97 results about "Watershed" patented technology

The invention discloses a space-time deep learning-based data-free watershed runoff prediction method and system. The method comprises the following steps: collecting static geographic raster data and dynamic hydro meteorological time series data of multiple watersheds, and performing preprocessing; constructing a double-flow deep learning model fusing space and time features; training a double-flow deep learning model based on the multi-region large-sample watershed data to obtain a general hydrological model; evaluating the adaptability of each network layer in the general hydrological model to a target watershed through a layered unfreezing test, and screening out a key adaptive layer; and under a leave-one-out method cross validation framework, layered progressive unfreezing transfer learning is carried out on the general hydrological model based on the key adaptation layer, and data-free drainage basin runoff prediction is realized. According to the method, multi-basin data driving, spatio-temporal feature fusion and a transfer learning mechanism are organically combined, the conversion of a hydrological modeling norm from local adaptation to global generalization is promoted, and a new path is provided for intelligent prediction of data-free basin runoff.

The invention discloses a landslide-debris flow disaster chain assessment method based on a graph neural network, and the method comprises the steps: generating a composite ground feature unit through the watershed segmentation of a composite curvature field and hydrological analysis, abstracting the composite ground feature unit as a geographic node, optimizing a multi-source environment factor through mutual information screening, constructing a directed weighted graph in combination with spatial similarity, and carrying out the reconstruction of a landslide-debris flow disaster chain. An improved GraphSAGE model which introduces neighbor weight, self-loop and residual error information and cancels random sampling is adopted, a sample set adaptive to a less-data area is constructed through a unit splitting-feature matching-terrain fitting mechanism, and model training and risk prediction are completed. The method solves the problems of single disaster assessment, strong sample dependence and high missed judgment rate in the traditional technology, has excellent performance indexes, effectively reduces the missed judgment risk of a high-risk area, and provides accurate and efficient technical support for disaster chain risk assessment of a small watershed in a small-data mountainous area.

The present invention relates to the technical field of soil and water conservation. Disclosed is a vegetation configuration adjustment method applicable to a hilly and gully area. The method comprises: on the basis of a digital elevation model and remote sensing image data of a target watershed, dividing the target watershed into a plurality of afforestation areas, wherein each afforestation area corresponds to one site type; conducting surveying and sampling on existing vegetation in the target watershed, and determining ecological service functions of the afforestation areas, wherein the ecological service functions comprise biodiversity, a water resource conservation function and a soil improvement function; on the basis of a soil loss amount of each area in the target watershed, determining the priority degree of water and soil loss control in each area; and for areas where ecological service functions of the existing vegetation in the target watershed do not match site types, using an analytic hierarchy process to adjust vegetation in the afforestation area in view of the priority degree of current water and soil loss control in the watershed. The present invention improves the efficiency of vegetation configuration.

The invention discloses a less-data watershed runoff prediction method fusing multi-scale features and prediction results. Determining a watershed with data most similar to the watershed with less data; dividing the watershed with data into different watershed clusters by using a clustering algorithm, and determining the watershed cluster to which the watershed with less data belongs; constructing a multi-scale watershed LSTM model, namely a regional watershed LSTM model, a cluster watershed LSTM model and an individual watershed LSTM model; extracting multi-scale features of the less-data watershed by using a multi-scale watershed LSTM model and obtaining a multi-scale runoff prediction result of the less-data watershed; and by taking the multi-scale features and the runoff prediction result of the less-data watershed as input features and taking the final runoff prediction result of the less-data watershed as an output label, constructing a less-data watershed runoff prediction model. The method can effectively improve the runoff prediction precision and generalization ability of the watershed with less data, and has a wide application prospect in the field of runoff prediction.

The invention discloses a watershed flood type identification method considering a topological relation, and the method comprises the steps: abstracting each hydrometric station in a watershed into graph nodes, and abstracting the upstream-to-downstream communication relation between the hydrometric stations into directed edges, thereby constructing a watershed directed topological graph; for each flood event, standardization processing is carried out on the multi-station features according to nodes and features, and flood identification features are added to each node. Then, a graph auto-encoder based on a graph attention mechanism is adopted to carry out unsupervised reconstruction learning on the graph structure sample so as to extract a graph-level embedded vector; and finally, performing K-Means clustering on the embedded vector, adaptively determining an optimal cluster number according to a contour coefficient, and finally outputting a type label of the flood sample. According to the method, drainage basin flood type supervision information can be generated without manual annotation, and the extracted feature representation can also be used for model training of downstream tasks such as subsequent flood type real-time prediction.

