257 results about "River network" patented technology

The invention relates to a disaster multi-source situation intelligent sensing management and control method and system. According to the method, hydrometeorological and topographic data are collected, and a standardized data set is generated through space-time alignment and anomaly cleaning; constructing a directed topological graph containing node and edge attributes based on the extracted river network topological relation; designing a neural network model, and training through a physical constraint loss function embedded in a water balance principle to obtain a flood dynamic routing prediction model; inputting real-time hydrological data into the model for graph convolution operation, and predicting water level, flow and split ratio changes of each node in a future time period; and finally, carrying out submerging simulation analysis in combination with a digital elevation model, and generating a flood control scheduling scheme and risk early warning information. The deep fusion of a physical mechanism and data driving is realized, the flood propagation rule under the river network topology constraint is effectively captured by using the graph neural network, the calculation efficiency is remarkably improved while the prediction precision is ensured, and real-time and reliable decision support is provided for flood disaster prevention and control in a complex river network region.

The invention discloses an automatic river network grading and river topological relation identification method based on a graph theory, and relates to the technical field of hydrological geographic information. The method comprises the following steps: acquiring and cleaning a vector river network, a key point location and DEM data of a target drainage basin; constructing an initial river network graph model based on the line element connection relationship; integrating DEM topographic evidence and graph theory connection features, constructing and solving a global potential energy field equation containing topographic driving and boundary constraint, and calculating flow potential energy attributes of nodes of the whole network to determine a flow relationship; based on the flow direction relation, identifying topology abnormal structures such as strong connectivity components in the network, and performing ring breaking processing by using direction confidence to generate a ring-free directed network structure; and performing river grade division based on a topology transfer rule, and associating the key point location to a river network skeleton. According to the method, through global potential energy field solving and topological optimization, the problems that the flow direction of the plain micro-geomorphic area is difficult to recognize and complex loops cannot be graded are solved, and automatic construction of the river network topology is achieved.

The invention discloses an adaptive neural network flood routing simulation and risk assessment method. The method comprises the following steps: step 1, generating multi-source hydrometeorological preprocessing data; 2, constructing a river network topological graph, generating node feature vectors and edge feature vectors, and forming time-space diagram input data; 3, inputting the liquid state time constant neural network to execute continuous time state updating, and generating a node hydrological state prediction vector set; step 4, injecting hydrodynamic physical consistency residual errors to generate a physical constraint hydrological state prediction vector set; 5, executing adaptive lag compensation, and generating a lag compensation hydrological state prediction vector set; 6, mapping to generate flow, water level and flood peak arrival time prediction data; and step 7, outputting a risk grading graph and early warning threshold list data. According to the invention, accurate prediction of the flood process and dynamic generation of the risk map are realized, and early warning precision and response efficiency are improved.

The invention discloses a river flow missing data reconstruction method, which comprises the following steps of S1, constructing a river network topological structure, and quantifying hydraulic correlation; s2, spatio-temporal feature fusion and multi-source information extraction; s3, performing multi-task cooperative flow reconstruction and confidence coefficient prediction; s4, dynamic weighting and result correction of meteorological factors; and S5, performing anomaly detection and model dynamic updating. According to the method, a river flow missing data reconstruction method is set, the steps are mutually fused and used, and topology-aware space-time diagram convolution is performed: a river network topology structure is encoded into a weighted adjacency matrix for the first time, space correlation features are extracted through a diagram convolution network, and the problem that a traditional method ignores hydraulic connection is solved; a multi-task collaborative learning mechanism: synchronously outputting a flow reconstruction value and confidence, combining topological smooth constraints, and realizing reconstruction reliability quantification while ensuring precision; and meteorological dynamic weighted correction: dynamically adjusting the node weight based on the real-time rainfall intensity, and accurately adapting to the nonlinear response of the water flow in the heavy rainfall period.

