Method, device, storage medium and electronic device for obtaining a pollutant mass load

Abstract

The present application relates to a kind of method for obtaining pollutant mass load, device, storage medium and electronic equipment, method includes: obtaining the topographic data of target river basin, based on the water flow accumulation amount of the water flow direction of grid unit based on the water flow accumulation amount of the water flow direction of candidate grid unit based on the water flow accumulation amount greater than water flow accumulation amount threshold, river outlet of topographic data and target river basin, carry out sub-basin division;The soil type data of each sub-basin, land use data and corresponding historical meteorological data, historical runoff data and historical water quality monitoring data according to time period, train hydrological simulation initial model, obtain hydrological simulation model;Based on the topographic data of the basin to be estimated, soil type data, land use data and the latest time period corresponding meteorological data and hydrological simulation model, obtain pollutant mass load.Can improve the precision of pollutant load estimation result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental science and hydrology, and particularly relates to a method and device for obtaining pollutant mass load, a storage medium and an electronic device. BACKGROUND

[0002] With the acceleration of industrialization and urbanization, water pollution problems caused by the acceleration are increasingly serious. Therefore, accurate assessment of pollutant mass load in a target basin is crucial for water environment management and water resource protection. In related technologies, statistical methods or empirical formulas are generally used to estimate the pollutant mass load of a target basin. However, due to the difficulty of statistical methods or empirical formulas in comprehensively considering various factors such as complex terrain, soil, land use and weather in the target basin, the accuracy of the estimated pollutant mass load is low, which cannot provide effective support for precise pollution prevention and control. Furthermore, the target basin generally covers a wide range, and the estimated pollutant mass load cannot accurately reflect the differentiated distribution of pollutants in the target basin. SUMMARY

[0003] Therefore, the present application provides a method and device for obtaining pollutant mass load, a storage medium and an electronic device.

[0004] Specifically, the present application is realized by the following technical solutions:

[0005] According to a first aspect of the present application, a method for obtaining pollutant mass load is provided, which comprises:

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

[0007] performing grid division on the non-depression terrain data according to a pre-set grid division strategy, obtaining the water flow direction of each grid cell according to a pre-set flow direction strategy, and obtaining 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, wherein the water flow direction data includes the water inflow into the grid cell and the water outflow out of the grid cell;

[0008] obtaining grid cells with water flow accumulation greater than a pre-set water flow accumulation threshold to obtain candidate grid cells, and dividing the target basin into multiple sub-basins 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;

[0009] obtain 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 in a preset time period;

[0010] For each sub-basin, the terrain data, soil type data, land use data, historical meteorological data of the sub-basin are taken as the input of the hydrological simulation initial model, the historical runoff data corresponding to the sub-basin is taken as the output of the hydrological simulation initial model, the hydrological simulation initial model is trained, and a hydrological simulation training model is obtained;

[0011] The historical runoff data corresponding to the sub-basin is taken as the input of the hydrological simulation training model, and the historical water quality monitoring data corresponding to the sub-basin is taken as the output of the hydrological simulation training model. The hydrological simulation training model is retrained to obtain a hydrological simulation model, and the output of the hydrological simulation model is set to be runoff data and water quality data.

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

[0013] Optionally, the hydrological simulation initial model is trained for each sub-basin, the terrain data, soil type data, land use data, historical meteorological data of the sub-basin are taken as the input of the hydrological simulation initial model, the historical runoff data corresponding to the sub-basin is taken as the output of the hydrological simulation initial model, and the hydrological simulation initial model is trained to obtain a hydrological simulation training model, comprising:

[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, a data pair is constructed;

[0015] According to the time period sequence from long to short from the current time length, the data pairs are sorted, and the convergence precision of the sorted data pairs is set. The longer the data pair is from the current time length, the lower the convergence precision is;

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

[0017] extracting historical meteorological data in the second ranked data pair as input of the hydrological simulation first iteration model, taking historical runoff data in the second ranked data pair as output of the hydrological simulation first iteration model, taking convergence precision of the second ranked data pair as a basis for ending training of the hydrological simulation first iteration model, training the hydrological simulation first iteration model to obtain a hydrological simulation second iteration model, until training of historical meteorological data in the last ranked data pair is completed, and a hydrological simulation training model is obtained.

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

[0019] According to the topographic data of the to-be-estimated basin, the hydrological simulation model is matched to obtain one or more sub-basins matched with the hydrological simulation model;

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

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

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

[0023] A sum value of the pollutant mass loads of the sub-basins is calculated to obtain the pollutant mass load of the target basin;

[0024] The pollutant mass loads of the sub-basins and the pollutant mass load of the target basin are displayed.

[0025] Optionally, the method further comprises:

[0026] According to the monitoring points arranged in each sub-basin, water quality measured data in the prediction time period is sampled to obtain predicted runoff of the sub-basin corresponding to the prediction time period as input of the hydrological simulation model, and water quality measured data of the sub-basin in the prediction time period as output of the hydrological simulation model, and the hydrological simulation model is retrained.

[0027] Optionally, the method further comprises:

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

[0029] The product of the predicted runoff and the water quality prediction data of each inflow sub-basin is calculated respectively to obtain the pollutant mass load flowing into the lake;

[0030] The product of the predicted runoff and the water quality prediction data of each outflow sub-basin is calculated 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, the pollution interception rate of the lake is obtained.