The application belongs to the technical field of data processing, and specifically discloses a watershed water and scenery uncertainty quantification method and device based on data driving. According to the application, based on a target watershed water and scenery prediction model, a watershed water and scenery prediction result is determined according to current watershed water and scenery data; and based on a data-driven watershed water and scenery uncertainty quantification model, the watershed water and scenery prediction result is quantified in uncertainty according to a target quantification algorithm. In the above manner, the target watershed water and scenery prediction model is determined based on the long-term dependence relationship of the watershed water and scenery data in time correlation and the dependence relationship in space correlation, the complex space-time relationship between the stations in the watershed is accurately expressed, and then the data-driven watershed water and scenery uncertainty quantification model is used for uncertainty quantification, so that the precision and reliability of the uncertainty quantification can be effectively improved without relying on data distribution assumptions and being able to cope with various complex environments.

The invention discloses a Yellow River basin midstream small watershed similarity discrimination and clustering method, system and device, and a storage medium, and the method comprises the steps: obtaining the static topographic index data and dynamic hydrological index data of a target small watershed, and carrying out the standardization processing of the static topographic index data and dynamic hydrological index data, and obtaining the standardized data; calculating variable coefficients of the static terrain indexes and the dynamic hydrological indexes, and determining weights of the static terrain indexes and the dynamic hydrological indexes according to the variable coefficients; on the basis of the weight and the standardized data, a weighted Euclidean distance algorithm is adopted to calculate the feature space distance between the small watersheds, and the similarity coefficient of the small watersheds is determined according to the feature space distance; and grouping the small watershed by using a K-means clustering algorithm, calculating a contour coefficient, determining an optimal clustering number, and obtaining a small watershed similarity clustering result. And the similarity degree between the drainage basins can be quantified comprehensively and accurately.

The application relates to the technical field of hydrology and water resources simulation, and discloses a SWAT watershed hydrology model task planning system, method and product, which comprises a knowledge graph construction module, an embedded index construction module, a query coding and two-stage retrieval module, a context construction module and a large language model task planning module. Through the complete planning process of knowledge graph modeling, vector index construction, two-stage retrieval, context generation and structured task output, the whole-process intelligentization from a user's natural language demand to SWAT watershed hydrology model executable task planning is realized, the SWAT file / parameter structure can be accurately matched, the professionalism, accuracy and executability of task planning are improved, and the problems of high operation threshold, large artificial workload, unstable and unexecutable output of a general large model of a traditional SWAT watershed hydrology model are solved.

The invention discloses a rainstorm torrential flood process, type and behavior change prediction method. The method comprises the following steps: step 1, collecting and sorting basic data of a research area; 2, determining a small watershed attribute type and a rainstorm torrential flood type of the research area; 3, constructing a small watershed rainstorm and mountain torrent comprehensive simulation model of the research area; 4, determining parameters of the small watershed rainstorm mountain torrent comprehensive simulation model; and 5, predicting the process, type and behavior change of the small watershed rainstorm torrential flood. According to the method, the mapping relation between the small watershed attribute characteristics and the flood behavior characteristic change types can be rapidly and efficiently determined, and rapid conversion from the small watershed physical attribute type to the rainstorm torrential flood type is realized; the comprehensive simulation accuracy of the small-watershed rainstorm torrential flood is accurately evaluated, the process, type and behavior changes of the small-watershed rainstorm torrential flood lacking data are predicted, the future flood change situation of the small-watershed is comprehensively represented, and technical support is provided for prediction and risk management of the small-watershed rainstorm torrential flood.

The invention discloses a moving abrasive particle detection and tracking method based on fusion of a Gaussian mixture model (GMM) and a watershed algorithm, which is characterized in that on the basis of acquiring abrasive particle moving targets based on the Gaussian mixture model (GMM), each extracted target is segmented by using the watershed algorithm based on distance transformation, so that the adhered abrasive particle targets can be accurately detected, and the detection accuracy is improved. Algorithm verification is carried out on abrasive particle videos in different stages, the detection accuracy of the abrasive particle adhesion target can reach 98.86%, the number, size and other characteristics of abrasive particles can be accurately extracted, and abrasion faults can be analyzed easily. In addition, on the basis of accurate detection of the adhered abrasive particle target, the SORT algorithm is further applied to track the multi-target abrasive particles in real time, and the result shows that the tracking effect of the adhered abrasive particle target is good.

