1736 results about "River water" patented technology

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention relates to the technical field of digital twinning, in particular to a high-density urban ponding evolution and early warning method based on digital twinning. The method comprises the following steps: real-time ponding depth, real-time river water level, real-time meteorological conditions and real-time drainage pipe network water level conditions corresponding to a city are collected in real time through ponding depth, river water level, meteorological monitoring and drainage pipe network sensors installed in the city, digital twinborn construction is carried out, and an urban inland inundation digital twinborn model is generated; obtaining drainage pipe network distribution and carrying out drainage grid division to generate an urban drainage grid model; acquiring a historical ponding event rule, and performing ponding area prediction analysis and ponding evolution coupling analysis to generate an urban ponding evolution distribution field; performing ponding space-time evolution analysis and ponding early warning processing on the urban ponding evolution distribution field so as to execute corresponding urban ponding emergency response work. According to the invention, accurate ponding evolution prediction and effective early warning can be provided.

The invention relates to a real-time river water level monitoring and early warning method and system based on deep learning. The method comprises the following steps: acquiring a target watershed remote sensing image and real-time hydrological data of multiple monitoring points; performing water flow direction topology analysis on the remote sensing image of the target watershed, and constructing a water flow topology network; performing basin structure dynamic change feature analysis on the target basin remote sensing image to generate basin structure dynamic change data; carrying out topology situation evolution fitting on the water flow topology network based on the basin structure dynamic change data to obtain a dynamic topology evolution network; performing multi-scale time sequence trend evolution analysis on the multi-monitoring-point real-time hydrological data to generate water flow time sequence evolution characteristics; and evolution characteristic space position mapping is performed on the dynamic topology evolution network based on water flow time sequence evolution characteristics, and a three-dimensional hydrological evolution model is constructed. According to the invention, timely and accurate river water level monitoring and early warning are realized.

The invention relates to the field of water quality evaluation, in particular to a rain source type river water quality evaluation method and system.The method comprises the steps that a sensor of a water quality monitoring system monitors micro-rain detection data of a micro-rain event, and the water quality monitoring system adjusts the sampling frequency of the sensor through an adaptive algorithm; obtaining water quality monitoring data through a micro-pollution mutation detection method; constructing a water quality recovery model of the micro-rain event through nonlinear kinetic analysis, and predicting a peak value and recovery time of water quality change of the micro-rain event; identifying a significant sudden change point of water quality change through a chaos theory algorithm, and adjusting the sampling frequency of the sensor through fuzzy logic control; key pollutants are obtained according to the peak value of the water quality change and the recovery time, and the key pollutants and pollution source data are subjected to data correlation analysis to judge the pollution source. The problems that the impact and the influence of a micro-rain event on the river water quality are not fully considered, so that the water quality evaluation is not timely and accurate enough, and the water quality change cannot be effectively captured and a pollution source cannot be analyzed are solved.

The invention relates to the technical field of hydrological monitoring, in particular to a multi-satellite collaborative hydrological monitoring system. The method comprises the following steps that a water level height measurement module obtains radar pulse recovery time delay data through a height measurement satellite and calculates the water level height, abnormal points are removed to generate river water level data, a water remote sensing module collects river water images through a remote sensing satellite to extract water boundary features, water area change data are deduced, and the water level height measurement module calculates the water level height. The meteorological wind shear analysis module obtains river channel meteorological data through a meteorological satellite, microwave scattering measurement and wind speed inversion are carried out, wind-induced shear stress parameters are generated, and the flow estimation module carries out section dynamic analysis and estimates the water flow in combination with river channel water level and water area data. And the flow correction module corrects the river water flow based on the wind-induced shear stress parameter and carries out real-time monitoring, and data are synchronously uploaded to the control terminal, so that comprehensive dynamic monitoring of the hydrological state of the river is realized. According to the invention, a more efficient multi-satellite collaborative hydrological monitoring system is realized.

The invention provides a river water quality parameter supervision method and system based on deep learning. The method comprises the steps of self-calibration multi-source data acquisition, diffusive water quality prediction, extreme water quality parameter simulation, reverse diffusion pollution positioning and water quality parameter intelligent supervision. The invention relates to the technical field of river water quality supervision, in particular to a river water quality parameter supervision method and system based on deep learning. By introducing a graph convolutional neural network and a physical diffusion constraint model, space-time diffusion trend modeling of pollutants in a river channel is realized; constructing an extreme pollution event simulation and attribution mechanism by combining a generative adversarial network and physical verification; further adopting a multi-modal Bayesian inversion model and a graph deconvolution structure to realize accurate source tracing of the pollution source; the system can dynamically sense hydrological changes, construct an adaptive threshold judgment mechanism, realize prediction, tracking and response to pollution risks, and provide efficient and intelligent technical support for river ecological safety management.

