2625 results about "Drainage basin" patented technology

The invention discloses a water quality prediction method based on graph neural network and spatial-temporal feature fusion, and the method comprises the steps: obtaining hydrological data and water quality data of a plurality of monitoring stations in a preset drainage basin in a preset time period, representing the monitoring stations as nodes of a graph, and representing the adjacency relation between the monitoring stations as edges of the graph, a graph topology network mapping multiple monitoring station pollutant data is constructed, a spatial-temporal feature fusion module is adopted to perform feature extraction and fusion on the graph topology network to obtain water pollution spatial-temporal feature data, and the spatial-temporal feature fusion module is obtained by training a graph convolutional network, a gating circulation unit and a self-attention mechanism. And performing fusion processing on the extracted feature data to form a time sequence data set of a graph topology structure, and finally predicting the data set through a graph time sequence prediction model to obtain water quality prediction results of the plurality of monitoring stations in a target time period. The method can effectively deal with the spatial and temporal change process of water pollution, reduce the water quality prediction cost and improve the water quality prediction precision.

The invention provides a drainage basin digital twinning construction method and system based on multi-source data fusion. The method is applied to the technical field of drainage basin digital twinning construction, and comprises the following steps: acquiring water level monitoring data, surface flood routing path data, weather forecast rainfall data and terrain slope change data; rainfall and topographic slope data are fused to generate a runoff distribution relation, and a high-risk area is identified in combination with a water level sudden change amplitude. A digital twinning preliminary framework is established, and related characteristic parameters are generated by utilizing reverse feedback of a flood path and water level data. And inputting meteorological data, constructing a twinborn model based on the boundary condition of the catchment area, and simulating flood direction abnormity and flooding early warning signals. And according to the early warning space-time distribution and the terrain elevation data, generating emergency schemes such as flood dividing gate scheduling and flood storage and detention area starting, and realizing risk hierarchical management and control. According to the technical scheme provided by the invention, the flood inundation prediction precision and the emergency response efficiency are improved, and precise decision support is provided for flood control of a drainage basin.

The invention provides a drainage basin water quality pollution traceability analysis method and system fused with space-time big data, and relates to the technical field of pollution analysis, and the method comprises the steps: collecting water quality, weather, surface runoff and other data, and generating a data set through space-time label association; and on the basis of the data set, constructing a pollutant migration and diffusion model containing a pollutant concentration attenuation and flow direction prediction sub-model, and calculating a pollutant concentration change rate and a migration path. And finally, the pollution sources are divided into point source pollution, non-point source pollution and endogenous pollution through the pollution source identification model, the contribution rate of each pollution source is calculated, and a pollution traceability analysis report is generated. The method can comprehensively consider the influence of various factors on the water quality, realizes the accurate positioning and quantitative analysis of the pollution source, and improves the traceability efficiency and accuracy.

The invention relates to the technical field of data processing, and discloses a water quality and water quantity accurate simulation method and system based on an SWAT model and a storage medium. The method comprises the following steps: analyzing rainfall and evapotranspiration data through a two-parameter trend test, and generating standard hydrological data by adopting a triple configuration algorithm; dividing sub-basins by combining topographic information, and calculating a hydrological response unit; performing water balance operation and water conservation evaluation; pollutant diffusion is analyzed, and a diffusion equation is solved; running state monitoring is implemented, and an early warning system is designed; and comparing and correcting measured data, and constructing a hierarchical monitoring mechanism. According to the invention, the whole-process precise management from hydrological data acquisition, water quality evaluation to early warning monitoring is realized, and the accuracy and reliability of water quality and water quantity monitoring are remarkably improved.

The invention provides a drainage basin flood runoff forecasting system and forecasting method based on water conservancy big data, relates to the technical field of flood runoff forecasting, and constructs the drainage basin flood runoff forecasting system based on the water conservancy big data by integrating the advantages of a physical model and a data driving model. The adopted bidirectional LSTM network is used for abnormal value interpolation, so that the data quality can be effectively improved; the HEC-HMS model is combined with a terrain correction factor to optimize the accuracy of the runoff process; the LSTM network enhances the data driving capability of flow prediction through the fusion of geographic space features and time series data, the model fusion module introduces an attention mechanism and water conservation constraints, the dynamic weight balance of a physical model and a data driving model is realized, the reliability and consistency of a prediction result are comprehensively improved, and the prediction efficiency is improved. And the model verification module realizes continuous comparison and verification of a forecast result and actually measured data through a closed-loop feedback mechanism of a total error and a dynamic threshold value.

