224 results about "Runoff volume" patented technology

The invention provides a plateau lake agricultural non-point source pollution treatment method which comprises the following steps: acquiring vegetation indexes and surface temperature field data by using a remote sensing satellite, and generating a pollution source space thermodynamic diagram in combination with water quality and soil parameters of ground sampling points; performing space-time alignment and data fusion on the thermodynamic diagram and real-time runoff and soil permeability data acquired by the hydrological sensor network, and constructing a structured pollution migration database; on the basis of the database, a hybrid neural network model embedded with physical constraints is utilized to predict pollutant concentration distribution within 72 hours in the future; inputting the predicted value into a multi-stage optimization controller, and generating a control parameter set comprising treatment intensity, engineering parameters and a fertilization ratio; generating a treatment strategy map covering the drainage basin through a GIS; and deploying an Internet of Things monitoring node to collect the treated water quality data to form a closed-loop control link. The treatment efficiency and effect can be improved, the treatment cost is reduced, and the negative influence on the ecological environment is reduced.

The invention discloses a river water ecological health monitoring method and a river water ecological health monitoring system, relates to the technical field of ecological health monitoring, and aims to solve the problem that a traditional water quality monitoring method mainly depends on water sample collection and laboratory analysis, and judges the water quality by measuring indexes such as dissolved oxygen, pH value, ammonia nitrogen, total phosphorus, heavy metal and dissolved organic matters in water; the river runoff is calculated from the precipitation and the drainage basin area, and the monitoring points are set in combination with the environmental information, so that an accurate data basis can be provided for subsequent water quality health assessment. The water quality condition is predicted by utilizing the water flow speed and the pollution point discharge amount and then compared with an actual monitoring result, so that potential problems can be found in time and a monitoring strategy can be optimized.

The invention discloses a watershed pollution prediction method based on multi-source data fusion, and belongs to the technical field of watershed pollution prediction. According to the method, water quality, runoff, land utilization and meteorological data sequences are firstly constructed, and then features of the sequences are extracted and spliced to form a fusion feature vector. And forming a training set by using a plurality of fusion feature vectors, inputting the training set into a random forest regression model for training, performing performance evaluation on the trained model by using a verification set, and finally using the model meeting evaluation conditions to predict the drainage basin pollution degree. According to the invention, through multi-source data fusion and scientific model training and evaluation processes, the accuracy of drainage basin pollution prediction is effectively improved.

The invention discloses a river organic carbon flux inversion method based on multi-source data and related equipment. The method comprises the following steps: acquiring multi-source data of a river basin range in a target research area; obtaining a normalized difference index based on remote sensing image conversion; river parameters of the target research area are obtained based on the normalized difference water body indexes and multi-source data processing, and river runoff is obtained through conversion of a hydraulic geometrical relationship; on the basis of the remote sensing image, the normalized difference chlorophyll index and the normalized difference turbidity index, the particle organic carbon concentration is obtained through inversion by means of a first inversion model; based on the population number and the rainfall capacity, a second inversion model is used for inversion to obtain the concentration of dissolved organic carbon; and converting the river runoff, the particle organic carbon concentration and the dissolved organic carbon concentration to obtain the river organic carbon flux. The method can significantly improve the accuracy and reliability of monitoring on a high-dynamic river, accurately realizes the inversion of the organic carbon flux of the river, and can be widely applied to the technical field of data processing.

The invention relates to the technical field of slope stability analysis and landslide disaster prevention and control, in particular to a slope three-dimensional stability calculation method and system based on a potential sliding direction. The method comprises the following steps: acquiring slope point cloud data, and constructing a digital elevation model according to the slope point cloud data; geologic structure data are obtained, and a side slope three-dimensional geologic model is constructed according to the geologic structure data and the digital elevation model; according to the digital elevation model, side slope flow direction and slope runoff characteristics are obtained, and according to the side slope flow direction and the slope runoff characteristics, the potential sliding direction of the side slope is determined; and according to the potential sliding direction of the slope and the three-dimensional geological model of the slope, constructing a three-dimensional stability calculation model of the slope, and according to the three-dimensional stability calculation model of the slope, calculating the three-dimensional stability of the slope. According to the method, the problem that the calculation result is not accurate enough due to the fact that calculation of the three-dimensional stability of the side slope is not comprehensively considered in the prior art is solved.