The invention discloses a pollution research and water quality prediction method based on river segmented water environment capacity analysis. The method comprises the following steps: accurately dividing a drainage basin into water environment capacity accounting units; obtaining a river water body image through satellite remote sensing; identifying and quantifying the positions and the intensities of point source and non-point source pollution sources in the drainage basin by utilizing the water quality parameters of the river obtained by the remote sensing image; establishing a river water quality prediction model; calculating the water environment capacity of each river reach under different surface water environment quality standards; comparing the estimated pollutant discharge amount with the water environment capacity under each water quality standard, and judging whether the pollutant discharge amount exceeds the capacity or not; and predicting future water quality change by using the model. According to the method, remote sensing, GIS and machine learning technologies are utilized, rapid analysis of the river segmented water environment capacity and accurate prediction of the water quality are achieved, dependence on a large amount of field sampling and complex software simulation is reduced, and the working efficiency is greatly improved.

The invention relates to the technical field of water resource scheduling, and provides an urban inland river system intelligent resource scheduling management system and method based on dynamic data collaboration, and the method comprises the following steps: S1, collecting river hydrological data, water quality indexes, urban water demands and water transfer facility state data in real time, and carrying out the time-space alignment and abnormal value filtering; s2, a hydrodynamic force-water quality coupling model of multiple water sources is established, a multi-objective optimization model is established in combination with constraint conditions of ecological base flow and an engineering upper limit, and the multiple water sources include but not limited to river water, underground water, reservoir water, reclaimed water, rainwater and desalinated seawater; and S3, carrying out global dynamic optimization on the water transfer proportion and the water transfer facility parameters by adopting a model algorithm, and minimizing the water transfer cost and the ecological influence. By quantifying the maximum flow upper limit and the ecological base flow threshold value of each water source and optimizing the water transfer proportion and facility parameters, the water resource utilization efficiency is improved, and it is guaranteed that the water transfer cost and the ecological influence are balanced in the urban river water dispatching process.

The invention discloses a river water surface two-dimensional flow field reconstruction method based on effective tracer detection and tracking, and the method comprises the steps: carrying out the distortion correction of an original video image sequence, and setting an ROI region according to the water level detection river water surface range; the method comprises the following steps: collecting a multi-scene floating object data set, screening floating objects with high relative brightness and clear outlines in different scenes, and training a YOLO model to detect the floating objects on the surface of a river; using an OCSORT matching tracking method to track adjacent frames of targets to obtain a trajectory, and filtering according to the length, angle and variance of the trajectory to obtain a stable trajectory; according to the method, object information obtained through target detection is utilized, according to the area, the area change rate and the length-width ratio of a floating object on an image, the following performance and stability of the floating object are evaluated, a track fusion weight is calculated, and the influence of unreliable floating objects is reduced; and gridding the river surface area after orthographic correction, and for a grid with a plurality of tracks, calculating the average flow velocity of the grid according to the confidence coefficient of each track to obtain a gridding two-dimensional flow velocity vector field. The method can accurately identify the floating object which meets the requirements of visibility, following performance and stability on the river surface as an effective water flow tracer, and is suitable for measuring the flow velocity of the river surface in complex scenes such as night, storm, flare, shadow and the like.

The invention discloses an inland river water transportation facility monitoring data secure transmission method based on a block chain technology, and the method comprises the following steps: selecting an optimal sensor for data collection according to the geographic position and operation environment of a facility through a dynamic sensor switching mechanism, and eliminating errors caused by equipment faults and environment interference, after the collected data is subjected to time-space aggregation processing and classified according to geographic areas and time windows, based on a block chain node selection algorithm, an optimal node is selected for data transmission, the data is encrypted by adopting a hierarchical encryption strategy, the encrypted data is stored in a block chain network, and the data is transmitted to the block chain network through a threshold-triggered smart contract. And monitoring the stored data in real time, and automatically adjusting a monitoring scheme or starting an emergency mechanism when the stored data exceed a preset threshold value. The invention aims to improve the security and integrity of monitoring data transmission.

