Method and device for acquiring pollutant mass load, storage medium and electronic equipment

Abstract

The invention relates to a method and a device for acquiring pollutant mass load, a storage medium and electronic equipment. The method comprises the following steps: acquiring topographic data of a target drainage basin, performing grid division based on depression-free shape data obtained through depression filling processing, and acquiring water flow cumulant based on the water flow direction of a grid unit; based on the water flow direction of the candidate grid units with the water flow cumulant larger than the water flow cumulant threshold value, the topographic data and the river outlet of the target drainage basin, sub-drainage-basin division is carried out; acquiring soil type data and land utilization data of each sub-drainage basin, and historical meteorological data, historical runoff data and historical water quality monitoring data corresponding to a time period, and training the hydrological simulation initial model to obtain a hydrological simulation model; and based on the topographic data, the soil type data and the land utilization data of the drainage basin to be estimated, and the meteorological data and the hydrological simulation model corresponding to the latest time period, obtaining the pollutant mass load. And the precision of a pollutant load estimation result can be improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of environmental science and hydrology, and particularly to a method, device, storage medium, and electronic device for obtaining pollutant mass load. Background Art

[0002] With the acceleration of the industrialization and urbanization processes, the problem of water pollution has become increasingly severe. Therefore, accurately assessing the pollutant mass load in a basin is crucial for water environment governance and water resource protection. In related technologies, generally, statistical methods or empirical formulas are used to estimate the pollutant mass load of the target basin. However, in this method, since it is difficult for statistical methods or empirical formulas to comprehensively consider various factors such as complex terrain, soil, land use, and meteorology in the target basin, the accuracy of the estimated pollutant mass load result is low, and it cannot provide effective support for precise pollution prevention and control. Further, generally, the scope covered by the target basin is relatively wide, and the obtained estimated pollutant mass load result cannot accurately reflect the differential distribution of pollutants in the target basin. Summary of the Invention

[0003] In view of this, the present invention provides a method, device, storage medium, and electronic device for obtaining pollutant mass load.

[0004] Specifically, the present invention is implemented through the following technical solutions:

[0005] According to a first aspect of the present invention, there is provided a method for obtaining pollutant mass load, and the method for obtaining pollutant mass load includes:

[0006] Obtain topographic data of a target basin, and perform depression filling processing on the depression topographic data caused by terrain undulation in the topographic data to obtain non-depression topographic data;

[0007] According to a pre-set grid division strategy, perform grid division on the non-depression topographic data, obtain the water flow direction of each grid unit according to a pre-set flow direction strategy, and based on the water flow direction data corresponding to the water flow direction of the grid unit, obtain the water flow accumulation amount of the grid unit, where the water flow direction data includes the water flow inflow amount flowing into the grid unit and the water flow outflow amount flowing out of the grid unit;

[0008] Obtain grid units with a water flow accumulation amount greater than a pre-set water flow accumulation amount threshold to obtain candidate grid units, and based on the river outlet of the target basin, the water flow direction of the candidate grid units, and the topographic data corresponding to the candidate grid units, perform sub-basin division on the target basin to obtain a plurality of sub-basins;

[0009] Obtain the soil type data, land use data of each sub-basin, as well as the historical meteorological data, historical runoff data and historical water quality monitoring data corresponding to each sub-basin according to a pre-set time period;

[0010] For each sub-basin, use the topographic data, soil type data, land use data, and historical meteorological data of the sub-basin as the input of the initial hydrological simulation model, and use the historical runoff data corresponding to the sub-basin as the output of the initial hydrological simulation model to train the initial hydrological simulation model and obtain a hydrological simulation training model;

[0011] Use the historical runoff data corresponding to the sub-basin as the input of the hydrological simulation training model, and use the historical water quality monitoring data corresponding to the sub-basin as the output of the hydrological simulation training model to retrain the hydrological simulation training model, obtain a hydrological simulation model, and set the output of the hydrological simulation model as runoff data and water quality data;

[0012] Obtain the topographic data, soil type data, land use data of the basin to be estimated, and the meteorological data corresponding to the latest time period, input them into the hydrological simulation model, obtain the predicted runoff and water quality prediction data of each sub-basin in the predicted time period, and obtain the pollutant mass load based on the predicted runoff and water quality prediction data.

[0013] Optionally, the step of using the topographic data, soil type data, land use data, and historical meteorological data of each sub-basin as the input of the initial hydrological simulation model, and using the historical runoff data corresponding to the sub-basin as the output of the initial hydrological simulation model to train the initial hydrological simulation model and obtain a hydrological simulation training model includes:

[0014] Based on the historical meteorological data, historical runoff data and historical water quality monitoring data of the target sub-basin in the same time period, construct data pairs;

[0015] Sort the data pairs in the order of time periods from the longest to the shortest distance from the current time, set the convergence accuracy for the sorted data pairs, and the longer the distance from the current time, the lower the convergence accuracy;

[0016] Extract the historical meteorological data in the first sorted data pair, use the historical meteorological data, the topographic data, soil type data, and land use data of the target sub-basin as the input of the initial hydrological simulation model, use the historical runoff data in the first sorted data pair as the output of the initial hydrological simulation model, and use the convergence accuracy of the first sorted data pair as the basis for ending the training of the initial hydrological simulation model to train the initial hydrological simulation model and obtain a hydrological simulation first iteration model;

[0017] Extract the historical meteorological data in the second sorted data pair, use the historical meteorological data as the input of the first iteration model of hydrological simulation, use the historical runoff data in the second sorted data pair as the output of the first iteration model of hydrological simulation, use the convergence accuracy of the second sorted data pair as the basis for ending the training of the first iteration model of hydrological simulation, train the first iteration model of hydrological simulation to obtain the second iteration model of hydrological simulation, and continue until the training is completed after extracting the historical meteorological data in the last sorted data pair to obtain the hydrological simulation training model.

[0018] Optionally, obtaining the predicted runoff and water quality prediction data of each sub-basin in the predicted time period includes:

[0019] Match the topographic data of the basin to be estimated with the hydrological simulation model to obtain one or more sub-basins that match the hydrological simulation model;

[0020] Based on the hydrological simulation model, obtain the predicted runoff and water quality prediction data of one or more sub-basins that match the hydrological simulation model in the predicted time period.

