801results about "Rainfall/precipitation gauges" patented technology

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.

This invention discloses a method for optimizing the layout of a rain gauge network in mountainous areas based on radar rainfall data, which includes Step 1: collecting rainfall data and dividing the target area into meshes, Step 2: identifying abnormal rain gauge station locations, Step 3: determining candidate locations for additional rain gauge stations, Step 4: evaluating the accuracy and effectiveness of different rain gauge network optimization methods, and Step 5: determining the optimal rain gauge network layout method. [Effects] The method of the present invention, combined with radar rainfall data, realizes effective planning and optimization of rainfall observation networks in complex mountainous terrain conditions, further improves the accuracy and reliability of precipitation observation in mountainous watersheds, and provides more accurate data support for the management and prevention of mountainous flood disasters.

The invention discloses a high-resolution peak accumulated snow depth inversion device and method based on a geographically weighted random forest, and the device comprises a data obtaining module which is used for obtaining the multi-source data of a research region, and the multi-source data comprise MODIS remote sensing accumulated snow data, Sentinel-1VV / VH backscattering data, topographic data, and meteorological station snow depth observation data; the high-resolution snow coverage data set construction module is used for processing the MODIS remote sensing snow data to obtain a high-resolution cloudless binary snow coverage data set; the feature construction module is used for constructing a comprehensive feature set containing a snow space-time process feature index, a back scattering feature index and a topographic feature index; and the model construction and analysis module is used for training a geographically weighted random forest model through the comprehensive feature set, and inverting the peak snow depth of the research area according to the optimal model. According to the invention, estimation of high-resolution peak snow depth can be realized.

The invention relates to the technical field of disaster early warning, and discloses a rainstorm early warning method based on multi-source forecast product dynamic fusion, which comprises the steps of collecting multi-mode rainfall forecast data, constructing a rainfall cumulative distribution function, performing dynamic updating based on Kalman filtering, and obtaining frequency correction rainfall forecast products of each mode; historical forecast and live monitoring data samples are obtained, comprehensive weight coefficients corresponding to all levels of rainfall are calculated, and a fusion initial rainfall distribution field is obtained; performing matching reconstruction on the corrected rainfall forecast field to generate a reconstructed rainfall distribution field; collecting latest real-time monitoring data, and carrying out space-time dynamic correction on the reconstructed rainfall distribution field to generate a real-time rainstorm potential field; and comparing the real-time rainstorm potential field with the multistage rainstorm early warning threshold, generating rainstorm early warning information, and issuing the rainstorm early warning information. According to the invention, the timeliness and accuracy of rainstorm early warning response are improved, the public personal safety is guaranteed, and the city emergency disposal capability is improved.

The invention discloses a multi-source data fusion method based on a water conservancy rain measuring radar, and belongs to the technical field of meteorological observation. Historical rainfall inversion, short temporary rainfall forecast and short-term rainfall forecast are realized based on multi-source data fusion, and a space-air-ground integrated rainfall observation and prediction system is constructed. According to the method, innovative technologies such as multi-source data, multi-parameter inversion, AI intelligent optimization and manual correction are fused, the limitation of a traditional rainfall inversion method is broken through, and the precision and service efficiency of quantitative rainfall estimation are remarkably improved; through multi-modal fusion and full-link integration, the method has significant technical advancement and business value in application in the fields of water conservancy and meteorology.

The present invention relates to a high temperature and drought composite disaster monitoring and early-warning method and system, belonging to the technical fields of disaster risk assessment and early warning. Internal correlation features and abnormality information of high temperature and drought events are input into a model, multi-time-space scale features of the high temperature and drought events can be identified accurately, high event identification accuracy and space resolution are achieved, the progress can be predicted progressively, the drought and high temperature threshold change can be monitored closely, and fine forecasting and early warning can be performed in different periods, regions and intensities, thereby ensuring that indicators are in the same time scale, avoiding the complication of the high temperature and drought process caused by frequent time and space discontinuities of the indicators in a single point or small region, and ensuring the suitability for any periods of the process.

The invention provides a hydrological forecasting method and system based on an adaptive correction chain, and relates to the field of data processing, and the method comprises the steps: generating a first reservoir inflow forecasting result according to a rainfall deviation traceability-dynamic correction chain and rainfall observation data; multi-dimensional features of the current scene are determined, and a second reservoir inflow forecasting result is generated according to the historical similar sample matching-rule application chain and the multi-dimensional features of the current scene; according to the confluence and production knowledge-threshold constraint chain, the underlying surface condition, the rainfall spatial distribution and the basin state, a third reservoir inflow forecasting result is generated; and determining the dynamic weights of the first reservoir inflow forecasting result, the second reservoir inflow forecasting result and the third reservoir inflow forecasting result, and generating a final reservoir inflow forecasting result, thereby improving the accuracy of the reservoir inflow forecasting result.