[0032] Based on the pollution interception rate of the lake, the management strategy for the lake is determined.

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

[0034] The method for obtaining the pollutant mass load in the technical solution comprises the following steps: obtaining terrain data of a target basin, performing depression filling on low-lying terrain data in the terrain data caused by terrain undulation to obtain non-low-lying terrain data; performing grid division on the non-low-lying terrain data according to a pre-set grid division strategy, obtaining a water flow direction of each grid unit according to a pre-set flow direction strategy, and obtaining a water flow accumulation amount of the grid unit based on water flow direction data corresponding to the water flow direction of the grid unit; obtaining a grid unit with a water flow accumulation amount greater than a pre-set water flow accumulation amount threshold to obtain a candidate grid unit, performing sub-basin division on the target basin based on a river outlet of the target basin, a water flow direction of the candidate grid unit, and terrain data corresponding to the candidate grid unit, and obtaining a plurality of sub-basins; obtaining soil type data and 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; for each sub-basin, taking the terrain data, the soil type data, the land use data, and the historical meteorological data of the sub-basin as inputs of a hydrological simulation initial model, taking historical runoff data corresponding to the sub-basin as an output of the hydrological simulation initial model, training the hydrological simulation initial model to obtain a hydrological simulation training model; taking the historical runoff data corresponding to the sub-basin as an input of the hydrological simulation training model, taking historical water quality monitoring data corresponding to the sub-basin as an output of the hydrological simulation training model, retraining the hydrological simulation training model to obtain a hydrological simulation model, and setting the output of the hydrological simulation model as runoff data and water quality data; obtaining terrain data, soil type data, land use data, and meteorological data corresponding to a latest time period of a basin to be estimated, inputting the hydrological simulation model, obtaining predicted runoff and water quality prediction data of each sub-basin in a prediction time period, and obtaining the pollutant mass load based on the predicted runoff and the water quality prediction data. In this way, the target sub-basin is divided into sub-basins, and a hydrological simulation model is constructed based on the divided sub-basins, which can consider various factors such as complex terrain, soil, land use, and weather in the target basin, effectively improve the accuracy of the pollutant mass load estimation result, and obtain a pollutant mass load estimation result that can finely reflect the differentiated distribution of pollutants in the target basin.

[0035] According to a second aspect of the present application, a device for obtaining a pollutant mass load is provided, comprising:

[0036] A first data acquisition module is configured to obtain terrain data of a target basin, perform depression filling on low-lying terrain data in the terrain data caused by terrain undulation to obtain non-low-lying terrain data;

[0037] The grid division module is configured to divide the non-depression terrain data according to a preset grid division strategy, obtain a water flow direction of each grid unit according to a preset flow direction strategy, and obtain a water flow accumulation of the grid unit based on water flow direction data corresponding to the water flow direction of the grid unit.

[0038] The sub-basin division module is configured to obtain grid units with a water flow accumulation greater than a preset water flow accumulation threshold to obtain candidate grid units, and divide the target basin into a plurality of sub-basins based on a river outlet of the target basin, the water flow direction of the candidate grid units, and terrain data corresponding to the candidate grid units.

[0039] The second data acquisition module is configured to obtain soil type data and land use data of each sub-basin, and historical meteorological data, historical runoff data, and historical water quality monitoring data corresponding to a preset time period of each sub-basin.

[0040] The first model training module is configured to train a hydrological simulation initial model by taking terrain data, soil type data, land use data, and historical meteorological data of each sub-basin as inputs of the hydrological simulation initial model, and taking historical runoff data corresponding to the sub-basin as an output of the hydrological simulation initial model, to obtain a hydrological simulation training model.

[0041] The second model training module is configured to retrain the hydrological simulation training model by taking historical runoff data corresponding to the sub-basin as an input of the hydrological simulation training model, and taking historical water quality monitoring data corresponding to the sub-basin as an output of the hydrological simulation training model, to obtain a hydrological simulation model, and set an output of the hydrological simulation model as runoff data and water quality data.

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

[0043] According to a third aspect of the present application, a storage medium is provided, which stores a computer program, and the program is executed by a processor to implement the steps of the method for obtaining a pollutant quality load in any possible implementation manner of the first aspect.

[0044] According to a fourth aspect of the present application, an electronic device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the method for obtaining a pollutant quality load in any possible implementation manner of the first aspect when executing the program. BRIEF DESCRIPTION OF DRAWINGS

[0045] The accompanying drawings, which are incorporated herein and constitute part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles of the application.

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or the related description will be briefly introduced. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without any creative effort.

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

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

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

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

[0051] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without any creative effort fall within the protection scope of the present application.

[0053] In the related art, by taking a target river basin as a whole, the product of the average concentration of a target pollutant in river water in the target river basin and the annual flow of the river is calculated based on the law of conservation of mass to obtain the mass load of the pollutant by using a statistical method or an empirical formula. Since the statistical method or the empirical formula is difficult to comprehensively consider various factors such as complex terrain, soil, land use and weather in the river basin, and the target river basin is taken as a whole, the spatial differences inside the river of the target river basin are ignored, resulting in low accuracy of the estimated mass load of the pollutant. With the acceleration of industrialization and urbanization, it is crucial to accurately evaluate the mass load of the pollutant in the target river basin for water environment management and water resource protection.