This application discloses a method, apparatus, storage medium, and processor for processing floodplain data. The method includes: monitoring river flow and determining the water level at various points along the riverbank in a watershed model based on the flow at the monitoring points. The watershed model is composed of elevation data within a target watershed, which includes at least a river and a riverbank. The method further includes determining floodplain points based on the water level and elevation of each point along the riverbank, resulting in at least one floodplain point. The method also includes determining floodplain links based on the water level and elevation of the surrounding terrain at each floodplain point, resulting in at least one floodplain link, where each floodplain link contains multiple points. Finally, the method involves simulating floodplain flow in the watershed model based on the at least one floodplain link. This application solves the problems of high computational load and demanding performance requirements for computing equipment in simulating floodplain conditions in related technologies.

The invention relates to the technical field of hydro meteorology, and discloses a method and a device for evaluating influence of reservoir construction on watershed hydrothermal balance. A hydrothermal balance relation prediction model in the invention considers various watershed environment data and hydrothermal coupling data parameters of a target watershed at the same time; the correlation features between the watershed environment features corresponding to various watershed environment data and the hydro-thermal coupling features are established, so that the hydro-thermal equilibrium relationship prediction model does not use the watershed environment data of a single element for learning; the method can accurately predict the relation between the hydrothermal coupling parameters and the reservoir underlying surface and other elements, further assesses the influence of reservoir construction on the hydrothermal coupling parameters of the target drainage basin, and finally facilitates the accurate assessment of the influence of reservoir construction on the hydrothermal balance of the target drainage basin.

This application discloses a method for determining the threshold of watershed ecological environment quality based on the PSR model, belonging to the field of watershed ecological assessment technology; it includes: determining the evaluation index items of the target watershed; arranging and combining all possible score values ​​of each index to obtain all score combinations; calculating the comprehensive score of each combination to generate a full sample space; performing fuzzy clustering on the full sample space, iteratively determining the membership degree and cluster center value of each sample to multiple preset cluster categories; sorting the cluster center values ​​of each cluster category in ascending order to form multiple health levels; classifying each sample to the cluster category with the largest membership degree based on the principle of maximum membership degree to obtain the sample set corresponding to each health level; fitting continuous distribution density curves to the sample sets of each health level, and determining the intersection of the distribution density curves of adjacent health levels as the critical threshold between adjacent health levels; and determining the health level of the target watershed based on each critical threshold.

The application discloses a kind of based on net ecological externality index's watershed ecological compensation zoning method, comprising: obtaining the geographical space data of target watershed, to calculate multiple preset positive, negative ecological externality index;Based on geographical space data, respectively for each spatial unit in the watershed Determine a positive ecological externality comprehensive index and a negative ecological externality comprehensive index;Based on positive, negative ecological externality comprehensive index, each spatial unit is calculated by hedge operation Net ecological externality index;According to the spatial distribution of each spatial unit net ecological externality index in target watershed, the ecological compensation zoning of target watershed is carried out.The application can more scientifically, comprehensively reflect the real ecological contribution of each region by constructing net value evaluation framework, and provides more accurate decision-making basis for formulating fair and reasonable ecological compensation strategy.

The invention relates to the technical field of mountain torrent flood control and disaster reduction, and discloses a small watershed mountain torrent risk assessment method and device, electronic equipment and a storage medium. Establishing a spatial association relationship between each danger area and the rainfall monitoring station based on the spatial association relationship between the small watershed and the danger area; based on the spatial association relationship between each danger area and the rainfall monitoring station, calculating the preset accumulated rainfall in each time period of each danger area in a rolling manner; and based on the relationship between the accumulated rainfall capacity of each preset time period of each danger area and the rainfall early warning threshold value of each preset time period, evaluating the small watershed mountain torrent risk. According to the method, a'small watershed-dangerous area-monitoring station 'space correlation system is established, refined description of rainfall spatial and temporal distribution and regional difference is realized, and a set of systematic and automatic mountain torrent risk assessment method is constructed by means of multi-period rolling accumulation calculation and dynamic threshold comparison.