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 discloses a rainfall intensity rapid identification method and system based on multi-sensor data fusion, and relates to the technical field of hydro-meteorological monitoring and urban drainage scheduling, and the method comprises the steps: obtaining a historical rainfall data set, drawing a peak curve, and extracting an evolution sequence feature before a peak value; correlation analysis is carried out on the data and complete rainfall process characteristics to establish a historical rule model; rainfall intensity data are collected in real time through multiple sensors, and space-time dimension features are fused; matching the fusion features with historical features, and screening target historical events; constructing a rainfall reproduction prediction model to reconstruct a current rainfall evolution process; and finally, calculating an inflow load based on a reconstruction result, and generating a dynamic drainage scheduling scheme. According to the invention, accurate prediction of the rainfall process and intelligent scheduling of the drainage system are realized, and the urban waterlogging prevention capability is 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 relates to a soil erosion amount dynamic monitoring method and system based on multi-source remote sensing data fusion. The method comprises the following steps: acquiring multi-source remote sensing data and auxiliary data, and performing geometric fine correction and atmospheric correction on an image based on high-resolution digital elevation data to obtain a data set with consistent space; performing inversion based on the data set to obtain a corrected terrain factor and a water and soil conservation measure factor; generating a time sequence rainfall erosivity factor and a time sequence vegetation coverage factor in combination with the multi-temporal satellite rainfall data; and calculating a preliminary soil erosion modulus in combination with the soil type map, and correcting by using multi-stage high-resolution digital elevation data to obtain a dynamic monitoring result of the soil erosion amount. By adopting the method, the problem of multi-source data fusion matching can be solved, and the factor inversion precision is improved, so that the accuracy of a dynamic monitoring result of the soil erosion amount is improved.

This invention relates to a rainfall observation method, apparatus, computer system, and medium, addressing the problem of high-reliability regional rainfall observation. First, reanalysis data and Earth system model output data are used to construct a first deep learning model for reconstructing a gridded long-series rainfall dataset by inverting rainfall from satellite data. Then, historical rainfall attenuation information is used as input, and different rainfall attenuation and intensity models are used to obtain corresponding historical rainfall simulation series. The weighted link method is then used to determine the regional rainfall under different rainfall attenuation and intensity models, and the optimal rainfall attenuation and intensity model is selected from the long-series gridded rainfall data. Further, a second deep learning model is constructed using satellite inversion data and rainfall data inverted from the rainfall attenuation and intensity model to achieve real-time correction of regional rainfall. Finally, the optimal rainfall attenuation and intensity model is used with real-time mobile signals as input to deduce real-time regional rainfall information and obtain the real-time corrected regional average rainfall.

The invention discloses an urban inland inundation dynamic early warning method fusing multi-source data and a high-performance numerical model, and belongs to the technical field of urban flood control and disaster reduction and disaster early warning, and the method comprises the following steps: 1, obtaining and preprocessing multi-source data; 2, constructing a high-performance urban flood coupling model; step 3, carrying out assimilation correction on the ensemble Kalman filter; 4, performing rolling prediction and dynamic updating; and step 5, risk identification and early warning release. According to the method, the advantages of multi-source data can be fully fused, and the problems of inaccurate rain condition and unclear waterlogging condition in rapid early warning of urban waterlogging are solved; through multi-source data fusion and high-performance numerical model real-time assimilation correction, minute-level rolling simulation and dynamic correction are achieved, the regional refined ponding risk can be forecasted in advance, the problems that traditional early warning is low in precision, updating lags behind, and the model lacks the self-updating capacity are solved, and the practical value is remarkable.