[0054] Referring to Figure 1 The embodiment of the present application provides a method for obtaining the mass load of the pollutant, which can include the following steps:

[0055] S101, obtaining terrain data of a target river basin, filling low-lying land in the terrain data due to terrain undulation to obtain non-low-lying land terrain data;

[0056] In the embodiment, as an optional embodiment, the terrain data of the target river basin can be obtained from a shared geological database on the network. As an optional embodiment, the terrain data includes but is not limited to digital elevation model (DEM) data.

[0057] In the embodiment, the terrain data is DEM data. Since the original DEM data obtained from the shared geological database may have data errors or low-lying land in the terrain data due to terrain undulation, these low-lying lands will affect the calculation of the subsequent water flow direction, and therefore need to be filled. As an optional embodiment, the DEM data is cleaned and corrected, and a pre-set software such as a "filling low-lying land" tool in ArcGIS software is used to process the DEM data to obtain non-low-lying land DEM data.

[0058] S102, performing grid division on the non-low-lying land terrain data according to a pre-set grid division strategy, obtaining the water flow direction of each grid unit according to a 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;

[0059] In this embodiment, as an optional embodiment, the water system such as rivers and lakes is obtained from the topographic map corresponding to the depressionless topographic data, and the grid unit is divided based on the rivers and lakes, for example, the grid unit is divided based on the topography of the rivers, the rivers entering the lakes and the rivers leaving the lakes. In this embodiment, the grid unit is divided in a graphical manner, and the divided grid unit is presented, and the divided grid unit can be corrected.

[0060] In this embodiment, as an optional embodiment, 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. The water flow direction of each grid unit is calculated according to the depressionless DEM data by using a pre-set software, for example, the "flow direction" tool of the ArcGIS software. In this embodiment, as an optional embodiment, the water flow direction includes 8 flow directions, and the water flow direction is represented by 8 direction encodings of 3-bit binary numbers, for example, 000 represents east direction, 001 represents northeast direction, and the like. By starting from the water flow direction, the water flow accumulation amount of the grid unit is finally obtained by using the pre-set algorithm in the software for step-by-step accumulation, and the water flow accumulation amount reflects the catchment amount of the upstream of the grid unit, which can provide a basis for determining the river network.

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

[0062] For each grid unit, the water flow accumulation amount (the total amount of water flow flowing into the grid unit from the upstream) of the grid unit is calculated, and the water flow accumulation amount of the grid unit is sequentially accumulated to the downstream grid unit from the grid unit at the edge of the target flow region according to the water flow direction converged by each grid unit, so as to obtain the confluence accumulation amount of the target flow region.

[0063] S103, obtaining the grid unit with the water flow accumulation amount greater than the pre-set water flow accumulation amount threshold value to obtain a candidate grid unit, performing sub-basin division on the target flow region based on the river outlet of the target flow region, the water flow direction of the candidate grid unit and the topographic data corresponding to the candidate grid unit, to obtain a plurality of sub-basins;

[0064] In this embodiment, the grid unit with the water flow accumulation amount greater than the water flow accumulation amount threshold value is extracted to obtain the river network. As an optional embodiment, the appropriate water flow accumulation amount threshold value is determined according to the actual situation and the topographic data characteristics of the target region. For example, in a mountainous region, through multiple tests and comparisons, it is determined that the grid unit with the water flow accumulation amount greater than 1000 can better represent the river network, and therefore, for the mountainous region, the water flow accumulation amount threshold value is set to 1000.

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

[0066] In this embodiment, based on the watershed outlet, the target watershed is divided into sub-watersheds according to the water flow direction of the grid cell and the corresponding terrain data. As an optional embodiment, the "watershed division" tool in the ArcGIS software can be used to divide the entire target watershed into multiple sub-watersheds according to the water flow direction and river network data, starting from the watershed outlet.

[0067] In this embodiment, as an optional embodiment, the method further comprises:

[0068] The acquired terrain data corresponding to the grid cell is converted into vector river network data using a pre-set vectorization strategy.

[0069] In this embodiment, as an optional embodiment, the "raster calculator" tool in the ArcGIS software is used to extract the grid cells with a water flow accumulation greater than a threshold value to form river network raster data. Then, the "vectorization" tool is used to convert the river network raster data into vector river network data to facilitate sub-watershed division.

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

[0071] In this embodiment, as an optional embodiment, the historical meteorological data includes but is not limited to precipitation data, air temperature data, 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 optional embodiment, the pre-set time period includes but is not limited to month, week, and season. Taking the month as an example, the monthly precipitation data, air temperature data, wind speed data, etc. of the sub-watershed are acquired. Among them, the historical meteorological data, the historical runoff and the historical water quality monitoring data are all for the same time period.

[0073] In this embodiment, as an optional embodiment, the acquired terrain data, soil type data, land use data, meteorological data and water quality monitoring data can also be preprocessed, such as format unification, missing value filling and outlier removal, to ensure the accuracy and availability of the data.