[0021] Optionally, obtaining the pollutant mass load based on the predicted runoff and water quality prediction data includes:

[0022] For each sub-basin, calculate the product of the predicted runoff and water quality prediction data of the sub-basin to obtain the pollutant mass load of the sub-basin;

[0023] Calculate the sum of the pollutant mass loads of each sub-basin to obtain the pollutant mass load of the target basin;

[0024] Display the pollutant mass load of each sub-basin and the pollutant mass load of the target basin.

[0025] Optionally, the method further includes:

[0026] According to the monitoring points arranged in each sub-basin, sample the measured water quality data in the predicted time period, use the predicted runoff of the corresponding sub-basin in the obtained predicted time period as the input of the hydrological simulation model, and use the measured water quality data of the sub-basin in the predicted time period as the output of the hydrological simulation model to retrain the hydrological simulation model.

[0027] Optionally, the method further includes:

[0028] Extract the lakes in the target basin, and for each lake, obtain the inflow sub-basins corresponding to the rivers flowing into the lake and the outflow sub-basins corresponding to the rivers flowing out of the lake;

[0029] Calculate the product of the predicted runoff and the predicted water quality data for each inflowing sub-basin respectively to obtain the pollutant mass load flowing into the lake;

[0030] Calculate the product of the predicted runoff and the predicted water quality data for each outflowing sub-basin respectively to obtain the pollutant mass load flowing out of the lake;

[0031] Based on the pollutant mass load flowing into the lake and the pollutant mass load flowing out of the lake, obtain the pollution interception rate of the lake;

[0032] Based on the pollution interception rate of the lake, determine the treatment strategy for the lake.

[0033] Optionally, the terrain data includes digital elevation model data, the historical meteorological data includes precipitation data, temperature data, and wind speed data, and the historical water quality monitoring data includes various pollutant concentration data.

[0034] The method for obtaining the pollutant mass load in this technical solution includes obtaining the topographic data of the target watershed, filling the depression topographic data caused by the terrain undulation in the topographic data to obtain the non-depression topographic data; dividing the non-depression topographic data into grids according to the pre-set grid division strategy, obtaining the water flow direction of each grid unit according to the pre-set flow direction strategy, and obtaining the water flow accumulation of the grid unit based on the water flow direction data corresponding to the water flow direction of the grid unit; obtaining the grid units with the water flow accumulation greater than the pre-set water flow accumulation threshold to obtain candidate grid units, and dividing the target watershed into multiple sub-watersheds based on the river outlet of the target watershed, the water flow direction of the candidate grid units, and the topographic data corresponding to the candidate grid units; obtaining the soil type data, land use data of each sub-watershed, and the historical meteorological data, historical runoff data, and historical water quality monitoring data corresponding to each sub-watershed according to the pre-set time period; for each sub-watershed, using the topographic data, soil type data, land use data, and historical meteorological data of the sub-watershed as the input of the initial hydrological simulation model, and using the historical runoff data corresponding to the sub-watershed as the output of the initial hydrological simulation model to train the initial hydrological simulation model to obtain a trained hydrological simulation model; using the historical runoff data corresponding to the sub-watershed as the input of the trained hydrological simulation model, and using the historical water quality monitoring data corresponding to the sub-watershed as the output of the trained hydrological simulation model to retrain the trained hydrological simulation model to obtain a hydrological simulation model, and setting the output of the hydrological simulation model as runoff data and water quality data; obtaining the topographic data, soil type data, land use data of the watershed to be estimated, and the meteorological data corresponding to the latest time period, inputting them into the hydrological simulation model to obtain the predicted runoff and water quality prediction data of each sub-watershed in the predicted time period, and obtaining the pollutant mass load based on the predicted runoff and water quality prediction data. In this way, by dividing the target sub-watershed and constructing a hydrological simulation model based on the divided sub-watersheds, various factors such as complex terrain, soil, land use, and meteorology in the target watershed can be considered, effectively improving the accuracy of the pollutant mass load estimation result, and the obtained pollutant mass load estimation result can finely reflect the differential distribution of pollutants in the target watershed.

[0035] According to the second aspect of the present invention, there is provided a device for obtaining the pollutant mass load, and the device for obtaining the pollutant mass load includes:

[0036] The first data acquisition module is used to obtain the topographic data of the target watershed, fill the depression topographic data caused by the terrain undulation in the topographic data to obtain the non-depression topographic data;

[0037] A grid division module, configured to perform grid division on the flat terrain data according to a preset grid division strategy, obtain the water flow direction of each grid cell according to a preset flow direction strategy, and obtain the water flow accumulation of the grid cell based on the water flow direction data corresponding to the water flow direction of the grid cell;

[0038] A sub-basin division module, configured to obtain grid cells with water flow accumulation greater than a preset water flow accumulation threshold to obtain candidate grid cells, and perform sub-basin division on the target basin based on the river outlet of the target basin, the water flow direction of the candidate grid cells, and the terrain data corresponding to the candidate grid cells, to obtain multiple sub-basins;

[0039] A second data acquisition module, configured to acquire the soil type data, land use data of each sub-basin, and the historical meteorological data, historical runoff data, and historical water quality monitoring data corresponding to each sub-basin according to a preset time period;

[0040] A first model training module, configured to, for each sub-basin, use the terrain data, soil type data, land use data, and historical meteorological data of the sub-basin as the input of the initial hydrological simulation model, and use the historical runoff data corresponding to the sub-basin as the output of the initial hydrological simulation model, and train the initial hydrological simulation model to obtain a hydrological simulation training model;

[0041] A second model training module, configured to use the historical runoff data corresponding to the sub-basin as the input of the hydrological simulation training model, and use the historical water quality monitoring data corresponding to the sub-basin as the output of the hydrological simulation training model, and retrain the hydrological simulation training model to obtain a hydrological simulation model, and set the output of the hydrological simulation model to runoff data and water quality data;

[0042] A pollutant mass load acquisition module, configured to acquire the terrain data, soil type data, land use data of the basin to be estimated, and the meteorological data corresponding to the latest time period, input them into the hydrological simulation model, obtain the predicted runoff and water quality prediction data of each sub-basin in the predicted time period, and obtain the pollutant mass load based on the predicted runoff and water quality prediction data.

[0043] According to a third aspect of the present invention, there is provided a storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method for obtaining pollutant mass load in any possible implementation manner of the first aspect are implemented.

[0044] According to a fourth aspect of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the steps of the method for obtaining pollutant mass load in any possible implementation manner of the first aspect are implemented. Description of the Drawings

[0045] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present invention and, together with the specification, are used to explain the principles of the present invention.

