Basin-scale water quantity and quality automatic regulation method and device, medium and equipment

Abstract

The present application relates to a kind of water quantity and water quality automatic regulation and control method, device, storage medium and electronic equipment at watershed scale, the method comprises: constructing watershed SWAT model, the model parameter of watershed SWAT model is verified, obtain the watershed SWAT model after verification, obtain the meteorological data of target region in target period predicted by atmospheric circulation model;The target water quantity and water quality of the target region in the target period are obtained, meteorological data is input into the watershed SWAT model after verification, obtain the water quantity and water quality of target region in target period, the water quantity and water quality regulation and control measure parameter of best management measure in the watershed SWAT model after verification is adjusted using non-dominated sorting genetic method with elite strategy, until the deviation between water quantity and water quality of target period and target water quantity and water quality is within the threshold range for stopping, obtain the water quantity and water quality regulation and control measure of the target region, reduce the artificial cost, improve the efficiency and precision of water quantity and water quality regulation and control.

Description

Technical Field

[0001] This invention relates to the field of water resource optimization and allocation technology, and in particular to a method, device, storage medium, and electronic equipment for automatic regulation of water quantity and quality at the watershed scale. Background Technology

[0002] Climate change is causing drastic changes in temperature, rainfall frequency, intensity, and spatiotemporal distribution, significantly impacting runoff-pollutant characteristics and potentially leading to increased flooding and water pollution. Best Management Practices (BMPS) are an effective method for mitigating watershed flooding and water quality deterioration. These practices, proposed by the U.S. Department of Agriculture in the mid-1970s, represent a series of methods and measures for reducing or preventing water environment problems.

[0003] Currently, different BMPS (Bill of Water Pollution) measures are used to simulate and explore which measures result in greater reductions in water quantity and quality, in order to formulate appropriate measures to alleviate watershed flooding and water quality deterioration. However, selecting suitable BMPS measures and their characteristic parameters requires technicians to have a deep understanding and practical experience of the study area and various BMPS measures. Otherwise, it is easy to make inappropriate selections of BMPS measures, lose the optimal BMPS configuration scheme, and thus reduce the efficiency of water quantity and quality reduction. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide a method, device, storage medium and electronic equipment for automatic regulation of water quantity and quality at the watershed scale, which has the advantages of reducing labor costs and improving the efficiency of water quantity and quality regulation.

[0005] According to a first aspect of the embodiments of this application, an automatic water quantity and quality control method at the watershed scale is provided, comprising the following steps:

[0006] Obtain topographic elevation data, land use data, soil data, meteorological data, and agricultural management data of the target area for a preset period before the target time period, and construct a watershed SWAT model based on the topographic elevation data, land use data, soil data, meteorological data, and agricultural management data;

[0007] The model parameters of the watershed SWAT model are validated to obtain the validated watershed SWAT model.

[0008] Acquire meteorological data of the target area predicted by the atmospheric circulation model during the target time period;

[0009] obtain target water quantity and quality of the target region in the target period; input the predicted meteorological data into the calibrated watershed SWAT model, and adjust water quantity and quality control measure parameters of the calibrated watershed SWAT model by using a non-dominated sorting genetic method with an elite strategy, so that a deviation between water quantity and quality output by the watershed SWAT model and the target water quantity and quality is within a preset threshold range;

[0010] use the adjusted water quantity and quality control measure parameters as water quantity and quality control measures of the target region in the target period.