The invention discloses a river water level dynamic monitoring and flood overflow risk prediction method based on deep learning, and the method comprises the following steps: S1, collecting multi-source hydrological data, and constructing a time series data set; s2, performing interpolation, denoising and normalization processing on the data to generate a unified time sequence format; s3, constructing a water level prediction model comprising a bidirectional long short-term memory network and an attention mechanism; s4, inputting the preprocessed data into the water level prediction model, and outputting a multi-time-step predicted water level sequence; s5, a dynamic threshold value is set according to the historical extreme value and the real-time hydrological condition, and the flood overflow risk is judged; s6, generating and caching a risk tag, and recording an error; s7, outputting a prediction result and risk information through a communication interface; and S8, periodically updating the input data in a rolling manner, and repeatedly executing the prediction and monitoring process. According to the invention, depth prediction and a dynamic threshold control mechanism are fused, and water level monitoring and flood overflow risk intelligent early warning are realized.

The invention discloses a river water ecological health monitoring method and a river water ecological health monitoring system, relates to the technical field of ecological health monitoring, and aims to solve the problem that a traditional water quality monitoring method mainly depends on water sample collection and laboratory analysis, and judges the water quality by measuring indexes such as dissolved oxygen, pH value, ammonia nitrogen, total phosphorus, heavy metal and dissolved organic matters in water; the river runoff is calculated from the precipitation and the drainage basin area, and the monitoring points are set in combination with the environmental information, so that an accurate data basis can be provided for subsequent water quality health assessment. The water quality condition is predicted by utilizing the water flow speed and the pollution point discharge amount and then compared with an actual monitoring result, so that potential problems can be found in time and a monitoring strategy can be optimized.

To have a high nature imitation effect of reference point micro inhabitation environment, hold water ecological space heterogeneity of a target water body at the maximum without changing a microorganism / organic feature in a natural water ecological system in the whole construction process, and realize a natural water ecological environmental status.SOLUTION: A selection and construction method of a reference point in interfered submergence type river water ecology includes steps of: dividing a basin basic space unit S1; analyzing an influence on an ecological environment stress of human activity in the basin basic space unit and extracting a water ecological feature S2; selecting a reference candidate point S3; evaluating an aquatic organism status of the reference candidate point S4; constructing a nature imitation reference point S5; and evaluating the aquatic organism status of the nature imitation reference point S6.SELECTED DRAWING: Figure 1

The invention discloses a boundary detection and positioning method suitable for trans-boundary water pollution, and relates to the technical field of river water quality monitoring. According to data obtained at the longitudinal and transverse positions of a river channel of each monitoring point of a selected drainage basin, a two-dimensional hydrodynamic model of the selected drainage basin is established; then, combining with the obtained target pollutant concentration to construct a diffusion model of the target pollutant, identifying and integrating a to-be-positioned region set in each time period, and screening out the concentration abrupt change regions which are spatially adjacent and consistent in concentration gradient change trend through clustering analysis by utilizing the spatial distribution positions and concentration gradient values of the concentration abrupt change regions; and the boundary contour of the pollutant is generated by fitting in combination with the direction information of the flow velocity field. According to the method, after concentration abrupt change areas identified in each time period are integrated, clustering analysis is carried out according to spatial distribution and gradient change trends of the concentration abrupt change areas, pollution boundary contour fitting is optimized in combination with flow velocity field direction information, and pollution boundary detection requirements in a complex water area environment are met.

The invention discloses a quantitative source tracing method for river and lake water pollution in combination with a knowledge graph and machine learning. The invention relates to the technical field of water quality pollution traceability, and the method comprises the steps: collecting and processing target river section data, constructing a hydrodynamic force-water quality model, and obtaining river downstream pollutant diffusion characteristics and target section concentration time sequence characteristics under different discharge situations of each discharge port based on the discharge port position of a target river section through a unit pulse response test; the method comprises the following steps: constructing a source intensity-time-concentration relation knowledge graph, randomly extracting a sample set from the graph, training the sample set by adopting a machine learning method, learning a nonlinear mapping relation between a downstream section concentration time sequence and multi-outlet source intensity, carrying out dynamic inversion of a river pollutant diffusion process, and realizing rapid positioning and contribution quantification of a pollution source. And finally, generating probability distribution of pollution source positions by adopting a Monte Carlo sampling method to assist in improving the traceability judgment priority, thereby improving the accuracy and response speed of river water quality pollution traceability.