The invention discloses a riverway flood control and disaster reduction risk point AI identification method and device based on a digital twinborn computable engine. The method comprises the following steps: acquiring three-dimensional scene basic data and hydraulic engineering parameters in a drainage basin; acquiring real-time rain condition, water condition, soil moisture content, reservoir condition and dike monitoring data to obtain real-time data; establishing a physical entity and a twin simulation scene according to the three-dimensional scene basic data and hydraulic engineering parameters; performing AI simulation calculation of water regimen according to the real-time data to obtain a simulation result; carrying out twinborn computable simulation according to the physical entity, the twinborn simulation scene and the simulation result to obtain a simulation result; and identifying the risk point of the river channel in the drainage basin according to the simulation result. By implementing the method, the problem that the prediction accuracy is affected due to the fact that the digital twinborn technology mainly lacks intelligent risk identification and multi-factor integration in flood control and disaster reduction of the river channel can be solved.

The invention discloses a grading, zoning and differentiating risk early warning method for mountain torrent disasters in medium and small watershed. Firstly, a drainage basin risk area is determined through mountain torrent disaster data analysis; the drainage basin delay time is calculated, the section water level and the drainage basin submerging range are simulated through the simplified hydrodynamic model, and quantitative analysis is conducted on the model; according to the method, a high-precision grid database is formed by combining unstructured grid division and risk source identification, and then clustering grading is performed on multi-dimensional characteristics of grids through a Gaussian mixture model (GMM) to obtain risk levels of the grids in a drainage basin. Finally, a differential early warning threshold value and a response strategy are formulated according to a grid partition grading result, and dynamic updating and linkage issuing are achieved on a GIS platform in combination with actual measurement data. According to the method, limited real conditions of small and medium-sized basin data and precision requirements of model simulation are taken into consideration, fine setting of the early warning threshold value is realized on the basis of grading and partitioning, and the accuracy of mountain torrent risk identification is also remarkably improved.

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 provides an intelligent management method for natural resource engineering data, and relates to the technical field of data processing, and the method comprises the steps: inputting a time-space coupling model into real-time weather forecast data, simulating water distribution schemes under different rainfall scenes, automatically recognizing a river reach position of which the ecological flow is lower than a design threshold value, and carrying out the calculation of the river reach position. In combination with a historical water replenishing strategy case library, generating a multi-objective optimization scheduling scheme including a water replenishing time sequence, a water replenishing path and a water replenishing amount; three monitoring points are arranged at a downstream section of an ecological protection area, a flood control section and a canal head of an irrigation area in a drainage basin, and ecological flow, flood control section water level and canal water level data are monitored in real time respectively; and dynamically correcting the water supplementing time sequence, the water supplementing path and the water supplementing amount in the multi-objective optimization scheduling scheme to obtain a corrected scheduling scheme. According to the method, refinement and toughness of drainage basin water resource management can be improved.

The invention provides a flood peak evolution path and dam break risk early warning method and system. According to the method, a time-space coupling water conservancy data set is constructed by fusing multi-dimensional water conservancy monitoring data and regional rainfall prediction information, and a basin topology perception model is established. And further performing mode matching on the parameters and a historical dam break event characteristic spectrum, and generating a dynamic response strategy set including a flood storage and detention area capacity allocation scheme by correcting a space weight of a matching result. And finally, based on a watershed topology perception model, matching the strategy set with historical dam break features, realizing accurate identification of a flood peak evolution path and graded early warning of dam break risks, outputting a water conservancy analysis report containing risk grades, and realizing closed-loop management from data fusion and dynamic prediction to risk decision. According to the technical scheme provided by the invention, the efficiency and accuracy of intelligent analysis of the water conservancy data can be improved.