The invention relates to a small watershed multi-element cooperative monitoring method, device and equipment and a medium. The method comprises the following steps: acquiring satellite remote sensing data, unmanned aerial vehicle observation data and ground observation data of a target drainage basin; preprocessing the satellite remote sensing data, the unmanned aerial vehicle observation data and the ground observation data to obtain multi-element observation parameters; carrying out basin live inversion according to the multi-element observation parameters to obtain land utilization grids, water body boundary-area data, runoff data and sediment data; and according to the land utilization grid, the water body boundary-area data, the runoff data and the sediment data, constructing a rainfall-runoff-sediment-water quality chain response relationship, and obtaining drainage basin multi-element scale coordination time sequence data, an element influence map and land utilization influence evaluation. By adopting the method, multi-element data space-time cooperative monitoring can be realized, and the full-chain demand of small-watershed ecological hydrological management is supported.

The invention discloses a knowledge decision supervision method for watershed flood prevention, and relates to the technical field of watershed flood prevention, and the method comprises the steps: obtaining watershed flood prevention data and real-time meteorological data of a target region; performing knowledge extraction on the flood prevention data of the drainage basin to obtain spatial element information and non-spatial element information; coding the space element information according to a preset identity coding rule to obtain coded space element information; performing spatial topological relation construction on the encoded spatial element information by utilizing a geographic information system, and constructing a knowledge relation of a root primitive according to the constructed spatial topological relation to obtain a spatial element knowledge relation; fusing the non-spatial element information and the spatial element knowledge relationship, and converting a fusion result into a knowledge graph to obtain a fused knowledge graph; and inputting the meteorological data and the fused knowledge graph into a preset knowledge aided decision-making model, and outputting a decision-making result. According to the invention, the decision-making precision, the response speed and the early warning response efficiency are improved.

The application discloses a kind of arid region small watershed vegetation ecological environment monitoring method, comprising: obtaining arid region small watershed surface image, arid region small watershed surface image is handled, obtains first vegetation distribution image;According to the matching of first vegetation distribution image and spectral feature library, obtain second vegetation distribution image;From second vegetation distribution image, the spatial distribution characteristics of vegetation community are extracted, the dynamic change trend of spatial distribution characteristics is calculated, and community structure change dataset is obtained;Based on community structure change dataset, short-term evolution trend of vegetation community structure is predicted using long short-term memory network, and dynamic change prediction result of vegetation community structure is obtained;According to dynamic change prediction result, combined with community structure change dataset, quantify biodiversity level, and obtain biodiversity evaluation result;From biodiversity evaluation result, key indicators are extracted, and according to key indicators, real-time monitoring vegetation ecological environment state data.

The invention discloses a closed watershed hydrological data networked organization method based on a ridge line, and relates to the technical field of hydrology and geography, and the method comprises the steps: obtaining and collecting DEM data of a region where a target watershed is located, extracting the ridge line through the region DEM data in combination with a GIS, determining a research region boundary according to the ridge line, and delimiting the closed watershed; dEM raster data in the delimited research area is converted into a DEM vector point set in combination with a GIS, and a network topology structure is constructed according to the vector point set; and obtaining and collecting composite hydrological data of the target drainage basin, associating the collected hydrological data with the DEM vector point set according to the space coordinates, and storing the hydrological data into corresponding nodes of the network topology structure. According to the method, the accurate definition of the basin boundary, the networked expression of the topographic structure and the networked organization of the multi-source hydrological data are realized, and an efficient and uniform data basis is provided for the networked simulation of the hydrological process.

The invention belongs to the technical field of data processing, and particularly discloses a watershed water, wind and light uncertainty quantification method and device based on data driving. According to the invention, based on the target watershed water-wind-light prediction model, the watershed water-wind-light prediction result is determined according to the current watershed water-wind-light data; and based on a data-driven watershed water-wind-light uncertainty quantification model, performing uncertainty quantification on a watershed water-wind-light prediction result according to a target quantification algorithm. Through the above mode, the watershed water-wind-light prediction result is determined by the target watershed water-wind-light prediction model constructed by the long-term dependency relationship of the watershed water-wind-light data in time correlation and the dependency relationship in consideration of spatial correlation, and the complex time-space relationship between stations in the watershed is accurately expressed. And then uncertainty quantification is carried out by using the data-driven watershed water-wind-light uncertainty quantification model, dependence on data distribution hypothesis is not needed, and various complex environments can be dealt with, so that the precision and reliability of uncertainty quantification can be effectively improved.