The invention discloses a modularized distributed cold region hydrothermal coupling hydrological model which comprises the following steps: S1, dividing a drainage basin into a plurality of grids, and preparing a file required by a confluence process; s2, preparing driving data and grid feature description data of each grid; s3, calculating an energy balance process of the soil and vegetation surfaces by using an energy balance module; s4, calculating an evapotranspiration process by using an evapotranspiration module; s5, calculating an ice and snow melting process by using an ice and snow melting water module and adopting a degree-day factor method; s6, calculating soil moisture infiltration and an internal hydrothermal transmission process by utilizing a soil hydrothermal transmission and infiltration module; s7, calculating the runoff volume by using a runoff production calculation and confluence module; and S8, evaluating the simulation performance of the model through the model evaluation index. The device has a flexible modular structure, can automatically adjust the time step length, improves the simulation capability of the runoff process in the cold region, and can provide scientific guidance for water resource management in the river source region.

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 hydrological prediction, in particular to a drainage basin distributed runoff prediction method and system based on a graph neural network, and the method comprises the following steps: obtaining a drainage basin multi-source runoff data set to construct a multi-relation dynamic graph structure, and extracting node feature vectors; based on the node feature vector, obtaining a watershed evolution trend forward feature by establishing a watershed diffusion fitting architecture; constructing a distributed runoff probability prediction model, and taking the watershed trend forward features as model input to obtain runoff initial condition probability distribution of each sub-watershed in multiple periods in the future; establishing a mixed loss function, and performing physical constraint optimization on the runoff initial condition probability distribution to obtain distributed runoff optimization probability distribution; and performing uncertainty quantification on the distributed runoff optimization probability distribution to obtain a drainage basin distributed runoff prediction result. According to the method, the hydrological process simulation capability of the complex watershed is improved, and accurate prediction of the distributed runoff volume is realized.

The invention discloses a flood lake wetland heat flux simulation method in a watershed hydrological model, and relates to the technical field of heat flux simulation, and the method comprises the following steps: dispersing a watershed space into a slope river channel and a lake wetland, simulating the water movement of a slope river channel unit, and simulating the water movement of the lake wetland; the method comprises the following steps: simulating surface energy exchange for a lake wetland unit on the basis of simulating a hydrological process, simulating land hydrology and lake inflow runoff of a sub-basin by adopting a grid-based distributed hydrological model, respectively dividing simulation grid units for a slope river channel and a lake wetland, establishing evapotranspiration, infiltration and surface runoff models, and establishing an evapotranspiration, infiltration and surface runoff model; a two-dimensional shallow water equation is used for processing slope confluence, a one-dimensional hydrodynamic model is used for calculating river channel confluence, a lake water quantity balance model is established for calculating the lake water level, the lake submerging process is simulated, and the lake wetland distributed heat flux process is simulated. And the technical blank of flooding wetland hydrothermal simulation is filled.

The invention belongs to the technical field of hydrological forecasting, and relates to a reservoir flood peak flow and time prediction method and system considering rainfall center movement. The method comprises the following steps: acquiring topographic data, historical meteorological data and historical runoff data of a target drainage basin; the target drainage basin is divided into a plurality of sub-drainage basins based on topographic data, hydrological models are constructed respectively, and parameters of the hydrological models of the sub-drainage basins are calibrated through historical meteorological data and historical runoff data; acquiring a moving track and rainfall intensity change of a rainfall center of the target drainage basin and real-time meteorological data and real-time runoff data of each sub-drainage basin, and inputting the moving track and the rainfall intensity change and the real-time meteorological data and the real-time runoff data into a hydrological model of the corresponding sub-drainage basin to obtain flood flow hydrograph corresponding to rainfall of a plurality of preset time steps; and superposing the flood flow hydrograph of a plurality of preset time steps to obtain a reservoir flood peak flow and time prediction result. According to the method, the flood prediction precision and reliability are improved, and a scientific basis is provided for flood scheduling and safety management of the hydropower station.