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or related technologies. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0047] Figure 1 A schematic flowchart of a method for obtaining the mass load of pollutants provided by an embodiment of the present invention;

[0048] Figure 2 A schematic diagram of sub - basins divided for a certain target basin and monitoring points arranged in a method for obtaining the mass load of pollutants provided by an embodiment of the present invention;

[0049] Figure 3 A schematic diagram of sub - basins divided for the target basin in a method for obtaining the mass load of pollutants provided by an embodiment of the present invention;

[0050] Figure 4 A schematic diagram of a device for obtaining the mass load of pollutants provided by an embodiment of the present invention;

[0051] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0052] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0053] In the related art, by using a statistical method or an empirical formula, taking the target watershed as a whole and based on the law of conservation of matter, the product of the average concentration of the target pollutant in the river water body of the target watershed and the annual flow of the river is calculated to obtain the pollutant mass load. Since it is difficult for the statistical method or the empirical formula to comprehensively consider various factors such as the complex terrain, soil, land use, and meteorology in the watershed, and at the same time, taking the target watershed as a whole ignores the spatial differences within the rivers in the target watershed, the accuracy of the estimated result of the pollutant mass load is low. With the acceleration of the industrialization and urbanization processes, accurately evaluating the pollutant mass load in the target watershed is crucial for water environment governance and water resource protection.

[0054] See Figure 1 , an embodiment of the present invention provides a method for obtaining a pollutant mass load, and the method may include the following steps:

[0055] S101. Obtain the terrain data of the target watershed, perform a depression filling process on the depression terrain data caused by the terrain undulation in the terrain data to obtain non-depression terrain data;

[0056] In this embodiment, as an optional embodiment, the terrain data of the target watershed may be obtained from a geological database shared on the network. Among them, as an optional embodiment, the terrain data includes, but is not limited to, Digital Elevation Model (DEM) data.

[0057] In this embodiment, the terrain data is DEM data. Since the original DEM data obtained from the shared geological database may have data errors or depressions caused by the terrain undulation in the terrain data, and these depressions will affect the subsequent calculation of the water flow direction, thus, filling is required. As an optional embodiment, data cleaning and calibration are performed on the DEM data, and by using a pre-set software, for example, in the ArcGIS software, the "Fill" tool is used to process the DEM data to obtain DEM data without depressions.

[0058] S102. Perform grid division on the non-depression terrain data according to a pre-set grid division strategy, obtain the water flow direction of each grid unit according to a pre-set flow direction strategy, and obtain the water flow accumulation amount of the grid unit based on the water flow direction data corresponding to the water flow direction of the grid unit;

[0059] In this embodiment, as an alternative embodiment, based on the topographic map corresponding to the flat-free terrain data, water systems such as rivers and lakes are obtained from the topographic map, and grid cells are divided based on the rivers and lakes. For example, grid cells are divided according to the terrain of the river, the inflowing rivers and outflowing rivers of the lake. In this embodiment, grid cells are divided in a graphical manner, and the divided grid cells are presented, and the divided grid cells can be corrected.

[0060] In this embodiment, as an alternative embodiment, the water flow direction data includes the water inflow volume flowing into the grid cell and the water outflow volume flowing out of the grid cell. Using pre-set software, for example, the "Flow Direction" tool of ArcGIS software, the water flow direction of each grid cell is calculated according to the flat-free DEM data. In this embodiment, as an alternative embodiment, the water flow direction includes 8 directions, and the 8 directions of the 3-bit binary number are used to encode the water flow direction. For example, 000 is used to represent the east direction, 001 is used to represent the northeast direction, etc. Starting from the water flow direction, through the algorithm pre-set in the software, cumulative summation is carried out step by step, and finally the water flow accumulation volume of the grid cell is obtained. The water flow accumulation volume reflects the water collection volume upstream of the grid cell and can provide a basis for determining the river network.

[0061] In this embodiment, as an alternative embodiment, the method further includes:

[0062] For each grid cell, the water flow accumulation volume (the total water volume flowing into the grid cell upstream) of the grid cell is calculated respectively. According to the water flow direction converged by each grid cell, starting from the grid cell at the edge of the target basin, the water flow accumulation volume of the grid cell is accumulated downstream to the grid cell in turn, and the runoff accumulation volume of the target basin is obtained.

[0063] S103: Obtain the grid cells with the water flow accumulation volume greater than the pre-set water flow accumulation volume threshold to obtain candidate grid cells. Based on the river outlet of the target basin, the water flow direction of the candidate grid cells, and the terrain data corresponding to the candidate grid cells, the target basin is divided into sub-basins, and multiple sub-basins are obtained;

[0064] In this embodiment, river network extraction is performed on the grid cells with the water flow accumulation volume greater than the water flow accumulation volume threshold. As an alternative embodiment, according to the actual situation of the target area and the characteristics of the terrain data, a suitable water flow accumulation volume threshold is determined. For example, in a mountainous basin, through multiple tests and comparisons, it is determined that the grid cells with the water flow accumulation volume greater than 1000 can better represent the river network. Therefore, for this mountainous basin, the water flow accumulation volume threshold is set to 1000.

[0065] In this embodiment, according to the actual situation of the target basin, the outlet position of the target basin is determined, that is, the basin outlet where the water flows out of the target area. For example, the position where the river enters the downstream reservoir can be selected as the basin outlet (river outlet).

[0066] In this embodiment, based on the basin outlet, the target basin is divided into sub-basins according to the flow direction of grid cells and the corresponding topographic data. As an alternative embodiment, the "Divide Watershed" tool in ArcGIS software can be used to divide the entire target basin into multiple sub-basins starting from the basin outlet according to the flow direction and river network data.

[0067] In this embodiment, as an alternative embodiment, the method further includes:

[0068] Using a pre-set vectorization strategy, the topographic data corresponding to the obtained grid cells is converted into vector river network data.

[0069] In this embodiment, as an alternative embodiment, tools such as the "Raster Calculator" in ArcGIS software are used to extract grid cells with a flow accumulation greater than the threshold to form river network raster data. Then, through the "Vectorize" tool, the river network raster data is converted into vector river network data to facilitate sub-basin division.

[0070] S104. Obtain the soil type data, land use data of each sub-basin, and the historical meteorological data, historical runoff data, and historical water quality monitoring data of each sub-basin corresponding to a pre-set time period;

[0071] In this embodiment, as an alternative embodiment, the historical meteorological data includes but is not limited to precipitation data, temperature data, and wind speed data, and the historical water quality monitoring data includes but is not limited to: various pollutant concentration data.

