50 results about "Erosion prediction" patented technology

According to the MoSi2 coating erosion rate prediction method based on the particle collision theory, a MoSi2 coating erosion rate prediction model is constructed based on the particle collision theory, and the dynamic damage process of a MoSi2 coating is simulated through a test measurement set obtained by carrying out a particle-containing high-temperature combustion gas flow erosion test. The mass loss of the MoSi2 coating formed under the erosion of the particle-containing high-temperature combustion gas flow is obtained, so that the prediction of the MoSi2 coating erosion rate prediction model is more accurate, and meanwhile, the limitation of a traditional erosion prediction model method on the characterization of a multi-factor coupling effect is broken through; in addition, the dynamic damage process of the MoSi2 coating is visualized by constructing a morphology graph prediction model, and a high-confidence-coefficient theoretical tool is provided for anti-erosion design of the MoSi2 coating.

The invention relates to the technical field of soil splash erosion, in particular to a soil splash erosion simulation analysis system which comprises a rainfall simulator for simulating rainfall with different intensities and different pH values; the wind simulator adjusts wind speed and wind direction; raindrop data are measured by raindrop characteristic measuring equipment; the splash erosion simulation equipment simulates the process that raindrops drop on target soil to cause splash erosion and generate splash erosion substances; the data acquisition equipment acquires the corresponding initial pH value and initial conductivity of the target soil before rainfall; collecting the target pH value, the target conductivity and the target denudation depth of the target soil after rainfall, and the average weight diameter and the total target splattering amount of the target splattering substance corresponding to the splattering substance; the electronic equipment determines and establishes a splash erosion prediction model based on the raindrop pH value, the raindrop data, the wind speed, the wind direction and the data acquired by the data acquisition equipment, and the soil splash erosion simulation analysis system capable of quantitatively analyzing soil splash erosion in real time is developed.

The invention relates to the technical field of industrial process prediction, in particular to a tundish erosion prediction method based on a hierarchical hybrid expert framework. The method comprises the following steps: collecting time sequence physical field data of the tundish, and screening features to construct a unified feature space; shunting the feature space to obtain a time sequence feature and a statistical aggregation feature; a statistical aggregation feature training classifier is utilized to generate a calibration posterior probability, and a gating network is constructed; generating an initial mode subset based on a posterior probability and training a corresponding expert model; the confidence of data to be measured is obtained by the gating network, and a single expert model is selected for prediction or multiple expert models are fused through a self-adaptive strategy for weighted prediction according to whether the confidence exceeds a threshold value or not; and finally, reconstructing the predicted value into an erosion thickness absolute value through inverse transformation. According to the method, the accuracy and adaptability of tundish erosion prediction are effectively improved.

The invention discloses a slope soil erosion prediction method, system, equipment and medium, and relates to the technical field of soil erosion, and the method comprises the following steps: taking slope soil with different grain composition as a research object, and carrying out a rainfall simulation experiment; constructing a production and construction project soil erosion prediction model according to the product of a subrainfall erosivity factor, a soil erodibility factor, a slope length factor, a slope factor and a slope surface area on the basis of the grain composition characteristic particle size; and predicting the soil erosion of the slope surface of the to-be-predicted production and construction project through the production and construction project soil erosion prediction model. According to the model disclosed by the invention, a direct quantitative relationship between the characteristic particle size and the erosion strength is established, so that an acquisition way of the soil erodibility factor is simplified, the dependence of the model on complex and expensive soil physical and chemical parameter analysis is greatly reduced, and the operability and the popularization efficiency of the model in a production field are greatly enhanced.

The invention relates to the technical field of steel structure detection, and discloses a coastal steel structure performance detection method and system, and the method comprises the steps: carrying out the clustering sequence division of all pixel points based on each pixel point and a clustering center point, determining a primary erosion value of to-be-detected steel according to local seawater data and local environment data, and carrying out the clustering sequence division of the to-be-detected steel; comparing the primary erosion value with the historical erosion data, judging whether the primary erosion value is correct or not according to a comparison result, and re-determining the primary erosion value of the to-be-detected steel according to the erosion prediction model when the primary erosion value is judged to be incorrect; the method comprises the steps of obtaining rainfall data, conducting rainfall scoring on each piece of rainfall data according to the standard rainfall pH value, determining a compensation coefficient of a primary erosion value according to the number of the rainfall data in a rainfall erosion set, and determining the performance defect grade of to-be-detected steel based on a target erosion value. And the reliability of performance detection of the coastal steel structure is improved.