[0011] According to a second aspect of the embodiments of the present application, a watershed-scale automatic water quantity and quality control device is provided, which comprises:

[0012] a data acquisition module configured to acquire terrain elevation data, land use data, soil data, meteorological data and agricultural management data of a target region in a preset period before a target period, and construct a watershed SWAT model according to the terrain elevation data, the land use data, the soil data, the meteorological data and the agricultural management data;

[0013] a parameter verification module configured to verify model parameters of the watershed SWAT model, and obtain a calibrated watershed SWAT model;

[0014] a meteorological data acquisition module configured to acquire meteorological data of the target region in the target period predicted by an atmospheric circulation model;

[0015] a measure parameter adjustment module configured to obtain target water quantity and quality of the target region in the target period; input the predicted meteorological data into the calibrated watershed SWAT model, and adjust water quantity and quality control measure parameters of the calibrated watershed SWAT model by using a non-dominated sorting genetic method with an elite strategy, so that a deviation between water quantity and quality output by the watershed SWAT model and the target water quantity and quality is within a preset threshold range;

[0016] a control measure obtaining module configured to use the adjusted water quantity and quality control measure parameters as water quantity and quality control measures of the target region in the target period.

[0017] According to a third aspect of the embodiments of the present application, an electronic device is provided, which comprises a processor and a memory; wherein the memory stores a computer program, and the computer program is adapted to be loaded and executed by the processor to implement the watershed-scale automatic water quantity and quality control method as described in any one of the above aspects.

[0018] According to a fourth aspect of the embodiments of the present application, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the automatic watershed scale water quantity and quality regulation method as described in any of the above.

[0019] The application acquires terrain elevation data, land use data, soil data, meteorological data and agricultural management data of a target region in a preset period before a target period, constructs a watershed SWAT model according to the terrain elevation data, the land use data, the soil data, the meteorological data and the agricultural management data, verifies model parameters of the watershed SWAT model to obtain a verified watershed SWAT model, acquires meteorological data of the target region in the target period predicted by an atmospheric circulation model, acquires target water quantity and quality of the target region in the target period, inputs the predicted meteorological data into the verified watershed SWAT model, adjusts water quantity and quality regulation measure parameters of the verified watershed SWAT model by using a non-dominated sorting genetic method with an elite strategy, so that a deviation between water quantity and quality output by the watershed SWAT model and the target water quantity and quality is within a preset threshold range, and uses the adjusted water quantity and quality regulation measure parameters as water quantity and quality regulation measures of the target region in the target period, thereby automatically determining water quantity and quality regulation measures suitable for climate change in the target period, reducing labor cost and improving efficiency and precision of water quantity and quality regulation.

[0020] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not limiting to the present application.

[0021] In order to better understand and implement, the present application is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 It is a flowchart of the watershed scale water quantity and quality automatic regulation method of the present application.

[0023] Figure 2 It is a flowchart of S20 in the watershed scale water quantity and quality automatic regulation method of the present application.

[0024] Figure 3 It is a flowchart of S50 in the watershed scale water quantity and quality automatic regulation method of the present application.

[0025] Figure 4 It is a structure block diagram of the watershed scale water quantity and quality automatic regulation device of the present application. DETAILED DESCRIPTION

[0026] In order to make the objects, technical solutions and advantages of the present application clearer, the following further describes the embodiments of the present application with reference to the drawings.

[0027] It should be noted that the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0028] The terms used in the embodiments of the present application are only for the purpose of describing the specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein means and includes any or all possible combinations of one or more associated listed items.

[0029] The following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of apparatuses and methods consistent with some aspects of the present application as detailed in the appended claims. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and do not necessarily describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0030] In addition, in the description of the present application, "a plurality of" means two or more, unless otherwise specified. The association between the associated objects is described by "and / or", which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after it.

[0031] Please refer to Figure 1 The embodiment of the present application provides a basin-scale water quantity and quality automatic regulation method, which comprises the following steps:

[0032] S10: Obtain topographic elevation data, land use data, soil data, meteorological data and agricultural management data of a target region in a preset period before a target period, and construct a watershed SWAT model according to the topographic elevation data, the land use data, the soil data, the meteorological data and the agricultural management data.

[0033] In the embodiments of the present application, the target region is a region to be determined for water quantity and quality regulation measures, and the target period is a certain future time period, for example, 5 or 10 years in the future. The preset period before the target period is the current time period or a certain past time period.