[0072] In this embodiment, as an alternative embodiment, the pre-set time period includes but is not limited to: month, week, quarter. Taking the time period as a month as an example, that is, obtain the monthly precipitation data, temperature data, wind speed data, etc. of the sub-basin. Among them, the historical meteorological data, historical runoff, and historical water quality monitoring data are all for the same time period.

[0073] In this embodiment, as an alternative embodiment, preprocessing operations such as format unification, missing value filling, and outlier removal can also be performed on the obtained topographic data, soil type data, land use data, meteorological data, and water quality monitoring data to ensure the accuracy and availability of the data.

[0074] S105. For each sub-basin, use the topographic data, soil type data, land use data, and historical meteorological data of the sub-basin as the input of the initial hydrological simulation model, and use the historical runoff data corresponding to the sub-basin as the output of the initial hydrological simulation model to train the initial hydrological simulation model to obtain a trained hydrological simulation model;

[0075] In this embodiment, as an alternative embodiment, for each sub-basin, the topographic data, soil type data, land use data, and historical meteorological data of the sub-basin are used as the input of the initial hydrological simulation model, and the historical runoff data corresponding to the sub-basin is used as the output of the initial hydrological simulation model to train the initial hydrological simulation model, resulting in a trained hydrological simulation model, including:

[0076] A11. Based on the historical meteorological data, historical runoff data, and historical water quality monitoring data of the target sub-basin in the same time period, construct data pairs;

[0077] A12. Sort the data pairs in the order of time periods from long to short from the current time, and set a convergence accuracy for the sorted data pairs. The longer the time period from the current time, the lower the convergence accuracy;

[0078] A13. Extract the historical meteorological data from the first sorted data pair, and use this historical meteorological data, the topographic data, soil type data, and land use data of the target sub-basin as the input of the initial hydrological simulation model, and use the historical runoff data in the first sorted data pair as the output of the initial hydrological simulation model. Use the convergence accuracy of the first sorted data pair as the basis for ending the training of the initial hydrological simulation model, and train the initial hydrological simulation model to obtain a first iterative hydrological simulation model;

[0079] A14. Extract the historical meteorological data from the second sorted data pair, use this historical meteorological data as the input of the first iterative hydrological simulation model, and use the historical runoff data in the second sorted data pair as the output of the first iterative hydrological simulation model. Use the convergence accuracy of the second sorted data pair as the basis for ending the training of the first iterative hydrological simulation model, and train the first iterative hydrological simulation model to obtain a second iterative hydrological simulation model, until the historical meteorological data in the last sorted data pair is extracted and the training is completed to obtain a trained hydrological simulation model.

[0080] In this embodiment, for the training algorithm adopted by the initial hydrological simulation model, specific references can be found in relevant technical literature and will not be elaborated here.

[0081] S106. Use the historical runoff data corresponding to the sub-basin as the input of the trained hydrological simulation model, and use the historical water quality monitoring data corresponding to the sub-basin as the output of the trained hydrological simulation model to retrain the trained hydrological simulation model to obtain a hydrological simulation model, and set the output of the hydrological simulation model as runoff data and water quality data;

[0082] In this embodiment, after obtaining the hydrological simulation training model, the data pair is used for training. When retraining, the convergence accuracy of the data pair is used as the basis for ending the training of the hydrological simulation training model. After the training of the hydrological simulation training model is completed, different from the model obtained by conventional training, the output of the hydrological simulation model needs to be reset, that is, the output during training, which is the water quality monitoring data, is set to the runoff data and water quality data, and the input data also needs to be reset.

[0083] S107. Obtain the topographic data, soil type data, land use data of the basin to be estimated, and the meteorological data corresponding to the latest time period, input them into the hydrological simulation model, obtain the predicted runoff and water quality prediction data of each sub-basin in the predicted time period, and obtain the pollutant mass load based on the predicted runoff and water quality prediction data.

[0084] In this embodiment, the basin to be estimated is the target basin or a part of the target basin. As an optional embodiment, obtaining the predicted runoff and water quality prediction data of each sub-basin in the predicted time period includes:

[0085] Match the topographic data of the basin to be estimated with the hydrological simulation model to obtain one or more sub-basins that match the hydrological simulation model;

[0086] Based on the hydrological simulation model, obtain the predicted runoff and water quality prediction data of one or more sub-basins that match the hydrological simulation model in the predicted time period.

[0087] In this embodiment, as an optional embodiment, obtaining the pollutant mass load based on the predicted runoff and water quality prediction data includes:

[0088] For each sub-basin, calculate the product of the predicted runoff and water quality prediction data of the sub-basin to obtain the pollutant mass load of the sub-basin;

[0089] Calculate the sum of the pollutant mass loads of each sub-basin to obtain the pollutant mass load of the target basin;

[0090] Display the pollutant mass load of each sub-basin and the pollutant mass load of the target basin.

[0091] In this embodiment, if the target basin has a large coverage area or significant differences in geological structures, then within the target basin, there will be great differences in topographic data, soil type data, land use data, meteorological data, and water quality data in different regions. For example, taking the target basin including mountainous basins and plain basins as an example, the water flow characteristics of mountainous basins and plain basins are different. Another example is taking the target basin including humid basins and arid basins as an example, the runoff and confluence mechanisms in humid area basins and arid area basins also have differences. Therefore, in this embodiment, by dividing the target basin into sub-basins, setting model parameters based on the soil type data and land use data of the sub-basins, constructing a hydrological ecological simulation unit that reflects the actual situation of the basin, combining meteorological data, and running a pre-constructed model to simulate the hydrological process for a long time series, a hydrological simulation model that can characterize the runoff and other water volumes of each sub-basin within the target basin can be obtained, which can effectively improve the estimation accuracy of pollutant mass load.

[0092] In this embodiment, by dividing the entire target basin into several sub-basins (HRU, Hydrologic Research Unit), setting a time period including a simulation time step and a simulation period, simulating the amount of pollutants discharged from each sub-basin into the rivers within the target basin, and considering factors such as hydrological processes (historical water quality monitoring data) and land use changes (land use data), calculating the pollutant emissions at different time scales, and obtaining the pollutant mass load.