[0074] S105, for each sub-watershed, taking the terrain data, soil type data, land use data, historical meteorological data of the sub-watershed as the input of the hydrological simulation initial model, and taking the historical runoff data corresponding to the sub-watershed as the output of the hydrological simulation initial model, training the hydrological simulation initial model to obtain a hydrological simulation training model;

[0075] In this embodiment, as an optional embodiment, for each sub-basin, the topographic data, soil type data, land use data, and historical meteorological data of the sub-basin are taken as the input of the hydrological simulation initial model, the historical runoff data corresponding to the sub-basin is taken as the output of the hydrological simulation initial model, the hydrological simulation initial model is trained to obtain a hydrological simulation training model, and the hydrological simulation training model comprises the following steps:

[0076] A11, based on the historical meteorological data, the historical runoff data, and the historical water quality monitoring data of the target sub-basin in the same time period, a data pair is constructed;

[0077] A12, the data pairs are sorted in the order of time periods from long to short from the current time length, and the convergence precision of the sorted data pairs is set, and the longer the data pair is from the current time length, the lower the convergence precision is;

[0078] A13, the historical meteorological data in the first sorted data pair is extracted, the historical meteorological data and the topographic data, the soil type data, and the land use data of the target sub-basin are taken as the input of the hydrological simulation initial model, the historical runoff data in the first sorted data pair is taken as the output of the hydrological simulation initial model, the convergence precision of the first sorted data pair is taken as the basis for ending the training of the hydrological simulation initial model, the hydrological simulation initial model is trained to obtain a first iteration model of the hydrological simulation;

[0079] A14, the historical meteorological data in the second sorted data pair is extracted, the historical meteorological data is taken as the input of the first iteration model of the hydrological simulation, the historical runoff data in the second sorted data pair is taken as the output of the first iteration model of the hydrological simulation, the convergence precision of the second sorted data pair is taken as the basis for ending the training of the first iteration model of the hydrological simulation, the first iteration model of the hydrological simulation is trained to obtain a second iteration model of the hydrological simulation, until the historical meteorological data in the last sorted data pair is extracted and the training is completed, and a hydrological simulation training model is obtained.

[0080] In this embodiment, the training algorithm used by the hydrological simulation initial model can be referred to related technical documents, which will not be described here.

[0081] S106, the historical runoff data corresponding to the sub-basin is taken as the input of the hydrological simulation training model, the historical water quality monitoring data corresponding to the sub-basin is taken as the output of the hydrological simulation training model, the hydrological simulation training model is retrained to obtain a hydrological simulation model, and the output of the hydrological simulation model is set as the runoff data and the water quality data;

[0082] In the embodiment, after obtaining the hydrological simulation training model, the data pairs are used for training, and the convergence accuracy of the data pairs 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, unlike the model obtained through the conventional training, the output of the hydrological simulation model needs to be reset, that is, the output during the training is set to the runoff data and the water quality data, and the input data also needs to be reset.

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

[0084] In the embodiment, the to-be-estimated basin is a 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 prediction time period includes:

[0085] According to the terrain data of the to-be-estimated basin, the hydrological simulation model is matched to obtain one or more sub-basins matched with the hydrological simulation model;

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

[0087] In the 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, the product of the predicted runoff and water quality prediction data of the sub-basin is calculated to obtain the pollutant mass load of the sub-basin;

[0089] The sum of the pollutant mass loads of the sub-basins is calculated to obtain the pollutant mass load of the target basin;

[0090] The pollutant mass loads of the sub-basins and the pollutant mass load of the target basin are displayed.

[0091] In the embodiment, the target basin covers a large range or has large differences in geological structure, and thus the terrain data, soil type data, land use data, meteorological data and water quality data of different regions in the target basin have large differences. For example, the water flow characteristics of a mountainous basin are different from those of a plain basin. For another example, the runoff and confluence mechanism of a humid basin is different from that of an arid basin. Therefore, in the embodiment, the target basin is divided into sub-basins, model parameters are set based on the soil type data and land use data of the sub-basins, a hydrological and ecological simulation unit reflecting the actual situation of the basin is constructed, meteorological data is combined, a pre-constructed model is run to simulate a long time sequence of hydrological processes, and a hydrological simulation model capable of representing the runoff and other water quantities of each sub-basin in the target basin is obtained, which can effectively improve the estimation accuracy of the pollutant mass load.

[0092] In the embodiment, the entire target basin is divided into a plurality of sub-basins (HRU, Hydrologic Research Unit), a time period including a simulation time step and a simulation period is set, the amount of pollutants discharged by each sub-basin into a river in the target basin is simulated, the pollutant discharge amount at different time scales is calculated by considering hydrological processes (historical water quality monitoring data), land use changes (land use data) and other factors, and the pollutant mass load is obtained.

[0093] In the embodiment, as an optional embodiment, the pollutant mass load is calculated by the following formula:

[0094]

[0095] wherein Q (mm / month / km2) is the total water quantity flowing into the divided sub-basin (HRU) per month, which is obtained by simulation by a pre-trained hydrological simulation model; and C (ng / L) corresponds to the pollutant concentration data of the sub-basin. 2

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

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

[0098] In the embodiment, as an optional embodiment, the water quality measured data is the measured data of each pollutant concentration.