The invention discloses a thermal power generating unit decommissioning path planning method and device based on meteorological and hydrological risks, equipment, a medium and a product, and relates to the field of power system decision optimization. The method comprises the steps of obtaining meteorological and hydrological risk data and a decommissioning index of a thermal power generating unit; according to the decommissioning index and the meteorological and hydrological risk data under the climate change, a progressive hierarchical screening logic method is adopted for screening and constraint progressive correction processing; performing mapping and nonlinear correlation interaction analysis by adopting Monte Carlo simulation and a gradient boosting decision tree and combining multiple groups of parameter combination data, such as water temperature and runoff volume in meteorological and hydrological variables; and performing collaborative optimization processing based on the adaptive strategy model and a set analysis function to obtain an optimal strategy, and then performing mapping and analysis processing in combination with an analysis result to obtain targeted strategy list information for planning the decommissioning path of the thermal power generating unit. The invention aims to realize the planning of the decommissioning path of the thermal power generating unit.

This invention proposes a method and system for optimizing water resource allocation, which relates to the technical field of water resource optimization. The method includes first obtaining historical hydrological data for a study area, then inputting the data into a preset river runoff prediction model, which then outputs a monthly runoff sequence for a specific year within the region. Based on the monthly runoff sequence, a multi-conditional constrained water resource optimization allocation model based on user allocation is established for the study area under extreme climate conditions. Finally, the multi-conditional constrained water resource optimization allocation model is solved to obtain a solution result, and water resource optimization is performed for users in the study area based on the solution result. This invention effectively achieves accurate and reasonable optimal allocation of water resources to users under multi-objective constraints.

The invention discloses a rice irrigation district irrigation amount simulation method and system, and the method comprises the steps: taking a region controlled by a backbone channel and a drainage main ditch as a standard for dividing sub-units based on a DEM and a digital canal system, carrying out the grid division of the sub-units, and obtaining an irrigation district sub-basin division result; constructing a dynamic irrigation triggering mechanism of each irrigation area sub-basin, and determining a multi-water-source cooperative scheduling scheme of each irrigation area sub-basin based on the division result of the irrigation area sub-basin; and when each irrigation sub-basin is irrigated according to the multi-water-source cooperative scheduling scheme, calculating the canal system leakage amount in each irrigation sub-basin, and determining a water distribution scheme of each irrigation sub-basin according to the canal system leakage amount. The method can be used for quickly and accurately simulating the irrigation amount and the runoff volume of the rice irrigation area, and provides a scientific basis for formulating agricultural management measures and screening water-saving high-yield technical modes.

The invention provides a water and soil loss early warning method and device based on climate change, and relates to the technical field of machine learning, and the method comprises the steps: collecting the surface temperature data and freeze-thaw state data of a target region in real time, and determining the freeze-thaw cycle state of a surface active layer based on a set freeze-thaw state criterion; calculating the melt water runoff generation amount of the surface active layer based on the freeze-thaw cycle state, and calculating the comprehensive runoff yield by combining rainfall forecast data; dynamically calculating the unit surface scouring force based on the comprehensive runoff runoff yield, and judging whether the unit surface scouring force reaches an erosion starting state or not based on a soil instability limit shear threshold value under the freezing-thawing interface; and triggering water and soil loss early warning when the superposing effective melt water runoff volume of the earth surface freeze-thaw conversion state reaches a preset percentile and the earth surface unit scouring force exceeds a soil instability limit shear threshold value. According to the invention, early warning can be carried out according to climate change for water and soil loss in alpine regions.

The invention discloses a runoff prediction method and device based on a Gaussian function and a medium, and relates to the technical field of runoff prediction, and the method comprises the steps: determining the fitting values of a plurality of Gaussian parameters of each historical year based on the monitoring value of the runoff of each month in each historical year; respectively inputting the fitted values of the Gaussian parameters in all historical years into a Gaussian parameter prediction model of the corresponding Gaussian parameters to obtain predicted values of the Gaussian parameters in the future year; determining a Gaussian function value of each month in the future year based on the predicted value of each Gaussian parameter in the future year; determining a predicted value of the runoff volume of each month in the future year based on the Gaussian function value of each month in the future year and a runoff volume prediction model; the runoff prediction model is obtained by training a back propagation neural network. According to the invention, the prediction precision of the runoff volume is improved.