The invention belongs to the technical field of hydrology and water resource intelligent management, and discloses an inter-basin water resource system cooperative scheduling and forward-reverse bidirectional coupling dynamic decision method and system, and the method comprises the steps: S1, constructing a multi-modal water conservancy infrastructure topological relation graph, and carrying out the bidirectional conduction optimization; s2, sensing hydrological gap risk situation and analyzing multi-source water supply potential coupling based on the multi-modal water conservancy infrastructure topological relation map and optimized bidirectional conduction; s3, based on hydrological gap risk situation and multi-source water supply potential coupling, generating an elastic grading water quantity distribution strategy and a cross-engineering replacement protocol; and S4, constructing and optimizing a positive-negative two-way rehearsal decision engine based on an elastic grading water distribution strategy and a cross-engineering replacement protocol. The technical problem that a traditional one-way scheduling model is difficult to deal with complex topological relations, sudden water shortage events and cross-administrative-region collaboration is solved.

The invention relates to the technical field of intelligent water conservancy, and discloses a cross-regional water transfer project intelligent scheduling method and system, and the method comprises the steps: building a digital twin system based on a geographic information system, hydrological monitoring data and a spatial topological structure, and integrating a meteorological evolution prediction model, a basin hydrological response model and a water demand prediction model; predicting a water demand and an adjustable water amount by using a space-time convolutional neural network and a gating circulation unit; establishing a multi-objective optimization model taking water supply benefit, ecological influence and energy consumption cost as optimization objectives; an optimal water transfer scheme is generated through a Markov decision process and multi-agent cooperation; and carrying out robustness evaluation on the scheme and generating an emergency scheduling plan. According to the method, the scheduling efficiency and adaptability of the water transfer project are remarkably improved, and efficient configuration of water resources and quick response under extreme situations are achieved.

The invention discloses a factor multiple optimization-based flood risk dynamic zoning method and system, and the method comprises the steps: constructing an initial flood risk assessment index set, carrying out the screening of the initial flood risk assessment index set, and calculating the frequency value of each index; constructing a comprehensive discrimination index value, and further screening to obtain a key flood risk assessment index set; constructing a risk assessment model and a key flood risk assessment index weight optimization model, and adopting a genetic-local search hybrid algorithm of simulated annealing optimization to calculate and obtain an optimal weight combination scheme; and calculating the similarity between the scene of each time point of the research area and a historical typical flood event, selecting an optimal weight combination scheme, and inputting the optimal weight combination scheme into the risk assessment model for calculation to obtain a flood risk dynamic division of the research area. According to the method, scientific and reasonable dynamic risk assessment can be provided, effective decision support can be provided for related decision makers, and the flood control toughness and the extreme environment coping capacity of the drainage basin area can be improved.

The invention relates to the technical field of hydrological data analysis, in particular to a dynamic hydrological coupling data analysis method and system for a complex watershed. The method comprises the following steps: acquiring historical meteorological data, historical hydrological data and historical remote sensing data, and performing hydrological cycle simulation analysis to generate a rainfall-evaporation-permeation-runoff hydrological cycle simulation model; performing drainage basin gridding processing on the rainfall-evaporation-permeation-runoff hydrological cycle simulation model to generate a hydrological cycle gridding model; obtaining actually measured meteorological data and actually measured hydrological data, and carrying out space-time simulation division and hydrological space-time correlation analysis to obtain a hydrological change space-time correlation correlation relationship among the sub-partitions of each drainage basin; and performing drainage basin space-time coupling early warning processing on the hydrological cycle gridding model to generate a hydrological change space-time coupling early warning response result corresponding to the complex drainage basin. The method can accurately analyze and predict the hydrological change and the mutual coupling relation thereof in the complex watershed.

