835 results about "Precipitation" patented technology

The invention provides a power equipment meteorological monitoring and early warning system based on artificial intelligence. The power equipment meteorological monitoring and early warning system based on artificial intelligence comprises a data acquisition module, a data preprocessing module, a spatio-temporal feature fusion module and a meteorological disaster prediction model, the meteorological disaster prediction model adopts a deep reinforcement learning framework, inputs a multi-dimensional spatio-temporal feature matrix, and carries out meteorological disaster prediction on the multi-dimensional spatio-temporal feature matrix. And outputting meteorological disaster risk levels and key parameter predicted values in a future preset time period, including a wind speed, precipitation, temperature anomaly and tropical cyclone path probability, a dynamic early warning threshold generation module, an early warning decision module and a model optimization module. The power equipment meteorological monitoring and early warning system based on artificial intelligence provided by the invention has the advantages that the data interpolation precision of a complex terrain region can be improved, high-precision prediction of a typhoon path, short-time strong wind and an icing risk can be realized, and the early warning accuracy and defense response efficiency of a power system to meteorological disasters can be comprehensively improved.

The invention discloses a regional water supply emergency scheduling method and system. The method comprises the following steps: fusing GIS and IoT data to construct a dynamic three-dimensional topology model; historical water consumption and meteorological data are decomposed through wavelet multi-scale decomposition, and a prediction model is input to obtain a water consumption prediction value of a scheduling day period; on the basis of reservoir water storage, flow, water quality, rainfall and pollution source data collected in real time, a flow-water quality coupling model is used for predicting the reservoir inflow and water quality of a dispatching day; the method comprises the following steps: establishing a multi-objective function by combining water consumption prediction, storage prediction and current water storage, embedding a water quality constrained multi-objective optimization model, and solving by adopting an NSGA-II algorithm to obtain a Pareto optimal scheduling scheme set; and finally, a scheme is selected to be issued and executed. According to the method, data-physical collaborative intelligent scheduling decision is realized, the water supply demand is met under the condition that the water supply quality is stable, the condition of water shortage of the user terminal is avoided, and the water quality of the user terminal can continuously reach the standard.

The invention belongs to the technical field of rainfall prediction, and discloses a short and temporary rainfall prediction method based on multi-model random scheduling integration, which develops a robust training and pushing framework based on a continuous rolling prediction strategy, and decomposes long-sequence prediction into manageable stages. According to the method, training is carried out through teacher forcing and planned sampling, error propagation is relieved, and the training process is stabilized. The invention further designs asymmetric encoder-decoders (DSE and AFD) that achieve lower FLOPs than competitive baselines under standardized assessment, where DSE selectively compresses significant features and AFD stepwise reconstructs details to mitigate excessive smoothing problems. Finally, an intensity weighted Gaussian KL divergence loss function is designed, and the key problem of data balance is solved by modeling and predicting on a distribution level and endowing a large weight to a meteorological important heavy rainfall event.

The invention relates to the technical field of building construction, in particular to a foundation pit dewatering prediction method based on a digital twinborn technology, which comprises the following steps of: 1, establishing a multi-source data fusion layer; 2, establishing a digital twin modeling layer; 3, establishing an intelligent prediction algorithm layer; 4, establishing a dynamic optimization decision layer, and generating a precipitation well optimization scheme based on a prediction result; according to the method, engineering geological data of a geological survey report before excavation of the building foundation pit, historical meteorology of a building location and hydrogeological data are utilized, digital twinning is carried out on precipitation during the whole construction period of the proposed building foundation pit on the basis of a digital twinning technology, the precipitation period, daily precipitation and influence of precipitation on foundation pit supporting are predicted, and the foundation pit supporting effect is improved. According to the method, the influence of settlement deformation of surrounding buildings is reduced, digital simulation is provided for smooth construction of a whole project, and measurement and prediction are provided for the project and the surrounding environment, so that the construction cost is greatly reduced.

