Real-time simulation method, device and equipment for agricultural non-point source pollution of session rainfall event

Abstract

The invention relates to the technical field of agricultural non-point source pollution load evaluation, and provides an agricultural non-point source pollution real-time simulation method, device and equipment for an event rainfall. The method comprises the following steps: acquiring preset basic data of a target area, corresponding forecast rainfall of each hydrological response unit under a time scale, and initial conditions of each hydrological response unit before a rainfall event occurs; according to the forecast rainfall capacity, preset basic data and initial conditions of the model, prediction is carried out in combination with an agricultural non-point source pollution model of the rainfall event, and the flow and nitrogen and phosphorus load simulation values at the hydrological monitoring node at the current time point are obtained; and real-time correction simulation of the session event process is carried out based on an ensemble Kalman filtering algorithm and real-time monitored and transmitted flow and nitrogen and phosphorus load observation values. According to the method, the precision of the initial condition of the agricultural non-point source pollution model of the session rainfall event can be effectively improved, and real-time simulation of the process of the agricultural non-point source pollution model of the session rainfall event is realized.

Description

Technical Field

[0001] This invention relates to the field of agricultural non-point source pollution load assessment technology, and in particular to a method, apparatus and equipment for real-time simulation of agricultural non-point source pollution during rainfall events. Background Technology

[0002] Agricultural non-point source pollution caused by rainfall events is an important concept in environmental science and agricultural management. It refers to events triggered by specific, short-term meteorological or hydrological events (such as rainfall) that cause pollutants generated during agricultural activities (such as fertilizers, pesticides, livestock manure, and soil particles) to enter water bodies in large quantities and in a concentrated manner within a short period of time through surface runoff, interflow, or groundwater leaching, resulting in significant deterioration of water quality. Unlike persistent, slowly released non-point source pollution, it is characterized by its suddenness, concentration, and high intensity, posing a significant threat to the water environment (especially lakes, reservoirs, and rivers).

[0003] Heavy rainfall events are the most direct driver of agricultural non-point source pollution. The randomness of rainfall events makes it difficult to accurately predict their occurrence and evolution, leading to short-term, pulsed surges in agricultural non-point source pollution output during rainfall events, making them extremely difficult to predict and control. Therefore, developing simulation models and real-time simulation methods suitable for non-point source pollution during rainfall events is of great significance for the forecasting, early warning, and precise control of agricultural non-point source pollution.

[0004] Existing models for agricultural non-point source pollution from rainfall events are mostly long-term assessment models, primarily simulating the long-term trend characteristics of agricultural non-point source pollution, with resolutions mainly at large scales (such as monthly and daily). The key initial parameters of these models are often manually defined or use default model values, directly impacting their simulation accuracy. Furthermore, existing models predict rainfall events before they occur and fail to interact with real-time monitoring of water quantity and quality data, thus hindering real-time simulation of the agricultural non-point source pollution process during these events. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a method, apparatus, and equipment for real-time simulation of agricultural non-point source pollution during a rainfall event.

[0006] This invention provides a method for real-time simulation of agricultural non-point source pollution from a single rainfall event, comprising: Obtain the preset basic data of the target area; Obtain the initial model conditions for each hydrological response unit in the target area before the rainfall event occurs; Obtain the forecasted rainfall for each hydrological response unit in the target area at a preset time step; Based on the forecasted rainfall, preset basic data, and initial model conditions, and combined with the agricultural non-point source pollution model for each rainfall event, simulation predictions are performed to obtain the simulated flow and nitrogen and phosphorus load values ​​at the hydrological monitoring nodes at the current time point.

[0007] According to the present invention, a real-time simulation method for agricultural non-point source pollution during a rainfall event is provided, the method further comprising: Obtain the real-time flow and real-time nitrogen and phosphorus concentrations at the hydrological monitoring nodes at the current time point; The actual nitrogen and phosphorus load at the current time point is obtained based on the real-time flow rate and real-time nitrogen and phosphorus concentration. Based on the simulated flow rate and nitrogen and phosphorus load values, as well as the real-time flow rate and actual nitrogen and phosphorus load, determine the flow rate error and nitrogen and phosphorus load error at the current time point.