[0099] ​In this embodiment, the water flow of each sub-basin is simulated in the hydrological simulation model in units of "month" or even shorter time periods, so that the pollutant mass load in different water periods (dry period, wet period, normal water period) can be compared. For example, samples are collected in the wet period (July) and dry period (February) of a year, and the corresponding pollutant concentration data is obtained by measurement. Based on the water flow in the wet period and the dry period respectively, the pollutant monthly mass load in the wet period and the dry period can be obtained, so that the predicted pollutant monthly mass load can be compared respectively, and the model parameters of the hydrological simulation model can be further adjusted. As an optional embodiment, the time period includes but is not limited to: year, month, day, hour. In this way, by using the granular time period, preferably month or day, the difference in water flow caused by the difference in water period can be reflected, so that the predicted differentiated pollutant mass load can better reflect the actual situation.

[0100] In this embodiment, the target basin includes a lake and rivers connected to the lake, for example, rivers flowing into the lake and rivers flowing out of the lake. Using the hydrological simulation model, the vector range (terrain data) of the target basin, the basin elevation data, the regional land use type data, the regional soil type classification data, and the regional meteorological data are obtained. The above data are sequentially input into the hydrological simulation model, the target region is divided into a plurality of sub-basins, and after simulation by the hydrological simulation model, a plurality of hydrology-related parameters of each sub-basin can be obtained, including but not limited to: the water flow discharged by the sub-basin to the nearest river. As an optional embodiment, the regional pollutant monitoring points are distributed in different sub-basins, and the water flow (sub-basin flow) discharged by each monitoring point to the nearby river is determined according to the regional pollutant monitoring points. The sub-basin flow is multiplied by the concentration value of any kind of pollutant to obtain the pollutant mass load of the monitoring point.

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

[0102] In this embodiment, as an optional embodiment, the method further comprises:

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

[0104] The product of the predicted runoff and the water quality prediction data of each inflow sub-basin is calculated to obtain the pollutant mass load flowing into the lake;

[0105] The product of the predicted runoff and the water quality prediction data of each outflow sub-basin is calculated 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, the pollution interception rate of the lake is obtained;

[0107] Based on the pollution interception rate of the lake, the management strategy for the lake is determined.

[0108] In this embodiment, the target basin is taken as the Dianchi basin, the target basin includes one out-of-lake river and 12 in-lake rivers, one monitoring point is arranged in the out-of-lake river, and one monitoring point is arranged in each in-lake river.

[0109] In this embodiment, the hydrological simulation model is used to divide the target basin into 23 sub-basins, the monthly water discharge of each sub-basin to the Dianchi (out-of-lake river and in-lake river) is simulated, and the pollution interception rate of the pollutant flowing into the Dianchi from the river is calculated through the following formula:

[0110]

[0111]

[0112]

[0113]

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

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

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

[0117] As Figure 2 and Figure 3As shown, the target basin includes a basin, a river, and a lake, 1-23 in the figure are each sub-basin divided, S1-S33 are each monitoring point (sample) arranged, WYLD is the total amount (mm) of water flowing from the sub-basin into the river, and flow_out is the average daily flow (cm / s) out of the river section. In this embodiment, the total water inflow (WYLD) into the main river in each time step is selected by using the pre-set hydrological process simulation algorithm, taking months, weeks, or days as the time period. The monthly flow data of the sub-basin generated by simulation is combined with the measured pollution concentration data obtained from the corresponding sampling points, so as to obtain the pollution load of each sub-basin output to the adjacent river. As an optional embodiment, the research results of a certain basin show that in the wet season of July 2022 and the dry season of February 2023, the total amount of pollutants (PFASs) discharged by 12 main rivers into the lake reaches 11.514 kg and 2.586 kg respectively, while the amount of perfluoroalkyl substances (PFASs) discharged by the outflow river is 0.073 kg and 0.034 kg respectively; in the corresponding period, the lake pollution retention rate is 93.66% and 86.76% respectively. Among them, the high retention rate of pollutants may be an important reason for Dianchi Lake as an endogenous pollution lake. From the investigation period, whether it is the pollution load or the pollution retention rate, the wet season is greater than the dry season. By taking months as the time step, the influence of hydrological factors such as river flow and water level on the migration, diffusion and dilution of pollutants in different periods can be clearly reflected, and the fluctuation of pollution load can be accurately grasped.

[0118] In this embodiment, for the same river, due to the influence of surface water inflow, groundwater inflow, rainfall and other factors, there are differences in the flow values of different river sections of the same river; and different monitoring points may have different pollution sources, so that the pollution data is also different. By obtaining the flow of each monitoring point (the flow of the divided sub-basin), and multiplying the pollution concentration value, the pollution load of each small sub-basin is obtained.

[0119] In this embodiment, visualization display is performed, the sub-basin division graph, the hydrological process dynamic simulation graph, the pollution load distribution contour graph and the like are displayed in an intuitive graphical manner by connecting a display or other visualization terminal, so as to facilitate researchers to analyze the characteristics of the basin and the pollution situation.

[0120] In this embodiment, as an optional embodiment, a variety of statistical analysis algorithms can also be integrated to compare and analyze, verify the accuracy and optimize the parameters of the pollution load results output by the hydrological simulation model according to the measured data, automatically generate verification reports and optimization suggestions, and assist users to improve the application effect of the model.

