein The fusing first satellite data of a target basin observed by a first satellite, second satellite data of the target basin observed by a second satellite, and water environment data of the target basin simulated by a dynamic basin model to obtain target water environment data of the target basin includes: Fusing the first satellite data and the water environment data to obtain first fused data; Fusing the first fused data and the second satellite data to obtain the target water environment data.

3. The method according to claim 2, wherein The fusing the first satellite data and the water environment data to obtain first fused data includes: Obtaining a first observation error of the first satellite according to the first satellite data and the water environment data; Correcting the first observation error by using a first Kalman gain to obtain a first target observation error; the first Kalman gain is used to measure the uncertainty of the water environment data; Obtaining the first fused data according to the water environment data and the first target observation error.

4. The method according to claim 2, characterized in that The fusing the first fused data and the second satellite data to obtain the target water environment data includes: Obtaining a second observation error of the second satellite according to the second satellite data and second fused data; the second fused data is obtained by disturbing a third fused data with a disturbance term, and the third fused data is the data corresponding to the first fused data; Correcting the second observation error by using a second Kalman gain to obtain a second target observation error; the second Kalman gain is used to balance the uncertainty of the water environment and the second observation error; Obtaining the target water environment data according to the first fused data and the second target observation error.

5. The method according to claim 1, wherein The inputting the basin flow, target water environment data, pollution source data, vegetation coverage, and meteorological data of the target basin into the dynamic basin model to obtain the pollutant concentration and outlet flow of the target basin includes: Selecting a plurality of target sub-basins from a plurality of sub-basins of the target basin; Inputting the basin flow, target water environment data, vegetation coverage, future meteorological data, and pollution source data of the plurality of target sub-basins of the target basin into the dynamic basin model to obtain the future pollutant concentration and outlet flow of the target basin.

6. The method according to claim 1, wherein The obtaining the water environment capacity of the target basin according to the pollutant concentration and the outlet flow of the target basin includes: Determine a target time period during which the pollutant concentration in the target watershed is less than the target pollutant concentration; For each time point in the target time period, obtain the pollutant flux at that time point based on the pollutant concentration and the outlet flow rate; Obtain the water environment capacity based on the pollutant fluxes at multiple time points in the target time period.

7. The method according to claim 1, characterized in that, The method further includes: Determine multiple elements that affect the water environment capacity, treatment resources, and ecological anomaly degree; the treatment resources are the resources consumed for treating pollutants in the target watershed, and the ecological anomaly degree indicates the ecological anomaly of the target watershed; With the goal of maximizing the water environment capacity, minimizing the treatment resources, and minimizing the ecological anomaly degree, screen out multiple target elements from the multiple elements; Respectively screen out multiple target parameters from the parameters of the multiple target elements.

8. The method according to claim 7, characterized in that, The step of screening out multiple target elements from the multiple elements with the goal of maximizing the water environment capacity, minimizing the treatment resources, and minimizing the ecological anomaly degree includes: Determine the penalty value when the compliance rate of the pollutant concentration does not meet the preset compliance rate; Use the penalty value to respectively correct the water environment capacity, the treatment resources, and the ecological anomaly degree to obtain the corrected water environment capacity, the corrected treatment resources, and the corrected ecological anomaly degree; With the goal of maximizing the corrected water environment capacity, the corrected treatment resources, and the corrected ecological anomaly degree, screen out the multiple target elements from the multiple elements.

9. An apparatus for determining the water environmental capacity, characterized in that, Includes: A fusion module configured to fuse the first satellite data of the target watershed observed by the first satellite, the second satellite data of the target watershed observed by the second satellite, and the water environment data of the target watershed simulated by the dynamic watershed model to obtain the target water environment data of the target watershed; A prediction module configured to input the watershed flow rate, the target water environment data, the pollution source data, the vegetation coverage, and the meteorological data of the target watershed into the dynamic watershed model to obtain the pollutant concentration and the outlet flow rate of the target watershed; A water environment capacity module configured to obtain the water environment capacity of the target watershed based on the pollutant concentration and the outlet flow rate of the target watershed; the water environment capacity indicates the capacity of the target watershed to accommodate pollutants.

10. An electronic device, characterized in that, Includes: A memory on which a computer program is stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1 to 8.

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