The invention relates to a multi-source data fusion rainfall revision model construction method based on dynamic physical constraints, and belongs to the technical field of artificial intelligence and meteorology and hydrology crossing. The method comprises the steps that multi-source heterogeneous data such as satellites, reanalysis, sites and terrains are acquired and fused, and a standardized space-time sample set is constructed; constructing a special deep learning network comprising a double-flow encoder, a cross-scale physical attention fusion module and a decoder; constructing a composite loss function fusing data fidelity, terrain uplift constraint and wind field advection constraint; designing a dynamic constraint gating mechanism, and adaptively adjusting a physical constraint weight according to a real-time atmospheric state; and a high-precision precipitation revision model is obtained by optimizing the composite loss function training network. According to the method, the key physical process is embedded into the model in a dynamic adjustable mode, the problems that a traditional method is poor in physical consistency and generalization ability are solved, and a revised precipitation field which is physically reasonable and higher in precision can be generated under complex terrains and changeable weather.

The invention relates to a rainfall nowcasting method based on U-KAN grading loss weighting and frequency self-adaption, which comprises the following steps: (1) carrying out quality control and screening on a radar puzzle, and establishing a data set; (2) dividing a training set, a verification set and a test set, and standardizing; (3) constructing a U-KAN model, selecting training parameters and inputting data: combining a traditional Unet structure with a KAN network to construct the U-KAN model; then performing model training to obtain a prediction result; the prediction result is restored to the original magnitude through destandardization; (4) introducing a loss function based on a root-mean-square error and grade weighting in a model training stage, and performing post-processing on model output by adopting a frequency deviation correction method; (5) integrating and averaging the forecast products processed by the two complementary strategies, and recording the forecast products as U-KANE; and (6) predicting a rainfall result in the next three hours by using radar echo data in the past one hour, and outputting a rainfall short-term and imminent forecast result by the U-KANE.

The invention provides a regional ecological environment quality evaluation method and system based on remote sensing data, and relates to the technical field of environment remote sensing and ecological monitoring, and the method comprises the steps: extracting red light and near-infrared reflectivity at the peak of a growing season by using Landsat8 satellite data, and calculating a mixed vegetation index; synchronously measuring vegetation indexes of pure vegetation and bare soil on site to obtain a vegetation coverage rate, and obtaining water transparency, suspended solid concentration and chlorophyll a concentration through on-site measurement on the basis of green light, red light, blue light and near-infrared reflectivity in a heavy rainfall period and a dry season; the method comprises the following steps: constructing a linear regression model of reflectivity and water quality parameters through a least square method, calculating a water quality index, constructing an extreme weather influence factor based on ten-year extreme weather data, and finally fusing a vegetation coverage rate, water quality, the extreme weather influence factor, annual precipitation, annual average temperature, optimal regional vegetation temperature and historical rainfall extremum. And constructing an ecological quality index, and dividing ecological environment quality grades.

The invention provides an intelligent path planning method and system based on dynamic road condition prediction. The method comprises the steps of firstly obtaining multi-source dynamic data of a target area; then, constructing a weather influence prediction model to predict weather influence parameters in a future time period; secondly, constructing a weather-traffic coupling model, and respectively establishing correlation models of corresponding precipitation, traffic flow density and average vehicle speed according to road types through historical data analysis, so as to estimate the traffic efficiency of each road section under a dynamic weather condition; and finally, generating a plurality of candidate paths according to the passing efficiency, screening out an alternative path set meeting a multi-target optimization condition from the candidate paths, performing simulation evaluation on the alternative path set, and determining an optimal path according to a simulation result. Compared with a traditional static path planning method, the method has the advantages that the responsiveness of a traffic system to meteorological disasters is remarkably improved, and predictable navigation service is provided for intelligent network connection vehicles.