The invention belongs to the related technical field of hydrology and water resource engineering, and discloses a drainage basin runoff prediction method and system fusing a graph attention network and physical constraints. The method comprises the steps of inputting preprocessed hydrological and geographic parameters of a to-be-processed watershed into an SWAT model, and obtaining attributes of each sub-watershed in the to-be-processed watershed, a topological relation of the watershed and simulated runoff volume of each sub-watershed in a specified time sequence; encoding the output of the SWAT model by using an encoder to obtain static features and dynamic features of each sub-basin; taking the static characteristics and the dynamic characteristics as input of a multi-head attention network to obtain a runoff corrected value of each sub-basin; and correcting the simulated runoff volume by using the runoff volume correction value to obtain a final predicted value of the runoff volume in a specified time sequence, so as to predict the runoff volume of the drainage basin. According to the invention, the problems of high parameter sensitivity, lack of physical constraints and simple attention mechanism design in hydrological prediction are solved.

The invention discloses a net ecology extrinsic index-based watershed ecological compensation zoning method, which comprises the following steps of: acquiring geographic space data of a target watershed so as to calculate various preset positive and negative ecology extrinsic indexes; based on the geographic space data, a positive ecological extrinsic comprehensive index and a negative ecological extrinsic comprehensive index are determined for each space unit in the drainage basin; based on the positive ecological extrinsic comprehensive index and the negative ecological extrinsic comprehensive index, a net ecological extrinsic index of each space unit is obtained through hedging calculation; and according to the spatial distribution of the net ecological extrinsic index of each spatial unit in the target drainage basin, performing ecological compensation partitioning on the target drainage basin. According to the method, the net value evaluation framework is constructed, so that the real ecological contribution of each region can be reflected more scientifically and comprehensively, and a relatively accurate decision basis is provided for formulating a fair and reasonable ecological compensation strategy.

The invention relates to the technical field of river hydrographic survey, in particular to a river hydrographic survey system based on big data. The system comprises a data perception integration module, a drainage basin feature analysis module, an adaptive parameter optimization module and a hydrographic measurement early warning module, and the drainage basin feature analysis module receives drainage basin feature data and performs drainage basin feature analysis on the drainage basin feature data to obtain drainage basin feature information; and finally, parameter optimization analysis is performed on the drainage basin feature information to obtain an early warning parameter adjustment strategy, so that the problem of early warning deviation caused by general parameters of a traditional system is avoided, and the pertinence and accuracy of hydrological early warning are improved. The drainage basin feature information and the drainage basin stability capability value are analyzed to output the final execution frequency, the final execution frequency is updated to the current collection execution frequency, the collection frequency is reduced when the drainage basin state is stable and fluctuation is small, the frequency is increased when the state is unstable, the system operation cost is reduced, and the monitoring efficiency is guaranteed.

This application discloses a water resources data correction method and system integrating multi-source feedback and iteration, involving hydrological scheduling and data processing technologies. The method includes: collecting multi-source data within a watershed; dividing the watershed into multiple sub-units and establishing hydrological models based on each sub-unit to simulate runoff generation and confluence processes; calculating the simulated flow rate of any cross-section based on a basic dataset and the constructed hydrological models of each sub-unit; determining the simulated flow rate under at least two hydrological models as feedback sources for any cross-section and calculating the deviation from the measured flow rate; constructing a parameter sensitivity matrix based on parameters in the basic dataset when the deviation exceeds a specified threshold; determining the parameter correction amount based on the calculated sensitivity, and using the parameter correction amount to correct the parameters of at least two of the hydrological models. This application considers the physical laws of actual hydrological processes to achieve water resources data correction and improve the accuracy of hydrological data processing.

This application relates to the field of zero-carbon determination technology, and provides a method for determining a zero-carbon watershed. The method includes: obtaining the annual average carbon emissions, annual average carbon sink, and annual average clean energy carbon neutrality contribution of a target watershed; and determining the target watershed as a zero-carbon watershed based on the annual average carbon emissions, annual average carbon sink, and annual average clean energy carbon neutrality contribution. This application enables the scientific determination of zero-carbon watersheds in a manner adapted to the characteristics of the watershed, thereby helping to clarify the boundaries and connotations of zero-carbon construction and realizing the systematic advancement and practical exploration of zero-carbon construction.