The invention relates to the technical field of hydrological simulation and flood forecasting, and particularly discloses a watershed runoff simulation method and device with a graph neural network fused with hydrological priori knowledge, and the method comprises the steps: carrying out the grid division of a target watershed, and building a watershed graph structure in combination with the hydrological priori knowledge; introducing a trainable weight into the graph neural network model, and performing time delay weighting based on a spatial distance to obtain a fusion network model; the meteorological driving data, the rainfall data and the drainage basin attributes serve as input, the convergence process is simulated through the fusion network model, and the predicted downstream runoff volume is obtained; and comparing the predicted downstream runoff volume with the actually measured downstream runoff volume, and optimizing all learnable parameters of the fusion network model. According to the method, the defects that a traditional physical model is large in calculation amount and long in consumed time are overcome, rapid simulation of the runoff process is achieved, and the accuracy and reliability of a data driving model in a runoff simulation task are improved by fusing hydrological priori knowledge and introducing physical constraints.

The invention provides a reservoir drainage basin daily runoff prediction method, system and device and a medium, and the method comprises the following steps: S1, obtaining daily runoff data in a certain drainage basin, carrying out the abnormal value detection of the daily runoff data through employing an isolated forest, and carrying out the data cleaning; s2, decomposing the cleaned daily runoff data by using data-driven adaptive chirp mode decomposition to obtain a plurality of components; constructing an input matrix for each component, and dividing the input matrix into a training set and a test set; s3, constructing a fusion model of the time convolution network and the non-inverse extreme learning machine, and training the fusion model; s4, introducing a half uniform initialization and variable helix updating strategy into a PID search algorithm to obtain an IPSA algorithm; using an IPSA algorithm to optimize the TCN-IFELM model; and S5, obtaining a prediction result of each component by using the test set, and adding the prediction results to obtain a predicted value of the daily runoff at the future moment. According to the method, the time sequence characteristic and the nonlinear relation of the runoff data can be effectively captured, so that the daily runoff prediction precision is improved.

The invention discloses a reservoir capacity prediction method considering underlying surface change conditions, and aims to solve the problems that the prediction value of an existing scheme is too large, deviation exists between the estimated reservoir capacity and the actual reservoir capacity, the functions of flood control, water supply, irrigation and the like of a reservoir are affected, and the economic benefits of the reservoir are directly affected. The method comprises the following steps: acquiring actually measured water and sediment series data of a reservoir in historical periods, analyzing the reservoir water and sediment change trend, determining reservoir runoff and reservoir sediment abrupt change points in combination with underlying surface factor changes, dividing different stages of reservoir sediment change, and calculating soil erosion amounts of drainage basins above the reservoir in different stages. And converting the reservoir sand amount in the early stage into the reservoir sand amount under the current underlying surface condition by using the conversion coefficient, estimating the reservoir sand amount in the planning level year or the design level year, and analyzing the reservoir capacity curve in the future. The reservoir capacity prediction method achieves the effects of more accurately predicting the reservoir capacity, exerting the reservoir function, improving the reservoir benefit and avoiding the waste of engineering investment.

The invention discloses a method for predicting the risk of snow-melting flood in spring. The method comprises the following steps: analyzing and calculating runoff contribution based on snow melting; extracting a snow melting period; establishing a relationship between accumulated snow in the snow melting period and the air temperature; establishing a relationship between the snow melting runoff and meteorological elements; the meteorological elements comprise air temperature, precipitation amount and snowmelt amount; and establishing a snow-melting runoff prediction model and predicting the snow-melting flood risk in spring. According to the method, the snow-melting runoff volume can be quickly estimated, and the risk can be quickly predicted.

The invention provides a flood model parameter calibration method and system based on isotopic tracing, and the method comprises the steps: determining a parameter set and prior distribution which need to be analyzed in a flood model, and the parameter set comprises parameters related to runoff volume and parameters related to isotope transmission in the flood model; randomly generating a group of parameter samples according to prior distribution of parameters; evaluating the sensitivity of each parameter in each group of parameter samples by analyzing the relationship between the parameter samples and the corresponding objective function values; screening out sensitive parameters according to a sensitivity evaluation result; according to the flood model parameter calibration method based on isotopic tracing provided by the invention, the problem of different parameters and the same effect of the flood model is solved, namely, the different parameter and the same effect of the model are greatly reduced (namely, the parameter range when the model evaluation index reaches a specific threshold value or above), and meanwhile, the simulation effect of the flood model in the verification period is greatly improved.

