175 results about "Point source pollution" patented technology

The invention discloses an intelligent monitoring and early warning system and method for agricultural non-point source pollution, and relates to the technical field of environmental monitoring, accurate prediction of water pollutant concentration is realized through multi-source heterogeneous data acquisition and fusion, a dynamic attention mechanism and a PINN-Transformer coupling model, the system extracts data spatial and temporal characteristics by using an optimized Transformer model, and the method is applied to the intelligent monitoring and early warning of agricultural non-point source pollution. A water pollution diffusion physical constraint is embedded, it is ensured that a prediction result conforms to an actual hydrodynamic law, and based on high-precision spatial-temporal distribution data, an intelligent algorithm is adopted to track a pollution diffusion path and rapidly lock a pollution source; meanwhile, the cellular automaton model simulates pollution risk dynamic diffusion and assists regional risk assessment, the system also combines a block chain technology to carry out credible evidence storage on key monitoring data, and real-time data processing and early warning pushing are realized through a cloud edge collaborative architecture. And an efficient and reliable technical solution is provided for agricultural water environment management and pollution prevention and control.

The invention provides a plateau lake agricultural non-point source pollution treatment method which comprises the following steps: acquiring vegetation indexes and surface temperature field data by using a remote sensing satellite, and generating a pollution source space thermodynamic diagram in combination with water quality and soil parameters of ground sampling points; performing space-time alignment and data fusion on the thermodynamic diagram and real-time runoff and soil permeability data acquired by the hydrological sensor network, and constructing a structured pollution migration database; on the basis of the database, a hybrid neural network model embedded with physical constraints is utilized to predict pollutant concentration distribution within 72 hours in the future; inputting the predicted value into a multi-stage optimization controller, and generating a control parameter set comprising treatment intensity, engineering parameters and a fertilization ratio; generating a treatment strategy map covering the drainage basin through a GIS; and deploying an Internet of Things monitoring node to collect the treated water quality data to form a closed-loop control link. The treatment efficiency and effect can be improved, the treatment cost is reduced, and the negative influence on the ecological environment is reduced.

The embodiment of the invention relates to the technical field of pollution traceability, and discloses a surface water multi-point source pollution traceability method, which comprises the following steps: preprocessing water quality time sequence data and environmental parameter information; performing feature extraction on the preprocessed water quality time sequence data to obtain water quality key features; the water quality key features are input into a pre-constructed deep learning model to be recognized so as to obtain a corresponding pollution recognition result, the deep learning model comprises a time sequence processing module, and the time sequence processing module is used for processing water quality time sequence features in the water quality key features; and outputting a corresponding pollution identification result, wherein the pollution identification result comprises pollution type information, pollution source position information and confidence degree information. According to the method, deep learning and classified calibration are performed on historical pollution source data through a deep learning algorithm, a pollutant label library and a knowledge library are formed, and support is provided for rapid identification of new pollution events.

The invention discloses a non-point source pollution and waterlogging collaborative four-pre management and control system based on short temporary rainfall forecast, which relates to the technical field of urban water environment management and comprises a short temporary rainfall forecast module, a non-point source pollution control module, a waterlogging management module, a four-pre management and control platform, an intelligent decision support system, a real-time monitoring and feedback system and a water ecological management module. By integrating meteorological monitoring, hydrological monitoring and environment monitoring data, high-precision short and temporary rainfall forecast is realized by using a machine learning algorithm, the operation strategies of the intelligent catch basin and the initial rain storage pond are dynamically adjusted to control non-point source pollution, and meanwhile, the waterlogging risk is predicted and preventive measures are taken in combination with real-time hydrological data. According to the technology, integrated management of forecasting, early warning, rehearsal and plans is achieved through a four-pre-management and control platform, an intelligent decision support system, a real-time monitoring and feedback system and a water ecological management module under the sponge city design concept are used in an auxiliary mode, and the efficiency and effect of urban water environment management are comprehensively improved.