The invention discloses a minute-level mountain approaching rainfall forecasting method and system based on improved Vision Transform topographic physical constraints, and the method comprises the steps: building a model training data set based on the multi-source meteorological observation station data and topographic feature data of a complex mountain region; a Vision Transform network is improved on the basis of a position adaptive dynamic convolutional network and a rain-free mask feature extraction strategy; a combined loss function capable of providing terrain forced precipitation physical constraints is constructed; the improved Vision Transform network is trained on the basis of the training data set, and an optimal approaching rainfall forecasting model is obtained; and inputting the real-time multi-source meteorological observation grid point data and the topographic feature grid point data into the optimal close rainfall forecast model to obtain close rainfall forecast. According to the method, the heavy rainfall position forecasting performance can be obviously improved, and the minute-level approaching heavy rainfall forecasting skill in the complex mountain environment is improved.

The invention discloses a deep learning and numerical mode seamless fusion-based short temporary rainfall forecasting method. The method comprises the steps of constructing a short temporary rainfall initial forecasting model based on a Vison Transform network, and constructing a deep learning and numerical mode seamless fusion-based short temporary rainfall correction forecasting model based on an improved U-Net network; and collecting various meteorological data in real time, preprocessing the data, inputting the preprocessed data into the trained short-temporary rainfall forecast optimal model to obtain 0-6h short-temporary rainfall forecast, fusing the short-temporary rainfall forecast with 2-6h short-temporary rainfall forecast in a real-time regional numerical mode, inputting the trained short-temporary rainfall optimal correction forecast model, and obtaining 0-6h seamless fused short-temporary rainfall forecast. The method solves the problems that in the prior art, deep learning extrapolation and numerical mode short and temporary rainfall fusion forecasting is discontinuous in time, inconsistent in spatial position and low in forecasting accuracy.

The invention discloses an aeronautical ice accretion identification method based on multi-source data, which solves the coverage blind area of a single data source (mode / satellite) through multi-source data fusion processing, improves the integrity of a three-dimensional space, and further establishes a scene classification foundation through cloud layer structure identification and accurate division of a cloud area / a precipitation area. Through membership function construction processing, a nonlinear relation between meteorological elements and icing is quantified, and a traditional threshold value method is replaced; furthermore, through physical scene classification processing, single-layer cloud / multi-layer cloud / freezing water freezing mechanisms are distinguished, and misjudgment is avoided; through initial index calculation, CTTmap is dynamically adjusted according to different scenes, and satellite detection deviation is corrected; finally, through dynamic index correction processing, vertical motion, supercooled water and a real-time report are introduced, and result self-adaptive optimization is achieved.

According to the typhoon disaster dynamic risk estimation method and system based on intelligent grid forecasting, meteorological data of an intelligent grid forecasting system with high refinement degree and good accuracy are adopted, intelligent grid forecasting data are corrected by fusing observation data, and data deviation is effectively reduced. The typhoon wind and rain comprehensive index can objectively reflect the typhoon composite disaster-inducing effect, and the typhoon wind and rain comprehensive index is based on the nature of a disaster-inducing mechanism, and the limitation of single-element evaluation is solved by dynamically fusing wind speed and rainfall data. The disaster-pregnant environment influence coefficient considering the dynamic influence of the terrain factors is constructed, the limitation of traditional fixed geographic parameters is broken through, and the risk assessment error caused by neglecting the dynamic nature of the disaster-pregnant environment in a traditional model is solved. A typhoon disaster risk assessment model based on index weight is designed, the risk assessment model focuses on disaster-causing risk, exposure degree and vulnerability key factors, the model structure is simplified, and the calculation efficiency is improved.

The invention belongs to the field of electric power, and discloses a downscaling method, device and equipment for a rainfall forecast field, and a storage medium, and the method comprises the steps: obtaining meteorological data, inputting the meteorological data into a fortune meteorological model, and enabling the fortune meteorological model to generate an initial forecast field based on the meteorological data; cutting the initial forecasting field to obtain an initial rainfall forecasting field of the target area, and inputting the initial rainfall forecasting field into a pre-trained downscaling model which comprises a shallow feature extraction module, a deep feature extraction module and a reconstruction module, a shallow feature extraction module extracts initial features from the initial rainfall forecast field based on a convolutional layer; the deep feature extraction module captures the spatial dependency relationship of the precipitation field from the shallow feature map through the residual block; the reconstruction module converts the deep feature map into a high-resolution downscaling precipitation field based on a pixel rearrangement operation. The spatial resolution and accuracy of rainfall forecast are improved through a downscaling model based on SwinIR.