The invention relates to the technical field of rainfall runoff forecasting, in particular to a physical mechanism guided deep learning rainfall runoff forecasting method and related equipment, and the method specifically comprises the steps: obtaining meteorological data, underlying surface data and runoff data, and determining target features through statistical analysis and causal analysis; constructing a water balance long-short term memory neural network; performing model training based on the target features, a long-short term memory neural network of water balance and a Parzen estimator of a structure tree so as to determine hyper-parameters, model weight information and threshold information of a prediction model, and obtaining the prediction model; and forecasting rainfall runoff based on the prediction model. Therefore, on the basis of ensuring the precision performance of the model, the problem that the prediction does not conform to the physical consistency can be avoided, and the practicability of the model is improved.

The invention provides an arid region mountain vegetation bearing capacity assessment method and device, a medium and equipment, and the method comprises the steps: obtaining the rainfall capacity, the soil water quantity and the underground runoff capacity of a sample site in a monitoring time period, the underground runoff volume comprises the upper boundary runoff volume and the lower boundary runoff volume of a bedrock weathered layer corresponding to the sample site; estimating the total amount of absorbable water according to the rainfall, the soil water amount and the underground runoff; and according to the total absorbable water amount and the vegetation water demand corresponding to the vegetation, generating a corresponding vegetation bearing capacity evaluation result. According to the method, a total moisture mode is integrated, thin film water and weathered debris water storage in soil are comprehensively considered, a method system for evaluating the vegetation bearing capacity of the mountain land is provided, total moisture dynamic modes of different soil types can be accurately captured, a vegetation bearing capacity evaluation result is obtained, and scientific and technological support is provided for ecological restoration, vegetation configuration and water resource management of an arid region.

The present application belongs to the technical field of geological disaster prevention and mitigation, and relates to a debris flow power early warning method, specifically comprising: obtaining a startable source amount of a debris flow basin and a possible runoff under a forecast rainfall condition, calculating a water-rock mixture density ρ d and a water-rock mixture density ρ h under an extreme hourly rainfall condition of a target channel under the forecast rainfall condition according to the startable source amount and the possible runoff respectively; obtaining a final critical density ρ of a water-rock mixture when a debris flow is started in a main sliding direction, and determining a first early warning level T1 under the forecast rainfall condition in combination with a daily scale water-rock mixture density early warning level determination rule, and determining a second early warning level T2 under the extreme hourly rainfall condition in combination with an hourly scale water-rock mixture density early warning level determination rule; when the first early warning level T1 and the second early warning level T2 are inconsistent, selecting a higher early warning level as a published early warning level T of a target debris flow basin, which can effectively prevent group debris flow disasters.

The invention belongs to the technical field of resource optimization scheduling, and provides a drainage basin water volume accurate scheduling method and system based on precipitation real-time monitoring, and the method comprises the following steps: collecting hydrological data, early-stage influence rainfall and soil water content of each hydropower station in a target drainage basin; the earlier-stage influence rainfall is corrected through the collected hydrological data, and the corrected earlier-stage influence rainfall is obtained; in combination with hydrological data and the corrected early-stage influence rainfall, carrying out runoff forecasting in a short-term period of the drainage basin and middle-and-long-term end-of-period water level forecasting in the drainage basin, and obtaining end-of-period water level information; determining a flood prevention early warning level and a drought resistance early warning level according to the end-of-period water level information; and a scheduling decision is made according to the flood prevention early warning level and the drought resistance early warning level. According to the scheduling method provided by the embodiment of the invention, accurate scheduling of the water volume of the drainage basin can be realized.