The invention discloses a land-sea system nitrogen and phosphorus pollutant collaborative source analysis method, and particularly relates to the field of environmental governance and water pollution traceability, which comprises a comprehensive method based on a multi-isotope tracer technology, an isotope mixed model and nitrogen and phosphorus molar ratio verification, and comprises the following steps: acquiring isotope characteristics of nitrogen sources and phosphorus sources in various forms of a land-sea system; in combination with hydrology and water quality monitoring data, the polymorphic contribution of river water, underground water, coastal terrestrial sources and estuary endogenesis to estuary nitrogen and phosphorus pollutants is quantitatively analyzed; through accurate calibration of underground water input, dynamic quantification of pollution flux and cooperative analysis of nitrogen and phosphorus pollutant sources, the problem of insufficient systematicness and accuracy in tracing of complex multi-source pollutants in the prior art is solved, and a basis is provided for drainage basin-estuary-bay integrated treatment.

The invention discloses a river water pollution real-time tracing method, which comprises the following steps: obtaining a pollution source position of a river basin, obtaining a pollutant type discharged by a pollution source, and obtaining pollutant particle parameters and pollutant types; obtaining a river connection relation of the river basin, and confirming an input flow and an output flow of pollutants; based on the output flow of the pollutants, the pollutant type of the river basin is obtained, and the input flow of the pollutants is obtained; obtaining a theoretical pollutant level based on the theoretical water flow velocity and pollutant particle parameters of the river basin; based on the actually measured pollutant concentration and the actually measured water flow velocity, acquiring an actually measured pollutant level; and obtaining the pollutant source based on the actually measured pollutant level and the theoretical pollutant level. The problems of difficulty in real-time detection and insufficient traceability precision in a water pollution analysis method based on sampling in the prior art are solved. The traceability speed and precision are fully improved, and various hydrological conditions in rivers can be handled.

The invention discloses a cave depot type data center based on underground river construction and a construction method and an operation method thereof, and belongs to the technical field of cave depot type data center construction. The data center comprises a mountain body, an underground river is developed in the mountain body, the underground river flows through a karst cave in the mountain body, a plurality of equipment storage tunnels are arranged on one side or two sides of the underground river in the mountain body, and the equipment storage tunnels are communicated with the karst cave through slope connecting tunnels. An equipment cabinet and a liquid heat exchanger are arranged in the equipment storage tunnel, a river water conveying assembly is connected to the liquid heat exchanger, and the end, away from the liquid heat exchanger, of the river water conveying assembly penetrates through the slope connection tunnel and extends into the underground river. The liquid cooling mode is adopted for cooling and heat dissipation of the equipment cabinet, the direct temperature control mode remarkably improves the heat dissipation and cooling effects of the IT equipment cabinet, meanwhile, the heat exchange efficiency is high, and the operation energy consumption of a data center can be reduced.

The invention relates to the technical field of water quality data analysis, and discloses a river water quality pollution assessment method and system based on big data analysis, and the method comprises the steps: building a water quality parameter set; obtaining a preprocessed water quality data set; obtaining a global statistical analysis result through descriptive statistical analysis; carrying out hierarchical multi-time evaluation analysis to obtain a hierarchical evaluation result; obtaining a deviation value between the current water quality data and standard water quality data of a laboratory; reliable water quality characteristic data are obtained; a difference trend analysis result is obtained; performing relevance verification on the river water quality distribution map and the time change map of the target area according to the difference trend analysis result to form a comprehensive analysis result; and finally generating a comprehensive water quality pollution assessment report containing water quality traceability information and a comprehensive analysis result. According to the invention, a multi-parameter sensing technology and intelligent data processing are organically combined, so that the water quality of the river is efficiently, accurately and carefully analyzed, and the pollution degree is evaluated.

The invention relates to the technical field of point cloud data extraction, in particular to a river water surface extraction method based on SWOT point cloud and center line data. The method comprises the following steps: acquiring PIXC point cloud data; performing data preprocessing on the PIXC point cloud data, and performing spatial index construction to obtain point cloud KD tree index data and center line KD tree index data; based on the point cloud KD tree index data and the center line KD tree index data, carrying out topology shape-preserving cutting to obtain river cutting center line data; performing candidate water surface point set construction on the river cutting center line data; therefore, through multi-level spatial index construction, topological conformal cutting, combined space-elevation modeling and structural processing of geometric and hydrological feature fusion, the problems of difficulty in data association, much space isolated noise and insufficient water surface continuity recognition in traditional river water surface extraction are solved; and the extraction precision and the processing efficiency of the river water surface point cloud data are improved.