The invention provides a mining ground fracture erosion prediction method, device and equipment under a freeze-thaw condition, and belongs to the field of geological research, and the method comprises the steps: obtaining the related data of a target mining ground fracture; calculating a frost heaving stress field and a mining-induced tensile stress field based on the related data, and performing superposition to obtain a total tensile stress field; when the total tensile stress field is greater than the tensile strength of the soil, calculating the coupling damage degree; based on the coupling damage degree, the effective cohesion and the effective internal friction angle of the soil are determined, and then the critical starting shear stress of the crack and the average flow velocity of the scouring critical state are determined; based on the critical starting shear stress and the average flow velocity, total energy consumed for erosion and collapse of the soil body in unit area is determined, and actual water flow power is calculated; and calculating the ratio of the total energy to the actual water flow power to obtain a physical index of the anti-erosion comprehensive capability of the soil so as to judge the erosion risk state of the soil crack. Therefore, space-time prediction and accurate early warning of the erosion risk can be realized.

The invention discloses a method for predicting surface erosion characteristics and temperature distribution of a thermal barrier coating based on numerical simulation. The method comprises the steps of establishing a grid model, establishing an erosion model considering dynamic physical property changes of the coating, establishing a thin-wall thermal resistance model supporting the dynamic thickness of the coating, setting a time amplification factor and the like. The characteristics that the porosity, Young modulus, Vickers hardness and other physical properties of the coating change along with the service time are considered. And in combination with a thin-wall thermal resistance model considering the dynamic thickness of the coating, the erosion characteristic of the coating surface and the temperature distribution condition of the coating surface at any moment in the future are quickly predicted and obtained. The method has the advantage of high simulation precision in coating erosion prediction. According to the method, the wall surface erosion characteristics and thermal barrier coating surface temperature distribution caused after erosion can be predicted, so that the service life conditions of different device surface coatings are effectively predicted, and reliable data support and guidance are provided for design of a thermal protection system and timely replacement of equipment.

This application relates to a method, apparatus, and equipment for soil erosion analysis. A pre-set model is trained by comprehensively considering soil erosion driving factor data, spatial structure characteristics, and soil erosion modulus data of the sample area. During training, the spatial autocorrelation of the prediction residuals of the soil erosion prediction model is ensured to meet a pre-set non-significant condition, thereby overcoming the problem of existing machine learning models neglecting spatial characteristics. In the prediction stage, the trained model is used in conjunction with the soil erosion driving factor data and spatial structure characteristics of the target prediction area to obtain accurate soil erosion modulus data, thus obtaining reliable soil erosion analysis information.

The present invention discloses a method for predicting and controlling erosion of a coal gasifier pipeline, which belongs to the technical field of safe transportation of industrial pipelines and is divided into four layers, wherein the first layer is a perception layer; the second layer is a monitoring layer; the third layer is a digital twin computing layer, and the fourth layer is a decision layer; the erosion morphology of the pipeline is displayed in real time, the erosion position of the pipeline is predicted, and the data of the collision of solid impurities in the pipeline with the pipe wall is collected, transmitted, analyzed and fed back in real time, so as to find the erosion position of the gas pipeline wall in advance and take protective measures as soon as possible. The core of the present invention is to connect CFD numerical simulation with the digital twin system, and use the intelligent solver to generate an intelligent algorithm. Complex physical behaviors can be monitored as the real process progresses, so as to respond to changes in real-time input data, which not only meets the needs of calculating real-time data, but also can improve the simulation accuracy of the digital twin, so that the constructed digital twin can be quickly applied to the erosion prediction of the pipeline.

The invention provides a coastal erosion prediction method and system based on adaptive STL decomposition and CNN-LSTM, and the method comprises the steps: obtaining the elevation scatter data of a coastal zone region in a specified time period, and constructing a three-dimensional spatio-temporal data set according to the elevation scatter data; subsequence data is extracted from the three-dimensional spatiotemporal data set through a specified sliding time window for calculating the window length, a seasonal stability index value is calculated according to the subsequence data, and the seasonal stability index value is converted into a corresponding adaptive seasonal window length according to a preset mapping relation; performing STL decomposition processing on the three-dimensional spatiotemporal data set in combination with the adaptive seasonal window length to obtain a seasonal item sequence, a trend item sequence and a residual item sequence; and performing elevation value prediction on the seasonal term sequence, the trend term sequence and the residual term sequence based on the trained CNN-LSTM combination model to obtain a final coast erosion prediction result. According to the method, the accuracy of coastal zone space-time erosion feature prediction can be improved.