[0008] According to the present invention, a real-time simulation method for agricultural non-point source pollution during a rainfall event is provided, the method further comprising: Obtain a preset number of simulated flow rate and simulated nitrogen and phosphorus load values ​​at the current time point, as well as real-time measured flow rate and nitrogen and phosphorus load values; Based on a preset number of simulated flow rates and simulated nitrogen and phosphorus load values ​​at the current time point, as well as real-time measured flow rates and nitrogen and phosphorus load values, an ensemble Kalman filter algorithm is used to determine the corrected simulated flow rates and simulated nitrogen and phosphorus load values ​​at the next time point.

[0009] According to the present invention, a real-time simulation method for agricultural non-point source pollution during a rainfall event is provided. The initial model conditions include soil moisture content, soil nutrient content, and crop leaf area index. Correspondingly, the initial model conditions for each hydrological response unit in the target area before the rainfall event are obtained, including: Obtain the soil moisture content of each hydrological response unit in the target area before the rainfall event; Obtain the soil nitrogen and phosphorus nutrient content of each hydrological response unit in the target area before the rainfall event; Obtain the leaf area index of each farmland hydrological response unit in the target area before the rainfall event.

[0010] According to the present invention, a real-time simulation method for agricultural non-point source pollution during a rainfall event includes obtaining the predicted rainfall for each hydrological response unit in the target area at a preset time step, comprising: Acquire rainfall forecast grid data for the target area at a preset time step; The rainfall forecast grid data is spatially linked with the hydrological response unit data to integrate it into an irregular hydrological response unit scale of forecast rainfall.

[0011] According to the present invention, a real-time simulation method for agricultural non-point source pollution during a rainfall event is provided. Based on the predicted rainfall, preset basic data, and initial model conditions, and combined with an agricultural non-point source pollution model for the rainfall event, predictions are made to obtain simulated flow values ​​and simulated nitrogen and phosphorus load values ​​at the hydrological monitoring node at the current time point, including: The intensity of rainfall events within the target area at a preset time step is determined based on the predicted rainfall. The rainfall parameters are determined based on the intensity of the rainfall event. Based on rainfall parameters, preset basic data, and initial model conditions, and combined with the agricultural non-point source pollution model of each rainfall event, the simulated values ​​of flow and nitrogen and phosphorus load at the hydrological monitoring nodes are obtained.

[0012] According to the present invention, a real-time simulation method for agricultural non-point source pollution during a rainfall event includes obtaining the real-time flow and real-time nitrogen and phosphorus concentrations at the hydrological monitoring node at the current time point, comprising: Obtain the preset nitrogen and phosphorus concentration-conventional water quality prediction model and the river cross-section water level-discharge relationship; Acquire dynamic water level data at hydrological monitoring nodes; Obtain routine water quality parameters at hydrological monitoring nodes; Based on the dynamic water level data and the river cross-section water level-discharge relationship, determine the real-time flow at the hydrological monitoring node at the current time point; Based on the conventional water quality parameters and the nitrogen and phosphorus concentration-conventional water quality prediction model, the real-time nitrogen and phosphorus concentrations at the hydrological monitoring nodes at the current time point are determined.

[0013] The present invention also provides a real-time simulation device for agricultural non-point source pollution during a rainfall event, comprising: The first acquisition module is used to acquire preset basic data of the target area; The second acquisition module is used to acquire the initial model conditions of each hydrological response unit in the target area before the occurrence of a precipitation event. The third acquisition module is used to acquire the forecast rainfall corresponding to each hydrological response unit in the target area at a preset time step. The data processing module is used to simulate and predict the flow rate and nitrogen and phosphorus load at the hydrological monitoring node at the current time point based on the forecast rainfall, preset basic data and initial model conditions, combined with the agricultural non-point source pollution model of the rainfall event.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the real-time simulation method for agricultural non-point source pollution of any of the above-described rainfall events.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a real-time simulation method for agricultural non-point source pollution of any of the above-described rainfall events.

[0016] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements a real-time simulation method for agricultural non-point source pollution of any of the above-described rainfall events.

[0017] This invention provides a method, apparatus, and equipment for real-time simulation of agricultural non-point source pollution during a rainfall event. Based on the forecast rainfall, preset basic data, and initial model conditions, the method combines the agricultural non-point source pollution model of the rainfall event to perform simulation and prediction, obtaining the simulated values ​​of flow and nitrogen and phosphorus load at the hydrological monitoring node at the current time point. Based on the ensemble Kalman filter algorithm and the real-time observation values ​​of flow and nitrogen and phosphorus load, the method performs real-time correction of the simulated values ​​at each time step of the event process, thereby realizing the real-time simulation of the agricultural non-point source pollution process during a rainfall event. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the real-time simulation method for agricultural non-point source pollution during rainfall events provided by this invention.