The invention discloses a flood disaster risk intelligent early warning method fusing multi-source information, and relates to the technical field of flood disaster prediction.The method comprises the steps that multi-source data of meteorology, hydrology, terrain, remote sensing, ground monitoring terminals and the like are obtained, and a unified data set is constructed through time synchronization, space resampling, abnormal value elimination and missing data filling; constructing multi-dimensional spatio-temporal features based on factors such as rainfall process, topographic features, surface coverage, drainage capacity, river network structure and soil humidity; inputting the spatial-temporal characteristics into a multi-source information fusion model, and predicting a flood occurrence probability in a future time period; flood risk indexes are generated according to the prediction probability, real-time monitoring and historical samples, and regional risks are divided into different grades; early warning information is automatically generated and issued according to the risk level; and the early warning result is fed back and corrected through field monitoring and patrol data, so that self-adaptive updating of early warning parameters and a model structure is realized.

The invention discloses a drainage basin nitrogen and phosphorus pollution prediction method based on rainfall runoff migration, and relates to the technical field of water quality pollution prediction, and the method comprises the steps: accurately obtaining land utilization and water body distribution through a remote sensing technology, carrying out the space division of a pollution source through combining a nitrogen and phosphorus load coefficient, and constructing a rainfall runoff model. The runoff volume and time and space distribution under different rainfall events are reflected, and the spatial dynamic change of pollutants is captured through a hydrodynamic model, in combination with a convection diffusion equation and by introducing a conversion model of nitrogen and phosphorus in various forms. By integrating remote sensing data, rainfall runoff simulation, hydrodynamic force and nitrogen and phosphorus form transformation, the space-time migration and transformation process of nitrogen and phosphorus pollution in a drainage basin is described, so that the coupling relation between pollution source distribution and runoff power is revealed, the conveying and diffusion rule of pollutants in a river network is dynamically reflected, and the drainage effect is improved. And refining to transformation evolution of different nitrogen and phosphorus forms, and generating intuitive concentration distribution and thermodynamic diagrams through spatial interpolation.

The invention relates to an intelligent regulation and control system for multi-phase conversion of water quality of a gate-controlled river reach, in particular to the field of water treatment, and realizes accurate dynamic perception of the multi-phase conversion process of the water quality of the gate-controlled river reach by constructing a high-fidelity digital twinborn body and fusing a real-time data assimilation technology; by means of a time-space diagram neural network agent model based on an attention mechanism, the system can predict a complex time-space pattern of pollutant concentration field and phase evolution under different regulation and control strategies in an ultra-fast manner; on the basis, a multi-agent reinforcement learning algorithm is adopted to automatically generate a globally optimal gate group coordinated regulation scheme, and multiple targets of water quality improvement, ecological protection, engineering operation and the like are effectively coordinated; and finally, a theoretical strategy is reliably converted into a physical action through rolling optimization and a closed-loop execution mechanism, and adaptive adjustment can be performed according to environmental feedback, so that the emergency response speed of sudden water pollution events, the scientificity of regulation and control decision and the intelligent level of water quality management of the whole river network are comprehensively improved.

The invention relates to the technical field of water conservancy informatization and intelligent early warning, in particular to a river network water regimen big data intelligent analysis early warning system which comprises a dynamic data fusion module, a topology sensing module, an overload propagation analysis module, an intelligent early warning decision module and an early warning execution module. Wherein the dynamic data fusion module is used for collecting multi-dimensional data of a river network area in real time; the topology sensing module is used for dynamically constructing a real-time topological relation graph of the river network connectivity; the overload propagation analysis module is used for extracting an overload coefficient of each node and outputting a risk diffusion path set; and the intelligent early warning decision module is used for generating a three-level early warning instruction by combining the positions of the engineering vulnerabilities in the real-time topological relation graph. According to the method, through fusion of the multi-source water regimen data and the dynamic topological structure, linkage of accurate identification and hierarchical response of the risk path is realized, and the intelligent early warning and rapid disposal capability of the sudden water regimen of the river network is remarkably improved.