The invention relates to the technical field of drainage basin water scheduling, and discloses a drainage basin water regulation and control optimization method based on ecological element changes. The method comprises the following steps: deploying a drainage basin monitoring system, and collecting ecological element real-time data such as a hydrological parameter sequence and a remote sensing image; after the data is cleaned and converted, hydrological trend features and spatial distribution features are extracted by adopting a feature learning model, and the hydrological trend features and the spatial distribution features are fused into unified ecological representation through a cross-modal alignment mechanism; inputting the unified ecological representation into a physically constrained neural network prediction model, and outputting a water regimen dynamic prediction value; and finally, based on the predicted value, a water resource regulation and control instruction is generated and executed by using a multi-objective decision algorithm so as to optimize the watershed water circulation process. According to the method, feature extraction comprehensiveness is improved through multi-source data fusion and cross-modal analysis, prediction reliability is enhanced in combination with physical constraints, reasonable allocation of water resources is achieved by means of multi-target decision, the ecological condition of a drainage basin can be improved, and the water utilization efficiency is improved.

The invention relates to the technical field of computer science and artificial intelligence, and discloses a digital twin basin flood disaster early warning plan generation system based on deep learning, and the system comprises a data collection and processing module which obtains and standardizes multi-source data of basin meteorology, hydrology, terrain, remote sensing and the like in real time, and provides real-time data input; the digital twinborn modeling module is used for constructing a digital twinborn model of a watershed hydrological dynamic process according to the real-time data and outputting a simulation result; the deep learning prediction module is used for receiving a simulation result, training a prediction model and outputting space and time distribution prediction of flood disasters; the emergency response generation module is used for generating an emergency plan of the flood disaster according to the prediction result; and the system feedback module is used for evaluating an emergency plan and a prediction effect and providing an adjustment basis for future disaster early warning. According to the invention, accurate flood disaster prediction based on real-time data can be realized, the accuracy and response efficiency of an emergency plan are improved, and disaster loss is reduced.

The invention relates to the technical field of water pollution traceability, and particularly discloses a river pollutant traceability system and method based on digital twinning, and the system comprises a water body sampling module which is used for obtaining the current water quality data of a preset region of a target river; the model construction module is used for constructing a digital twinborn model according to the river topographic data and the current water quality data and by fusing the historical hydrological data, the real-time meteorological data and the drain outlet distribution topological graph; the data analysis module is used for performing spatial-temporal feature extraction on a pollutant diffusion path in the digital twinborn model by utilizing a graph convolutional neural network, applying dynamic correction pollution source position probability distribution in combination with Bayesian inference, and generating a traceability path thermodynamic diagram; and the pollution traceability module is used for obtaining a pollutant traceability result according to the confidence coefficient threshold of the traceability path thermodynamic diagram. According to the method, the efficiency and the accuracy of tracing the river pollutants in the complex dynamic environment can be remarkably improved, and technical support is provided for ecological safety and precise treatment of a drainage basin.

The invention discloses a small watershed hydrological runoff and pollution load short-term prediction refinement method, which comprises the following steps of: constructing a modeling data set, constructing a small watershed foundation base model, segmenting hydrological response units according to land utilization types, crop planting structures and reservoir pond / wetland distribution data in a refinement manner respectively, and calculating the hydrological runoff and pollution load short-term prediction of the small watershed foundation base model. A small watershed foundation base model corresponding to the optimal segmentation scheme is screened out, and a small watershed depth base model is generated; constructing a reservoir pond / wetland simulation module; coupling the small-watershed depth base model and the reservoir pond / wetland simulation module to form a small-watershed hydrology and water quality early warning model, and optimizing the small-watershed hydrology and water quality early warning model; rolling to generate a hot start file for short-term prediction; constructing a future short-term weather enhancement forecasting model; the small watershed hydrology and water quality early warning model is driven to operate, the future water volume and load output are calculated in a rolling mode, and hydrology and water quality results are output; and calibrating and updating the small watershed hydrology and water quality early warning model. The precision and timeliness of hydrology and water quality prediction of the small watershed are remarkably improved.