The invention discloses an urban area runoff pollutant transportation process simulation forecasting method, which is characterized in that a building area runoff production calculation method under the influence of a wind field is provided, and the building area runoff production calculation method is coupled with an SWMM rainfall flood model; secondly, constructing a two-dimensional hydrodynamic model based on an FVCOM and a Lagrange particle model, coupling the two-dimensional hydrodynamic model with the SWMM rainfall flood model, and driving the two-dimensional hydrodynamic model by taking the overflow flow and the pollutant concentration simulated by the SWMM rainfall flood model as boundary conditions; simulating a transportation process of pollutants along with evolution of surface ponding by establishing a'concentration-quantity 'conversion relation between the pollutants and particles at grid units and nodes, and obtaining a time sequence pollutant concentration data set; and training and establishing a pollutant concentration prediction model based on the obtained data, and then predicting the pollutant concentration of each prediction point after specific time in an actual rainfall event. According to the method, the runoff pollutant concentration can be predicted, and the urban non-point source pollution risk under the rainstorm weather is reduced.

The invention discloses a non-point source pollution emergency bypass decision control method, particularly relates to the technical field of automation and process control, and aims to solve the problems that existing scheduling cannot align multi-source measurement and evaluate credibility in minutes under a unified time base, and is lack of risk measurement integrated with a tide level phase and a moisture regain path and same-layer gating. The bypass rhythm is easy to misjudge, and the oscillation is difficult to audit. A minute-level risk density is constructed through multi-source alignment and credibility grading under a unified time reference, injected tide level phases and moisture regaining paths, bypass rhythms are constrained by same-layer gating and tide windows, and beam search, boundary arbitration and fixed window closed-loop correction are matched, so that the risk density is improved. Therefore, under the superposition of rainstorm and tide, decision making and execution are stably limited in a non-backflow red line, bearing and equipment limit, misjudgment and oscillation are remarkably reduced, the problems of unavailability and difficult auditing caused by multi-source asynchronization and moisture regain coupling are solved, intelligent decision support is provided for non-point source pollution treatment, and it is ensured that the treatment technology is accurate and efficient.

The invention discloses a watershed non-point source pollution load dynamic measurement and calculation method and system based on a distributed process model, and the method comprises the steps: firstly collecting the meteorological, hydrological, water quality, soil and terrain, land utilization, vegetation coverage, atmospheric settlement and surface pollution source data of a target watershed, and constructing a basic database of the target watershed; performing integration processing on different types of data in the basic database of the target drainage basin by adopting geospatial interpolation, spatial resampling and frequency matching; according to the invention, a function of coupling a runoff-sediment-nitrogen and phosphorus physical process on the basis of a distributed hydrological model and combining social economic development driving factors, land utilization change and land pollution source dynamic change to carry out space-time quantitative measurement and calculation on the drainage basin non-point source pollution load under a change scene is realized; the method not only can better capture and predict the dynamic change of the input data, but also avoids the separation of social economic development driving factors, land utilization change and land pollution source dynamic change, and is suitable for wide popularization and use.

The invention relates to the technical field of drainage basin water environment pollution traceability, and discloses a drainage basin non-point source pollution accurate traceability method based on deep learning, which comprises the following steps: constructing a generation and verification dual-network collaborative framework comprising a pollution source hypothesis generator and a hypothesis verifier, and establishing a collaborative optimization mechanism of the pollution source hypothesis generator and the hypothesis verifier; the generation and probability evaluation of the pollution source hypothesis are realized, and an evidence link tracker is constructed to analyze the relationship between the pollution source candidate hypothesis and the support evidence; further analyzing the relationship between the pollution source candidate hypothesis and the support evidence; the analysis result of the relation between the pollution source candidate hypothesis and the support evidence is integrated, and the calculation efficiency and the system performance are optimized; according to the method, probability evaluation is provided for each pollution source hypothesis through a Bayesian posterior inference framework, the uncertainty of a traceability result is quantified, and a reliable reliability index is provided for decision making.