[0020] Figure 2 This is an overall architecture diagram of the real-time simulation method for agricultural non-point source pollution during rainfall events provided by this invention.

[0021] Figure 3 This is a schematic diagram of the structure of the real-time simulation device for agricultural non-point source pollution during rainfall events provided by the present invention.

[0022] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] The following is combined with Figures 1-4 This invention describes a method, apparatus, equipment, and storage medium for real-time simulation of agricultural non-point source pollution during rainfall events.

[0025] Figure 1 This invention provides a flowchart illustrating a real-time simulation method for agricultural non-point source pollution during a rainfall event. (See attached diagram.) Figure 1 The method includes the following steps: Step 11: Obtain the preset basic data of the target area.

[0026] Step 12: Obtain the initial model conditions for each hydrological response unit in the target area before the precipitation event.

[0027] Step 13: Obtain the forecast rainfall corresponding to each hydrological response unit in the target area at a preset time step.

[0028] Step 14: Based on the forecast rainfall, preset basic data and initial model conditions, and combined with the agricultural non-point source pollution model of the rainfall event, conduct simulation and prediction to obtain the simulated flow value and nitrogen and phosphorus load value at the hydrological monitoring node at the current time point.

[0029] Regarding steps 11-14, it should be noted that agricultural non-point source pollution from rainfall events is an important concept in environmental science and agricultural management. It refers to events triggered by specific, short-term meteorological or hydrological events (such as rainfall), causing pollutants generated during agricultural activities (such as fertilizers, pesticides, livestock manure, and soil particles) to enter water bodies in large quantities and in a concentrated manner within a short period through surface runoff, interflow, or groundwater leaching, resulting in significant water quality deterioration. Unlike persistent, slowly released non-point source pollution, it is characterized by its suddenness, concentration, and high intensity, posing a significant threat to the water environment (especially lakes, reservoirs, and rivers).

[0030] Heavy rainfall events are the most direct driver of agricultural non-point source pollution. The randomness of rainfall events makes it difficult to accurately predict their occurrence and evolution, leading to short-term, pulsed surges in agricultural non-point source pollution output during rainfall events, making them extremely difficult to predict and control. Therefore, developing simulation models and real-time simulation methods suitable for non-point source pollution during rainfall events is of great significance for the forecasting, early warning, and precise control of agricultural non-point source pollution.

[0031] Existing models for agricultural non-point source pollution from rainfall events are mostly long-term assessment models, primarily simulating the long-term trend characteristics of agricultural non-point source pollution, with resolution mainly at large scales (such as monthly and daily). The key initial conditions of these models are often manually defined or based on default model values, directly impacting their simulation accuracy. Furthermore, existing models predict rainfall events before they occur and fail to interact with real-time monitoring of water quantity and quality data, thus hindering real-time simulation of the agricultural non-point source pollution process during these events.

[0032] To this end, the present invention provides a real-time simulation method for agricultural non-point source pollution during rainfall events. Based on the Kalman filter algorithm, it realizes the interaction between the model simulation value and the real-time observation value, and provides accuracy in simulating and predicting the flow rate and nitrogen and phosphorus load of rainfall events.

[0033] The agricultural non-point source pollution model for rainfall events is obtained through traditional hydrological model calibration—using historical, synchronous measured flow-water quality data to calibrate, validate, and perform uncertainty analysis on the model parameters. Therefore, this invention requires dynamic adjustment of the model parameters through multi-source data fusion. Thus, this invention necessitates the acquisition of multi-source data.

[0034] In this invention, preset basic data for the target area (the area requiring simulation and prediction) is acquired. Acquiring this preset basic data is equivalent to establishing a database. This preset basic data includes the watershed boundaries, surface spatial characteristics, meteorological driving data, agricultural management characteristics, and hydrological and water quality characteristics for each area.

[0035] Specifically: 1.1 Vector Boundary of the Region: Based on high-resolution satellite imagery, and combined with the D8 algorithm (a single-flow-direction model in hydrological analysis) and manual visual interpretation techniques, the watershed boundary of the region is obtained, including key watershed features such as ridgeline connections, major road networks (railways, expressways, national roads and rural roads, etc.), and river embankments, to determine the regional boundary, river network and outlet location, etc.