The application provides a soil erosion prediction method, and belongs to the technical field of soil erosion. The method comprises the following steps: obtaining a target data set corresponding to each grid, obtaining a rainfall erosion force factor, a soil erosion factor, a slope length and slope gradient factor, a vegetation coverage factor and a water and soil conservation factor of each grid based on the target data set; calculating the soil erosion amount of each grid by using a RUSLE model based on the above factors; correcting the soil erosion amount of each grid in two adjacent grids based on the geological features and environmental features between the two adjacent grids to obtain the corrected soil erosion amount of each grid; obtaining the predicted soil erosion amount of each grid based on the corrected soil erosion amount of each grid and the soil erosion amount in each period in history; and obtaining the predicted soil erosion amount of a target region based on the predicted soil erosion amount of each grid. The application can improve the prediction accuracy of the soil erosion amount in the target region.

The application discloses a kind of well down pipe string erosion risk assessment method based on gas field production parameters, comprising the following steps, step 1, based on the relationship between the deformation wear amount, cutting wear amount and particle impact load of parent material, the production situation is established;Step 2, based on the production pipe string structure, the gas-solid erosion prediction model of production pipe string is established, and the grid-independent solution is obtained;Step 3, according to the relationship between the deformation wear amount, cutting wear amount and particle impact load in step 1, the gas-solid erosion prediction model is optimized to the error within 5% range;Step 4, based on the direct relationship between the optimized gas-solid erosion prediction model and production parameters, determine the production parameters corresponding to the erosion degree;Step 5, establish three-dimensional critical production parameter chart containing yield, pressure, sand production, according to the production parameters in step 4, erosion risk assessment is carried out on production pipe string of different materials.

This application relates to a method and system for monitoring soil and water conservation using a combination of UAV low-altitude remote sensing and ground-based measurements. The method includes: acquiring remote sensing images, meteorological data, and ground-based measured data of a target area; fusing the features of the remote sensing images, meteorological data, and ground-based measured data to obtain comprehensive target features; filtering the comprehensive target features using a feature selector to obtain key comprehensive features; and analyzing the key comprehensive features based on a hybrid architecture integrating GCN and TCN to obtain soil erosion prediction results. Specifically, the GCN module in the hybrid architecture is used to mine spatial correlations between geographical areas, while the TCN module is used to capture the temporal nonlinear changes in soil erosion. This approach ensures both spatial accuracy of the prediction results and temporal continuity, improving the accuracy and reliability of soil erosion prediction and providing more scientific technical support for soil and water conservation planning and ecological restoration decisions.

The present invention discloses a topsoil retention method and system for brown soil farmland, relating to the field of land erosion monitoring technology. The method comprises: retrieving brown soil farmland regional management data to collect farmland data, constructing a soil erosion prediction model and inputting the data to obtain prediction results, formulating a topsoil retention plan based on the results and matching the regional management data, executing the plan and activating a monitoring mechanism for evaluation, and optimizing the plan based on the evaluation results to intelligently retain the topsoil. This method solves the technical problems of existing topsoil retention technologies, such as inaccurate soil erosion prediction, lack of scientific and targeted plan formulation, and lack of effective monitoring and feedback optimization mechanisms. By optimizing the entire process of topsoil retention in brown soil farmland, the method achieves the technical effects of reducing soil erosion, improving soil fertility, and increasing crop yield and quality.

The invention discloses a river shoreline form prediction method and device. The river shoreline form prediction method comprises the following steps: acquiring a deposition prediction model and an erosion prediction model; training the model; obtaining current shoreline coordinate information, current water level data, current flow data, current rainfall data, current osmotic pressure data, current seepage data and current geological data; generating fusion features according to the current shoreline coordinate information, the current water level data, the current flow data, the current rainfall data, the current osmotic pressure data, the current seepage data and the current geological data; and inputting the fusion features into the siltation prediction model and / or the erosion prediction model so as to obtain siltation prediction information and / or erosion prediction information. According to the method, the neural network and the historical element data of shoreline erosion and siltation are combined for learning to generate the corresponding erosion and siltation prediction model, so that the prediction accuracy and reliability are improved.