The invention discloses a multi-drive process multi-factor agricultural non-point source pollution prediction method based on coupling meteorological numerical forecasting. The method comprises the following steps: firstly, introducing numerical weather forecast data, and constructing a high-precision weather driving field with kilometer-level space and hour-level time resolution by combining WRF dynamic downscaling, DEM terrain correction and conservation resampling; secondly, establishing a multi-drive process coupling model system which comprises a meteorological drive layer, a hydrological response layer, a pollutant migration layer and a crop feedback layer and is used for simulating runoff production, sediment erosion, nitrogen and phosphorus migration and transformation and crop transpiration and root nutrient absorption processes; thirdly, performing precision verification on a simulation result by using observation data, and identifying key meteorological and hydrological factors through an error transfer matrix and a sensitivity analysis method; and finally, realizing parameter adaptive correction by adopting a long short-term memory network, finishing parameter optimization in combination with a multi-target genetic algorithm, packaging the model chain through a containerization technology, and realizing cross-platform deployment and visual output of a pollution load result. The method can be used for agricultural non-point source pollution prediction and management.

The invention provides a river flood season pollution intensity prediction and early warning method and system, and relates to the technical field of hydraulic engineering, and the method comprises the steps: obtaining multi-source data in a drainage basin, and carrying out the preprocessing of the multi-source data; constructing a hydrological water quality model for calculating the hydrological cycle process of the whole drainage basin from the upstream to the downstream; calculating point source and non-point source pollution loads to obtain water quality concentration; constructing a prediction data set according to the water quality concentration, and training the XGBoost machine learning model by using the prediction data set; and utilizing the trained XGBoost machine learning model to predict the pollutant concentration in the flood season under the influence of different meteorological conditions and carry out standard exceeding concentration early warning. The method can predict a short-term river water quality change peak value in advance by fusing weather forecast, a hydrological model and real-time monitoring data, realizes early warning of water quality change in advance, and is finally integrated to a drainage basin management decision-making platform, so that advanced regulation and control of a management department and advanced prevention and control of pollution are realized.

The invention discloses a field-ditch-pond extraction method based on unmanned aerial vehicle photogrammetry and deep learning, and belongs to the technical field of remote sensing technology and agricultural informationization, and the method comprises the steps: employing an unmanned aerial vehicle to carry a multispectral camera and an optical lens, and obtaining original image data through an oblique photogrammetry technology; preprocessing the obtained original image data to generate a multispectral image map (DOM) and a digital surface model (DSM); starting from the spectrum, terrain and texture features of a small watershed field-ditch-pond system, a U-net algorithm with a built-in attention mechanism, an image segmentation model supporting zero sample learning, a multi-scale segmentation technology and a multiple extraction framework of a shape feature extraction algorithm are constructed, image segmentation, classification and interpretation are carried out, and a multi-scale image segmentation model is constructed. Fine extraction of the small watershed field-ditch-pond system is realized; the design can improve the extraction precision and efficiency under complex terrains, and is suitable for current small watershed agricultural non-point source pollution migration trajectory simulation and farmland fine management.

The invention relates to the technical field of water environment treatment, in particular to a water quality and water quantity joint scheduling method based on a distributed hydrological model. The method comprises the steps of arranging high-precision monitoring equipment to collect multi-source data, constructing a basic data set, analyzing spatial and temporal distribution characteristics and dynamic change rules of pollutants, designing a multilayer neural network model for training optimization, and generating a water quality prediction result and a scheduling strategy. The invention solves or at least alleviates the capability limitation problem of a non-point source pollution model in the aspect of describing a pollutant migration path and mechanism, and provides a water quality and water quantity joint scheduling method based on a distributed hydrological model. According to the method, through fusion of multi-source data and a dynamic optimization regulation strategy, accurate prediction and efficient treatment of pollutants under complex hydrological conditions are realized.

The invention relates to the technical field of non-point source pollution decision, and discloses a non-point source pollution simulation method and a non-point source pollution intelligent interaction method based on an agent, and the simulation method comprises the steps: extracting land and river attribute data of a target region, and constructing a nitrogen emission coefficient database; associating the land attribute type with a nitrogen emission coefficient library, calculating the plot emission amount through a reference area correction model nitrogen emission coefficient, converting the plot emission amount into emission amount grid data, and generating a nitrogen emission distribution map; converting the emission raster data into point data, calculating each pollution point and a river reach influence area, and constructing a river directed graph network; calculating a river reach absorption coefficient by adopting an exponential decay model, and calculating a pollution load transmission quantity and a river reach absorption quantity step by step along a river digraph network topology through breadth-first search by combining the river reach absorption coefficient based on the digraph network; the total consumption amount of the influence river reach of each pollution point is accumulated, the net discharge load distribution diagram and the consumption amount distribution diagram are generated, the calculation process is high in real-time performance, and the method is suitable for various application scenes.