[0034] The SWAT (Soil and Water Assessment Tool) hydrological model is a distributed hydrological physical model developed by the United States Department of Agriculture. It can simulate the hydrological processes, soil erosion, chemical processes, agricultural management measures and biomass changes of a watershed for a long period of time continuously, and can predict the impact of human activities on the above processes under different soil conditions, land use types and management measures. In the embodiments of the present application, the terrain elevation data is obtained from the geographic spatial data cloud platform of the Chinese Academy of Sciences Computer Network Information Center, and the spatial resolution is 90 m. The land use data is obtained from the Resource and Environment Science Data Center of the Chinese Academy of Sciences. The soil data uses the HWSD data provided by the Food and Agriculture Organization of the United Nations. The meteorological data selects the daily rainfall data of 21 rainfall stations in the watershed from 2000 to 2015, and the temperature, wind speed, relative humidity and sunshine duration data are selected from the Chinese Ground Climate Data Daily Value Dataset. The agricultural management data is obtained from the local statistical yearbook.

[0035] The process of constructing the watershed SWAT model according to the terrain elevation data, the land use data, the soil data, the meteorological data and the agricultural management data is: based on the terrain elevation data, setting the sub-watershed area threshold and the watershed outlet, generating the watershed river network, and dividing the watershed into a plurality of sub-watersheds. Further, the sub-watershed is further subdivided into a plurality of hydrologic response units (HRU) according to soil, land use and slope classification data. Each sub-watershed can input differentiated meteorological, soil characteristics, vegetation cover and terrain features, while each HRU generally has the same land use status, soil type and other characteristics, and the HRU is assumed to be independent units without interaction. The input meteorological data includes five types of daily scale rainfall, temperature, solar radiation, wind speed and relative humidity. The input agricultural management data includes crop types, fertilizer types, application methods and application amounts. The watershed SWAT model is run, first on the HRU for runoff and water quality simulation calculation, and the runoff and non-point source pollutants of each HRU are collected to the outlet of the sub-watershed, and then based on the river network, the simulation calculation is performed at the sub-watershed level to obtain the simulation results of the watershed outlet.

[0036] S20: verifying the model parameters of the watershed SWAT model to obtain a verified watershed SWAT model.

[0037] The simulation process of the watershed SWAT model involves a plurality of parameters. In the calibration process of the model parameters, some parameters have a great influence on the results, while some have little effect on the improvement of the results. The calibration of the model parameters is to find the parameters that make the simulation values of the model consistent with the corresponding measured values as much as possible. In the embodiments of the present application, the non-dominated sorting genetic method (NSGA-II) with an elitist strategy is used to automatically calibrate the model parameters of the watershed SWAT model. The non-dominated sorting genetic algorithm (NSGA) is a genetic algorithm based on the concept of Pareto optimality. The NSGA-II introduces an elitist strategy on the basis of the NSGA to expand the sampling space. The parent population and the offspring population generated by the parent population are combined to compete to generate the next generation of population, which is conducive to keeping the excellent individuals in the parent generation into the next generation, and through the hierarchical storage of all individuals in the population, the best individual cannot be lost, and the population level can be rapidly improved.

[0038] In an optional embodiment, referring to Figure 2 , the step S20 includes S201-S204, and specifically as follows:

[0039] S201: A Latin hypercube sampling is used to select a set of model parameters from the preset model parameters of the watershed SWAT model and the range of the model parameters;

[0040] S202: The watershed SWAT model is run according to the set of model parameters to obtain the water quantity and water quality results output by the watershed SWAT model;

[0041] S203: A target function is calculated according to the output water quantity and water quality results and the measured water quantity and water quality results; wherein the target function includes a Nash efficiency coefficient, a certainty coefficient and a deviation percentage;

[0042] S204: The model parameters of the watershed SWAT model are calibrated according to the target function and the non-dominated sorting genetic method with an elitist strategy to obtain the calibrated watershed SWAT model.