The invention discloses a runoff production determination method, device, equipment and medium for a complex terrain region, and relates to the technical field of watershed hydrological process simulation research, and the method comprises the steps: obtaining digital elevation data DEM of the region; acquiring gradient data of each grid unit of the region by using ArcGIS software, and converting the gradient data into integer; constructing a regional slope raster data attribute table by utilizing the integer slope data of each raster unit in ArcGIS software; obtaining a grid number corresponding to each gradient value in the gradient raster data attribute table, calculating a weight of the grid number corresponding to each gradient value to obtain a weight value, carrying out weighted average on the weight value corresponding to each gradient value to obtain a gradient value of the region, and obtaining a regional gradient factor according to the regional gradient value; and obtaining the regional water supply degree according to the gradient factor, and introducing the regional water supply degree into a double-excess runoff production model to obtain regional runoff production. According to the method, an ArcGIS tool is used for carrying out gradient analysis on digital elevation data DEM corresponding to a complex terrain of a region so as to obtain a gradient value of the region and a gradient factor of the region, and the regional water supply degree is obtained according to the gradient factor and introduced into a double-excess runoff production model to obtain regional runoff production. The method reasonably considers the influence of the gradient in the complex terrain area on the runoff production of the whole area, is high in prediction result precision, and is wide in application range.

The invention belongs to the technical field of agricultural remote sensing, and relates to a fine field extraction method based on multi-dimensional feature fusion and a semantic guidance watershed. The method comprises the steps of performing multi-dimensional feature extraction, and constructing a feature subset corresponding to each dimension; splicing all the feature subsets in a channel dimension; constructing a double-branch multi-scale convolution module to perform semantic segmentation on the multi-channel feature tensor; taking the plot boundary probability map as an energy topographic map, and generating a constraint mask; extracting instance seed points; generating a mark map, and performing watershed transformation in combination with the energy topographic map and the constraint mask; and superposing to obtain a farmland plot segmentation result. According to the method, multi-channel input is constructed, and the perception and analysis capability of the model is improved; through a double-branch multi-scale convolution module, the instance segmentation precision and robustness of the farmland plot are significantly improved; taking the plot boundary probability graph as an energy topographic map of a watershed algorithm, and finding a unique instance seed for each independent plot; the method can effectively prevent the segmented region from leaking to the background.

The invention discloses a drainage basin measure optimization configuration method considering sediment function connectivity. The method comprises the following steps of simulating the total sediment production amount of a drainage basin, obtaining measure data, establishing a multi-target optimization system, dynamically adjusting measure layout, generating a leading edge optimal solution set, and obtaining a final measure space distribution diagram and the achievement effect of a plurality of targets. According to the method, measures are preferentially arranged in the functional connectivity hot spot area, so that the erosion intensity is controlled from the source, and the hydrological connectivity of the upstream and the downstream is weakened. The accurate intervention strategy of the key nodes is focused, high cost of global spreading can be avoided, downstream flood and deposition risks can be relieved by interfering the peak flow of substance transportation, and therefore the purpose of obtaining high ecological benefits with the small disturbance rate is achieved.

This invention discloses a flood simulation method and system that couples the dynamic characteristics of the underlying surface and the river channel, relating to the fields of watershed hydrological simulation and disaster prevention and mitigation. The method includes: multi-source data acquisition and preprocessing; prediction of underlying surface evolution and hydrophysical parameterization; inversion of river channel dynamic roughness based on water-sediment coupling simulation and inverse Manning's formula; coupling the future runoff generation parameter field and dynamic roughness sequence to a distributed hydrological model for simulation; and quantitatively decoupling the contributions of underlying surface and river channel changes to flood characteristics through multi-scenario comparative experiments. This invention overcomes the shortcomings of existing technologies, such as the disconnect between underlying surface and river channel processes and the static nature of roughness parameters, achieving full-chain dynamic coupling simulation. It significantly improves the physical realism and prediction accuracy of flood simulation under complex environments, and is particularly suitable for data-scarce high-altitude watersheds.

The application discloses an erosion gully classification method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring satellite images of a research area, and performing geographical coordinate calibration on the satellite images; acquiring a digital elevation model of the research area based on the calibrated satellite images, and performing pretreatment on the digital elevation model, and generating a contour map according to the pretreated digital elevation model; dividing the research area into a plurality of small watersheds based on the contour map; performing visual interpretation operation on each small watershed, and performing erosion gully classification processing on the research area based on each small watershed after the visual interpretation operation and a preset classification criterion. The application solves the technical problem that the current erosion gully classification method cannot scientifically classify erosion gullies according to the development topography of the erosion gullies.