[0093] In this embodiment, as an alternative embodiment, the following formula is used to calculate the pollutant mass load:

[0094]

[0095] Where: Q (mm / month / km 2 ) is obtained by simulation using a pre-trained hydrological simulation model and is the total amount of water flowing into the divided sub-basin (HRU) per month; C (ng / L) is the pollutant concentration data corresponding to the sub-basin.

[0096] In this embodiment, as an alternative embodiment, the method further includes:

[0097] According to the monitoring points arranged in each sub-basin, sampling the measured water quality data for the predicted time period, using the predicted runoff of the sub-basin corresponding to the obtained predicted time period as the input of the hydrological simulation model, and using the measured water quality data of the sub-basin in the predicted time period as the output of the hydrological simulation model to retrain the hydrological simulation model.

[0098] In this embodiment, as an alternative embodiment, the measured water quality data is the measured data of the concentrations of various pollutants.

[0099] In this embodiment, with a time period of "month" or even shorter, the water flow of each sub-basin is simulated in the hydrological simulation model. Therefore, the pollutant mass loads in different water periods (dry season, wet season, normal season) can be compared. For example, samples are collected in the wet season (July) and dry season (February) of a certain year, and the corresponding pollutant concentration data are measured. Then, based on the water flows in the wet season and dry season respectively, the monthly pollutant mass loads in the wet season and dry season can be obtained, and thus compared with the predicted monthly pollutant mass loads respectively, and the model parameters of the hydrological simulation model can be further adjusted. As an alternative embodiment, the time period includes but is not limited to: year, month, day, hour. In this way, through the granulated time period, preferably using month or day, the water volume differences caused by water period differences can be reflected, so that the predicted differentiated pollutant mass loads can better reflect the actual situation.

[0100] In this embodiment, the target basin contains lakes and rivers connected to the lakes, such as inflowing rivers and outflowing rivers. Using the hydrological simulation model, the vector range (topographic data), basin elevation data, regional land use type data, regional soil type classification data, and regional meteorological data of the target basin are obtained. The above data are input into the hydrological simulation model in sequence, and the target area is divided into multiple sub-basins. After being simulated by the hydrological simulation model, various hydrological-related parameters of each sub-basin can be obtained, including but not limited to: the water flow discharged from the sub-basin to the nearest river. As an alternative embodiment, the regional pollutant monitoring points are distributed in different sub-basins. According to the regional pollutant monitoring points, the water flow (sub-basin flow) discharged from each monitoring point to the nearby river is determined, and the pollutant mass load of the monitoring point is obtained by multiplying the sub-basin flow by the concentration value of any pollutant.

[0101] The method of this embodiment can calculate the mass load of any pollutant by arranging monitoring points in the target basin, where the pollutant mass load = (any) pollutant concentration × (each sub-basin) flow. Calculating the flow of each sub-basin with year, month, day, and hour as time steps can effectively improve the accuracy of the obtained pollutant mass load.

[0102] In this embodiment, as an alternative embodiment, the method further includes:

[0103] Extracting the lakes in the target basin, and for each lake, obtaining the inflowing sub-basin corresponding to the river flowing into the lake and the outflowing sub-basin corresponding to the river flowing out of the lake;

[0104] Calculating the product of the predicted runoff and water quality prediction data of each inflowing sub-basin respectively to obtain the pollutant mass load flowing into the lake;

[0105] Calculate the product of the predicted runoff and water quality prediction data for each outflow sub-basin respectively to obtain the pollutant mass load flowing out of the lake.

[0106] Based on the pollutant mass load flowing into the lake and the pollutant mass load flowing out of the lake, obtain the pollution interception rate of the lake.

[0107] Based on the pollution interception rate of the lake, determine the treatment strategy for the lake.

[0108] In this embodiment, taking the Dianchi Basin as the target basin, the target basin includes 1 out-of-lake river and 12 in-lake rivers. A monitoring point is arranged on the out-of-lake river, and a monitoring point is arranged on each in-lake river respectively.

[0109] In this embodiment, using a hydrological simulation model, the target basin is divided into 23 sub-basins, and the monthly drainage volume of each sub-basin into Dianchi Lake (out-of-lake river, in-lake river) is simulated. The pollution interception rate of pollutants flowing into Dianchi Lake from the river is calculated by the following formula:

[0110]

[0111]

[0112]

[0113]

[0114] Where: is the pollutant mass load of 12 in-lake rivers (kg); is the pollutant mass load flowing out of the lake (kg), and lv(%) can reflect the interception and accumulation ability of the lake for pollutants. is the cumulative time period.

[0115] Figure 2 is the schematic diagram of the sub-basins and the arranged monitoring points obtained by dividing a certain target basin in the method for obtaining pollutant mass load provided by the embodiment of the present invention;

[0116] Figure 3 is the schematic diagram of the sub-basins obtained by dividing the target basin in the method for obtaining pollutant mass load provided by the embodiment of the present invention.

[0117] Such as Figure 2 and Figure 3As shown in the figure, the target basin includes a basin, a river, and a lake. In the figure, 1-23 are the sub-basins obtained by division, S1-S33 are the monitoring points (Samples) arranged, WYLD is the total amount of water flowing from the sub-basin into the river (mm), and flow_Out is the average daily flow rate of the outflowing river section (cm / s). In this embodiment, the time period is in months, weeks, or days. Using a pre-set hydrological process simulation algorithm, the total water volume (WYLD) index flowing into the main river within each time step is selected. The monthly flow data of the sub-basins generated by the simulation is combined with the measured pollution concentration data obtained at the corresponding sampling points, so as to obtain the pollutant mass load output from each sub-basin to the adjacent river. As an optional embodiment, the research results of a certain basin show that during the high-water period in July 2022 and the low-water period in February 2023, the total amounts of pollutants (PFASs) discharged from a total of 12 main rivers flowing into the lake reached 11.514 kg and 2.586 kg respectively. At the same time, the perfluoroalkyl substances (PFASs) discharged through the outflowing rivers were 0.073 kg and 0.034 kg respectively; during the corresponding periods, the pollutant retention rates of the lake were 93.66% and 86.76% in turn. Among them, the high pollutant retention rate may be an important reason for Dianchi Lake being an endogenous pollution lake. From the perspective of the investigation period, whether it is the pollution load into the lake or the pollutant retention rate, the high-water period is greater than the low-water period. By taking a month as the time step, it can clearly reflect the influence of hydrological elements such as river flow and water level on the migration, diffusion, and dilution of pollutants in different periods, and accurately grasp the fluctuation of pollutant load.