[0043] Latin hypercube sampling (LHS) is a method of approximate random sampling from a multivariate parameter distribution, which belongs to stratified sampling technique, and is often used in computer experiments or Monte Carlo integration, etc. The Nash-Sutcliffe efficiency coefficient (NSE) is generally used to verify the goodness of the simulation results of a hydrological model. The degree of coincidence between the predicted process and the measured process is represented by the coefficient of determination, and the value of the coefficient of determination is between 0 and 1. The percentage bias identifies the degree of deviation between the measured value and the accurate value. In the embodiments of the present application, Latin hypercube sampling is performed on the model parameters of the watershed SWAT model to obtain a random parameter combination; then the random parameter combination is input into the watershed SWAT model to observe the change and perturbation difference between the simulation value and the observed value, and to calculate the Nash-Sutcliffe efficiency coefficient, the coefficient of determination and the percentage bias. According to the objective function and the non-dominated sorting genetic method with an elite strategy, the model parameters of the watershed SWAT model are verified, the matching degree between the simulation value and the observed value is judged, and after multiple iterations, when the simulation effect meets the set judgment value, the verification is passed, and the verified watershed SWAT model is obtained.

[0044] S30: Obtain meteorological data of the target region in the target period predicted by the atmospheric circulation model.

[0045] General Circulation Models (GCMs) are an important tool for quantitative research on climate change, and have been widely used in research on different levels and different climate change problems such as global and regional. Because the increase in the frequency of rainfall events and the rise in temperature under future climate change will significantly affect the efficiency of the best management practices of the watershed, the best management practices of the watershed should be adjusted according to climate change. Therefore, in the embodiments of the present application, the meteorological data of the target region in the target period is predicted by the atmospheric circulation model, i.e. the future meteorological data of the target region is predicted, which facilitates the subsequent input of the future meteorological data into the watershed SWAT model to obtain the water quantity and quality of the target region in a certain period in the future.

[0046] In an optional embodiment, the meteorological data of the target region in the target period includes daily rainfall, air temperature, solar radiation, wind speed and relative humidity data in the target period.

[0047] S40: obtaining a target water quantity and quality of the target region in the target period; inputting the predicted meteorological data into the calibrated watershed SWAT model, adjusting water quantity and quality control measures parameters of the calibrated watershed SWAT model by using a non-dominated sorting genetic method with an elite strategy, so that a deviation between the water quantity and quality output by the watershed SWAT model and the target water quantity and quality is within a preset threshold range.

[0048] In the embodiment of the present application, the daily scale rainfall, air temperature, solar radiation, wind speed and relative humidity data of the target region in the target period are input into the calibrated watershed SWAT model, and the water quantity and quality output by the watershed SWAT model is obtained, so as to predict the runoff water quantity and quality characteristics of the watershed under future climate change and analyze the influence of future climate change on the runoff water quantity and quality of the watershed. The water quality includes total nitrogen concentration and total phosphorus concentration in the watershed, etc.

[0049] If the deviation between the output water quantity and quality and the target water quantity and quality is not within the preset threshold range, it is determined that the water quantity and quality control measures parameters of the best management measures are unreasonable, and the water quantity and quality control measures parameters of the best management measures need to be adjusted again until the deviation between the output water quantity and quality and the target water quantity and quality is within the preset threshold range. The water quantity and quality control measures parameters of the best management measures in the watershed SWAT model include FILTER_RATIO (field area to plant buffer zone area ratio), FILTER_CON (representing the part of the total runoff of the most concentrated 10% of farmland entering the plant buffer zone), FILTER_CH (representing the part of the runoff passing through the completely channeled plant buffer zone), GWATN (Manning coefficient n value of slope overland flow), GWATSPCON (linear parameter for calculating sediment in grass waterway), GWATD (channel depth of grass waterway), GWATW (average width of grass waterway), WET_NSA (wetland surface area under normal water level), WET_NVOL (wetland storage under normal water level), WET_MXSA (wetland surface area under maximum water level), WET_MXVOL (wetland storage under maximum water level), WET_K (hydraulic conductivity or permeability coefficient through the bottom of the wetland), etc.

[0050] S50: taking the adjusted water quantity and quality control measures parameters as the water quantity and quality control measures of the target region in the target period.