The invention discloses a short temporary rainfall forecasting method and system based on a dynamic neural network architecture. According to the method, training sample distribution is optimized through a space-time gradient driven resampling strategy, a time-frequency double-branch dynamic network architecture is adopted, a time domain branch extracts multi-scale space features through an encoder-decoder structure, a frequency domain branch adaptively activates an FFT calculation module through a content awareness dynamic network to capture multi-scale time features, and the multi-scale time features are obtained. And two key problems of meteorological data distribution imbalance and deep learning model optimization imbalance are solved in combination with a balance regression loss function. According to the method, adaptive computing resource allocation for meteorological events with different complexities is realized, the forecasting precision and computing efficiency of heavy rainfall events are remarkably improved, and an efficient and reliable technical solution is provided for short temporary rainfall business forecasting.

The invention discloses a rainfall inversion external expansion method based on mask knowledge distillation and self-mask fine tuning. The rainfall inversion external expansion method comprises the following steps: acquiring a track multi-modal data set and a full-width infrared data set; a teacher model is established, and a track multi-modal data set is adopted for training; establishing a student model and performing mask knowledge distillation training; training the student model after mask knowledge distillation training by adopting a full-width infrared data set based on low-rank adaptation and a self-mask fine tuning strategy, and completing rainfall inversion external expansion; and inputting the full-width infrared data to be measured and the third mask of the whole one into the student model after rainfall inversion external expansion, and obtaining corresponding rainfall intensity and rainfall area as rainfall inversion results. Under single-mode infrared data, high-precision rainfall estimation in a full-width range is realized by means of rich physical information contained in a multi-mode teacher model.

The embodiment of the invention provides an intelligent electric precipitation system. The intelligent electric precipitation system comprises a power management module, a working condition sensing module, a dynamic optimization module and a cooperative control module, the power supply management module comprises a high-frequency power supply unit and a power-frequency power supply unit, and the high-frequency power supply unit adopts a full-bridge series-parallel hybrid resonance circuit; the working condition sensing module is configured with a distributed sensor network; the dynamic optimization module establishes a multi-objective optimization model; the cooperative control module realizes a four-level control architecture; the system realizes optimal balance of dust removal efficiency and energy consumption by collecting working condition parameters in real time and dynamically adjusting working parameters of a power supply. According to the intelligent electric dust removal system, through a hybrid power supply framework, a multi-sensor network and four-stage cooperative control, the dust removal efficiency is improved, and the equipment reliability and the intelligent level are improved.

The invention provides a rainfall sampler for meteorological monitoring, and relates to the technical field of water sampling, the rainfall sampler comprises a placing box, and a water quality collecting mechanism is arranged in the placing box; the water collecting mechanism comprises a shaft rod, a circular plate, a turntable, an up-down moving part and an annular concave-convex plate; the shaft rod is connected into the containing box, the circular plate is connected with the shaft rod in the containing box, a gear disc is arranged on the shaft rod, arc-shaped grooves are formed in the gear disc and the circular plate, the rotating disc is in threaded connection with the shaft rod, the up-down moving part is slidably connected to the rotating disc and is opposite to the arc-shaped grooves, the annular concave-convex plate is fixedly connected into the containing box, and the up-down moving part annularly rotates on the annular concave-convex plate. The inner top wall of the placing box is communicated with an elastic plugging pipe mechanism, under the assistance of an annular concave-convex plate, up-down moving pieces are inserted into arc-shaped grooves in a circular plate and a gear disc at intervals to control the circular plate to rotate and ascend, when the circular plate rotates, respective sampling of a plurality of sampling bottles is achieved, and when the circular plate ascends, water overflow is avoided under the assistance of the elastic plugging pipe mechanism.