[0121] Based on the same inventive concept, as Figure 4 The embodiment of the present application also provides a device for obtaining a pollutant mass load, which comprises:

[0122] The first data obtaining module 401 is configured to obtain terrain data of a target river basin, and perform depression filling on low-lying terrain data in the terrain data caused by terrain undulation to obtain non-low-lying terrain data.

[0123] The grid division module 402 is configured to perform grid division on the non-low-lying terrain data according to a pre-set grid division strategy, obtain a water flow direction of each grid cell according to a pre-set flow direction strategy, and obtain a water flow accumulation of the grid cell based on water flow direction data corresponding to the water flow direction of the grid cell.

[0124] The sub-basin division module 403 is configured to obtain grid cells with a water flow accumulation greater than a pre-set water flow accumulation threshold to obtain candidate grid cells, and divide the target river basin into a plurality of sub-basins based on a river outlet of the target river basin, the water flow direction of the candidate grid cells and terrain data corresponding to the candidate grid cells.

[0125] The second data obtaining module 404 is configured to obtain soil type data and land use data of each sub-basin, and historical meteorological data, historical runoff data and historical water quality monitoring data of each sub-basin corresponding to a pre-set time period.

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

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

[0128] Based on the historical meteorological data, the historical runoff data and the historical water quality monitoring data of the target sub-basin corresponding to the same time period, a data pair is constructed.

[0129] The data pairs are sorted according to time periods from long to short to the current time length, and the sorted data pairs are set with convergence accuracies, and the longer the distance from the current time length, the lower the convergence accuracy.

[0130] extracting historical meteorological data in the first sorted data pair, taking the historical meteorological data as input of the hydrological simulation initial model, taking historical runoff data in the first sorted data pair as output of the hydrological simulation initial model, taking convergence precision of the first sorted data pair as a basis for ending training of the hydrological simulation initial model, training the hydrological simulation initial model to obtain a hydrological simulation first iteration model;

[0131] extracting historical meteorological data in the second sorted data pair, taking the historical meteorological data as input of the hydrological simulation first iteration model, taking historical runoff data in the second sorted data pair as output of the hydrological simulation first iteration model, taking convergence precision of the second sorted data pair as a basis for ending training of the hydrological simulation first iteration model, training the hydrological simulation first iteration model to obtain a hydrological simulation second iteration model, until training of historical meteorological data in the last sorted data pair is completed to obtain a hydrological simulation training model.

[0132] In this embodiment, as an optional embodiment, the terrain data includes digital elevation model data, the historical meteorological data includes precipitation data, air 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 configured to take historical runoff data corresponding to the sub-basin as input of the hydrological simulation training model, take historical water quality monitoring data corresponding to the sub-basin as output of the hydrological simulation training model, 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.

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

[0135] In this embodiment, as an optional embodiment, the quality load acquisition module 407 is specifically configured to:

[0136] match the terrain data of the to-be-estimated basin with the hydrological simulation model to acquire one or more sub-basins matched with the hydrological simulation model;

[0137] obtain predicted runoff and water quality prediction data of the one or more sub-basins matched with the hydrological simulation model in the prediction time period based on the hydrological simulation model.

[0138] As another optional embodiment in this embodiment, the quality load obtaining module 407 is specifically further used for:

[0139] For each sub-basin, the product of the predicted runoff and the water quality prediction data of the sub-basin is calculated to obtain the pollutant quality load of the sub-basin;

[0140] The sum of the pollutant quality loads of the sub-basins is calculated to obtain the pollutant quality load of the target basin;

[0141] The pollutant quality loads of the sub-basins and the pollutant quality load of the target basin are displayed.

[0142] As an optional embodiment in this embodiment, the device further comprises:

[0143] The correction module (not shown in the figure) is used for obtaining water quality measured data of a prediction time period by sampling according to the monitoring points arranged in the sub-basins, taking the predicted runoff of the sub-basin corresponding to the prediction time period as the input of the hydrological simulation model, taking the water quality measured data of the sub-basin in the prediction time period as the output of the hydrological simulation model, and retraining the hydrological simulation model.

[0144] As another optional embodiment in this embodiment, the device further comprises:

[0145] The pollution interception rate obtaining module is used for extracting the lakes in the target basin, obtaining the inflow sub-basin corresponding to the river flowing into each lake and the outflow sub-basin corresponding to the river flowing out of the lake for each lake;

[0146] The product of the predicted runoff and the water quality prediction data of each inflow sub-basin is calculated to obtain the pollutant quality load flowing into the lake;

[0147] The product of the predicted runoff and the water quality prediction data of each outflow sub-basin is calculated to obtain the pollutant quality load flowing out of the lake;

[0148] Based on the pollutant quality load flowing into the lake and the pollutant quality load flowing out of the lake, the pollution interception rate of the lake is obtained;

[0149] Based on the pollution interception rate of the lake, the management strategy for the lake is determined.

[0150] This embodiment comprehensively considers the natural geographical and ecological process characteristics of the basin, combines the refined sub-basin division, and realizes high-precision estimation of the pollutant quality load, providing reliable technical support for precise water environment management. The device can integrate data management, model operation, visualization display and result verification functions in one, is convenient to operate, greatly improves the work efficiency of researchers, can quickly respond to the pollutant quality load analysis requirements of different basins, has good universality and popularization value.