The invention provides a water environment model coupling data processing method and system, and relates to the technical field of hydrology and water quality monitoring. The method comprises the following steps: determining a soil permeability characteristic descending trend and estimating an uncertainty range of a runoff coefficient; obtaining source model data according to the uncertainty range of the runoff coefficient by using a regional pollution production and convergence model; using the one-dimensional river network model to obtain error information according to the source model data, and outputting prediction data; and determining a prediction deviation mode by comparing the prediction data with the real-time monitoring data, and correcting parameters of all related models in the whole model chain. The method aims at solving the technical problem that the permissible discharge amount set based on a three-model coupling system does not conform to the actual absorption capacity of a water body due to continuous nonlinear deviation of river network model prediction and actually measured water quality data in the three-model coupling system, and accurate analysis of the urban pollution load and water environment quality response relation is achieved. And real-time and self-adaptive adjustment of the permissible emission amount is carried out.

The invention relates to a river network grading system and method based on a graph theory and confluence cumulant, and belongs to the technical field of water conservancy projects and geographic information. Comprising the following steps: acquiring original DEM data and water system data, and converting the water system data to a preset projection coordinate system; extracting a primary river network based on the DEM data, and screening the primary river network in combination with the length of the river network and the area of the drainage basin to obtain a simplified river network; river network flow calculation: carrying out flow assignment on each river network element in the simplified river network based on the confluence cumulant; graph theory modeling: abstracting the simplified river network into a directed graph model; and river network grading: reverse tracing is carried out by taking the water outlet as a starting point, and the hierarchical relationship between the trunk and the branch is determined by combining the in-degree / out-degree and the edge weight of the node, so that river network grading is realized. According to the method, river network grading is upgraded from morphological description to hydrological function description, and the method is suitable for high-precision river network refined analysis, intelligent water conservancy emergency management and other scenes.

The invention relates to the field of process control, in particular to a multi-target particle swarm optimization scheduling system for flood control and waterlogging removal in a plain river network polder area. The method comprises the specific implementation steps that a coupling hydrodynamic model module is constructed, the water flow exchange amount between a one-dimensional hydrodynamic model and a two-dimensional hydrodynamic model is calculated, multiple times of simulation are carried out to generate a training data set, and a neural network agent model is trained and generated; generating an initial population based on topological structure information of the plain river network and a heuristic rule by adopting a particle swarm optimization algorithm; according to external work condition information, dynamically adjusting feasible region constraints of an optimization algorithm, inputting candidate scheduling schemes conforming to the constraints into the neural network agent model, and outputting a predicted scheduling state; based on the predicted scheduling state and the weight coefficient, the fitness value of each candidate scheduling scheme is calculated, the optimal scheduling scheme is output through iterative optimization, and the decision-making efficiency, robustness and safety of the scheduling system in the face of complex working conditions and emergencies are improved.

The invention belongs to the technical field of hydrology and water resources, and discloses a cold and dry mountainous area drainage basin runoff simulation method based on an MIKE SHE / MIKE 11 coupling model in combination with GIS / RS data. According to the multi-source data fusion technology, a cold and dry mountainous area distributed input data set is constructed through TRMM satellite rainfall data, an MODIS LAI product and an ASTER DEM, and the rainfall space representation precision is improved by 40%. According to the model coupling mechanism, an MIKE SHE / MIKE 11 collaborative simulation framework is provided, earth surface-groundwater flow data interaction is achieved through river channel linkage, and the complex river network flood routing simulation error is reduced to 10% or below. According to the parameter partition calibration method, a drainage basin is divided into five hydrogeological regions, sensitive parameters such as a Manning coefficient and a hydraulic conduction coefficient are optimized in combination with a genetic algorithm, and the basic flow simulation precision is improved by 25%.

The invention discloses a business district drawing method and device based on geographic space aggregation, and the method comprises the steps: carrying out the spatial density clustering of all network points in a to-be-drawn space, and obtaining a plurality of network point clusters; correspondingly generating a plurality of convex hulls based on the plurality of dot clusters; and performing boundary optimization on the convex hull according to the collected river network data, and determining the optimized convex hull as the business circle in the space to be drawn. According to the method, the problems of fuzzy boundary, data lag, poor strategy adaptability and the like in the traditional technology can be effectively solved, accurate definition of the contour of the business district and standardized output of grade division are realized, and then the efficiency of drawing the business district is improved.