[0118] In this embodiment, for the same river, due to the influence of various factors such as surface water inflow, groundwater inflow, and rainfall, there will be differences in the flow values of different river sections of the same river; near different monitoring points, there may be different pollution sources, and thus, the pollution data will also be different. By obtaining the flow rate of each monitoring point (the flow rate of the sub-basins divided from the basin), and then multiplying it by the pollutant concentration value, the pollutant load of each small basin is obtained.

[0119] In this embodiment, for visual display, by connecting a monitor or other visual terminals, the sub-basin division map, the dynamic simulation map of the hydrological process, the contour map of the pollutant load distribution, etc. are displayed in an intuitive graphical manner, which is convenient for researchers to analyze the basin characteristics and pollution situation.

[0120] In this embodiment, as an optional embodiment, a variety of statistical analysis algorithms can also be integrated to conduct comparative analysis, accuracy verification, and parameter optimization on the pollutant mass load results output by the hydrological simulation model based on the measured data, automatically generate verification reports and optimization suggestions, and assist users in improving the application effect of the model.

[0121] Based on the same inventive concept, as Figure 4 shown, an embodiment of the present invention further provides a device for obtaining the mass load of pollutants. The device includes:

[0122] A first data acquisition module 401, configured to acquire topographic data of a target basin, perform depression filling processing on the depression topographic data caused by terrain undulation in the topographic data, and obtain non-depression topographic data;

[0123] A grid division module 402, configured to divide the non-depression topographic data according to a pre-set grid division strategy, obtain the water flow direction of each grid unit according to a pre-set flow direction strategy, and obtain the water flow accumulation of the grid unit based on the water flow direction data corresponding to the water flow direction of the grid unit;

[0124] A sub-basin division module 403, configured to acquire grid units with water flow accumulation greater than a pre-set water flow accumulation threshold to obtain candidate grid units, and perform sub-basin division on the target basin based on the river outlet of the target basin, the water flow direction of the candidate grid units, and the topographic data corresponding to the candidate grid units, so as to obtain a plurality of sub-basins;

[0125] A second data acquisition module 404, configured to acquire soil type data, land use data of each sub-basin, and historical meteorological data, historical runoff data, and historical water quality monitoring data corresponding to each sub-basin according to a pre-set time period;

[0126] A first model training module 405, configured to, for each sub-basin, use the topographic data, soil type data, land use data, and historical meteorological data of the sub-basin as the input of a hydrological simulation initial model, and use the historical runoff data corresponding to the sub-basin as the output of the hydrological simulation initial model to train the hydrological simulation initial model to obtain a hydrological simulation training model;

[0127] In this embodiment, as an optional embodiment, the first model training module 405 is specifically configured to:

[0128] Construct data pairs based on the historical meteorological data, historical runoff data, and historical water quality monitoring data of the target sub-basin in the same time period;

[0129] Sort the data pairs in the order of time periods from long to short from the current time, set a convergence accuracy for the sorted data pairs, and the longer the time period from the current time, the lower the convergence accuracy;

[0130] Extract the historical meteorological data in the first sorted data pair, and use this historical meteorological data, the topographic data, soil type data, and land use data of the target sub-basin as the input of the initial hydrological simulation model. Use the historical runoff data in the first sorted data pair as the output of the initial hydrological simulation model, and use the convergence accuracy of the first sorted data pair as the basis for ending the training of the initial hydrological simulation model. Train the initial hydrological simulation model to obtain the first iterative hydrological simulation model;

[0131] Extract the historical meteorological data in the second sorted data pair, use this historical meteorological data as the input of the first iterative hydrological simulation model, use the historical runoff data in the second sorted data pair as the output of the first iterative hydrological simulation model, and use the convergence accuracy of the second sorted data pair as the basis for ending the training of the first iterative hydrological simulation model. Train the first iterative hydrological simulation model to obtain the second iterative hydrological simulation model, until the training is completed after extracting the historical meteorological data in the last sorted data pair, and obtain the trained hydrological simulation model.

[0132] In this embodiment, as an optional embodiment, the topographic data includes digital elevation model data, the historical meteorological data includes precipitation data, temperature data, and wind speed data, and the historical water quality monitoring data includes various pollutant concentration data.

[0133] The second model training module 406 is used to retrain the hydrological simulation training model with the historical runoff data corresponding to the sub-basin as the input of the hydrological simulation training model and the historical water quality monitoring data corresponding to the sub-basin as the output of the hydrological simulation training model, to obtain the hydrological simulation model, and set the output of the hydrological simulation model to runoff data and water quality data;

[0134] The mass load acquisition module 407 is used to acquire the topographic data, soil type data, land use data of the basin to be estimated, and the meteorological data corresponding to the latest time period, input them into the hydrological simulation model, obtain the predicted runoff and water quality prediction data of each sub-basin in the predicted time period, and obtain the pollutant mass load based on the predicted runoff and water quality prediction data.

[0135] In this embodiment, as an optional embodiment, the mass load acquisition module 407 is specifically used for:

[0136] Match the topographic data of the basin to be estimated with the hydrological simulation model to obtain one or more sub-basins that match the hydrological simulation model;

[0137] Based on the hydrological simulation model, obtain the predicted runoff and water quality prediction data of one or more sub-basins that match the hydrological simulation model in the predicted time period.

[0138] In this embodiment, as another alternative embodiment, the mass load acquisition module 407 is further specifically configured to:

[0139] For each sub-watershed, calculate the product of the predicted runoff and the water quality prediction data of the sub-watershed to obtain the pollutant mass load of the sub-watershed;

[0140] Calculate the sum of the pollutant mass loads of each sub-watershed to obtain the pollutant mass load of the target watershed;

[0141] Display the pollutant mass load of each sub-watershed and the pollutant mass load of the target watershed.

[0142] In this embodiment, as an alternative embodiment, the device further includes:

[0143] A calibration module (not shown in the figure), which is used to sample the measured water quality data of the predicted time period according to the monitoring points arranged in each sub-watershed, use the predicted runoff of the sub-watershed corresponding to the obtained predicted time period as the input of the hydrological simulation model, and use the measured water quality data of the sub-watershed in the predicted time period as the output of the hydrological simulation model to retrain the hydrological simulation model.

