164 results about "River network" patented technology

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 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 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 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.

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 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 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.

The invention provides a high-fidelity generalization method and system for plain river network water flow simulation, and relates to the technical field of model simulation. The method comprises the steps that a node river network is constructed, constructing a physical river network topological structure comprising a main river channel node, a river reach, a regulation and storage node and a virtual river reach; a dynamic parameter configuration step: configuring hydraulic parameters for the section in the physical river network topological structure; a boundary coupling step: distributing water catchment unit runoff obtained by calculation of the hydrological model to corresponding regulation and storage nodes as boundary input conditions; and a simulation execution step: performing hydrodynamic simulation calculation based on the constructed physical river network topological structure, the configured hydraulic parameters and the distributed boundary input conditions. Through a high-low water level dynamic parameter method, a hydrodynamic model parameter calibration method is optimized, especially for rivers with large water level fluctuation amplitude, and the accuracy of simulation results is ensured.

The application discloses a kind of automatic realization small watershed mountain torrent flood flow and inundation prediction method, including steps: step S1, the parameters of the preset basin are obtained, including grid water flow direction, grid slope, basin river network, river network width;Step S2, the CN value and K value of each grid in the preset basin are obtained;Step S3, the flow and confluence of each grid in the preset basin are calculated;Step S4, a mountain torrent inundation model based on the open source model LISFLOOD-FP is constructed;Step S5, the basin flow prediction value and flood inundation range are automatically calculated and output by the mountain torrent inundation model.The present application can realize the automatic real-time prediction from rainfall to flow prediction to inundation range.

The invention discloses a rapid treatment device and method for neonicotinoid pesticide polluted water in a river network area, and belongs to the technical field of water treatment. The device comprises a mixing unit, a settling separation unit and an adsorption medium circulating unit, the mixing unit is provided with a tangential water inlet and an adsorption medium feeding port; the settling separation unit is used for realizing rapid gravity separation of the adsorbed medium and water; and the adsorption medium circulation unit is used for medium recovery and regeneration. The invention also discloses a special core-shell structure composite particle adsorption medium and a preparation method thereof, and a rapid adsorption treatment method based on the device. Through the physical process of eddy enhanced mixing-rapid settling separation, the problems that a traditional adsorption technology is low in treatment speed, prone to blockage and low in flux are solved, and the device and the method are particularly suitable for emergency treatment of sudden and large-flow trace neonicotinoid pesticide polluted water in river network areas and have the advantages of being rapid in response, large in treatment flux, reliable in operation and low in cost.

An urban-flooding-oriented optimization method for real-time control rules for a river network. The method comprises: S1, collecting data of a river network; S2, constructing an urban hydrological-hydrodynamic coupling model; S3, on the basis of a rainstorm intensity formula, simulating and calculating flooding processes in different design rainstorm scenarios, and comprehensively identifying drainage outlets within a study area that are susceptible to backwater effects from water levels in the river network; S4, using a clustering algorithm to perform spatial clustering on the backwater-affected drainage outlets, so as to define regulation and control zones, further performing feature clustering on the backwater-affected drainage outlets on the basis of spatial clustering, and outputting a cluster center with the greatest influence and using same as a river network regulation and control point; S5, using the river network regulation and control point as a regulation and control reference object, and formulating optimized gate and pump control rules on the basis of warning water level data and ecological water level data of river channels; and S6, inputting the optimized gate and pump control rules into the urban hydrological-hydrodynamic coupling model, using a rainstorm event to evaluate the performance of the method, and quantitatively analyzing the response of flooding to the gate and pump control rules. The method enables the regulation and control of water levels in a river network during a rainstorm, thereby mitigating the risk of flooding.