The invention discloses a plain river gatekeeper group intelligent regulation and control method based on deep reinforcement learning. The method comprises the steps that a flow prediction module, a knowledge constraint reinforcement learning module combining domain knowledge and an experience library and a multi-agent reinforcement learning module are built; integrating the calibrated knowledge constraint reinforcement learning module with a flow prediction module and a multi-agent reinforcement learning module to form a complete plain river network intelligent regulation and control model; and on the basis of the calibrated plain river network intelligent regulation and control model, future meteorological and hydrological prediction data are accessed, and a reasonable gate group scheduling scheme is rapidly output. The method has the advantages that the scheduling scheme cluster is intelligently generated, the complex and dynamically changing plain river gatekeeper group scheduling problem is efficiently solved, the flexibility, timeliness and intelligent level of a gatekeeper group scheduling system are improved, the possible'non-realistic solution 'problem of a machine learning method in practical application is solved, the interpretability of a result and the practical operability of a scheme are enhanced, and the method is suitable for large-scale popularization and application. And a self-adaptive closed loop of model-environment-feedback-regulation and control correction is formed.

The invention discloses a watershed multi-scale water quality target overall regulation and control method, which is characterized in that a regulation and control unit is delimited based on DEM data, a multi-scale spatial analysis framework is constructed through a hydrological topology and watershed nesting relationship, and the coupling process analysis of pollutant discharge, absorption and transportation is realized; deducing a water quality control target of each catchment area step by step through a process coupling reverse algorithm by taking a watershed outlet section water quality assessment target as a reference; dynamically quantifying a balance relation among pollutant discharge, land area system interception and river network system absorption based on a source-sink profit and loss intensity index, and realizing source-sink state diagnosis of different regulation and control units; and finally, generating a basin overall regulation and control scheme by delimiting a pollution emission hot spot area and a weak absorption capacity area and analyzing a regulation and control scene. The method can be widely applied to water environment refined treatment scenes such as river and lake water quality target management and pollution control zoning, and provides scientific basis and technical support for watershed water environment protection and overall restoration.

The invention relates to a coastal city flood multi-system coupling evaluation method and system, and the method comprises the steps: obtaining multi-source data, and carrying out the unified projection and time resolution normalization processing of the data; constructing an upstream river network one-dimensional hydrodynamic model, an urban drainage pipe network one-dimensional hydrodynamic model and an urban two-dimensional surface water dynamic model based on the multi-source data, and setting tide boundary conditions; coupling each model and the tide boundary; performing model calibration and error constraint on the coupled multi-dimensional hydrodynamic coupling model; constructing a composite scene, taking each group of scenes as an input boundary condition to drive the multi-dimensional hydrodynamic coupling model, and obtaining a flood evolution result; and performing risk assessment and sensitivity analysis based on a flood evolution result. According to the method, a unified river network-pipe network-earth surface-tide multi-scale coupling framework is constructed, so that the defect that a traditional single model or result superposition method is difficult to reflect multi-system interaction is effectively overcome.

The invention relates to the technical field of water quality simulation, in particular to a river network water quality simulation method and system based on a cloud platform. The method comprises the following steps: obtaining river network basic data, river channel section data, river channel topographic data, water quality monitoring data and water level flow data, and constructing a river network hydrodynamic model according to the river network basic data, the river channel topographic data and the river channel section data, determining water quality component categories according to the river network hydrodynamic model in combination with the water quality monitoring data and the water level flow data, and constructing a preliminary coupling model; performing water quality initial field determination processing on the preliminary coupling model according to the water quality monitoring data to obtain a water quality initial concentration field model; and performing Nash efficiency coefficient-based model calibration and precision evaluation processing on the water quality initial concentration field model to obtain a hydrodynamic water quality coupling model. The invention provides a dual interpolation algorithm combining an inverse distance weighting method and time sequence data correction, and effectively solves the problem of insufficient precision of a traditional initial field determination method.