[0144] In this embodiment, as another alternative embodiment, the device further includes:

[0145] A pollution interception rate acquisition module, which is used to extract the lakes in the target watershed, and for each lake, obtain the inflow sub-watershed corresponding to the river flowing into the lake and the outflow sub-watershed corresponding to the river flowing out of the lake;

[0146] Calculate the product of the predicted runoff and the water quality prediction data of each inflow sub-watershed respectively to obtain the pollutant mass load flowing into the lake;

[0147] Calculate the product of the predicted runoff and the water quality prediction data of each outflow sub-watershed respectively to obtain the pollutant mass load flowing out of the lake;

[0148] Based on the pollutant mass load flowing into the lake and the pollutant mass load flowing out of the lake, obtain the pollution interception rate of the lake;

[0149] Based on the pollution interception rate of the lake, determine the treatment strategy for the lake.

[0150] This embodiment comprehensively considers the characteristics of the natural geography and ecological processes of the watershed, combines the refined sub-watershed division, realizes the high-precision estimation of the pollutant mass load, and provides reliable technical support for precise water environment management. The device can integrate functions such as data management, model operation, visualization display, and result verification, is easy to operate, greatly improves the work efficiency of researchers, can quickly respond to the analysis requirements of the pollutant mass load in different watersheds, and has good versatility and promotion value.

[0151] Based on the same inventive concept, an embodiment of the present invention further provides a storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the method for obtaining the pollutant mass load in any of the above possible implementation manners are implemented.

[0152] Optionally, the storage medium may be a non-transitory computer-readable storage medium. For example, the non-transitory computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0153] Based on the same inventive concept, see Figure 5 , an embodiment of the present invention further provides an electronic device, including a memory 101 (such as a non-volatile memory), a processor 102, and a computer program stored on the memory 101 and executable on the processor 102. When the processor 102 executes the program, the steps of the method for obtaining the pollutant mass load in any of the above possible implementation manners are implemented, which is equivalent to the device for obtaining the pollutant mass load as described above. Of course, the processor may also be used to process other data or perform operations. The electronic device may be a device such as a PC, a server, or a terminal.

[0154] As Figure 5 shown, the electronic device generally may further include: a memory 103, a network interface 104, and an internal bus 105. In addition to these components, other hardware may also be included, which will not be elaborated herein.

[0155] It should be noted that the above device for obtaining the pollutant mass load may be implemented by software. As a logically meaningful device, it is formed by the processor 102 of the electronic device where it is located reading the computer program instructions stored in the non-volatile memory into the memory 103 and running them.

[0156] The embodiments of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or in a combination of one or more of them. The embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non-transitory program carrier to be executed by, or to control the operation of, a data processing apparatus. Alternatively or additionally, the program instructions can be encoded on an artificially generated propagated signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode information and transmit it to a suitable receiver apparatus for execution by the data processing apparatus. A computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.

[0157] The processes and logical flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform the corresponding functions by operating on input data and generating output. The processes and logical flows can also be performed by, for example, FPGA (Field Programmable Gate Array) or ASIC (Application Specific Integrated Circuit) as special logic circuitry, and the apparatus can also be implemented as special logic circuitry.

[0158] Computers suitable for executing a computer program include, for example, general and / or special purpose microprocessors, or any other type of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory and / or a random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, etc., or the computer will be operatively coupled to such mass storage devices to receive data from them or to transmit data to them, or both. However, a computer is not necessarily required to have such devices. In addition, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name just a few.

[0159] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, for example, semiconductor memory devices (such as EPROM, EEPROM, and flash memory devices), magnetic disks (such as internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. The processor and the memory may be supplemented by, or incorporated in, special purpose logic circuitry.

[0160] Although this specification contains many specific implementation details, these should not be construed as limiting the scope of any invention or the scope of what is claimed, but rather as mainly being used to describe the features of specific embodiments of a particular invention. Certain features that are described in multiple embodiments in this specification may also be implemented in combination in a single embodiment. On the other hand, the various features described in a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination. Additionally, although features may act in certain combinations and even be initially claimed as such, one or more features from a claimed combination may in some cases be removed from that combination, and the claimed combination may be directed to a sub-combination or a variation of a sub-combination.

[0161] Similarly, although operations are depicted in the drawings in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or sequentially, or that all illustrated operations be performed, to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Additionally, the separation of the various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0162] Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the acts recited in the claims may be performed in a different order and still achieve the desired result. Additionally, the processes depicted in the drawings are not necessarily in the particular order or sequential order shown to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0163] It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0164] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for obtaining the mass load of pollutants, characterized in that, Including: Obtain the topographic data of the target basin, perform depression filling processing on the depression topographic data caused by terrain undulation in the topographic data to obtain depression-free topographic data; According to the pre-set grid division strategy, divide the depression-free topographic data into grids, obtain the water flow direction of each grid cell according to the pre-set flow direction strategy, and based on the water flow direction data corresponding to the water flow direction of the grid cell, obtain the water flow accumulation of the grid cell. The water flow direction data includes the water inflow volume flowing into the grid cell and the water outflow volume flowing out of the grid cell; Obtain the grid cells with water flow accumulation greater than the pre-set water flow accumulation threshold to obtain candidate grid cells, and based on the river outlet of the target basin, the water flow direction of the candidate grid cells and the topographic data corresponding to the candidate grid cells, divide the target basin into sub-basins to obtain multiple sub-basins; Obtain the soil type data, land use data of each sub-basin, and the historical meteorological data, historical runoff data and historical water quality monitoring data of each sub-basin corresponding to the pre-set time period; For each sub-basin, use the topographic data, soil type data, land use data, and historical meteorological data of the sub-basin as the input of the initial hydrological simulation model, and use the historical runoff data corresponding to the sub-basin as the output of the initial hydrological simulation model to train the initial hydrological simulation model to obtain a hydrological simulation training model; Use the historical runoff data corresponding to the sub-basin as the input of the hydrological simulation training model, and use the historical water quality monitoring data corresponding to the sub-basin as the output of the hydrological simulation training model to retrain the hydrological simulation training model to obtain a hydrological simulation model, and set the output of the hydrological simulation model as runoff data and water quality data; Obtain the topographic data, soil type data, land use data of the basin to be estimated and the meteorological data corresponding to the latest time period, input them into the hydrological simulation model, obtain the predicted runoff and water quality prediction data of each sub-basin in the predicted time period, and obtain the pollutant mass load based on the predicted runoff and water quality prediction data.