The invention discloses a rainstorm ground waterlogging prediction method, device, equipment and product, and relates to the technical field of urban waterlogging prediction.The rainstorm ground waterlogging prediction method comprises the steps that ephemeris and ground observation station data are obtained, the atmospheric moisture delay amount and the high-altitude water vapor conversion factor are calculated respectively, the historical water vapor content is calculated, and the future atmospheric precipitable water amount is predicted; rainfall time distribution data are obtained through rainstorm intensity decomposition; calculating the rainwater overflow amount of the catchment area in combination with the water discharge amount time distribution data of the urban drainage pipe network; and iterative calculation is carried out by using a digital elevation model grid to obtain the waterlogging depth, and waterlogging early warning information is sent out. By constraining the total rainfall amount, the rainfall amount can be effectively constrained by predicting the precipitable water amount based on the satellite ephemeris; the underground pipe network drainage is restrained, and a water flow condition in a pipe channel is simulated by adopting an SWMM model; and finally, the waterlogging accumulated water volume is calculated, constraint verification is carried out on each link of urban waterlogging, and relatively high precision and relatively long timeliness are ensured.

The invention discloses a unified rainfall downscaling method based on a multi-stream analysis diffusion model, and the method comprises the steps: collecting the meteorological data of a target region, including low-resolution rainfall data and preset auxiliary variable data, generating a low-resolution rainfall field, and calculating the rainfall deviation between a high-resolution real rainfall field and the low-resolution rainfall field; based on preset auxiliary variable data, combining the low-resolution rainfall data to form multi-modal condition input; constructing a precipitation downscaling model which takes the analysis diffusion model as a framework and is combined with a mixed attention U-shaped network, and predicting precipitation deviation; and training the rainfall downscaling model, performing reasoning by adopting the rainfall downscaling model, inputting meteorological data of the target area, outputting predicted rainfall deviation, and superposing the predicted rainfall deviation to the low-resolution rainfall field to obtain super-resolution rainfall. According to the method, multi-source meteorological and geographic information is effectively fused, high-fidelity and high-resolution rainfall data is generated, the generalization ability is high, and the calculation efficiency is high.

The invention provides a hydrological flow long sequence prediction method and system of an improved state space model. The method comprises the steps of collecting multi-source data of a drainage basin to be predicted; constructing a time sequence sample pair by the processed multi-source data through a sliding window method, wherein the time sequence sample pair comprises an input sequence and a target sequence; a HydroMama model is constructed according to the time sequence sample pair, a HydroMama prediction model is trained, and an optimal hyper-parameter combination is searched for; based on the trained HydroMama model, traffic prediction and result restoration are realized by adopting an autoregression mechanism; the calculation efficiency is high, and the continuous state equation discretization of the state space model is utilized, so that the long-distance meteorological-hydrological hysteresis effect which is difficult to capture by a traditional cycle model can be captured; according to the method, skewed distribution of hydrological data is processed through logarithmic transformation, and an anti-overfitting objective function is combined, so that the prediction performance of the model on unseen data is remarkably improved, and the practical application value is high.

The invention belongs to the technical field of water conservancy informatization and automatic control, and discloses an intelligent water conservancy-oriented multi-source hydrological data monitoring and safety early warning system, which comprises a monitoring link for acquiring water level, rainfall, flow velocity, flow and meteorological elements, and acquiring environment information in combination with videos and remote sensing images; the communication processing link supports multi-link transmission and generates an integrity mark for the data; in the data processing link, field mapping is realized based on a unified semantic model, conflict judgment is carried out through a timestamp, an integrity mark and an adjacent monitoring point weighting sequence, and an abnormal mark is generated at the same time; in the digital modeling link, three-dimensional twin models of a river channel, a reservoir and a dam are established, and abnormal factors are written in; in the risk prediction link, a risk assessment result is output in a multi-factor weighting mode; the early warning control link determines a release object based on the three-dimensional permission matrix and executes a boundary rule; and the linkage link issues a scheduling instruction after manual reexamination and confirmation, and feeds back an audit record to the model to form closed-loop correction.

The invention relates to a rainfall observation method and device, a computer system and a medium, and solves the problem of high-reliability observation of regional rainfall. The method comprises the following steps: firstly, adopting reanalysis data and earth system mode output data to construct a first deep learning model of satellite inversion rainfall so as to reconstruct a lattice long-series rainfall data set; then, taking the rainfall attenuation information of the historical period as input, obtaining a rainfall simulation series of the corresponding historical period through different rainfall attenuation and rainfall intensity models, determining regional rainfall under the different rainfall attenuation and rainfall intensity models through a weighted link method, and preferably selecting an optimal rainfall attenuation and rainfall intensity model through long-series lattice rainfall data of the historical period; furthermore, a second deep learning model is constructed by adopting satellite inversion data and rainfall data inverted by a rainfall attenuation and rainfall intensity model, real-time correction of regional precipitation is realized, finally, real-time rainfall information of the region is deduced by adopting the optimal rainfall attenuation and rainfall intensity model and taking a real-time mobile signal as input, and the regional average precipitation corrected in real time is obtained.