[0051] In the embodiment of the present application, the water quantity and quality control measures parameters corresponding to the deviation between the water quantity and quality output by the watershed SWAT model and the target water quantity and quality within the preset threshold range are taken as the water quantity and quality control measures of the target region in the target period.

[0052] In an optional embodiment, the optimal management measures include setting a plant buffer zone, a plant waterway, and an artificial wetland.

[0053] The plant buffer zone is a water and soil conservation treatment measure, which refers to a three-dimensional plant zone of trees, shrubs, and grasses constructed in a certain area at the junction of a river and land, which plays a certain buffering role between farmland and a river. The plant waterway is to remove NH3, NO2, and NO3 in water through aquatic plants in the waterway. The artificial wetland is a ground similar to a marsh constructed and controlled by humans, and sewage and sludge are controlled and injected into the artificial wetland. In the process of flowing in a certain direction, the sewage and sludge are mainly treated by the triple synergistic action of soil, artificial medium, plants, and microorganisms. In the embodiments of the present application, the total nitrogen concentration and the total phosphorus concentration in the basin are reduced, and the water quantity and quality control efficiency is improved by setting the plant buffer zone, the plant waterway, and the artificial wetland.

[0054] In an optional embodiment, referring to Figure 3 , the step S50 includes S501-S504, and specifically as follows:

[0055] S501: Obtain the target water quantity and quality of the target region in the target period, input the predicted meteorological data into the calibrated SWAT model of the basin, and obtain the water quantity and quality output by the SWAT model of the basin;

[0056] S502: If the deviation between the output water quantity and quality and the target water quantity and quality is not within the preset threshold range, obtain the initial population of the target region; wherein each individual of the initial population is used to indicate a set of water quantity and quality control measure parameter set of the optimal management measure;

[0057] S503: Perform crossover and mutation on the initial population to obtain an offspring population, and calculate the fitness value of the offspring population; wherein the fitness value is the deviation between the output water quantity and quality and the target water quantity and quality;

[0058] S504: Merge and non-dominant sort the initial population and the offspring population, calculate the crowding degree of the individuals in the same non-dominant layer, and select appropriate individuals to form a new population;

[0059] S505: Perform crossover and mutation on the new population to obtain a new offspring population, and repeatedly iterate the above selection, crossover, and mutation steps, so that the generated population individuals continuously approach the Pareto optimal solution, until the deviation between the output water quantity and quality and the target water quantity and quality is within the preset threshold range.

[0060] In the embodiment of the present application, the water quantity and water quality regulation and control measure parameters of the optimal management measure in the verified watershed SWAT model are adjusted by using the non-dominated sorting genetic method with an elitist strategy, so that the deviation between the water quantity and water quality output by the watershed SWAT model and the target water quantity and water quality is within a preset threshold range, and manual setting of the water quantity and water quality regulation and control measure parameters of the optimal management measure is no longer needed, thereby improving the efficiency and accuracy of water quantity and water quality regulation and control.

[0061] By applying the embodiment of the present application, topographic elevation data, land use data, soil data, meteorological data and agricultural management data of a target region in a preset period before a target period are acquired, and a watershed SWAT model is constructed according to the topographic elevation data, the land use data, the soil data, the meteorological data and the agricultural management data; model parameters of the watershed SWAT model are verified to obtain a verified watershed SWAT model; meteorological data of the target region in the target period predicted by an atmospheric circulation model is acquired; target water quantity and water quality of the target region in the target period are acquired; the predicted meteorological data is input into the verified watershed SWAT model, and a non-dominated sorting genetic method with an elitist strategy is used to adjust water quantity and water quality regulation and control measure parameters of the verified watershed SWAT model, so that the deviation between the water quantity and water quality output by the watershed SWAT model and the target water quantity and water quality is within a preset threshold range; the adjusted water quantity and water quality regulation and control measure parameters are used as water quantity and water quality regulation and control measures of the target region in the target period, so that water quantity and water quality regulation and control measures suitable for climate change in the target period are automatically determined, labor cost is reduced, and the efficiency and accuracy of water quantity and water quality regulation and control are improved.