The invention relates to the technical field of civil engineering, and discloses a road subgrade settlement prediction method and system based on multi-source data. The method comprises the following steps: acquiring and correcting a precipitation sequence and roadbed soil moisture distribution data; analyzing rainfall accumulation characteristics and carrying out penetration risk classification, thereby extracting a penetration depth estimation value, and utilizing finite element analysis to simulate a soil body saturation state change trend; quantifying a soil body supporting capacity reduction range, constructing and calibrating a settlement initiation probability calculation model, and obtaining settlement probability distribution; identifying a high-risk evolution area, integrating path weights and accumulated influence factors, and generating a settlement prediction interval subjected to multi-dimensional verification; and fusing the prediction interval and actual roadbed structure data through a geographic information system to generate a comprehensive evaluation report. According to the method, the whole-process accurate evaluation of the roadbed settlement risk from multi-source perception, mechanism simulation to space prediction is realized, and the accuracy and timeliness of the roadbed settlement risk prediction and the pertinence of engineering maintenance are improved.

The invention discloses a multi-mode set heavy rainfall forecasting method fusing space loss function deep learning, which comprises the following steps: acquiring rainfall site observation data, meteorological element data and various physical factor data to form multivariate meteorological factor data; processing the multivariate meteorological factor data into equal-resolution lattice point data and preprocessing the lattice point data; screening out meteorological element and physical factor data of which the importance measurement value is greater than a threshold value, and dividing a data set according to research requirements; constructing a mixed loss function fusing precipitation spatial features; a mixed loss function is developed to train the U-NET deep learning neural network model, and the performance of the model is evaluated; and inputting the multi-element meteorological factor data of the multi-mode output real-time forecast into the model, and generating the real-time heavy rainfall forecast of the multi-mode set. According to the method, precipitation space structure characteristics are integrated into a deep learning model, the problems of deep learning forecast averaging and peak loss caused by a traditional point-to-point strength loss function are solved, and the method aims at improving the precision of heavy precipitation forecast.

The invention relates to the technical field of dangerous rock monitoring, in particular to a dangerous rock monitoring system and a risk assessment method thereof. According to the method, environmental factors such as wind speed and vegetation are accurately modeled by introducing the graph convolutional neural network, the limitation of a traditional wind power calculation model is overcome, and the influence of wind power tension is corrected, so that the stability of the rock mass is more accurately evaluated; by introducing a dynamic adjustment mechanism of an environment change coefficient and a vibration propagation coefficient, changes of environment factors such as temperature, humidity and rainfall can be adapted in real time, and the adaptability and precision of the model are improved; besides, spatial interaction and dynamic effect among rock mass units are considered, an instability propagation equation is established, and a rock mass instability propagation process is captured, so that physical attributes, spatial interaction and environmental change of the rock mass are comprehensively considered, and more accurate and real-time instability prediction is provided. And effective risk early warning and emergency decision-making assistance are provided for projects such as mining and slope construction.

The invention provides a method for simulating runoff in a cold and cold mountainous area by combining LSTM (Long Short Term Memory) to improve a VI-glacier model. The method is used for solving the technical problems that runoff components in the cold and cold mountainous area are diverse and each runoff source cannot be directly distinguished. The method comprises the following steps: firstly, determining model input data including DEM data, vegetation data, soil data, meteorological data, hydrological data and glacier boundary data; a GPM rainfall product is adopted to drive meteorological data; secondly, introducing a degree-day factor model to quantify the glacier ablation process, coupling the degree-day factor model with the VIC model, and constructing a VI-glacier model; then, an SCE-UA automatic optimization algorithm and an artificial trial and error method are combined to calibrate the VICs-glacier model, and an optimal parameter value is obtained; and meanwhile, error correction is carried out on a simulation result of the VI-glacier model through the LSTM model, so that runoff correction is realized. The model provided by the invention can relieve the uncertainty of water resources caused by glacier changes, and the future runoff can be predicted more accurately.