The invention discloses an ecological restoration quantification method based on a watershed ecological safety complex network. The method comprises the following steps: S1, obtaining multi-time-sequence watershed multi-source heterogeneous spatial data of a target area; s2, taking the grid as an evaluation unit, establishing a drainage basin ecosystem service function evaluation system, and obtaining spatial-temporal change characteristics of an ecological safety function; s3, analyzing the spatial-temporal change characteristics of the ecological land scale of the watershed; s4, constructing a drainage basin ecological resistance surface; s5, identifying an ecological corridor; s6, constructing an ecological safety complex network model by taking the ecological source as a node, the ecological corridor as an edge, the scale of the ecological source as a point weight and the proportional relation between the connection efficiency of the ecological corridor and the corridor resistance as an edge weight, and extracting spatial-temporal evolution characteristics of an ecological safety structure; s7, a coupling coordination degree model is adopted to simulate and analyze the characteristic relation among the safety function, the safety structure and the land use scale, and the coupling coordination degree is obtained; and S8, guiding and optimizing an ecological restoration implementation strategy. According to the method, ecological security network structure features on the watershed system scale are quantified.

A storm water trough filter system is mountable in a storm vault having interior walls and a storm water inlet formed in at least one of the interior walls which allows storm water to flow therethrough and into the storm vault. The storm water trough filter system has at least one trough section which defines a water trough for receiving storm water flowing into the storm vault through the storm water inlet thereof. The storm water trough filter system further includes at least one mounting bracket which is affixable to at least one of the interior walls of the storm vault, the at least one trough section being mountable on the at least one mounting bracket. The storm water trough filter system further includes a filtering apparatus having a water permeable filtering medium. The water trough defined by the at least one trough section is in fluid communication with the water permeable filtering medium of the filtering apparatus to direct storm water received by the water trough to the filtering medium of the filtering apparatus.

The invention relates to the technical field of river channel desilting treatment, in particular to a dynamic simulation and optimization decision-making-based river channel silt treatment method, which comprises the following steps of: acquiring and treating historical and real-time multi-dimensional environmental data of a to-be-treated river channel and an influence area of the to-be-treated river channel; a river basin process simulation system capable of simulating river water power, sediment transportation, pollutant migration and transformation and ecological response processes is constructed, decision variables forming a river regulation scheme are defined in a parameterized mode, a multi-target optimization problem is defined and set in a formalized mode, a multi-target optimization engine is adopted, and the river regulation scheme is optimized. The method comprises the following steps: embedding a drainage basin process simulation system as a core evaluation module into an optimization iterative loop, implementing an optimization scheme, and continuously monitoring the environmental state change in the implementation process and after the implementation; the method can accurately predict the long-term and short-term influence of different treatment schemes in multiple aspects of hydrodynamic force, silt, pollutant migration and transformation, ecological response and the like, and solves the problem of comprehensive treatment of sludge.

The invention relates to the field of hydrology and water resource management and karst hydrodynamics, and discloses a karst basin coupling hydrology model prediction method, which comprises the following steps: S1, collecting and standardizing multi-source heterogeneous monitoring data of a karst basin; s2, constructing a hydrological model for dynamic coupling of surface runoff, fracture seepage and a pipeline network; s3, performing global optimization on the multi-dimensional hydrological parameters by adopting a hybrid intelligent optimization method; s4, performing iterative correction on the model based on a multi-level verification system; and S5, outputting a hydrological prediction result and generating disaster early warning information. By adopting a dynamic adjusting technology of karst area underground river and surface water flow network joint modeling, multi-karst water-containing medium characteristic analysis and integration of underground river and surface water system response characteristics, the problems that a traditional method cannot accurately predict underground water flow and flow velocity, and is difficult to deal with complexity of a karst water system, lack of data and the like are solved.

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 invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The invention discloses a reinforcement learning-based intelligent flood discharge scheduling method for a small reservoir. The method comprises the following steps of S1, forming a complete reservoir data set; s2, constructing an environment state vector required by reinforcement learning based on the reservoir data set; s3, generating an initial flood discharge scheduling strategy; s4, establishing an improved reinforcement learning model based on the environment state vector and the initial flood discharge scheduling strategy; s5, updating the strategy network in the improved reinforcement learning model to obtain an optimal flood discharge decision scheme; s6, gate operation is carried out according to the optimal flood discharge decision scheme, and the water level change of the small reservoir, the safety state of the downstream drainage basin and the ecological demand satisfaction degree after execution are monitored; and S7, inputting the feedback information into the improved reinforcement learning model again, and performing iterative updating and strategy correction on the improved reinforcement learning model in combination with the reservoir data set and the environment state vector. According to the invention, the efficiency and reliability of flood discharge scheduling of the small reservoir are obviously improved.

