1876 results about "Drainage basin" patented technology

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 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 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 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 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 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 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 relates to the technical field of water resource analysis, and discloses a water resource predictive analysis method based on artificial intelligence. The method relates to the technical field of water resource analysis, and comprises the following steps: acquiring an original hydrological data set including rainfall intensity, river flow and the like through a sensing terminal, and performing multi-modal data alignment to generate a hydrological space-time tensor; constructing a dynamic water level threshold response mechanism in combination with watershed topographic features to obtain a partition water level calibration matrix; inputting the hydrological feature map into a spatial-temporal feature coupling network containing a long-short-term memory module and a spatial self-attention module to generate a hydrological feature map; constructing a multi-dimensional abnormal association tensor based on the multi-dimensional abnormal association tensor, and identifying rainfall flood event nodes by using an adaptive sliding window detection algorithm; and an optimal hydrological parameter set is obtained through genetic algorithm optimization, and the three-dimensional hydrological dynamic model is driven to establish a mapping relation chain. The method can effectively fuse hydrological data spatio-temporal characteristics, and improves the accuracy and efficiency of water resource prediction analysis.

The invention discloses a valley-type tailing pond flood runoff prediction method based on underlying surface parameter dynamic correction, which comprises the following steps: S1, acquiring valley-type tailing pond multi-source data for preprocessing, and constructing a basic database; s2, underlying surface parameters of the valley-type tailings pond are obtained, and the initial value range of each parameter is determined; s3, establishing an underlying surface parameter dynamic correction model, setting differentiated production and confluence parameters for different areas, and performing dynamic correction; s4, constructing a coupled hydrological-hydrodynamic model, simulating a runoff forming process and time-varying characteristics, outputting predicted values of runoff flow, flood peak time and flood peak water level, and comparing the predicted values with actual measured values of a historical flood area of a satellite remote sensing image for verification and calibration; and S5, inputting the real-time data into the hydrological-hydrodynamic model, and predicting the submerging range, submerging time and submerging degree of the flood runoff based on a geographic space analysis method. According to the method, the timeliness and the accuracy of runoff prediction of small watershed areas without runoff data such as valley type tailings ponds are improved.

The invention relates to the technical field of ecological restoration prevention and control, in particular to a sea-entering river pollution source targeted blocking and ecological restoration integrated prevention and control system which comprises a pollution source recognition unit, a targeted blocking unit, an ecological restoration unit and a cooperative control platform. The recognition unit combines a multispectral unmanned aerial vehicle with a water quality sensor and positions a pollution source through artificial intelligence traceability; the targeted blocking unit implements physical-chemical combined blocking according to pollution types; the ecological restoration unit purifies water and reconstructs ecology through a modular constructed wetland and the like; the cooperative control platform optimizes each unit strategy based on hydrological data; each unit correlation formula accurately calculates parameters, such as a recognition algorithm, a gate dam opening control formula and the like. According to the invention, multi-technology fusion and system collaboration are realized, pollution events can be quickly responded, measures are accurately applied, the sea-entering river pollution prevention and control efficiency is effectively improved, the treatment cost is reduced, and drainage basin water environment ecological restoration and sustainable development are promoted.

The invention relates to the technical field of migration simulation, in particular to a drainage basin water pollution migration and transformation simulation method and system based on an MT-DHM model. The method comprises the following steps: carrying out data acquisition on a watershed multi-source space, carrying out data preprocessing, and generating a hydrological response unit standard data set; performing vertical hierarchical calculation based on the hydrological response unit standard data set to obtain land runoff vertical analysis data; carrying out land pollutant migration simulation on the land runoff vertical analysis data, and carrying out real-time correction with an iteration step length of 1 h to obtain a dissolved state / adsorption state load matrix; therefore, by constructing a full-process multi-source heterogeneous data fusion and pollution migration simulation mechanism, the problems that a traditional model is insufficient in data uniformity, low in parameter calibration precision and weak in spatial distribution simulation capability are solved, and the precision, stability and management and control operability of drainage basin pollution load calculation are improved.

The invention discloses a reservoir group delivery flow prediction method considering climate change and artificial regulation and storage. The method comprises the following steps: collecting hydro meteorological data of a research basin; determining an upstream inlet reservoir, an interval reservoir and an outlet reservoir according to the reservoir position distribution of the research basin; determining reservoir inlet flow calculation modes of the reservoirs at different positions and reservoir outlet flow models of the reservoirs; and for the outlet reservoir, according to the outlet flow of the upstream reservoir and the hydrometeorological data, the reservoir inlet flow of the reservoir is obtained through a vector autoregression model or a multiple linear regression model, the outlet flow simulation of the reservoir is carried out, and an outlet flow residual prediction model based on deep learning is established, so that the outlet flow prediction of the outlet reservoir is realized. According to the method, the influence of the artificial regulation and storage effect of the reservoir on the downstream reservoir is considered on the basis of the inflow and outflow hydraulic relation between the reservoir groups, and the outflow process of the downstream reservoir is predicted by combining the conceptual rainfall runoff model, the reservoir regulation model and the deep learning model.