[0062] Corresponding to the method embodiment, refer to Figure 4 The embodiment of the present application provides a watershed-scale automatic water quantity and water quality regulation and control device 6, which comprises:

[0063] A data acquisition module 61 is configured to acquire topographic elevation data, land use data, soil data, meteorological data and agricultural management data of a target region in a preset period before a target period, and construct a watershed SWAT model according to the topographic elevation data, the land use data, the soil data, the meteorological data and the agricultural management data;

[0064] A parameter verification module 62 is configured to verify model parameters of the watershed SWAT model to obtain a verified watershed SWAT model;

[0065] A meteorological data acquisition module 63 is configured to acquire meteorological data of the target region in the target period predicted by an atmospheric circulation model;

[0066] The measure parameter adjustment module 64 is configured to obtain target water quantity and quality of the target region in the target period; input the predicted meteorological data into the calibrated watershed SWAT model; adjust water quantity and quality control measure parameters of the calibrated watershed SWAT model by using a non-dominated sorting genetic method with an elitist strategy, so that a deviation between water quantity and quality output by the watershed SWAT model and the target water quantity and quality is within a preset threshold range;

[0067] The control measure obtaining module 65 is configured to use the adjusted water quantity and quality control measure parameters as water quantity and quality control measures of the target region in the target period.

[0068] Optionally, the parameter calibration module 62 comprises:

[0069] The parameter set selection unit 621 is configured to select a set of model parameter sets from preset model parameters and model parameter ranges of the watershed SWAT model by using Latin hypercube sampling;

[0070] The result obtaining unit 622 is configured to run the watershed SWAT model according to the model parameter sets, and obtain water quantity and quality results output by the watershed SWAT model;

[0071] The objective function calculation unit 623 is configured to calculate an objective function according to the output water quantity and quality results and the measured water quantity and quality results; the objective function comprises a Nash efficiency coefficient, a certainty coefficient and a deviation percentage;

[0072] The parameter calibration unit 624 is configured to calibrate model parameters of the watershed SWAT model according to the objective function and the non-dominated sorting genetic method with an elitist strategy, and obtain a calibrated watershed SWAT model.

[0073] Optionally, the measure parameter adjustment module 64 comprises:

[0074] The water quantity and quality obtaining unit 641 is configured to obtain target water quantity and quality of the target region in the target period; input the predicted meteorological data into the calibrated watershed SWAT model, and obtain water quantity and quality output by the watershed SWAT model;

[0075] The initial population obtaining unit 642 is configured to obtain an initial population of the target region if a deviation between the output water quantity and quality and the target water quantity and quality is not within a preset threshold range; each individual of the initial population is used to indicate a set of water quantity and quality control measure parameters of the best management measure;

[0076] The fitness value calculation unit 643 is configured to perform cross and mutation on the initial population to obtain a child population, and calculate a fitness value of the child population, wherein the fitness value is a deviation between the output water quantity and water quality and the target water quantity and water quality.

[0077] The crowding degree calculation unit 644 is configured to merge and non-dominantly sort the initial population and the child population, perform crowding degree calculation on individuals in the same non-dominant layer, and select appropriate individuals to form a new population.

[0078] The population cross and mutation unit 645 is configured to perform cross and mutation on the new population to obtain a new child population, and repeatedly iterate the selection, cross and mutation steps to make the generated population individuals approach the Pareto optimal solution, until the deviation between the output water quantity and water quality and the target water quantity and water quality is within a preset threshold range.