The invention discloses a pond multi-objective optimization management method oriented to agricultural watershed non-point source pollution regulation and control, and relates to the technical field of agricultural watershed non-point source pollution abatement. Multi-dimensional attributes such as pond application types, morphological characteristics and sediment nutrient states are integrated into a unified quantitative framework for the first time, and a generalized additive model is constructed to obtain a multi-objective optimization model; nonlinearity and interaction of pond attributes on river water quality influence can be accurately captured, and the accuracy of river nitrogen and phosphorus concentration prediction is improved. According to the method, pond management measures are quantified into controllable decision variables, a multi-target dynamic optimization model for balancing environmental benefits and economic benefits is established, a Pareto optimal solution is used for replacing a traditional experience decision, a scientific, efficient and quantitatively-implemented pond management scheme is provided, the one-sidedness of a single target is avoided, and the method is suitable for large-scale popularization and application. The cost minimization is realized while the non-point source pollution reduction is maximized, and the accurate configuration and scientific quantitative decision of pond management resources are realized.

The invention discloses a key index screening method and system for investigation and monitoring of watershed agricultural non-point source pollution, and relates to the technical field of watershed agricultural non-point source pollution prevention and control. The method comprises the following steps: acquiring multi-source spatio-temporal data of a target area about watershed agricultural non-point source pollution; the multi-source spatio-temporal data comprises water quality monitoring data, geographic spatial data, yearbook statistical data and meteorological data; constructing a feature variable set and a target variable set by using a source-migration-sink theory based on the multi-source spatio-temporal data; inputting the feature variables in the feature variable set and the target variables in the target variable set into a pre-trained machine learning model to obtain a feature importance value of the contribution degree of each feature variable to each target variable; and screening out key indexes in the feature variable set according to the feature importance value. According to the method, a scientific and unified index system can be established, the monitoring cost is remarkably reduced, and the objectivity and comparability of evaluation are improved.

The invention provides a Yellow River water irrigation area non-point source pollution load space-time analysis method, and relates to the technical field of Yellow River water irrigation area non-point source pollution monitoring and analysis, and the method comprises the following steps: S1, collecting and preprocessing multi-source basic data; according to the Yellow River water irrigation area non-point source pollution load space-time analysis method, total nitrogen and ammonia nitrogen are accurately recognized as core pollutants through three-stage fuzzy comprehensive evaluation, the recognition accuracy is improved to 90% or above, and the problem that recognition is fuzzy in a traditional method is solved; by combining nitrogen and oxygen isotopes and a flora active chip, the manure sewage is determined as a main source of nitrogen for the first time, the traceability error is controlled within 10%, and the pollution source analysis blank is filled up; a core pollution area is defined through a gravity center migration model, the pollution load space-time distribution visualization degree is improved by 60%, and area differentiation treatment is achieved; a coupling migration model is constructed, the mechanism of reverse migration of pollutants caused by irrigation return water is clarified for the first time, and the interpretation precision reaches 85%.

The invention relates to an urban non-point source pollution identification method and system based on rainfall events. The method comprises the following steps: step S100, decomposing rainfall data of a continuous time sequence into independent rainfall events and snowfall events; step S200, dividing grid units, and performing runoff calculation in each unit so as to obtain runoff information of spatial distribution; wherein the rainfall event runoff is estimated by adopting a runoff curve hydrological model method; calculating snow melting runoff by adopting a temperature index snow melting model; s300, estimating the average event concentration of the rainfall event by adopting an accumulation and scouring formula, and estimating the average event concentration of the snow melting event by using average observation values in different land utilization types; and step S400, realizing refined identification and accurate estimation of urban non-point source pollution load by integrating urban functional area characteristics, surface runoff simulation and event average concentration simulation of pollutants. The method is suitable for estimating the rainfall runoff pollution load in a large-scale region with relatively limited water quality monitoring data.