The invention discloses a sandstorm erosion prediction and adaptability evaluation method for a photovoltaic panel in a Saggob area. The method comprises the following steps: collecting sandstorm environment parameters and panel state parameters by arranging monitoring equipment such as a micro meteorological station and an unmanned aerial vehicle aerial photography system to form a multi-source data basis; key erosion dynamics characteristics are extracted based on the wind sand dynamics principle; performing an accelerated test by using a wind-sand erosion simulation device to quantify the relationship between the material wear rate and the performance attenuation; constructing a time sequence deep learning network to establish an erosion evolution prediction model; and finally, according to a prediction result, evaluating an adaptability level and formulating a targeted protection strategy. According to the method, multi-source monitoring data and a machine learning technology are creatively fused, high-precision prediction and dynamic evaluation of the wind-sand erosion process of the photovoltaic panel are realized, prediction accuracy and mechanism interpretability are remarkably improved, a scientific basis can be provided for active operation and maintenance of a power station, and the method is suitable for popularization and application. And the long-term stability and the operation and maintenance efficiency of the photovoltaic power station in a severe environment are effectively enhanced.

The present application relates to the technical field of erosion prediction and equipment health assessment of polysilicon cold hydrogenation device, and discloses a kind of based on mechanism and data fusion's silicon-based new material cold hydrogenation reactor conveying elbow pipe's erosion rate prediction method, obtains elbow pipe inner diameter, curvature ratio, bending angle, particle mass flow rate, particle size and particle velocity, establishes parameterized elbow pipe geometric model, and based on verified gas-solid two-phase flow erosion numerical model, the wall surface erosion rate under sample working condition is calculated, and the maximum erosion rate is extracted to construct LHS-CFD sample database;Erosion rate prediction model consisting of mechanism trend term and Gaussian process residual term is constructed;After inputting target working condition parameters into the trained model, the maximum erosion rate prediction value, prediction variance and prediction interval are output.The present application combines high-fidelity numerical samples with mechanism-constrained proxy modeling, which can reduce the cost of repeated CFD calculation for new working conditions, improve the efficiency of multi-condition erosion rate rapid assessment, parameter screening and high erosion condition identification.

The application relates to the technical field of industrial process prediction, in particular to a tundish erosion prediction method based on a hierarchical hybrid expert framework. The method comprises the following steps: collecting tundish time series physical field data, screening features and constructing a unified feature space; shunting the feature space to obtain time series features and statistical aggregation features; training a classifier using the statistical aggregation features to generate calibrated posterior probability and construct a gating network; generating an initial mode subset based on the posterior probability and training a corresponding expert model; for the to-be-tested data, obtaining the confidence thereof from the gating network, and selecting a single expert model for prediction or fusing multiple expert models for weighted prediction through an adaptive strategy according to whether the confidence exceeds a threshold; and finally inversely transforming and reconstructing the prediction value into an absolute value of the erosion thickness. The application effectively improves the accuracy and adaptability of tundish erosion prediction.

The present application relates to a kind of cavity erosion prediction method considering cavity collapse and its energy transport process, belong to the technical field of hydrodynamics research.The cavity erosion prediction method includes the following steps: solving cavitation flow field to the object to be predicted;Based on the physical property change of cavity collapse, identify collapse region and record cavity collapse pressure;According to cavity collapse pressure, calculate collapse energy;Analysis of the transport process of collapse energy radiated to the wall to be predicted position, calculate transfer parameter and attenuation parameter, and the transport parameter is obtained by multiplication;Combined with collapse energy and transport parameter, calculate the cavity erosion load of collapse energy radiated to the wall, and the cumulative cavity erosion load of the current predicted position of wall is obtained by accumulation;According to cumulative cavity erosion load, determine the cavity erosion region.The cavity erosion prediction method considers the real physical process of cavity collapse, ensures the authenticity of collapse region identification, and considers the transfer and dissipation process in energy transport, ensures the accuracy of cavity erosion region prediction.