The utility model discloses a plain river network reciprocating flow choking and guiding device, which relates to the field of river channel guiding facilities and comprises a plurality of guiding plates arranged on two sides of a river channel bank, the appearance of each guiding plate is in a Tesla valve shape, and all the guiding plates are arranged on the river channel bank in the same direction; the fixed seats are arranged in one-to-one correspondence with the guide plates, and the fixed seats are arranged on the bank of the river channel; one end of the fixing frame is rotationally arranged on the fixing seat, and the other end of the fixing frame is connected with the flow guide plate; according to the device, a simple flow blocking-guiding structure is constructed in a plain river network area by utilizing a Tesla valve principle, so that the flow direction of a river channel water body can be controlled under a certain hydrological condition, accurate prevention and control of a protection area can be realized, and the flow blocking-guiding structure has the characteristics of simplicity in design and construction, low economic cost, good prevention and control effect and the like.

This application provides a method and system for extracting water body contours from high-orbit satellite imagery, comprising the following steps: cutting the original image into a sliding window to generate an image patch sequence; inputting the image patch sequence into a heterogeneous dual-stream encoder to extract dual-branch heterogeneous features; performing deep interaction on the dual-branch heterogeneous features in the heterogeneous dual-stream encoder to obtain deep heterogeneous collaborative features and shallow heterogeneous collaborative features at multiple feature extraction stages; processing the deep heterogeneous collaborative features and the shallow heterogeneous collaborative features to obtain a probability prediction map; using a square-shaped weighted fusion strategy to reassemble the probability prediction maps of all image patches to obtain a full-image probability prediction map; and performing binary partitioning on the probability prediction map to obtain the final binary mask map. This invention solves the problems inherent in high-orbit SAR imagery, such as uneven texture within water bodies due to noise, difficulty in maintaining the topological integrity of small river networks, and gaps and misalignments left during reassembly and stitching.

The application discloses a kind of complex tide river network's multi-source compound disaster inundation risk simulation method, contains the construction method of astronomic tide-storm surge-flood-salinity-falling multi-scale dynamic process full coupling model, can analyze complex river network in climate change scenario mountain torrent, storm surge, salinity and rainstorm etc. extreme compound disaster's inundation and salinity upstream risk.Through potential inundation area determination, model grid optimization, numerical format setting and parallel acceleration etc. technical means, on the basis of guaranteeing momentum and material flux conservation in river network, maximum degree optimizes calculation efficiency, can realize in estuary-coastal cross-scale spatial region high-precision simulation of the purpose of giving priority to multi-source disaster inundation and saltwater stratification.Based on the application, the problem that water dynamics, salinity, relatively isolated nearshore complex topography cannot interact in traditional simulation can be solved, technical support is provided for scientific evaluation of estuary area multi-source disaster risk and return period, with outstanding social benefits and disaster prevention and mitigation economic benefits.

The application discloses a surveying-based binary structure small watershed division method and system, and particularly relates to the field of watershed division. Basic data processing of the application includes collecting and preprocessing topography, remote sensing and other data, establishing a basic database, converting vector contour line data into DEM and filling in depressions, generating a non-depression DEM, determining water flow direction by using a D8 algorithm, calculating cumulative flow, and generating a river network. The river channel is converted into a vector runoff network, the sub-watershed range is extracted and mapped, the outcrop point of underground river or karst spring is taken as a corrosion reference, the watershed outlet is determined in combination with the main river channel of a large river, and a complete correlation system is constructed. Finally, the development of the underground water system pipeline is determined through hydrogeological investigation, geophysical prospecting, drilling and pumping test, the results of comprehensive investigation, geophysical prospecting, drilling and pumping test are comprehensively considered to accurately correct the boundary of the underground water system, and the underground water system distribution map conforming to the actual conditions is formed in combination with remote sensing images and administrative division maps.