The invention relates to a construction method and application of a pollutant non-point source migration prediction model based on a gated circulation unit neural network, and belongs to the urban non-point source pollution prediction technology, and the construction method is characterized by comprising the following steps: S1, collecting historical data of a to-be-detected region, dividing the historical data into a plurality of sub-basin data, and carrying out the standardized integration to form a feature data set; s2, establishing a pollutant non-point source migration prediction model through the feature data set; the pollutant non-point source migration prediction model comprises a hydrological simulation module, a hydraulic simulation module and a pollutant fate module; and S3, training the hydraulic simulation module and the pollutant fate module, and optimizing module parameters.

The invention belongs to the technical field of regional soil pollution evaluation and risk management, and particularly relates to a pollution grading evaluation and point source pollution identification method based on a regional soil environment background. The method comprises the following steps: determining a target area range and soil target pollutants; obtaining a current-stage soil environment background concentration data set of the target area, and calculating a statistical index of the data set; arranging regional soil pollution monitoring points, and monitoring the target pollutant content of the soil sample; and finally, the constructed pollution index evaluation model based on the soil environment background is adopted to calculate the single or combined pollution index of the soil at each point, the regional soil pollution grade and the environment quality are quantitatively evaluated, and the soil pollution point source is further identified. According to the method provided by the invention, based on the soil environment background content, the uncertainty of overestimation of non-point source pollution in a traditional soil pollution evaluation method is optimized, and the point source pollution point location can be accurately positioned.

The application discloses a kind of unmanned aerial vehicle photogrammetry and depth learning's field-gully-pond extraction method, belong to remote sensing technology and agricultural informatization technical field, method includes: using unmanned aerial vehicle carries multispectral camera and optical lens, obtains original image data by tilt photogrammetry technique;The original image data obtained is preprocessed, generates multispectral image (DOM) and digital surface model (DSM);From the spectral, topography and texture features of small watershed field-gully-pond system, construct the multiple extraction framework of U-net algorithm with built-in attention mechanism, image segmentation model supporting zero sample learning, multi-scale segmentation technology and shape feature extraction algorithm, carry out image segmentation, classification and interpretation, realize the fine extraction of small watershed field-gully-pond system;The design can improve the extraction accuracy and efficiency under complex terrain, applicable to current small watershed agricultural non-point source pollution migration trajectory simulation and farmland fine management.

The invention relates to the technical field of pollution risk assessment, and particularly discloses a non-point source pollution risk assessment method based on machine learning, and the method comprises the steps: collecting the water quality monitoring data of a target drainage basin under various meteorological conditions in a historical period, and synchronously collecting the meteorological monitoring data of a corresponding period; the method comprises the steps that meteorological extreme events and water quality response characteristics corresponding to the meteorological extreme events are extracted, a meteorological-water quality associated data set is constructed, a prediction model between meteorological factors and pollution load peak values is trained through a machine learning method, and the short-term risk level of non-point source pollution is evaluated based on a prediction result; and generating a risk early warning signal according to the risk level obtained by evaluation, and through multi-source data fusion and machine learning modeling, providing high-timeliness and high-precision risk decision support for drainage basin management, significantly enhancing the ability to cope with sudden pollution events, and improving the risk early warning effect. And meanwhile, key technical guarantee is provided for optimizing and treating resource allocation and reducing water environment disaster risks.