[0151] Based on the same inventive concept, the embodiment of the present application further provides a storage medium, which stores a computer program, and the program is executed by a processor to realize the steps of the method for acquiring the mass load of pollutants in any possible implementation manner.

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

[0153] Based on the same inventive concept, see Figure 5 , the embodiment of the present application further provides an electronic device, which comprises a memory 101 (for example, a non-volatile memory), a processor 102, and a computer program stored in the memory 101 and executable on the processor 102, and the processor 102 realizes the steps of the method for acquiring the mass load of pollutants in any possible implementation manner when executing the program, which can be equivalent to the above-mentioned device for acquiring the mass load of pollutants, of course, the processor can also be used to process other data or operations. The electronic device can be a PC, a server, a terminal, etc.

[0154] As shown in Figure 5 , the electronic device can generally further comprise a memory 103, a network interface 104, and an internal bus 105. In addition to these components, other hardware can also be included, which will not be described here.

[0155] It should be pointed out that the above-mentioned device for acquiring the mass load of pollutants can be realized by software, which is a logically meaningful device, and is formed by reading the computer program instructions stored in the non-volatile memory into the memory 103 and running by the processor 102 of the electronic device where it is located.

[0156] Embodiments of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly-embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. 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 for execution by, or to control the operation of, data processing apparatus. Alternatively or in addition, the program instructions can be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. The computer storage medium can 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 logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit), and the apparatus can be implemented as special purpose logic circuitry.

[0158] Computers suitable for the execution of a computer program include, by way of example, general and / or special purpose microprocessors, or any other kind 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 essential elements of a computer are a central processing unit for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, 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 (e.g., 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 by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0160] While the specification contains many specifics, these should not be construed as limiting the scope of any invention or of the required claims in any way. The specification and the described embodiments are illustrative of specific ways to make and use the invention, but various other ways can be employed by persons skilled in the art. Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring or implying that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing can be advantageous. Moreover, the separation of various system modules and components in the embodiments described above should not be understood 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.

[0161] Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring or implying that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing can be advantageous. Moreover, the separation of various system modules and components in the embodiments described above should not be understood 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 following claims. In some cases, actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0163] It has to be noted that, in the present document, relational terms are intended only to convey a possible relationship between elements or

[0164] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, which modifications and changes are to be understood as intended to be encompassed by the general scope of the application. Accordingly, the application is not to be limited to the above described or illustrated embodiments, but is intended to encompass all embodiments consistent with the principles of the application.

Claims

1. A method of obtaining a mass loading of a pollutant, characterized by, The method comprises the following steps: obtaining terrain data of a target basin, filling in low-lying terrain data in the terrain data due to terrain undulations to obtain non-low-lying terrain data; dividing the non-low-lying terrain data into grids according to a preset grid division strategy, obtaining a water flow direction of each grid unit according to a preset flow direction strategy, and obtaining a water flow accumulation amount of the grid unit based on water flow direction data corresponding to the water flow direction of the grid unit, wherein the water flow direction data comprises a water flow inflow amount into the grid unit and a water flow outflow amount out of the grid unit; obtaining grid units with a water flow accumulation amount greater than a preset water flow accumulation amount threshold to obtain candidate grid units, and dividing the target basin into sub-basins based on a river outlet of the target basin, the water flow directions of the candidate grid units and terrain data corresponding to the candidate grid units to obtain a plurality of sub-basins; obtaining soil type data and 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; training a hydrological simulation initial model by taking the terrain data, soil type data, land use data and historical meteorological data of each sub-basin as inputs of the hydrological simulation initial model and taking historical runoff data corresponding to each sub-basin as outputs of the hydrological simulation initial model to obtain a hydrological simulation training model; retraining the hydrological simulation training model by taking historical runoff data corresponding to each sub-basin as inputs of the hydrological simulation training model and taking historical water quality monitoring data corresponding to each sub-basin as outputs of the hydrological simulation training model to obtain a hydrological simulation model, and setting outputs of the hydrological simulation model as runoff data and water quality data; obtaining terrain data, soil type data, land use data and meteorological data corresponding to a latest time period of a basin to be estimated, inputting the hydrological simulation model, obtaining predicted runoff and water quality prediction data of each sub-basin in a prediction time period, and obtaining pollutant mass load based on the predicted runoff and water quality prediction data.

2. The method of acquiring a mass of contaminant of claim 1, wherein, The method of training the hydrological simulation initial model by taking the terrain data, soil type data, land use data and historical meteorological data of each sub-basin as inputs of the hydrological simulation initial model and taking historical runoff data corresponding to each sub-basin as outputs of the hydrological simulation initial model to obtain a hydrological simulation training model comprises the following steps: constructing a data pair based on historical meteorological data, historical runoff data and historical water quality monitoring data of a target sub-basin in a same time period; sorting the data pairs in a time period order from long to short, setting a convergence precision for the sorted data pairs, and the longer the distance from the current time length, the lower the convergence precision. extracting historical meteorological data in the first sorted data pair, taking the historical meteorological data as input of the hydrological simulation initial model, taking historical runoff data in the first sorted data pair as output of the hydrological simulation initial model, taking convergence precision of the first sorted data pair as a basis for ending training of the hydrological simulation initial model, training the hydrological simulation initial model to obtain a hydrological simulation first iteration model; extracting historical meteorological data in the second sorted data pair, taking the historical meteorological data as input of the hydrological simulation first iteration model, taking historical runoff data in the second sorted data pair as output of the hydrological simulation first iteration model, taking convergence precision of the second sorted data pair as a basis for ending training of the hydrological simulation first iteration model, training the hydrological simulation first iteration model to obtain a hydrological simulation second iteration model, until training of historical meteorological data in the last sorted data pair is completed to obtain a hydrological simulation training model.