[0036] 1.2 Surface Spatial Characteristics: Based on the vector boundary of the region, complete the cropping of major basic spatial data such as DEM (Digital Elevation Model), land use type, and soil type, and obtain parameters such as elevation, slope, land use type, soil type, soil hydrophysical properties and chemical properties of the region. Use these parameters to complete the calculation process suitable for obtaining surface spatial characteristics.

[0037] 1.3 Meteorological driving data: Collect historical meteorological data for the region, with rainfall time resolution at the hourly scale, and temperature, wind speed, relative humidity, and solar radiation at the daily scale; establish a meteorological driving data lookuptable file and complete the meteorological data linking.

[0038] 1.4 Agricultural Management Measures: Investigate routine farmland management information, including farming methods, planting systems, irrigation systems, fertilization information, and other agricultural management characteristics, construct an agricultural management measures database, and input the above information into the model through parameterized expression.

[0039] 1.5 Hydrological and Water Quality Monitoring Data: Using a multi-parameter online water quality monitoring platform, high-frequency water quality monitoring data under various events are acquired, including water temperature, dissolved oxygen, pH value, oxidation-reduction potential, conductivity, and turbidity; using the river cross-section water level-flow relationship, dynamic flow data is acquired based on real-time water level data to construct a hydrological and water quality characteristic database.

[0040] In this invention, a Hydrological Response Unit (HRU) is a relatively homogeneous spatial unit divided based on factors such as soil type, land use / cover, and topographic slope, with irregular shapes and sizes. Rainfall is allocated to these irregular HRUs. Therefore, the forecast rainfall corresponding to each hydrological response unit in the target area is obtained at a preset time step. To avoid using a resolution primarily at large scales (such as months or days), the preset time step in this invention is an hourly or minute-based scale.

[0041] It should also be noted that the forecasted rainfall for each hydrological response unit in the target area at a preset time step is obtained as follows: Acquire rainfall forecast grid data for the target area at a preset time step; Rainfall forecast grid data is spatially linked with hydrological response units to integrate them into forecast rainfall at the scale of irregular hydrological response units.

[0042] To address this, this invention acquires kilometer-scale and hourly-scale rainfall forecast gridded data within the region, enabling hourly rolling forecasts for flood season precipitation events, which serve as rainfall-driving data for an agricultural non-point source pollution model for each rainfall event. Next, an area-weighted algorithm is used to integrate multiple regular rainfall forecast gridded data into irregular hydrological response unit (HRU)-scale forecast rainfall amounts, achieving high-resolution input of forecast rainfall data to overcome the problem of existing models' poor ability to capture the spatial heterogeneity of rainfall data.

[0043] In this invention, to avoid using large-scale resolutions (such as months or days), the preset time step is set to hourly or minute-scale. The initial model conditions for each hydrological response unit in the target area before the rainfall event are obtained.

[0044] Further explanation is needed. The initial conditions for the model include soil moisture content, soil nutrient content, and crop leaf area index. Therefore, the specific steps to obtain the initial conditions include: Obtain the soil moisture content of each hydrological response unit in the target area before the occurrence of the rainfall event; Obtain the soil nutrient content of each hydrological response unit in the target area before the rainfall event; Obtain the leaf area index of each farmland hydrological response unit in the target area before the rainfall event.

[0045] A1. Real-time update of soil moisture content: Using high-resolution satellite imagery data as the data source, combined with measured soil moisture data at key ground points, deep learning technology is used to obtain the spatial distribution data of soil moisture in the target area before the rainfall event; using an area-weighted algorithm, soil moisture content data at the HRU scale is obtained, thus determining the initial conditions of soil moisture before the rainfall event.

[0046] A2. Dynamic Renewal of Soil Nutrients: The underlying surface refers to the physical layer of the Earth's surface that allows for direct exchange of energy (heat, radiation) and matter (water vapor, gases, particulate matter, etc.) with the atmosphere. It is a key interface for the interaction of the climate system, hydrological cycle, and ecosystem.

[0047] By utilizing rapid soil measurement data from the underlying surface (including crop type and soil type), dynamic correction of soil nutrient content can be achieved, including organic nitrogen, nitrate nitrogen, ammonium nitrogen, organic phosphorus, and inorganic phosphorus.