The invention discloses a river nitrogen pollutant source analysis method based on drainage basin nitrogen turnover and isotope, and particularly relates to the technical field of water treatment.The river nitrogen pollutant source analysis method comprises the following steps that a drainage basin to be researched is selected, river water and soil samples in the drainage basin are systematically collected, and physical and chemical indexes and isotope indexes # imgabs0 # of the collected samples are tested. Nitrate marked by nitrogen isotope delta 15N / delta 18O and # imgabs1 # are used as key indication marks to perform high-precision identification on nitrogen sources in a river and an environmental system, the contribution proportion of different nitrogen sources to the nitrate concentration in a water body is quantified through a stable isotope analysis model, and the concentration of the nitrate in the water body is calculated. And in combination with data, the rates of denitrification and anaerobic ammonia oxidation in environmental samples, sediments and soil are directly measured through combined application of an isotopic tracer technology and a membrane inlet mass spectrometer technology.

The invention discloses a method for dynamically predicting the underground water level during excavation of an overtopping ground layer foundation pit, and relates to the technical field of dynamic prediction of the underground water level, which comprises the following steps of: acquiring multi-source data related to the underground water level in an engineering range; comprising information of soil moisture content, stratum permeability coefficient, rainfall capacity, foundation pit excavation depth, foundation pit dewatering scheme and foundation pit waterproof and drainage measures; historical underground water level, underground aquifer data and Yangtze River water level change information are obtained. According to the method, data quality is optimized through multi-source data acquisition, correlation analysis and z-score standardization, nonlinear modeling is realized in combination with a support vector machine (SVM) and Bayesian optimization, and model precision and generalization ability are improved. Model parameters are dynamically adjusted through a real-time monitoring and feedback optimization mechanism, the adaptability to sudden hydrological changes is enhanced, engineering risks are effectively reduced, and construction safety and economical efficiency are guaranteed.

The invention discloses a river water quality prediction method combining dual signal decomposition and deep learning, which comprises the following steps: S1, carrying out feature selection through random forest feature selection (RF), and extracting water quality data related to water quality prediction; s2, in the preprocessing of the water quality data, performing preliminary decomposition on the water quality data by adopting time-varying filter empirical mode decomposition (TVF-EMD), enabling an internal mode function (IMF) component generated by decomposition to contain a large number of complex signals, and performing secondary decomposition on the complex internal mode function (IMF) component by adopting sparrow search algorithm-variational mode decomposition (SSA-VMD); and S3, using a sparrow algorithm to optimize a convolutional long short-term memory neural network model (SSA-CNN-LSTM) to predict the data processed in the step S2. The constructed prediction model is excellent in performance in water quality prediction, particularly in short-term prediction, the prediction precision of the prediction model on the water quality index is remarkably superior to that of a single decomposition model, and short-term dynamic changes of the water quality index can be accurately captured.

The invention relates to a method for predicting river nitrogen metabolism gene abundance and key influence factors thereof based on automatic machine learning. Comprising the following steps: collecting a river water sample, detecting water quality physicochemical indexes, and performing metagenome sequencing to obtain nitrogen metabolism gene abundance; taking conventional water quality physicochemical indexes as explanatory variables, taking nitrogen metabolism gene abundance as a target variable, adopting automatic machine learning to construct a model, evaluating the performance of the model through a mean square error (MSE) and a fitting coefficient (R), and selecting an optimal algorithm for prediction; the model analyzes the relative contribution of each water quality index through an SHAP value graph, and reveals the relationship between each water quality index and gene abundance by using a partial dependency graph (PDP). The method shows high precision and good generalization performance in the aspect of predicting the abundance of the nitrogen metabolism gene, and is suitable for describing the nitrogen dynamic process of a river water body. The model can explain, analyze and quantify the influence of different water quality indexes on the nitrogen metabolism gene afterwards, and a scientific basis is provided for river water quality management and nitrogen metabolism optimization regulation and control.