The present application relates to the technical field of soil splash erosion, and particularly relates to a soil splash erosion simulation analysis system, which comprises: a rainfall simulator for simulating rainfall with different intensities and different pH values; a wind simulator for adjusting wind speed and wind direction; a raindrop characteristic measurement device for measuring raindrop data; a splash erosion simulation device for simulating the process of raindrop falling on target soil to cause splash erosion and generate splash erosion material; a data acquisition device for acquiring the initial pH value and initial conductivity of the target soil before rainfall, and acquiring the target pH value, target conductivity, target erosion depth of the target soil after rainfall, and the target splash erosion material average weight diameter and target total splash erosion amount of the splash erosion material; and an electronic device for determining and establishing a splash erosion prediction model based on the raindrop pH value, raindrop data, wind speed, wind direction and data acquired by the data acquisition device, and developing a soil splash erosion simulation analysis system capable of real-time and quantitative analysis of soil splash erosion.

The invention discloses a soil erosion intelligent prediction and restoration decision-making method and system, and relates to the technical field of ecological environment monitoring and restoration, and the method comprises the steps: building a multi-source data space-time coupling database, and achieving the dynamic updating and refining of land utilization classification data; a land utilization classification-oriented soil erosion prediction model is constructed based on a neural network model, and sensor data time correction and trend fusion analysis are combined, so that the erosion intensity prediction precision is improved; and meanwhile, a land utilization type-erosion risk-restoration strategy knowledge base is established, differential restoration schemes are generated, and risk early warning is carried out. Through multi-source data fusion and intelligent model application, full-process management of soil erosion from dynamic prediction to precise restoration is realized, the restoration scheme mismatching rate is reduced, and scientific support is provided for ecological restoration decision making.

The application discloses a method for analyzing the erosion characteristics of a steam turbine regulating valve based on a modified Oka erosion model, which comprises the following steps: S1, modifying the parameters of the Oka erosion model; S2, analyzing the flow field characteristics; and S3, analyzing the erosion characteristics. By constructing a single particle impact three-dimensional model and performing explicit dynamic finite element simulation, the particle size, impact velocity and impact angle parameters in the Oka erosion model are modified, so that the erosion prediction model is matched with the actual valve core material and high temperature and high pressure working conditions, and the accuracy of the erosion rate calculation is improved. By establishing a three-dimensional entity model of the regulating valve and extracting a fluid domain for flow field numerical simulation, the distribution of the velocity field and the pressure field in the valve is obtained, the modified Oka erosion model is introduced and coupled with the solid particle parameters, the particle motion equation and the particle-wall collision model are solved, and the distribution of the erosion and wear of the valve core surface is obtained, so that the positioning accuracy of the erosion area under the complex gas-solid two-phase flow is improved.

The invention discloses a multi-scale three-dimensional monitoring method and a multi-scale three-dimensional monitoring system for ecological hydrology of an agricultural and pastoral zone. The multi-scale three-dimensional monitoring method comprises the following steps: acquiring space-based data, air-based data and foundation data by using a multi-source satellite source, aerial survey of an unmanned aerial vehicle and a point-line-plane three-level sensing network; integrating space-based data through a space-time fusion algorithm by using a multi-level fusion framework, performing feature extraction on the space-based data through LiDAR point cloud analysis and thermal infrared analysis, and performing model calculation on the foundation data through soil water isotope traceability and livestock capacity calculation; performing space-time collaboration and data fusion on the space-based data with the space-based data and the foundation data, and performing super-resolution reconstruction and dynamic erosion prediction; the foundation data and the air-based data are fused to calculate a grazing intensity index, and real-time inversion of an ecological hydrological process is carried out; grazing early warning and grazing route optimization are carried out through a multi-module cooperation mechanism and model deduction of sky-ground data calibration, air-ground trigger response and livestock-ground feedback control.

The invention discloses a POD-Kriging-based pipeline erosion condition prediction method, and relates to the field of pipeline erosion prediction, and the method comprises the steps: determining an input parameter set and an output physical quantity of a reduced-order model based on an ROM database and in combination with a variable range of an industrial condition; selecting a plurality of design points based on a parameter value interval of the input parameter set, obtaining an output physical quantity corresponding to each design point, and constructing a training data set; based on the training data set, an intrinsic orthogonal decomposition method and a spatial interpolation algorithm are executed to construct an initial order reduction model; carrying out collaborative optimization on the number of intrinsic modes and the number of design points in the initial reduced-order model to obtain an optimized reduced-order model; according to the method, parameter mapping is established through the intrinsic orthogonal decomposition and spatial interpolation algorithm, and cooperative improvement of calculation efficiency and precision in industrial pipeline erosion field prediction is realized in combination with cooperative optimization of the modal number and the design point.