[0048] A3. Dynamic Updates of Crop Growth: High-resolution EVI data refers to Enhanced Vegetation Index (EVI) datasets with high spatial resolution (typically ≤30 meters). Acquired through remote sensing satellites or UAV platforms, it can finely depict the distribution, growth status, and dynamic changes of surface vegetation, and is one of the core data sources for ecological, agricultural, forestry, and environmental monitoring. Using high-resolution EVI data and deep learning methods, the leaf area index (LAI) of crops at different growth stages in farmland units is calculated, and measured LAI data is used to replace model calculations, enabling dynamic adjustments to crop growth.

[0049] In this invention, after the acquisition of the above-mentioned multi-source data, the multi-source data is used as the initial conditions for the agricultural non-point source pollution model of the rainfall event. The forecast rainfall, the preset basic data and the initial conditions of the model are input into the agricultural non-point source pollution model of the rainfall event to realize the simulation and prediction of the hydrological monitoring node, and obtain the simulated flow value and simulated nitrogen and phosphorus load value of the hydrological monitoring node at the current time point.

[0050] The present invention provides a real-time simulation method for agricultural non-point source pollution during a rainfall event. This method obtains the real-time corrected forecast rainfall, preset basic data, and initial model conditions for the target area. Based on the forecast rainfall, preset basic data, and initial conditions, and combined with the agricultural non-point source pollution model of the rainfall event, it performs simulation and prediction to obtain the simulated flow and nitrogen and phosphorus load values ​​at the hydrological monitoring node at the current time point. Based on the ensemble Kalman filter algorithm and the real-time observation values ​​of flow and nitrogen and phosphorus load, the simulated values ​​at each time step of the event process are corrected in real time, thereby realizing the real-time simulation of the agricultural non-point source pollution process during a rainfall event.

[0051] In a further step of the above method, in order to achieve real-time correction of the model simulation values ​​at each time step, it is necessary to obtain the real-time observed values ​​of flow rate and nitrogen and phosphorus load at the current time point. In this invention, the real-time flow rate and real-time nitrogen and phosphorus concentration at the hydrological monitoring node at the current time point are obtained, and then the observed value of nitrogen and phosphorus load at the current time point is obtained based on the real-time flow rate and real-time nitrogen and phosphorus concentration.

[0052] Furthermore, it obtains the real-time flow and real-time nitrogen and phosphorus concentrations at the hydrological monitoring nodes at the current point in time, including: Obtain the preset nitrogen and phosphorus concentration-conventional water quality prediction model and the river cross-section water level-discharge relationship; Acquire dynamic water level data at hydrological monitoring nodes; Obtain routine water quality parameters at hydrological monitoring nodes; Based on dynamic water level data and the water level-discharge relationship at river cross-sections, the real-time flow at the hydrological monitoring node at the current time point is determined; Based on conventional water quality parameters and a conventional water quality prediction model, the real-time nitrogen and phosphorus concentrations at the hydrological monitoring nodes at the current time point are determined.

[0053] It should be noted that, using historical synchronous hydrological and water quality data, conventional water quality parameters (such as water temperature, dissolved oxygen, pH, redox potential, conductivity, and turbidity) are used as independent variables, and nitrogen and phosphorus concentrations (nitrate nitrogen, ammonia nitrogen, organic nitrogen, total nitrogen, total phosphorus, and dissolved phosphorus) are used as dependent variables. A data-driven nitrogen and phosphorus concentration-conventional water quality prediction model is constructed using deep learning algorithms.

[0054] This invention also constructs the river cross-section water level-flow relationship, and obtains the real-time flow at the hydrological monitoring node at the current time point based on dynamic water level data.

[0055] By using a multi-parameter water quality sensor, the conventional water quality parameters at the hydrological monitoring node at the current time point are obtained. Then, by using the nitrogen and phosphorus concentration-conventional water quality prediction model, the real-time nitrogen and phosphorus concentrations at the hydrological monitoring node at the current time point are obtained.

[0056] Based on real-time flow rate and real-time nitrogen and phosphorus concentration, a numerical integration algorithm is used to calculate the actual nitrogen and phosphorus load at the current time point.

[0057] In a further step of the above method, compared to existing methods for non-point source pollution models that lack real-time simulation, this invention can utilize real-time observed values ​​of flow rate and nitrogen and phosphorus load, along with a Kalman filter algorithm, to achieve real-time correction of the simulated values ​​of flow rate and nitrogen and phosphorus load in the event model. Specifically, this invention provides the simulated values ​​of flow rate and nitrogen and phosphorus load at the current time point, as well as the real-time observed values ​​of flow rate and nitrogen and phosphorus load.