The invention relates to the technical field of environmental governance, and discloses an alpine region reservoir area water and sediment scheduling and water quality scenario prediction method comprising the following steps: river network segmentation and node classification; embedding ice kinetic parameters; transporting and correcting low-temperature silt; carrying out low-temperature adaptation on water quality parameters; performing dynamic boundary transfer and joint solution; and carrying out synchronous real-time iterative calculation. According to the method, ice kinetic parameters are embedded in a one-dimensional Saint-Venant equation, and the coupling relation between the thickness of the ice cover and the hydraulic element is dynamically solved by correcting the effective cross-section area in a continuous equation and the friction gradient in a momentum equation and adopting an iterative algorithm. Therefore, the water level fluctuation in the ice dam forming and outburst process is accurately described, and the problem that an existing model is insufficient in response to ice period hydraulic sudden change is solved.

The invention relates to the technical field of urban drainage and waterlogging prevention, in particular to a plain river network large-scale drainage and waterlogging prevention project group optimization scheduling method and system. The method comprises the steps of determining a typical composite waterlogging event of a plain river network, establishing a hydrodynamic model to analyze the typical composite waterlogging event to identify key nodes of waterlogging, determining a linkage relationship between a drainage facility scheduling behavior and hydrological response of the key nodes, and establishing a multi-target optimization scheduling model with waterlogging risk minimization and operation cost optimization. And gradually decomposing a global scheduling target into a local scheduling target, establishing a mapping relationship between the local scheduling target and a flood drainage facility scheduling decision, obtaining a multi-target optimization scheduling model for potential individuals predicted in different candidate scheduling scheme populations, performing real solution, constructing an expectation improvement matrix, and selecting an optimal solution to obtain an optimal scheduling scheme. According to the method, the scheduling timeliness requirement is effectively met in combination with the machine learning prediction capability and the optimization algorithm, and optimal scheduling of the large-scale drainage and waterlogging prevention project group in the plain river network urban area is realized.

The invention belongs to ecological environment engineering, and particularly relates to a river and lake wetland zoning and classifying ecological water demand fine accounting method, which comprises the following steps of S1, performing zoning and classifying on a river and lake wetland of a research area; s2, establishing a space-time coupling hydrodynamic model based on a partition classification relationship; s3, constructing a multi-factor ecological response model, and calculating an ecological suitability index of each partition; s4, establishing a multi-objective optimization model, and performing optimization solution on the ecological water demand of the partition by combining ecological benefits, water balance and process smoothness objectives; and S5, performing robust correction on an optimization result by adopting an uncertainty analysis method to obtain a final ecological water demand distribution result. By establishing a river and lake wetland partition classification spatial database based on digital elevation, river network distribution, land utilization and ecological attribute information, fine description and dynamic association of different ecological functional areas are realized, and the effects of ecological process differentiation characterization and remarkable improvement of water demand accounting precision are obtained.

This invention discloses an adaptive neural network method for flood evolution simulation and risk assessment, comprising the following steps: Step 1, generating multi-source hydrological and meteorological preprocessing data; Step 2, constructing a river network topology map and generating node feature vectors and edge feature vectors to form a spatiotemporal graph input data; Step 3, inputting a liquid time constant neural network to perform continuous time state updates, generating a set of node hydrological state prediction vectors; Step 4, injecting hydrodynamic physical consistency residuals to generate a set of physically constrained hydrological state prediction vectors; Step 5, performing adaptive lag compensation to generate a set of lag-compensated hydrological state prediction vectors; Step 6, mapping and generating predicted data for flow, water level, and peak arrival time; Step 7, outputting a risk classification map and a list of early warning thresholds. This invention achieves accurate flood process prediction and dynamic generation of risk maps, improving early warning accuracy and response efficiency.