This invention provides a method for determining the sample for verifying the proportion of house damage caused by rainstorm and flood disasters. The method includes the following steps: S1, collecting the average rainfall, maximum rainfall, runoff, proportion of brick-and-wood structure houses, and proportion of house damage for each administrative unit to be investigated after a rainstorm and flood disaster, denoted as n for the number of administrative units to be investigated; S2, processing the data; S3, calculating and generating a semi-variant scatter plot of the house damage proportion matrix Y as a function of distance within a four-dimensional attribute space matrix X; S4, determining the sampling sample. This method makes the measurement of spatial correlation of samples more reasonable, has stronger interpretability, reduces the sample size required in actual investigations, helps to make faster and more accurate assessments of losses after rainstorm and flood disasters, and ensures the accuracy of the assessment results.

The invention discloses a river runoff prediction method, system, equipment and medium under a rainfall condition, and the method comprises the steps: collecting data information of a cascade hydropower station drainage basin and an adjacent region, and carrying out the data preprocessing of the collected data; performing sample construction on the preprocessed data; a meteorological prediction model in the cascade hydropower station drainage basin is constructed based on an auto-encoder, and meteorological auto-regression prediction is carried out; and based on a meteorological autoregression prediction result, using a neural network model to automatically weight and predict the rainfall, using the predicted rainfall, and calling software to predict the river runoff. Through a physical model, the interpretability of the method model is enhanced, and complementation of a physical mechanism and data driving is realized.

The application discloses a runoff prediction method and device based on a Gaussian function and a medium, relates to the technical field of runoff prediction, and comprises the following steps: determining fitting values of a plurality of Gaussian parameters of each historical year based on monitored values of runoff of each month in each historical year; inputting the fitting values of each Gaussian parameter of all historical years into a Gaussian parameter prediction model of the corresponding Gaussian parameter respectively to obtain prediction values of each Gaussian parameter of a future year; determining Gaussian function values of each month in the future year based on the prediction values of each Gaussian parameter of the future year; and determining prediction values of runoff of each month in the future year based on the Gaussian function values of each month in the future year and a runoff prediction model. The runoff prediction model is obtained by training a back propagation neural network. The application improves the prediction accuracy of runoff.

The application provides a soil salinization response simulation method and device, the method comprises the following steps: obtaining the soil type, terrain grid data and meteorological data of a target area; inputting the terrain grid data into a first processing model to obtain a sub-basin division result; inputting the meteorological data and soil type into the first processing model according to the sub-basin division result to obtain sub-basin daily precipitation data and sub-basin evapotranspiration; inputting the sub-basin daily precipitation data and sub-basin evapotranspiration into a second processing model to obtain soil salt content and crop yield in combination with sub-basin irrigation; obtaining soil salinization prevention and control measures according to the soil salt content and crop yield; wherein the first processing model is obtained by calibrating a preset processing module according to the difference between the monthly runoff of a basin and the measured calibration data. The application can improve the accuracy, applicability and operability of soil salinization prevention and control.

The present disclosure relates to the technical field of watershed water and sediment regulation, and provides a method and device for predicting a watershed sediment transport ratio. The method comprises: obtaining a slope sediment yield parameter and a runoff parameter of a target watershed; calculating a target slope sediment yield according to the slope sediment yield parameter; calculating a target runoff according to the runoff parameter; and introducing the target slope sediment yield and the target runoff into a preset sediment transport ratio prediction model to predict a target sediment transport ratio. The present disclosure can greatly improve the application range and prediction accuracy of the prediction of the watershed sediment transport ratio.

The application discloses a snowmelt runoff prediction method and system based on direct measurement of snow water equivalent, and belongs to the technical field of hydrological monitoring. The system comprises a cooperative observation network composed of an acoustic source array emitting coded acoustic waves and a sensing optical fiber laid on the ground surface; a distributed optical fiber acoustic wave sensing demodulator for receiving a snow layer modulated vibration signal; a fusion inversion calculation unit adopting a physical information neural network algorithm, embedding an acoustic wave fluctuation equation as a constraint into network training, and directly inverting a continuous spatial distribution of the snow water equivalent from the signal; and a runoff prediction module for predicting runoff by using the distributed data. The application breaks through the limitations of traditional point measurement and satellite remote sensing, realizes direct, high-resolution and high-precision surface measurement of the snow water equivalent, fundamentally improves the input data quality of the snowmelt runoff prediction model, and has the characteristics of high precision, strong reliability and cost-effectiveness.