The invention discloses a water conservancy regulation and control information fused plain river network area pollutant flux simulation correction method, which realizes deep fusion of a physical mechanism and deep learning, utilizes a physical reference of an SWAT model as a basic constraint, utilizes deep learning to excavate a nonlinear residual error, and cooperates with ReLU physical constraint and a graph topology transmission mechanism to realize simulation correction of the flux of pollutants in a plain river network area. The method has the interpretability of a physical model and the high precision of a data-driven model, and is particularly suitable for monitoring a complex watershed with scarce data. High-precision inversion and correction are carried out on the day-by-day runoff, the total nitrogen flux and the total phosphorus flux of the whole watershed through a small amount of key node monitoring data, and powerful support is provided for space-time analysis of plain river network area non-point source pollution and water environment fine management. The problem of false runoff production in a plain river network area is solved, cascade correction logic with runoff as quality is established, and the problems of runoff simulation distortion and large pollution flux estimation error of a traditional hydrological model are avoided.

This invention relates to the field of water pollution control technology, and discloses a method and system for river section pollution source tracing analysis based on pollution source inflow calculation. The method includes the following steps: collection of basic watershed data; construction of river network topology; calculation of pollution inflow into sub-watersheds, obtaining the pollution discharge amount of each pollutant indicator for each pollution source in each sub-watershed based on the spatial relationship between administrative regions and sub-watersheds, and obtaining the pollutant inflow amount of each pollutant indicator for each pollution source in each sub-watershed based on the inflow coefficient; pollution source tracing analysis: based on the results of the sub-watershed pollution inflow calculation, combined with the river network topology and the monitoring section data, calculating and analyzing the upstream pollution inflow situation and the contribution ratio of various pollution sources at the selected target monitoring section. This invention achieves comprehensive coverage of multiple pollution sources within a large watershed, meets the needs of precise source tracing in large watersheds, and provides a scientific basis for watershed water pollution control.

The application discloses a simulation and dispatching control method and system for urban flood process. The application belongs to the technical field of urban water conservancy system control. The method comprises the following steps: collecting and constructing a specific type of basic data set, respectively constructing a hydrological runoff model, a one-dimensional water power model of a drainage pipe network, a river network mixed water power model and an engineering dispatching model; performing three-dimensional dynamic coupling to form a water resource full-space dynamic model; using at least one historical flood event in historical flood data to calibrate parameters and verify accuracy of the water resource full-space dynamic model; after verification, using the water resource full-space dynamic model to build a digital simulation platform to simulate flood and determine a dispatching scheme. The technical scheme can realize multi-scenario dynamic simulation, automatic comparison and selection after generation of a dispatching scheme, enhance system adaptability and response timeliness under extreme weather, and provide an efficient and accurate flood control and drainage scheme generation mechanism for urban area flood control and drainage.

This invention relates to a river network classification system and method based on graph theory and confluence accumulation, belonging to the fields of water conservancy engineering and geographic information technology. The method includes the following steps: acquiring raw DEM data and river system data, and converting the river system data to a preset projected coordinate system; extracting a primary river network based on the DEM data, and filtering the primary river network by combining river network length and drainage area to obtain a simplified river network; calculating river network flow: assigning flow values ​​to each river network element in the simplified river network based on confluence accumulation; graph theory modeling: abstracting the simplified river network into a directed graph model; river network classification: tracing back from the outlet as the starting point, combining node in-degree / out-degree and edge weights to determine the hierarchical relationship between the main stream and tributaries, thus realizing river network classification. This invention upgrades river network classification from "morphological description" to "hydrological function characterization," and is suitable for high-precision refined analysis of river networks and smart water conservancy emergency management scenarios.

The invention discloses a complex river network flow prediction method based on a diffusion space-time diagram neural network guided by physical information, and relates to the technical field of intelligent water conservancy and hydrological time sequence prediction. According to the method, a diffusion space-time diagram neural network (PI-Diffusion STG) guided by physical information is constructed aiming at the problems of lack of physical consistency and insufficient long-distance space-time dependence capture capability of an existing data-driven model. According to the model, a bidirectional graph diffusion convolution module is designed, forward-flow hydraulic conduction and reverse-flow jacking effects in a river network are simulated through forward diffusion and backward diffusion respectively, and bidirectional spatial-temporal characteristics are effectively extracted; performing multi-step traffic prediction by adopting a sequence-to-sequence architecture; meanwhile, a Saint-Venant equation set describing fluid mechanics is used as a physical constraint regularization term to be deeply fused into a loss function, and a physical residual error is calculated by utilizing a hybrid strategy combining automatic differential and finite difference on a graph. According to the method, the prediction precision of the complex river network under extreme conditions can be remarkably improved, the prediction result is ensured to accord with the law of conservation of momentum and quality, and the method has relatively high robustness and physical interpretability.