The invention relates to an agricultural non-point source polluted soil nitrate nitrogen in-situ monitoring system and method, and aims to solve the technical problem that accurate in-situ monitoring of soil leaching nitrate nitrogen and a migration rule is difficult to realize. Wherein the soil leaching solution extraction device comprises moisture sensors buried in different soil depths, a leaching solution extraction probe, a solution storage container and a negative pressure source, and the detection device comprises at least two groups of micro-flow pool channels connected in parallel and a photoelectric detection path corresponding to nitrate nitrogen ultraviolet light absorption and non-absorption wavebands; the liquid pumping pipeline is respectively communicated with the liquid storage container and the micro-flow pool channel; the control module receives information collected by the moisture sensor and determines whether to send a negative pressure source starting instruction or not after comparing the information with a set value, a negative pressure cavity is formed in the leaching solution extraction probe, and soil leaching solution collection is achieved. The in-situ monitoring of the leaching nitrate nitrogen can be realized without disturbing the soil structure; the result is continuous, accurate and reliable, and a reliable way is provided for obtaining accurate data information related to agricultural non-point source pollution distribution, migration, development trend and the like.

The invention relates to the technical field of ridge construction, and discloses an ecological ridge construction method for slowing down lateral seepage and intercepting non-point source pollution, which comprises the following steps of: extracting soil texture, elevation and area information of a target area, and scientifically delimiting a ridge and a farmland boundary by combining a spatial analysis and function partitioning algorithm; dynamically determining the height of the buffer zone according to the height difference relation and the flood and drought stress characteristics of plants; hydraulics characteristics are obtained through hydrological simulation, the width of the buffer zone and the filler amount are calculated based on the length-width ratio and hydrodynamic force coupling mechanism, optimal design of water flow regulation and control and pollution interception is achieved, and the stability and purification efficiency of the ridge system in different environments are ensured. According to the invention, the ecological ridge system with strong adaptability is constructed through cooperative analysis of multi-source terrain environment and ecological parameters.

The invention discloses a non-point source pollution intensity evaluation method based on multi-source data fusion. The non-point source pollution intensity evaluation method comprises the following steps: S1, collecting multi-source environmental data of a target drainage basin through a space-air-ground collaborative network; s2, constructing an environmental resistance-pollution source intensity coupling model, and calculating a pollution migration index and a dynamic load; and S3, calculating a non-point source pollution intensity index, and carrying out non-point source pollution risk grading. Through air-space-ground multi-source data collaboration, the space-based satellite remote sensing system, the air-based unmanned aerial vehicle monitoring system and the foundation internet-of-things sensing system are used for collecting environment data of the target drainage basin, and the environment data are used for non-point source pollution intensity calculation and non-point source pollution risk evaluation of the target drainage basin.

The invention discloses a watershed agricultural non-point source pollution on-line monitoring method and system, and relates to the technical field of agricultural non-point source pollution monitoring, a plurality of monitoring nodes are arranged in a watershed to collect water quality parameters in real time and regularly collect water body samples to obtain a monitoring data set, data preprocessing is completed through two times of frame interpolation processing and maximum and minimum normalization, and the watershed agricultural non-point source pollution on-line monitoring system is obtained. And performing real-time early warning on agricultural non-point source pollution in the drainage basin according to the established drainage basin agricultural non-point source pollution monitoring model and a preset early warning threshold value. According to the invention, the limitations of low manual sampling efficiency, incapability of real-time monitoring and limited remote sensing monitoring precision are broken through, and global dynamic monitoring is realized through multi-node deployment and real-time monitoring; a real-time early warning mechanism can timely find pollution and trigger response, and technical support is provided for efficient treatment of drainage basin agricultural non-point source pollution.

The invention relates to the technical field of agricultural environmental engineering and control, and discloses an agricultural non-point source pollution multistage control planning system and method based on a discussion hall architecture, and the system comprises an organization-level module, an adaptation-level module, a coordination-level module, an operation control-level module, a controlled-level module, a comprehensive integration discussion hall module, and an open external interface module. The organization level uses deep learning and causal inference to generate a full-watershed strategy; the discussion hall module fuses expert experience conversion constraints and carries out case migration; the adaptation level utilizes a recursive algorithm to dynamically correct model parameters; the coordination level adopts a hybrid optimization algorithm to solve a multi-objective optimization problem; the operation control level is combined with a feedforward feedback mechanism to generate an instruction; the controlled level executes actions and collects feedback; and the interface module is responsible for security encryption and verification of data interaction. According to the method, the expert intelligence of the discussion hall and the five-level hierarchical architecture are fused, qualitative and quantitative combination and full-basin dynamic collaborative optimization are realized, and the management and control precision and the response speed are improved.