The invention relates to the technical field of water quality monitoring, in particular to a drainage basin water quality interpretable prediction method based on a space-time diagram attention network, which comprises the following steps: S1, obtaining and preprocessing drainage basin data; s2, decomposing a multi-scale time sequence; s3, constructing a river network graph structure; s4, spatio-temporal feature extraction and fusion; s5, water quality prediction output; s6, carrying out interpretability analysis; according to the method, the spatial dependency relationship and water quality time evolution characteristics among monitoring stations in a drainage basin can be effectively captured, and precise modeling of space-time coupling characteristics is realized; meanwhile, prior knowledge in the field of watershed hydrology is fused, physical significance is given to model learning features, scientific reliability and interpretability of prediction results are improved, causal association between input features and the prediction results is clearly revealed, and high-precision and credible decision support is provided for practical application such as watershed water environment management, pollution traceability and early warning transmission.

The invention discloses an urban flood simulation and safety evaluation method, and belongs to the technical field of hydrological data processing and flood rehearsal. The method comprises the following steps: firstly, collecting DEM data of a to-be-processed range, performing projection depression filling preprocessing through ArcGIS software, calculating a flow direction and a flow rate, extracting a river network, and constructing a catchment area and a rainfall flood model; carrying out rainfall simulation and one-dimensional hydrodynamic force calculation by using SWMM software to obtain a river channel and node time sequence flow; then, the flow data are input into HEC-RAS software, two-dimensional hydrodynamic simulation is completed in combination with the terrain, and the flood inundation range and depth are obtained; finally, repeatedly simulating rainfall in different recurrence periods, and dividing flood risk areas according to indexes such as a water depth threshold value and a flooded area. According to the method, full-process automatic processing from rainfall input to flood routing is realized, the timeliness and precision of flood rehearsal are improved, and support is provided for disaster prevention and reduction decisions.

The invention relates to the technical field of water quality monitoring, and provides a water quality prediction method based on multi-source data and a graph attention long-short term memory network, and the method comprises the steps: obtaining the annual multi-source data of a target monitoring section, carrying out the preprocessing of the multi-source data, obtaining the to-be-detected data, and carrying out the detection of the to-be-detected data; and inputting the to-be-detected data into the trained water quality prediction model, and obtaining a water quality prediction result of a preset number of days. The water quality prediction model is trained based on multi-source sample data of a plurality of monitoring sections, a river channel topological graph structure and water quality label data, and the river channel topological graph structure is determined according to an upstream and downstream relationship of a target monitoring section in a target regional river network and is constructed based on a graph attention network and a long and short term memory network; comprising a two-channel graph attention network module, a time sequence feature extraction module and a prediction output module, the upstream and downstream relation of a monitoring section is fully considered, multi-source data are fused, spatial features and time features are fully acquired, and the accuracy of the obtained water quality prediction result is improved.

The invention discloses a construction method of a river network hydrodynamic condition visualization system based on digital twinning, and the method comprises the steps: employing a dual gradual change dynamic visual angle switching algorithm, and carrying out the smooth transition to a three-dimensional visualization environment through user interaction in a two-dimensional interface; a semantic deployment modeling mechanism and a three-dimensional river mechanism based on water level interpolation are utilized to automatically generate a building model and a river path in a river-side area, and a water flow material and a flow animation are constructed through a Cesium platform to realize flow effect rendering; water depth and flow velocity data under a continuous time sequence are dynamically acquired, real-time rendering is performed on a Cesium platform, continuity and smoothness of a visual effect are guaranteed by using a nonlinear response interpolation algorithm based on animation driving, and a simulation time step length is controlled through a timer mechanism, so that real-time simulation of the water depth and the flow velocity is realized. And accurate visual expression of the hydrodynamic force change process under different time scales is supported. And users are supported to carry out interactive operation under different visual angles and time dimensions, so that the visual expressive power of the river network hydrodynamic data and the system response efficiency are effectively improved.

The invention discloses a river network modeling and question-answering method based on a time sequence knowledge graph, and the method comprises the steps: fusing geographic information data and hydrological and hydrodynamic data, constructing a river network ontology model with a time evolution capability, obtaining a high-frequency concept through semantic clustering, and designing a multi-granularity time relation; carrying out entity and relation joint extraction on heterogeneous data by adopting an OneRel model, designing a SelfKG-T algorithm to complete entity alignment, and constructing a complete time sequence knowledge graph; and realizing semantic question answering and dynamic response to river network knowledge in combination with an RAG architecture and a large language model. The method has the advantages of knowledge graph construction automation, knowledge expression timing, question and answer interaction intelligence and the like, and is suitable for intelligent water conservancy scenes such as flood control scheduling and water resource management.