The invention discloses a gas well oil pipe erosion safety evaluation method and a related device. The method comprises the following steps: acquiring key parameters of a target well; according to the obtained key parameters of the target well, an erosion test is conducted, and a test result is obtained; constructing a gas well oil pipe erosion mathematical model according to the test result; the erosion mathematical model is used for conducting erosion prediction on a target well pipe column; and judging the erosion safety of the oil pipe under the current gas production amount according to the erosion prediction. By obtaining the key parameters of the target well, the test result closer to the actual condition can be obtained, the erosion condition of the oil pipe can be reflected more accurately, the mathematical model constructed based on the erosion test result can simulate and predict the erosion condition of the oil pipe under different gas production rates, the erosion mechanism can be understood, and the method is suitable for popularization and application. According to the method, firstly, a theoretical basis is provided for subsequent optimization design and safety management, secondly, erosion prediction can be conducted on the tubular column of the target well by means of the established erosion mathematical model, and therefore safety accidents caused by erosion are avoided.

The invention provides a coal mining subsidence area slope erosion intensity prediction method under freezing and thawing conditions, and belongs to the field of soil erosion prediction.The method comprises the steps that initial data of a coal mining area to be predicted are obtained, and the initial slope shape after subsidence is calculated; simulating dynamic changes of the soil temperature, the volume ice content and the water content along with time, and calculating a dynamic soil erodibility factor under the freezing and thawing action based on the freezing and thawing cycle times and the initial water content of the soil; calculating the runoff depth and the unit width flow based on the gradient and the slope length of the sunken slope surface, the dynamic soil erodibility factor and rainfall data; based on the dynamic soil erodibility factor, the runoff depth and the unit width flow, the soil critical anti-shearing force influenced by the volume ice content is introduced, and the slope soil erosion intensity is calculated. Therefore, the influence of natural excursion force and human activities is organically fused, various key factors of the special area are fully considered, and the blank of the method for predicting the slope scale soil erosion intensity of the special area is filled.

The invention discloses a subsea tunnel ion erosion analysis method based on an embedded physical information neural network. The subsea tunnel ion erosion analysis method comprises the steps of solving and constructing a pore water pressure control equation and obtaining a pore water pressure field; acquiring a seepage velocity field according to the pore water pressure field; inputting the seepage velocity field as a key parameter into a convection-dispersion equation so as to obtain the erosion ion concentration representing the migration process of the erosion ions under the driving of the pore water pressure; acquiring a training set and a test set; performing model training on the constructed physical information neural network model through the training set to obtain a trained physical information neural network model; based on the constructed physical loss function, performing model verification on the trained physical information neural network model to obtain an optimal prediction model; therefore, analysis and prediction of subsea tunnel ion erosion are realized. According to the method, the problem that the accuracy and calculation efficiency of ion erosion prediction of the subsea tunnel are low due to the fact that an existing research method still has many defects in the aspects of calculation precision, efficiency and applicability is solved.

A beach erosion detection and quantitative calculation method based on unmanned aerial vehicle mapping is provided, including the following steps: S1, configuring unmanned aerial vehicles; S2, obtaining beach aerial image data in different periods by using the unmanned aerial vehicle; S3, delineating a plurality of sections on the beach, and extracting elevation data of the sections from the multi-period data by using the aerial images processed; S4, in order to unify the reference boundary of multi-period aerial photography data, a unified lowest water line is defined, and the upper boundary is the coastline; S5, automatically identifying and predicting factors affecting beach erosion; S6, performing real-time erosion prediction and future erosion trend simulation; S7, calculating the total amount of beach erosion, and comparing the sand amount in each grid to get the distribution characteristics of beach erosion change.

The application discloses a kind of hydropower station dam body erosion prediction method, it is related to hydropower station engineering safety monitoring technical field, comprising the following steps: S1, obtains the historical image of dam body spillway apron at each period of history, and the historical image is preprocessed;S2, according to the image after preprocessing, erosion area is identified using deep learning instance segmentation model, and the erosion volume of each period is calculated in combination with digital elevation model difference method;S3, extract the discharge parameter synchronous with historical image from hydropower station monitoring system;The erosion prediction method of the application uses multiple factors such as discharge capacity, sediment scouring capacity and apron structure parameters, and combines historical erosion volume to predict the future erosion volume of hydropower station dam body, through erosion prediction and risk assessment, can reasonably arrange maintenance work, avoid excessive maintenance or insufficient maintenance, improve the operation efficiency and economic benefit of hydropower station.