[0079] By applying the embodiment of the present application, the terrain elevation data, the land use data, the soil data, the meteorological data and the agricultural management data of a target region in a preset period before a target period are acquired, a watershed SWAT model is constructed according to the terrain elevation data, the land use data, the soil data, the meteorological data and the agricultural management data, the model parameters of the watershed SWAT model are verified to obtain a verified watershed SWAT model, the meteorological data of the target region in the target period predicted by an atmospheric circulation model is acquired, the target water quantity and water quality of the target region in the target period are acquired, the predicted meteorological data is input into the verified watershed SWAT model, the water quantity and water quality control measure parameters of the verified watershed SWAT model are adjusted by using a non-dominant sorting genetic method with an elite strategy, the deviation between the water quantity and water quality output by the watershed SWAT model and the target water quantity and water quality is within a preset threshold range, and the adjusted water quantity and water quality control measure parameters are used as the water quantity and water quality control measures of the target region in the target period, so that the water quantity and water quality control measures suitable for climate change in the target period are automatically determined, the labor cost is reduced, and the efficiency and accuracy of water quantity and water quality control are improved.

[0080] The present application also provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program which is adapted to be loaded and executed by the processor to implement the method steps of the above-mentioned embodiments.

[0081] The present application also provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the method steps of the above-mentioned embodiments.

[0082] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, and the present application also intends to include these modifications and improvements.

Claims

1. A method for automatically regulating water quantity and quality at a watershed scale, characterized in that, The method comprises the following steps: obtaining terrain elevation data, land use data, soil data, meteorological data and agricultural management data of a target region in a preset period before a target period, and constructing a watershed SWAT model according to the terrain elevation data, the land use data, the soil data, the meteorological data and the agricultural management data; verifying model parameters of the watershed SWAT model to obtain a verified watershed SWAT model; obtaining meteorological data of the target region in the target period predicted by an atmospheric circulation model, wherein the meteorological data of the target region in the target period comprises daily rainfall, air temperature, solar radiation, wind speed and relative humidity data in the target period; obtaining target water quantity and quality of the target region in the target period; inputting the predicted meteorological data into the verified watershed SWAT model, adjusting water quantity and quality control measure parameters of the verified watershed SWAT model by using a non-dominated sorting genetic method with an elite strategy, so that the deviation between the water quantity and quality output by the watershed SWAT model and the target water quantity and quality is within a preset threshold range; using the adjusted water quantity and quality control measure parameters as the water quantity and quality control measures of the target region in the target period.

2. The method according to claim 1, wherein the step of obtaining target water quantity and quality of the target region in the target period, inputting the predicted meteorological data into the verified watershed SWAT model, and adjusting water quantity and quality control measure parameters of the verified watershed SWAT model by using a non-dominated sorting genetic method with an elite strategy, so that the deviation between the water quantity and quality output by the watershed SWAT model and the target water quantity and quality is within a preset threshold range, comprises: obtaining target water quantity and quality of the target region in the target period, inputting the predicted meteorological data into the verified watershed SWAT model, and obtaining water quantity and quality output by the watershed SWAT model; if the deviation between the output water quantity and quality and the target water quantity and quality is not within the preset threshold range, obtaining an initial population of the target region; wherein each individual of the initial population is used to indicate a set of water quantity and quality control measure parameters of the best management measures; crossing and mutating the initial population to obtain a child population, and calculating the fitness value of the child population; wherein the fitness value is the deviation between the output water quantity and quality and the target water quantity and quality; merging and non-dominantly sorting the initial population and the child population, calculating the crowding degree of individuals in the same non-dominant layer, and selecting appropriate individuals to form a new population; crossing and mutating the new population to obtain a new child population, and repeatedly iterating the above selection, crossing and mutation steps, so that the generated population individuals continuously approach the Pareto optimal solution, until the deviation between the output water quantity and quality and the target water quantity and quality is within the preset threshold range. ​ 3. The watershed-scale water quantity and quality automatic regulation method according to claim 1, characterized in that, the step of verifying the model parameters of the watershed SWAT model to obtain the verified watershed SWAT model comprises: selecting a set of model parameters from the preset model parameters and model parameter ranges of the watershed SWAT model by using Latin hypercube sampling; running the watershed SWAT model according to the set of model parameters to obtain water quantity and quality results output by the watershed SWAT model; calculating an objective function according to the output water quantity and quality results and the measured water quantity and quality results; wherein the objective function includes a Nash efficiency coefficient, a certainty coefficient and a deviation percentage; verifying the model parameters of the watershed SWAT model according to the objective function and a non-dominated sorting genetic method with an elitist strategy to obtain the verified watershed SWAT model.