2. The method for obtaining the mass load of pollutants according to claim 1, wherein For each sub-basin, using the topographic data, soil type data, land use data, and historical meteorological data of the sub-basin as the input of the initial hydrological simulation model, and using the historical runoff data corresponding to the sub-basin as the output of the initial hydrological simulation model to train the initial hydrological simulation model to obtain a hydrological simulation training model, includes: Construct data pairs based on the historical meteorological data, historical runoff data and historical water quality monitoring data of the target sub-basin in the same time period; Sort the data pairs in the order of the time period from long to short from the current time, set the convergence accuracy for the sorted data pairs, and the longer the time period from the current time, the lower the convergence accuracy; Extract the historical meteorological data in the first sorted data pair. Use this historical meteorological data, along with the terrain data, soil type data, and land use data of the target sub-basin, as the input of the initial hydrological simulation model. Use the historical runoff data in the first sorted data pair as the output of the initial hydrological simulation model. Use the convergence accuracy of the first sorted data pair as the basis for ending the training of the initial hydrological simulation model, and train the initial hydrological simulation model to obtain the first iterative hydrological simulation model. Extract the historical meteorological data in the second sorted data pair. Use this historical meteorological data as the input of the first iterative hydrological simulation model. Use the historical runoff data in the second sorted data pair as the output of the first iterative hydrological simulation model. Use the convergence accuracy of the second sorted data pair as the basis for ending the training of the first iterative hydrological simulation model, and train the first iterative hydrological simulation model to obtain the second iterative hydrological simulation model. Repeat this process until the training is completed after extracting the historical meteorological data in the last sorted data pair, and obtain the trained hydrological simulation model.

3. The method for obtaining the mass load of pollutants according to claim 1, characterized in that, The obtaining of the predicted runoff and water quality prediction data for each sub-basin in the predicted time period includes: According to the terrain data of the basin to be estimated, match it with the hydrological simulation model to obtain one or more sub-basins that match the hydrological simulation model. Based on the hydrological simulation model, obtain the predicted runoff and water quality prediction data for one or more sub-basins that match the hydrological simulation model in the predicted time period.

4. The method for obtaining the mass load of pollutants according to claim 3, wherein The obtaining of the pollutant mass load based on the predicted runoff and water quality prediction data includes: For each sub-basin, calculate the product of the predicted runoff and water quality prediction data of this sub-basin to obtain the pollutant mass load of this sub-basin. Calculate the sum of the pollutant mass loads of each sub-basin to obtain the pollutant mass load of the target basin. Display the pollutant mass load of each sub-basin and the pollutant mass load of the target basin.

5. The method for obtaining the mass load of pollutants according to any one of claims 1 to 4, characterized in that The method further includes: According to the monitoring points arranged in each sub-basin, sample to obtain the measured water quality data in the predicted time period. Use the predicted runoff corresponding to the obtained predicted time period of the sub-basin as the input of the hydrological simulation model, and use the measured water quality data of this sub-basin in the predicted time period as the output of the hydrological simulation model to retrain the hydrological simulation model.

6. The method for obtaining the mass load of pollutants according to any one of claims 1 to 4, characterized in that, The method further includes: Extract the lakes in the target basin. For each lake, obtain the inflow sub-basins corresponding to the rivers flowing into the lake and the outflow sub-basins corresponding to the rivers flowing out of the lake. Calculate the product of the predicted runoff and water quality prediction data of each inflow sub-basin respectively to obtain the pollutant mass load flowing into the lake. Calculate the product of the predicted runoff and water quality prediction data of each outflow sub-basin respectively to obtain the pollutant mass load flowing out of the lake. Based on the pollutant mass load flowing into the lake and the pollutant mass load flowing out of the lake, obtain the pollution interception rate of the lake. Based on the pollution interception rate of the lake, determine the treatment strategy for this lake.

7. The method for obtaining the mass load of pollutants according to any one of claims 1 to 4, characterized in that, The terrain data includes digital elevation model data, the historical meteorological data includes precipitation data, temperature data, and wind speed data, and the historical water quality monitoring data includes various pollutant concentration data.

8. A device for obtaining the mass load of pollutants, characterized in that, The device for obtaining the pollutant mass load includes: A first data acquisition module, configured to acquire terrain data of a target basin, perform depression filling processing on the depression terrain data caused by terrain undulation in the terrain data to obtain terrain data without depressions; A grid division module, configured to divide the terrain data without depressions according to a preset grid division strategy, obtain the water flow direction of each grid cell according to a preset flow direction strategy, and obtain the water flow accumulation of the grid cell based on the water flow direction data corresponding to the water flow direction of the grid cell; A sub-basin division module, configured to obtain grid cells with a water flow accumulation greater than a preset water flow accumulation threshold to obtain candidate grid cells, and perform sub-basin division on the target basin based on the river outlet of the target basin, the water flow direction of the candidate grid cells, and the terrain data corresponding to the candidate grid cells to obtain multiple sub-basins; A second data acquisition module, configured to acquire soil type data, land use data of each sub-basin, and historical meteorological data, historical runoff data, and historical water quality monitoring data corresponding to each sub-basin according to a preset time period; A first model training module, configured to, for each sub-basin, use the terrain data, soil type data, land use data, and historical meteorological data of the sub-basin as the input of the initial hydrological simulation model, and use the historical runoff data corresponding to the sub-basin as the output of the initial hydrological simulation model to train the initial hydrological simulation model to obtain a hydrological simulation training model; A second model training module, configured to use the historical runoff data corresponding to the sub-basin as the input of the hydrological simulation training model, and use the historical water quality monitoring data corresponding to the sub-basin as the output of the hydrological simulation training model to retrain the hydrological simulation training model to obtain a hydrological simulation model, and set the output of the hydrological simulation model as runoff data and water quality data; A mass load acquisition module, configured to acquire the terrain data, soil type data, land use data of the basin to be estimated, and meteorological data corresponding to the latest time period, input them into the hydrological simulation model, obtain the predicted runoff and water quality prediction data of each sub-basin in the predicted time period, and obtain the pollutant mass load based on the predicted runoff and water quality prediction data.

9. A storage medium, characterized in that, A program or instruction is stored on a storage medium, and when the program or instruction is run by a processor, the steps of the method for obtaining the pollutant mass load as described in any one of claims 1 to 7 are implemented.

10. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, the steps of the method for obtaining the pollutant mass load as described in any one of claims 1 to 7 are implemented.

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