The invention discloses a method for evaluating the influence of a water system structure on a river network hydrological process. The method comprises the following steps: calculating water system structure indexes; a hydrological model and a river network hydrodynamic model of the research area are constructed, and under the same typical annual rainfall condition, hydrological process parameters under river network water systems in different historical periods are obtained through simulation of the hydrological hydrodynamic model; and constructing a machine learning model fused with physical constraints, analyzing the relationship between the water system structure change and the hydrological process response based on the water system structure index and the hydrological process parameter, and identifying the water system structure index having a key driving effect on the hydrological process through a feature importance analysis method. According to the method, a hydrological hydrodynamic model is coupled through a control variable method, and the independent influence of river network structure change on the hydrological process is accurately quantified; meanwhile, a complex nonlinear response relationship between a water system structure and a hydrological process is deeply analyzed by utilizing a machine learning model and feature importance analysis, and key water system structure indexes for regulating and controlling the hydrological process are identified.

The invention discloses a river water level inversion method taking altimeter footprint heterogeneity into consideration. The method comprises the following steps: acquiring an observation waveform of an SAR altimeter; space geometric parameters are calculated through footprint gridding and observation geometric modeling; constructing a water distribution scene based on the water mask and the river network, and classifying the water into a main river channel, a flood area and an adjacent isolated water body; electromagnetic scattering numerical integration is carried out based on the differentiated surface roughness parameters, and a simulated echo waveform is generated; waveform retracking and parameter inversion are realized through coarse grid search and local optimization solution; calculating an absolute water level and carrying out geophysical correction; performing multi-index quality control based on the interpretation rate of the analog waveform and the residual whitening degree; and finally outputting a water level product. According to the method, the defects of insufficient inversion precision and robustness of an existing method in a complex earth surface mixed scene are overcome, and the accuracy, reliability and physical interpretability of inland river water level monitoring are remarkably improved.

The invention provides a protection method, device and equipment for power grid facilities under natural disasters and a medium. The method comprises the following steps: firstly, acquiring an initial data set, wherein the initial data set comprises a rainfall data set, a normalized vegetation index, land utilization data, road network data, soil type data, river network density data, elevation data, slope data, rock data and a surface deformation rate; and then, based on the initial data set, simulating a position point of a landslide barrier dam generated in a river after landslide is generated by rainfall, and geometric parameters of the landslide barrier dam. A first evolution law of outburst flood generated by the landslide barrier dam after outburst is then simulated based on the location points and the geometric parameters. And finally, based on the first evolution rule of the outburst flood, determining emergency protection measures of power grid facilities at the downstream of the landslide barrier dam. The protection accuracy of power grid facilities under natural disasters in the prior art is improved.

The invention relates to the technical field of knowledge graph construction and water quality analysis, in particular to a water quality anomaly attribution identification method based on a water quantity and water quality coupling knowledge graph. The method comprises the following steps of: accessing hydrological data and water quality data of a drainage basin monitoring section, engineering scheduling data of an associated gate dam and drainage basin pollution source multi-source data to form a standardized water quantity and water quality associated data set; taking a monitoring section, a gate dam and a drain outlet as core entities based on a water quantity and water quality associated data set; spatial correlation is established according to a river network topological structure, time response correlation is bound based on time sequence monitoring data, and a dynamic coupling mechanism of water volume change and water quality evolution and a dynamic coupling mechanism of sewage draining exit discharge and water quality response are embedded, so that a water volume and water quality coupling knowledge graph is constructed. According to the method, the water quantity and water quality coupling knowledge graph is constructed, multi-source data are fused, intelligent and accurate attribution of water quality abnormity is realized, and the decision scientificity of intelligent water conservancy is improved.