[0058] Then, based on a preset number of simulated flow rates and simulated nitrogen and phosphorus load values ​​at the current time point, as well as the real-time measured flow rates and nitrogen and phosphorus load values, an ensemble Kalman filter algorithm is used to determine the corrected simulated flow rates and simulated nitrogen and phosphorus load values ​​at the next time point.

[0059] Specifically, based on a preset number of simulated flow rates and nitrogen and phosphorus load values ​​for the current time point, as well as the actual real-time flow rates and nitrogen and phosphorus load values, an ensemble Kalman filter algorithm is used to determine the corrected flow rates and nitrogen and phosphorus load values ​​for the next time point. Finally, based on the corrected flow rates and nitrogen and phosphorus load values ​​for the next time point, the simulated flow rates and nitrogen and phosphorus load values ​​for the next time point are corrected in real time.

[0060] It should be noted that this invention makes full use of real-time sensor observation data and combines it with ensemble Kalman filtering technology to obtain the model simulation error during the event process in real time. Then, it uses the Kalman gain value of the error at the current time point to dynamically correct the model prediction value at the next time point in real time.

[0061] B1. Initial State Set Construction: An initial state set for flow rate and nitrogen / phosphorus load is generated using Monte Carlo sampling, denoted as... , where N is the number of set members (i.e. the preset number mentioned above), which is usually 30 to 100.

[0062] B2. Calculate the Kalman gain: Calculate the Kalman gain based on the relative importance of the simulated flow rate, simulated nitrogen and phosphorus load, and real-time observed values. The specific formula is as follows:

[0063] in, The covariance between the simulated value and the real-time observed value at the current time point t. The variance of the real-time observations at the current time point t. The variance of the actual value. It is the Kalman gain.

[0064] B3. Update the simulated flow rate for the next time step: Once the ground sensor obtains the real-time observation value at the current time point, the posterior estimate of the model's state variables is updated using Kalman filtering. The specific calculation formula is as follows:

[0065] Where i is the number of states in the ensemble Kalman filter algorithm, i.e., i ; 'a' represents the updated value label (i.e., adjust) of the model simulation result; therefore This represents the simulated update value of the flow rate and nitrogen and phosphorus load state variables at the current time point t corresponding to the i-th set number. The observed values ​​of flow rate and nitrogen and phosphorus load at the current time point t. This is a set of observation operator functions, which are used to map state variables to the observation space.

[0066] A further step in the above method mainly involves explaining the process of obtaining simulated flow and nitrogen and phosphorus load values ​​at the current time point by predicting rainfall, using forecasted rainfall, preset basic data, and initial model conditions, combined with an agricultural non-point source pollution model for a rainfall event. The details are as follows: Determine the intensity of rainfall events within the target area at a preset time step based on the forecasted rainfall amount; Determine rainfall parameters based on the intensity of rainfall events; Based on rainfall parameters, preset basic data, and initial model conditions, simulations and predictions were conducted using an agricultural non-point source pollution model for each rainfall event to obtain simulated values ​​of flow and nitrogen and phosphorus loads at hydrological monitoring nodes.

[0067] It should be noted that, using historical observation data, sensitivity analysis of model parameters was completed according to the intensity level of rainfall events (light rain, moderate rain, heavy rain, rainstorm, torrential rain, and extremely heavy rain), and sensitivity parameters under different rainfall intensities were selected. Computer-aided automatic parameter tuning technology was used to obtain the values ​​of key model parameters under different rainfall intensities, forming a backup library of model parameters for different rainfall scenarios.

[0068] The intensity of rainfall events within the target area at a preset time step is determined based on the forecast rainfall amount, and the model parameters are determined based on the intensity of the rainfall events.

[0069] Finally, based on rainfall parameters, preset basic data and initial conditions, and combined with the agricultural non-point source pollution model of the rainfall event, the simulated values ​​of flow and nitrogen and phosphorus load at the hydrological monitoring nodes are obtained.

[0070] Based on the above description, see [link / reference]. Figure 2 The overall architecture of the method of the present invention can be shown.

[0071] The following describes the real-time simulation device for agricultural non-point source pollution during a rainfall event provided by the present invention. The real-time simulation device for agricultural non-point source pollution during a rainfall event described below can be referred to in correspondence with the real-time simulation method for agricultural non-point source pollution during a rainfall event described above.