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 discloses a UE5-based dynamic simulation method for digital twin flood inundation of small and medium-sized watersheds. The method comprises the following steps: 1, constructing a two-dimensional hydrodynamic numerical model; step 2, creating a flood inundation simulation UE5 plug-in project; step 3, creating a flood role class; 4, constructing a method for drawing triangular mesh primitives of the flood model; 5, constructing a flood hydrological feature and flood flow state rendering method; and 6, realizing a dynamic simulation process of digital twinborn flood submerging. According to the invention, dynamic rendering and high-fidelity visualization of an ultra-large data volume flood model in a small and medium-sized basin digital twinning three-dimensional scene are realized, and multi-dimensional flood inundation numerical simulation results of water level, water depth, flow velocity, flow direction and the like and complex flood flow states of backflow, convergent flow, bifurcated flow, overflat flow, turbulent flow and the like are expressed; the authenticity and accuracy of flood inundation simulation are improved, and the flood risk assessment and flood control plan decision support capability of small and medium-sized drainage basins can be enhanced.

The invention relates to a method, device and product for monitoring river flow in real time, and the method comprises the steps: obtaining tower footing video data of a target river reach which comprises a target section and a pier buoy; inputting the tower footing video data into a water level and flow velocity identification model, and obtaining the water level elevation and the surface flow velocity of at least part of the cross section in the target section through the water level and flow velocity identification model; inputting the water surface elevation into a three-dimensional digital river channel model to obtain a section water passing area of a corresponding cross section; and obtaining the cross section flow of the target cross section according to the cross section water passing area and the surface flow velocity. According to the embodiment of the invention, automatic flow estimation based on the tower footing video can be realized, the manual measurement cost is reduced, the spatial-temporal resolution of flow monitoring is improved, and the method can be used for large-range river monitoring, especially in areas lacking hydrometric stations, and can provide data support for drainage basin water resource management and flood prevention early warning.

The invention discloses a drainage basin maintenance intelligent management system based on the Internet of Things and intelligent decision, and relates to the technical field of computers. Comprising a multi-source perception and edge access module which is used for realizing multi-protocol access, time and session alignment, data quality labeling, breakpoint resume and edge side anomaly preliminary screening of industrial data, low-power-consumption point location data and video and thermal image data. According to the invention, through the multi-source sensing and edge access module, various types of data such as industrial data, low-power-consumption point location data, video and thermal image data can be processed, the technical problems of multi-protocol access, time and session alignment, data quality labeling and breakpoint resume and the like are solved, the integrity and reliability of the data on the edge side are ensured, and the service life of the data is prolonged. The edge side anomaly preliminary screening function significantly improves the timeliness of early fault discovery, reduces the network bandwidth pressure, effectively solves the problems of anomaly cooperative detection and alarm flooding in a complex system, and improves the accuracy and timeliness of fault early warning.

The invention discloses a water quality pollution source reverse tracking method based on an LSTM model and a pollution scene database, belongs to the technical field of water quality monitoring and pollution tracing, and is used for solving the problem that a pollution source and a pollution type are difficult to accurately identify under a limited data condition. According to the technical key points, a model used for describing migration and diffusion of pollution factors in a drainage basin is constructed according to a pollutant diffusion equation; mapping pollution source characteristic parameters input into the model, a pollution factor concentration change curve output by the model, a residual value and a corresponding pollution scene, and storing the mapping in a database; training a machine learning model by using the data in the database; the pollution source characteristic parameters predicted and output by the machine learning model are matched in the database, and pollution scenes corresponding to the matched pollution source characteristic parameters in the database are obtained, so that the pollution source tracking accuracy and efficiency can be effectively improved, and the method can be used for drainage basin pollution abatement and environment monitoring and early warning; and key technical support is provided for drainage basin water environment management.