The invention discloses a method for predicting the risk of a valley-type tailing pond being threatened by extra-small watershed flood, and the method comprises the steps: S1, obtaining the soil type, vegetation coverage and gradient of the extra-small watershed of the valley-type tailing pond, and the spatial distribution data of a valley, and building an underlying surface classification system; s2, non-linear key inducements in the rainfall-runoff process in the valley terrain extra-small watershed are extracted, dominant factors influencing runoff formation are recognized, and a non-linear confluence calculation numerical model is constructed; s3, constructing a distributed sensing monitoring network through a rainfall monitoring station and a runoff monitoring station; s4, constructing a layered three-dimensional hydrological hydrodynamic model of coupling rainfall runoff generation-slope confluence-shallow groundwater flow; and S5, establishing a risk assessment index system and a risk prediction model through a layered three-dimensional hydrological hydrodynamic model in combination with flood risk influence factors. According to the method, accurate prediction of the valley-type tailings pond under the flood threat in the extra-small watershed is realized, and the problem of poor predictability of the flood threat in the extra-small watershed is solved.

A water pollution risk early warning and tracing method based on multi-source data fusion belongs to the technical field of water pollution monitoring and early warning, and comprises the following steps: step 1, constructing a multi-source heterogeneous data acquisition network and realizing real-time data transmission; 2, performing multi-source data fusion and feature enhancement processing based on space-time reference; 3, constructing a water pollution risk dynamic early warning system based on a WOA-LSSVM model; fourthly, reverse positioning of the pollution source is completed on the basis of a CNN-GRU-SE Attention model; and 5, carrying out development and emergency response on a multi-dimensional visual decision support system, and positioning a pollution source. Multi-source information such as water quality sensor data, unmanned aerial vehicle image data and geographic information data is fused, and an intelligent monitoring network is constructed; through multi-source data fusion and an intelligent algorithm, water pollution risk early warning accuracy and traceability efficiency are effectively improved, and the method has the advantages of high response speed, wide monitoring range, accurate positioning and the like, can be widely applied to the fields of urban water supply, drainage basin management and the like, and meets the requirements of water environment safety guarantee.

The invention discloses a multi-scale fusion ecological hydrological interaction quantification method, and relates to the technical field of ecological hydrology, and the method comprises the steps: 1, collecting environment driving data and remote sensing data; 2, preprocessing the remote sensing data, and carrying out hydrological process dynamic monitoring and ecological parameter collaborative inversion in combination with environment driving data to respectively obtain hydrological data and ecological indexes; 3, constructing a spatio-temporal data set by using the hydrological data, the ecological indexes and the environment driving data; 4, constructing a bidirectional LSTM neural network model, and performing training optimization by using the spatio-temporal data set to obtain a water volume change predicted value, lag time and corresponding ecological variables; 5, lag effect analysis is carried out according to the water volume change predicted value, the lag time and the corresponding ecological variables, and an interaction quantification network diagram of the ecological hydrological elements is constructed. According to the method, the dynamic coupling relation of the ecological hydrological process of the sand lake basin is quantified, and theoretical support is provided for resource optimization management and ecological restoration engineering.

The invention belongs to the field of hydrological data processing, and particularly discloses a runoff data interpolation model construction method and system. The method comprises the following steps: constructing a time sequence runoff data set on the basis of spatial and temporal distribution characteristics of hydrometric stations of a river basin; a space-time coupling interpolation model architecture is constructed and comprises an encoder and a decoder, the encoder comprises a Bi-LSTM module and a multi-head attention module, the Bi-LSTM module extracts forward and backward local time sequence characteristics of time sequence data layer by layer through a bidirectional information transmission mechanism, the multi-head attention module is used for capturing a space-time relationship in the time sequence data, and the time sequence data is subjected to time sequence data processing. The decoder adopts a mask self-attention mechanism to combine with a feature weight fusion module to dynamically correct a missing value; and performing model training and optimization on the interpolation model by using the time sequence runoff data set. According to the invention, interpolation of key time sequence dynamic information in the hydrological field can be realized.

The invention discloses a river and lake water level and flow prediction method and system in the field of remote sensing hydrology and hydraulic engineering, and the method comprises the steps: obtaining satellite radar altimeter data, river and lake water system data, rainfall inversion data and surface temperature inversion data based on a target river, a target lake and a watershed range file; taking the cross interval as a virtual hydrometric station observation point, extracting and processing waveform data, calculating water surface elevation information through a wavelet tracking algorithm, constructing an elevation profile group to establish an initial water level time sequence, and generating a river and lake water level inversion data set through fitting and elevation reselection. Meanwhile, NDWI data are obtained from satellite radar altimeter data, water surface width data are extracted, an inversion data set is formed by combining actually-measured river channel section data and a Manning formula inversion river channel flow rate, and finally various kinds of data are input into a pre-constructed intelligent prediction model to obtain future river and lake water level and flow rate change conditions. According to the invention, the precision of river and lake water level and flow prediction in areas lacking data is effectively improved.

The invention discloses a lake water resource-water safety collaborative annual-scale robust scheduling logic generation method, system and device and a storage medium, and the system comprises a watershed hydrology-lake body hydrodynamic coupling modeling module which is used for constructing a distributed watershed hydrology model and a lake body hydrodynamic model; the comprehensive meteorological uncertainty boundary file generation module is used for obtaining a watershed meteorological boundary file and a lake body meteorological boundary file; the drainage basin hydrological simulation module is used for forming a drainage basin inflow-water taking boundary file set covering meteorological uncertainty; the lake water level control strategy generation module can obtain a plurality of water level control boundary files; a lake body hydrodynamic simulation module; and the water level control strategy determination module is used for determining a final annual scale robust water level scheduling strategy. According to the method, the spatial-temporal heterogeneity of rainfall and a multi-scene water level control strategy are comprehensively considered, the accuracy and robustness of lake water resource management are remarkably improved, and reliable operation of the system under various meteorological conditions is guaranteed.