4. The method according to claim 1, wherein, The best management measures include setting plant buffer zones, plant waterways and artificial wetlands.

5. A device for automatically regulating water quantity and quality at a river basin scale, characterized in that, comprise: a data acquisition module configured to acquire topographic elevation data, land use data, soil data, meteorological data and agricultural management data of a target region in a preset period before a target period, and to construct a watershed SWAT model according to the topographic elevation data, the land use data, the soil data, the meteorological data and the agricultural management data; a parameter verification module configured to verify model parameters of the watershed SWAT model to obtain a verified watershed SWAT model; a meteorological data acquisition module configured to acquire meteorological data of the target region in the target period predicted by an atmospheric circulation model, wherein the meteorological data of the target region in the target period includes daily rainfall, air temperature, solar radiation, wind speed and relative humidity data in the target period; a measure parameter adjustment module configured to acquire target water quantity and quality of the target region in the target period, input the predicted meteorological data into the verified watershed SWAT model, and adjust water quantity and quality regulation measure parameters of the verified watershed SWAT model by using a non-dominated sorting genetic method with an elitist strategy, so that the deviation between the water quantity and quality output by the watershed SWAT model and the target water quantity and quality is within a preset threshold range; a regulation measure acquisition module configured to acquire the adjusted water quantity and quality regulation measure parameters as the water quantity and quality regulation measures of the target region in the target period.

6. The device according to claim 5, wherein The measure parameter adjustment module comprises: a water quantity and quality acquisition unit configured to acquire target water quantity and quality of the target region in the target period, input the predicted meteorological data into the verified watershed SWAT model, and obtain water quantity and quality output by the watershed SWAT model; an initial population acquisition unit configured to acquire an initial population of the target region if the deviation between the output water quantity and quality and the target water quantity and quality is not within the preset threshold range; wherein each individual of the initial population is used to indicate a set of water quantity and quality regulation measure parameters of the best management measures. An adaptation value calculation unit is configured to perform cross and mutation on the initial population to obtain a child population, and calculate an adaptation value of the child population, wherein the adaptation value is a deviation between the output water quantity and quality and a target water quantity and quality. A crowding degree calculation unit is configured to merge and non-dominant sort the initial population and the child population, perform crowding degree calculation on individuals in a same non-dominant layer, and select appropriate individuals to form a new population. A population cross and mutation unit is configured to perform cross and mutation on the new population to obtain a new child population, and repeatedly iterate the selecting, cross and mutation steps until the generated population individuals approach the Pareto optimal solution, and the deviation between the output water quantity and quality and the target water quantity and quality is within a preset threshold range.

7. The watershed-scale water quantity and quality automatic regulation device according to claim 5, characterized in that the parameter verification module comprises: a parameter set selection unit configured to select a group of model parameter sets from preset model parameters and model parameter ranges of a watershed SWAT model by using Latin hypercube sampling; a result obtaining unit configured to run the watershed SWAT model according to the model parameter sets to obtain water quantity and quality results output by the watershed SWAT model; a target function calculation unit configured to calculate a target function according to the output water quantity and quality results and measured water quantity and quality results, wherein the target function comprises a Nash efficiency coefficient, a certainty coefficient and a deviation percentage; a parameter verification unit configured to verify model parameters of the watershed SWAT model according to the target function and a non-dominant sorting genetic method with an elite strategy to obtain a verified watershed SWAT model. The computer program is executed by the processor to implement the watershed-scale water quantity and quality automatic regulation method according to any one of claims 1 to 4.

8. An electronic device, comprising: The computer program is executed by the processor to implement the watershed-scale water quantity and quality automatic regulation method according to any one of claims 1 to 4. ​ 9. A computer readable storage medium having stored thereon a computer program, characterized in that, ​

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