The invention relates to the technical field of meteorology and hydrology, in particular to a rainfall space reconstruction method, system and device based on terrain and meteorological multi-factor fusion driving and a medium, and the method comprises the steps: obtaining space terrain factors and meteorological observation data of a target area, and employing a BP neural network to progressively interpolate missing measurement values of meteorological driving variables in a layered manner; setting a missing detection elimination rule based on a wet season and a dry season to process precipitation data; constructing a 14-dimensional high-dimensional input feature system fusing a space terrain factor, a time sequence factor and a meteorological driving factor; nonlinear models such as an XGBoost model, a BP neural network model or an LSTM model are used for training, and finally the monthly scale precipitation space reconstruction of the grid is achieved. According to the method, the problems that a traditional interpolation method is poor in adaptability in a complex terrain area and insufficient in multi-factor driving relation description are effectively solved, and the precision and the physical consistency of precipitation space distribution are remarkably improved.

The invention discloses a slope stability analysis method and system based on data analysis, belongs to the technical field of slope safety analysis, and aims to solve the problems of dependence on hypothesis simplified model and neglect of real-time dynamic change in the prior art. S2, dynamic environment factor analysis; s3, slope stability analysis; s4, establishing an intelligent decision-making and early warning system; s5, system integration and user interaction; s6, performing system optimization and continuous learning; by comprehensively considering complex soil body characteristics such as heterogeneity and anisotropy of the slope and dynamic changes of environmental factors such as external rainfall, meteorology and earthquakes, the system can provide a more real and accurate slope stability analysis result, an intelligent algorithm and a data analysis technology are adopted, and the stability of the slope is improved. The dependence on a simplified physical model and a static hypothesis is reduced, so that the scientificity and the practicability of an analysis result are improved.

The invention discloses a multi-factor coupled mountain area mountain torrent dynamic partition early warning method, belongs to the technical field of mountain torrent defense, and realizes dynamic partition early warning of mountain area mountain torrent disasters by systematically integrating multi-source data, quantifying multi-factor synergistic effect and constructing a differentiated early warning model. The method comprises the following steps: firstly, collecting and preprocessing high-resolution topographic data, multi-source meteorological and hydrological observation data and soil attribute data, dividing hydrological partitions and constructing a basic database; secondly, screening small watershed units with similar parameters by adopting a control variable method, and simulating and analyzing a coupling driving mechanism of rainfall, terrain and soil through a distributed hydrological model; constructing a dynamic partition early warning model based on a multi-factor action rule, and dynamically adjusting an early warning threshold by combining real-time data; and finally, outputting the mountain torrent risk levels of different hydrological partitions, and providing quantitative support for mountain torrent early warning in a mountainous area.

The invention discloses an approaching rainfall forecasting method and system based on attention mechanism and frequency domain fusion, and belongs to the technical field of weather forecast, and the method comprises the steps: constructing a radar echo sequence sample data set; constructing an approaching rainfall forecasting model; a radar echo sequence sample data set is adopted to train an approaching rainfall forecasting model; and performing radar echo data prediction by adopting the trained approaching rainfall forecasting model, and converting the radar echo data into rainfall data so as to realize rainfall forecasting. According to the method, the generation and elimination evolution rule of the radar echo can be effectively reflected, the forecasting accuracy of the high-intensity value of the radar echo is improved, the situation that a previous model is insufficient in severe convection forecasting accuracy is effectively improved, and the method has great significance in effectively preventing various losses caused by severe convection weather.

The invention discloses a super-set deterministic weather forecast method and device based on machine learning, and the method comprises the steps: obtaining multi-source meteorological data of a target region, carrying out the meshing of the multi-source meteorological data, carrying out the historical static feature analysis and dynamic feature analysis of the meshing features, obtaining the feature weight of each mode, and carrying out the recognition of the multi-source meteorological data. The method comprises the following steps: constructing a prediction sub-model according to an extreme event, obtaining enhanced numerical prediction data, obtaining posterior probability distribution of grid points through a conditional generative adversarial network and a Bayesian neural network based on the numerical prediction data, a gridding feature and a feature weight, taking a maximum probability value as a deterministic weather forecast, and calculating a confidence interval. And obtaining a joint probability product including wind speed and rainfall joint distribution and the characteristic contribution degree. According to the method, numerical forecasting set products of different mode centers are utilized to fuse probability forecasting information, deterministic weather forecasting is obtained, smoothing of extreme events is reduced, deterministic maximum value output is provided, and meanwhile good interpretability is achieved.