The invention discloses a simulation basin non-point source pollution priority control area identification method and system, and relates to the technical field of water environment monitoring and pollution abatement, and the method comprises the steps: collecting basin basic data, and carrying out the preprocessing of the basin basic data; key resistance factors are selected, the weight of each factor is determined, a minimum cumulative resistance model is established based on a source-sink landscape theory, a source landscape is set as a pollution starting point, and the cumulative resistance value of pollutants in the process of migrating from the starting point to a receiving water body is calculated through the minimum cumulative resistance model; according to the calculated accumulated resistance value, pollution risk levels in different periods are calculated; and based on the risk level, introducing a governance feasibility factor to determine a priority control region. According to the method, the source-sink landscape theory, the minimum accumulated resistance model and multi-factor dynamic analysis are integrated, so that the priority control area which is low in data dependence, high in precision and strong in dynamic property and meets the treatment requirement is recognized, and a scientific basis is provided for targeted treatment of drainage basin non-point source pollution.

The invention relates to the technical field of environmental protection, in particular to a drainage basin pollution load accounting system and a source-sink response simulation method thereof.The drainage basin pollution load accounting system comprises a drainage basin data module, a non-point source pollution module, a point source emission module and a hydrodynamic simulation module, and the drainage basin data module inputs information and simulates hydraulic and water quality scenes; the non-point source pollution module simulates non-point source pollution amounts of different land utilization types; and the point source emission module calculates the point source pollution emission. The hydrodynamic simulation module performs simulation in combination with drainage basin data and meteorological boundary conditions; the dynamic TMDL allocation module realizes dynamic allocation of the total amount of pollutants based on a source-sink response result, and the accounting precision is improved through coupling of an SWAT model and an EFDC model; according to the system, an administrative village is adopted as a minimum calculation unit, and an APCS-MLR model is combined to realize accurate quantification of non-point source pollution; according to the system, land and water systems are effectively integrated, and the accuracy and efficiency of drainage basin pollution load accounting and management are improved.

The invention discloses a rainstorm runoff flow limiting and overflowing impact resistance device and method, and belongs to the technical field of runoff non-point source pollution abatement. The device comprises a flow dividing well body and further comprises a water inlet, a flow limiting opening and an overflowing opening; by adopting a double-stage shunting mode of initial-stage interception and middle-later-stage overflow and combining the design of horizontal symmetrical distribution of the water inlet and the flow limiting port and vertical distribution of the overflow port and the water inlet, water flow can be guided to form a stable transverse flowing path, so that high-pollution rainwater is intercepted in the initial stage of rainfall by virtue of a gravity flow principle, and high-pollution rainwater is intercepted in the middle-later stage of rainfall. Clean rainwater is rapidly discharged through the flow limiting opening, so that the short-time heavy rainfall condition can be effectively handled, the flow dividing stability in different rainfall stages can be guaranteed, the impact load resistance of the device is greatly improved, the system overload risk caused by sudden flow change is reduced, and it is ensured that the efficient pollution treatment effect is always kept in long-term operation.

The invention provides a low-hill mountainous area non-point source pollution intelligent prevention and control and gradient utilization system, and relates to the technical field of low-hill mountainous area agricultural non-point source pollution prevention and control. The system comprises a drainage ditch, a water quality on-line monitoring module, an intelligent gate, a multi-stage ecological ditch pond, a direct drainage channel, an internet-of-things intelligent management and control platform and a stepped rice field. The system drives the intelligent gate to perform diversion according to a pollution index threshold value based on monitoring data, high-pollution water enters the multi-stage ecological ditch pond to be purified, low-pollution water is discharged through the direct discharge channel, and purified tail water is reused for the cascade rice field. Self-flow conveying is achieved by means of the regional height difference, a collaborative prevention and control mechanism of process interception, tail end purification and resource recycling is constructed, scientific prevention and control of pollution, intelligent management and efficient utilization of resources are achieved, the non-point source pollution treatment effect is remarkably improved, and continuous circulation of water and fertilizer resources is promoted.