The invention discloses a river water quality prediction method fusing quantum-like features and a graph-time sequence model. The river water quality prediction method comprises the following steps: acquiring multivariable water quality observation data, time sequence meteorological data and static basin attribute data of a plurality of river sites; preprocessing the data; generating a meteorological quantum feature vector and a static basin attribute quantum feature vector; after the meteorological quantum feature vector and the static watershed attribute quantum feature vector are spliced and fused with the spatial dependence features, inputting the spliced and fused data and the preprocessed multivariable water quality observation data into a graph-time sequence model for training; and performing reverse normalization processing on simulation output obtained by calculation of the trained graph-time sequence model to obtain continuous day-by-day river water quality simulation data, and evaluating model performance to predict day-by-day continuous river water quality data. According to the method, the corresponding space-time class quantum feature vectors are generated by executing high-dimensional class quantum feature transformation on the multi-source heterogeneous data, so that the characterization capability of the model on the nonlinear interaction relationship of the space-time features is enhanced.

The present invention discloses a cave-type data center constructed based on an underground river, as well as its construction method and operation method, belonging to the technical field of cave-type data center construction. The data center includes a mountain body, in which an underground river is developed, and the underground river flows through a karst cave in the mountain body. On one side or both sides of the underground river in the mountain body, a number of equipment storage tunnels are provided. The number of the equipment storage tunnels are respectively connected to the karst cave through a slope connecting tunnel. An equipment cabinet and a liquid heat exchanger are arranged in the equipment storage tunnel. A river water conveying assembly is connected to the liquid heat exchanger. One end of the river water conveying assembly far away from the liquid heat exchanger passes through the slope connecting tunnel and extends into the underground river. The liquid cooling method is used to cool and dissipate heat from the equipment cabinet. This direct temperature control method significantly improves the heat dissipation and cooling effect on the IT equipment cabinet. At the same time, the heat exchange efficiency is high, and the operation energy consumption of the data center can be reduced.

The invention discloses a crop flood disaster large-area quantitative evaluation method and system, and the method comprises the steps: obtaining flood disaster data and SAR time sequence remote sensing data, obtaining a flood disaster submerging space range based on a deep learning image semantic segmentation model according to an SAR remote sensing image, and obtaining a crop flood disaster flood area based on landform and river water level observation data. Estimating the flood submerging depth of the DEM pixel scale by using a submerging depth model; recognizing a flood disaster area by combining SAR (Synthetic Aperture Radar) time sequence remote sensing data, the precipitation observed on the ground and the water level change of a river channel; core evaluation indexes of crop flood submerging range, submerging duration and submerging depth are calculated, flood disaster events are associated with crop periods, and a flood hazard degree equation is constructed by taking flood stress sensitivity as a basis. According to the method, local crop characteristics of a research area are fused, a flood disaster event is associated with a crop period, and the flood hazard degree equation is constructed by taking the flood stress sensitivity as one of parameters, so that quantitative calculation of the flood hazard degree is realized.

The invention relates to the technical field of water quality monitoring, in particular to an artificial intelligence river water quality pollutant spatial form monitoring system and method, and the system comprises a concentration trend analysis module, a spatial form monitoring module, a spatial trend analysis module and a pollution early warning evaluation module. According to the method, the water quality is monitored at a plurality of river water area nodes, the pollutant concentration data can be accurately captured, the change rate of the pollutant concentration can be predicted, the understanding of the pollution trend is improved, the response speed to the pollution event is enhanced, the image monitoring data is combined, the convolutional neural network is used for analyzing the river water area image, and the accuracy of the analysis is improved. The spatial area of the pollutants is analyzed, so that the spatial distribution state of the pollutants is evaluated in detail, the severity level of the pollutants can be evaluated, managers are notified in real time, meanwhile, dynamic change information of pollutant concentration and spatial distribution is integrated, an early warning mechanism is more agile and accurate, and the early warning efficiency is improved. And the effectiveness and the accuracy of pollution early warning are improved.

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 relates to the technical field of water regimen forecasting, in particular to a river channel water regimen forecasting method and system based on digital twinning. The method comprises the following steps: arranging sensor nodes along a river channel and carrying out real-time monitoring on the river channel environment water regimen along the river channel to obtain real-time data of the water level along the river channel, real-time data of the flow along the river channel and real-time data of the weather along the river channel; performing digital twinborn construction and risk estimation division along the riverway to generate a riverway water regimen flood risk estimation period and a riverway water regimen drought risk estimation period; the method comprises the following steps: performing flood risk forecast processing in a river water regimen flood risk estimation period to execute corresponding river water regimen flood risk management decision-making work; and performing drought risk forecast processing in the river channel water regimen drought risk estimation time period to execute corresponding river channel water regimen drought risk management decision-making work. According to the invention, efficient and accurate water regimen prediction and decision support can be realized.