The invention discloses a mountain torrent automatic identification and real-time early warning system based on deep learning, and the system comprises a data collection and quality control module which is used for collecting rainfall and topographic data, and generating basic observation data; the rainfall correction and fusion module is used for generating a rasterized rainfall field and uncertainty measurement; the drainage basin topology construction module is used for carrying out concave-pit filling and flat-pit communication on the topographic data and constructing a drainage basin topology; the spatial-temporal feature generation module is used for performing topology alignment on the rasterized precipitation field and the drainage basin to obtain a spatial-temporal feature sequence; the depth prediction module is used for inputting the spatial-temporal feature sequence into a spatial-temporal deep learning model to form a depth prediction result; the hydrodynamic simulation module is used for setting initial and boundary conditions on the unstructured grid and outputting a hydrodynamic simulation result; and the coupling correction and early warning issuing module is used for updating prediction and boundaries, generating graded early warning and completing archiving. According to the invention, automatic mountain torrent identification and real-time early warning are realized.

The invention discloses a numerical simulation-based long-time-sequence figure operation effect quantitative evaluation method, which comprises the following steps of: performing image processing to determine sub-evaluation time periods, a grid representative region and a region weight, and acquiring driving data, live precipitation field data and artificial influence operation data of each sub-evaluation time period of a region to be evaluated; carrying out the numerical simulation of each sub-evaluation time period, carrying out the data alignment of the numerical simulation result of each sub-evaluation time period and the actual rainfall field data, carrying out the accuracy test, calculating the grid precipitation increasing rate in each sub-evaluation time period, calculating the time period precipitation increasing amount of each sub-evaluation time period of the to-be-evaluated region according to the grid precipitation increasing rate and the region weight, and carrying out the calculation of the time period precipitation increasing amount of each sub-evaluation time period of the to-be-evaluated region. And accumulating the time period rainfall increment of each sub-evaluation time period to obtain a long-time-sequence rainfall increment evaluation result of the to-be-evaluated region. According to the method, the long-time-sequence figure operation effect can be evaluated more comprehensively and more accurately, and meanwhile, the method has important significance for promoting scientific management and reasonable distribution of water resources.

The invention relates to the technical field of machine learning models, in particular to a geological disaster risk dynamic assessment method based on multi-source data fusion, which comprises the following steps: constructing a basic geographic information database; dividing geological disaster risk areas by adopting a machine learning algorithm; calculating the contribution degree of each environment factor to the geological disaster through an information amount model; fusing the dynamic rainfall data, carrying out weighted fusion on the dynamic rainfall data and the static geological disaster factors, and calculating a comprehensive risk value; dividing risk levels according to the risk values, and generating a geological disaster risk zoning map; compared with the prior art which mainly depends on single static geological data for analysis and has the problems of incomplete evaluation dimensions and poor timeliness, the scheme realizes multi-dimensional fusion analysis of geological conditions and real-time meteorological factors by constructing the geographic information database integrating the multi-source environmental factors and the dynamic rainfall data; and the comprehensiveness and the momentality of risk assessment are obviously improved.

The embodiment of the invention discloses a low-altitude wind shear early warning method and device based on multi-source meteorological data. A specific embodiment of the method comprises the steps of generating a first region of interest for a target region according to a contour topographic map; determining a second region of interest according to the real-time rainfall distribution diagram and the real-time air temperature distribution diagram; performing region optimization on the first region of interest according to the real-time wind field graph to obtain a third region of interest; generating a fourth region of interest according to the second region of interest and the third region of interest; and according to the fourth region of interest and a pre-trained low-altitude wind shear prediction model, generating wind shear region early warning information. According to the embodiment, accurate prediction of the wind shear is realized, so that early warning is performed on the low-altitude aviation equipment, and the navigation safety of the low-altitude aviation equipment is ensured.