The application discloses a kind of tidal river network sluice operation data model processing method, system and terminal, it is related to sluice operation analysis technical field, and its technical scheme main points are: initial outer river tidal level is carried out noise reduction pretreatment, and sliding average is carried out;Select the smooth result of the highest matching degree of rising and falling tide trend, and carry out abnormal fluctuation elimination processing;The rising and falling tide trend in standard smooth result is analyzed;Rising and falling tide trend result and water level difference threshold are loaded in Infoworks RTC module;Different water level difference threshold is tried, and the optimal water level difference threshold of sluice opening and closing is determined, to obtain final Infoworks RTC module.The application is carried out to the smooth processing of outer river tidal level data, and the outer river tidal drop process is judged, according to the inner and outer river tidal difference to control the opening and closing of sluice, reach water quality model operation working condition and actual engineering working condition consistent, provide strong support for later water power water quality model rating.

The present application belongs to the technical field of space-to-earth observation (satellite remote sensing) and watershed hydrology, geographical environment disciplines, and discloses a watershed scale high spatio-temporal resolution river network remote sensing information extraction method and system. After cloud and cloud shadow processing of optical remote sensing images, a simple non-iterative clustering superpixel segmentation algorithm is used to segment homogeneous landscape objects, and a hierarchical decision tree is constructed using a water body index and a vegetation index. In the area where the optical remote sensing image is seriously covered by clouds or the image is missing, the SAR image is filtered, the dual polarization water body index is calculated using the VV and VH polarization images, the maximum inter-class variance method is executed, and the image is binarized. After obtaining the water body result extracted from the remote sensing image, the river cut-off is repaired in combination with the water system diagram generated by the DEM model. The present application can automatically extract accurate, continuous and high spatio-temporal resolution river network remote sensing information, and can be expanded to global scale river network feature extraction.

The application 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 the downward trend of soil permeability characteristics and estimating the uncertainty range of runoff coefficient; using a regional source-sink pollution model, obtaining source model data according to the uncertainty range of runoff coefficient; using a one-dimensional river network model, obtaining error information and outputting prediction data according to the source model data; and determining the prediction deviation mode by comparing the prediction data and real-time monitoring data and correcting the parameters of all related models in the whole model chain. The method aims to solve the technical problem that the prediction of the river network model deviates from the actual measurement data in a nonlinear manner in a three-model coupling system, which ultimately leads to the inconsistency between the permitted discharge amount set based on the system and the actual water body absorption capacity, and to realize the accurate analysis of the response relationship between the urban pollution load and the water environment quality and the real-time and self-adaptive adjustment of the permitted discharge amount.

The application discloses a kind of topography and geomorphology automatic classification method based on local reference bottom surface, steps are as follows: based on the original DEM data of study area to generate confluence accumulation data;Based on confluence accumulation to construct river network, with river network point as the elevation control point of base terrain, construct local reference bottom surface DEM;With original DEM data subtracts local reference bottom surface DEM, obtains the surface relief of study area, according to the range of relief degree classification mountain plain, according to relief degree further divides mountain;With elevation as classification criterion to carry out global elevation classification to study area, comprehensively relief degree classification result and elevation classification result, obtain final classification result;Classification result post-processing.The application effectively improves the precision and regional integrity of topography and geomorphology classification by using TIN to construct local reference bottom surface, can realize the high-precision automatic division of global topography and geomorphology class in the case of only using DEM as original data, provides solid data foundation for global geomorphology research.