[0072] Figure 3 This diagram illustrates the structure of a real-time simulation device for agricultural non-point source pollution during a rainfall event, provided by the present invention. (See attached diagram.) Figure 3 The device includes a first acquisition module 31, a second acquisition module 32, a third acquisition module 33, and a data processing module 34, wherein: The first acquisition module is used to acquire preset basic data of the target area; The second acquisition module is used to acquire the initial model conditions of each hydrological response unit in the target area before the occurrence of a precipitation event. The third acquisition module is used to acquire the forecast rainfall corresponding to each hydrological response unit in the target area at a preset time step. The data processing module is used to simulate and predict the flow rate and nitrogen and phosphorus load at the hydrological monitoring node at the current time point based on the forecast rainfall, preset basic data and initial model conditions, combined with the agricultural non-point source pollution model of the rainfall event.

[0073] Since the apparatus of this invention is based on the same principle as the corresponding method described above, more detailed explanations will not be repeated here.

[0074] It should be noted that, in the embodiments of the present invention, the relevant functional modules can be implemented by a hardware processor.

[0075] The real-time simulation device for agricultural non-point source pollution of a single event provided by this invention obtains the real-time corrected forecast rainfall, preset basic data and initial model conditions for the target area, and makes predictions based on the forecast rainfall, preset basic data and initial model conditions, combined with the agricultural non-point source pollution model of the single rainfall event, to obtain the simulated flow value and simulated nitrogen and phosphorus load value at the hydrological monitoring node at the current time point. Based on the real-time measured values ​​of flow and nitrogen and phosphorus load at the current time point and the ensemble Kalman filter algorithm, the corrected simulated flow value and simulated nitrogen and phosphorus load value at the next time point are determined.

[0076] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4As shown, the electronic device may include: a processor 41, a communication interface 42, a memory 43, and a communication bus 44. The processor 41, communication interface 42, and memory 43 communicate with each other via the communication bus 44. The processor 41 can call logical instructions in the memory 43 to execute a real-time simulation method for agricultural non-point source pollution during a specific event. This method includes: acquiring preset basic data for the target area; acquiring the initial model conditions for each hydrological response unit in the target area before the occurrence of a precipitation event; acquiring the predicted rainfall for each hydrological response unit in the target area at a preset time step; and, based on the predicted rainfall, preset basic data, and initial model conditions, combining the agricultural non-point source pollution model for the specific precipitation event to make predictions and obtain simulated values ​​of flow and nitrogen and phosphorus load at the hydrological monitoring node at the current time point.

[0077] Furthermore, the logical instructions in the aforementioned memory 43 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0078] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the real-time simulation method for agricultural non-point source pollution of a given event provided by the above methods. The method includes: acquiring preset basic data of a target area; acquiring the initial model conditions of each hydrological response unit in the target area before the occurrence of a precipitation event; acquiring the forecast rainfall corresponding to each hydrological response unit in the target area at a preset time step; and making predictions based on the forecast rainfall, preset basic data, and initial model conditions, combined with the agricultural non-point source pollution model of the precipitation event, to obtain the simulated values ​​of flow and nitrogen and phosphorus load at the hydrological monitoring node at the current time point.

[0079] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a real-time simulation method for agricultural non-point source pollution of a given event, as provided by the methods described above. The method includes: acquiring preset basic data of a target area; acquiring the initial model conditions of each hydrological response unit in the target area before the occurrence of a precipitation event; acquiring the forecasted rainfall amount corresponding to each hydrological response unit in the target area at a preset time step; and making predictions based on the forecasted rainfall amount, the preset basic data, and the initial model conditions, combined with the agricultural non-point source pollution model of the precipitation event, to obtain the simulated values ​​of flow and nitrogen and phosphorus load at the hydrological monitoring node at the current time point.

[0080] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0081] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for real-time simulation of agricultural non-point source pollution from a single rainfall event, characterized in that, include: Obtain the preset basic data of the target area; Obtain the initial model conditions for each hydrological response unit in the target area before the precipitation event occurs; Obtain the forecasted rainfall for each hydrological response unit in the target area at a preset time step; Based on the forecasted rainfall, preset basic data, and initial model conditions, and combined with the agricultural non-point source pollution model for each rainfall event, the simulated values ​​of flow and nitrogen and phosphorus load at the hydrological monitoring node at the current time point are obtained.