3. The method of obtaining a mass loading of contaminants of claim 1, wherein, The method further comprises: matching the terrain data of the to-be-estimated basin with the hydrological simulation model to obtain one or more sub-basins matched with the hydrological simulation model; obtaining predicted runoff and water quality prediction data of the one or more sub-basins matched with the hydrological simulation model in a prediction time period based on the hydrological simulation model.

4. The method of acquiring a mass of contaminant of claim 3, wherein, The method further comprises: calculating a product of the predicted runoff and water quality prediction data of each sub-basin to obtain pollutant mass load of the sub-basin; calculating a sum of the pollutant mass load of each sub-basin to obtain pollutant mass load of the target basin; displaying the pollutant mass load of each sub-basin and the pollutant mass load of the target basin.

5. The method of obtaining a mass load of a contaminant according to any one of claims 1 to 4, wherein, The method further comprises: retraining the hydrological simulation model according to water quality measured data in the prediction time period sampled from the monitoring points arranged in each sub-basin, taking predicted runoff of the sub-basin corresponding to the prediction time period as input of the hydrological simulation model, and taking water quality measured data of the sub-basin in the prediction time period as output of the hydrological simulation model.

6. A method of obtaining a mass load of a contaminant according to any one of claims 1 to 4, characterised in that, The method further comprises: extracting lakes in the target basin, and for each lake, obtaining inflow sub-basins corresponding to rivers flowing into the lake and outflow sub-basins corresponding to rivers flowing out of the lake; calculating a product of predicted runoff and water quality prediction data of each inflow sub-basin to obtain pollutant mass load flowing into the lake; calculating a product of predicted runoff and water quality prediction data of each outflow sub-basin to obtain pollutant mass load flowing out of the lake; obtaining pollution interception rate of the lake based on the pollutant mass load flowing into the lake and the pollutant mass load flowing out of the lake; determining a governance strategy for the lake based on the pollution interception rate of the lake.

7. A method of obtaining a mass load of a contaminant according to any one of claims 1 to 4, characterised in that, The terrain data comprises digital elevation model data, the historical meteorological data comprises precipitation data, air temperature data, and wind speed data, and the historical water quality monitoring data comprises various pollutant concentration data.

8. An apparatus for obtaining a mass loading of a contaminant, comprising: The device for obtaining the pollutant mass load comprises: a first data acquisition module, configured to acquire terrain data of a target watershed, perform depression filling processing on low-lying terrain data in the terrain data caused by terrain undulations, and obtain non-low-lying terrain data; a grid division module, configured to perform grid division on the non-low-lying terrain data according to a pre-set grid division strategy, acquire a water flow direction of each grid cell based on a pre-set flow direction strategy, and acquire a water flow accumulation amount of the grid cell based on water flow direction data corresponding to the water flow direction of the grid cell; a sub-watershed division module, configured to acquire grid cells with a water flow accumulation amount greater than a pre-set water flow accumulation amount threshold, to obtain candidate grid cells, and to divide the target watershed into a plurality of sub-watersheds based on a river outlet of the target watershed, water flow directions of the candidate grid cells, and terrain data corresponding to the candidate grid cells; a second data acquisition module, configured to acquire soil type data and land use data of each sub-watershed, and historical meteorological data, historical runoff data, and historical water quality monitoring data corresponding to each sub-watershed according to a pre-set time period; a first model training module, configured to, for each sub-watershed, take terrain data, soil type data, land use data, and historical meteorological data of the sub-watershed as inputs of a hydrological simulation initial model, take historical runoff data corresponding to the sub-watershed as an output of the hydrological simulation initial model, train the hydrological simulation initial model, and obtain a hydrological simulation training model; a second model training module, configured to take historical runoff data corresponding to each sub-watershed as an input of the hydrological simulation training model, take historical water quality monitoring data corresponding to the sub-watershed as an output of the hydrological simulation training model, retrain the hydrological simulation training model, obtain a hydrological simulation model, and set an output of the hydrological simulation model as runoff data and water quality data; a mass load acquisition module, configured to acquire terrain data, soil type data, land use data, and meteorological data corresponding to a latest time period of a to-be-estimated watershed, input the hydrological simulation model, obtain predicted runoff and water quality prediction data of each sub-watershed in a prediction time period, and acquire a pollutant mass load based on the predicted runoff and the water quality prediction data.

9. A storage medium, characterized by A program or instruction is stored on a storage medium, and the program or instruction is run by a processor to implement steps of the method for obtaining a pollutant mass load according to any one of claims 1 to 7.

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, The processor implements steps of the method for obtaining a pollutant mass load according to any one of claims 1 to 7 when executing the program.

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