2. The method for real-time simulation of agricultural non-point source pollution from a single rainfall event according to claim 1, characterized in that, The method further includes: Obtain the real-time flow and nitrogen and phosphorus concentrations at the hydrological monitoring nodes at the current point in time; The measured value of nitrogen and phosphorus load at the current time point is obtained based on the real-time flow rate and nitrogen and phosphorus concentration. Based on the simulated flow rate and nitrogen and phosphorus load values, as well as the real-time measured flow rate and nitrogen and phosphorus load values, determine the simulation error of the flow rate and nitrogen and phosphorus load at the current time point.

3. The method for real-time simulation of agricultural non-point source pollution during a rainfall event according to claim 2, characterized in that, The method further includes: Obtain a preset number of simulated flow rate and simulated nitrogen and phosphorus load values ​​at the current time point, as well as real-time measured flow rate and nitrogen and phosphorus load values; Based on a preset number of simulated flow rates and simulated nitrogen and phosphorus load values ​​at the current time point, as well as real-time measured flow rates and nitrogen and phosphorus load values, an ensemble Kalman filter algorithm is used to determine the corrected simulated flow rates and simulated nitrogen and phosphorus load values ​​at the next time point.

4. The method for real-time simulation of agricultural non-point source pollution from a single rainfall event according to claim 1 or 3, characterized in that, The initial conditions of the model include soil moisture content, soil nutrient content, and crop leaf area index. Correspondingly, the initial conditions of the model for each hydrological response unit in the target area before the precipitation event include: Obtain the soil moisture content of each hydrological response unit in the target area before the rainfall event; Obtain the soil nutrient content of each hydrological response unit in the target area before the precipitation event; Obtain the leaf area index of each farmland hydrological response unit in the target area before the precipitation event.

5. The method for real-time simulation of agricultural non-point source pollution from a single rainfall event according to claim 4, characterized in that, The acquisition of the predicted rainfall corresponding to each hydrological response unit in the target area at a preset time step includes: Acquire rainfall forecast grid data for the target area at a preset time step; The rainfall forecast grid data is spatially linked with the hydrological response unit data to integrate it into an irregular hydrological response unit scale of forecast rainfall.

6. The method for real-time simulation of agricultural non-point source pollution from a single rainfall event according to claim 5, characterized in that, Based on the predicted rainfall, preset basic data, and initial model conditions, and combined with the agricultural non-point source pollution model for this rainfall event, the simulated flow and nitrogen and phosphorus load values ​​at the hydrological monitoring nodes at the current time point are obtained, including: The intensity of rainfall events within the target area at a preset time step is determined based on the predicted rainfall. The rainfall parameters are determined based on the intensity of the rainfall event. Based on rainfall parameters, preset basic data, and initial model conditions, and combined with the agricultural non-point source pollution model of the rainfall event, the simulated values ​​of flow and nitrogen and phosphorus load at the hydrological monitoring nodes are obtained.

7. The method for real-time simulation of agricultural non-point source pollution from a single rainfall event according to claim 2, characterized in that, The acquisition of real-time flow and nitrogen and phosphorus concentrations at the hydrological monitoring node at the current time point includes: Obtain the preset nitrogen and phosphorus concentration-conventional water quality prediction model and the river cross-section water level-discharge relationship; Acquire dynamic water level data at hydrological monitoring nodes; Obtain routine water quality parameters at hydrological monitoring nodes; Based on the dynamic water level data and the river cross-section water level-discharge relationship, determine the real-time flow at the hydrological monitoring node at the current time point; Based on the conventional water quality parameters and the nitrogen and phosphorus concentration-conventional water quality prediction model, the real-time nitrogen and phosphorus concentrations at the hydrological monitoring nodes at the current time point are determined.

8. A real-time simulation device for agricultural non-point source pollution during a rainfall event, characterized in that, include: The first acquisition module is used to acquire preset basic data of the target area; The second acquisition module is used to acquire the initial model conditions of each hydrological response unit in the target area before the occurrence of a precipitation event. The third acquisition module is used to acquire the forecast rainfall corresponding to each hydrological response unit in the target area at a preset time step. The data processing module is used to simulate and predict the flow rate and nitrogen and phosphorus load at the hydrological monitoring node at the current time point based on the forecast rainfall, preset basic data and initial model conditions, combined with the agricultural non-point source pollution model of the rainfall event.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the real-time simulation method for agricultural non-point source pollution of a rainfall event as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the real-time simulation method for agricultural non-point source pollution of a rainfall event as described in any one of claims 1-7.

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