The invention discloses a hydrological sequence missing data complementation and trend prediction system and method, and belongs to the technical field of water conservancy information. The system comprises a data acquisition and preprocessing module, a spatial-temporal feature fusion module, a physical constraint completion module, an uncertainty prediction module, a credible evidence storage and verification module and a visual tracing module which are connected in sequence. The method comprises the following steps: collecting and preprocessing multi-source hydrological data; combining the spatial-temporal features through a dual-channel network to generate joint representation; generating and checking a completion sequence based on joint representation and coupling physical constraints such as a unit line method and a Manning formula; performing uncertainty trend prediction by using an integrated predictor; the key data of the whole process are linked and stored, and are automatically verified through an intelligent contract; and full-life-cycle visual tracing is provided. According to the method, the complementation robustness and accuracy in a high-missing-rate scene are effectively improved, the physical consistency of a complementation result is ensured, and credible evidence storage and transparent decision of the whole process are realized.

The invention discloses a radar short-time heavy rainfall estimation method based on classification echoes and environmental physical constraints, and belongs to the technical field of meteorological detection, and the method comprises the following steps: S1, multi-source data integrated fusion and cooperative gridding preprocessing; s2, rainfall type dynamic identification and Z-R relation self-adaptive primary selection based on multi-feature fusion; s3, adaptive correction of the estimation result driven by the environmental physical process is carried out; s4, estimating sequence optimization and systematic deviation correction based on a sliding time window and live feedback; and S5, multi-source information optimal fusion and refined heavy rainfall product generation. According to the method, the problems that a traditional fixed Z-R relation and single data source estimation method is insufficient in precision and insufficient in physical mechanism consideration are effectively solved, the accuracy of short-time heavy rainfall estimation is remarkably improved, and the method has obvious service application value.

The invention is suitable for the technical field of meteorological engineering, and provides a rainfall forecast time downscaling method based on data assimilation and source backtracking, which comprises the following steps: firstly obtaining a first forecast time sequence, a second forecast time sequence and an observation time sequence, downscaling the second sequence and then combining with the first sequence to generate a third sequence, and then generating a fourth sequence by adopting an interpolation method, the method comprises the following steps of: acquiring observation data, interpolating the observation data to a lattice point to generate an observation field consistent in space, calculating space and time gradients in a sliding time window, resolving an average motion vector, determining an upstream source based on backtracking of the average motion vector, carrying out dynamic assimilation correction, generating an hour-by-hour fifth sequence, and finally adjusting the fifth sequence through a 3-hour cumulant conservation constraint to obtain an observation result. And outputting a final downscaling result. By fusing observation data, backtracking rainfall sources and introducing conservative constraints, hourly rainfall forecasting with finer time scale, more accurate spatial sources and stronger physical constraints is realized.

The invention discloses a data processing method applied to digital twinborn hydraulic engineering, and relates to the technical field of hydraulic engineering, the decision reliability of the digital twinborn hydraulic engineering in a rainstorm scene is significantly improved through algorithm architecture reconstruction, a two-channel cooperation mechanism deeply couples water level time sequence monitoring and rainfall space analysis, and the accuracy of data processing is improved. A graph convolutional network is used for extracting drainage basin level rainfall thermodynamic diagram features, self-attention dynamic noise filtering is combined, space-time alignment of meteorological data and ground sensing is achieved, false alarms caused by equipment interference or data asynchronization are restrained from the source, it is ensured that an early warning instruction is only triggered under the real flood situation condition, and the early warning efficiency is improved. The edge intelligent closed-loop decision breaks through the cloud dependence bottleneck, the lightweight model locally generates a scheduling strategy at a gate station node, the reinforcement learning driven gate control takes the water level deviation as an optimization target, and the loss of frequent actions on equipment is synchronously constrained, so that the flood discharge response is changed from passive rule execution to active risk stabilization.

The invention discloses a closed pit mine water pollution four-water conjugate cooperative monitoring and early warning method and system, and relates to the technical field of water quality analysis, and the method comprises the following steps: collecting water level and water quality data of corresponding positions of atmospheric precipitation, surface water, underground water aquifer and goaf ponding; performing format standardization, exception elimination, deletion processing and space-time alignment on the monitoring data, and constructing a four-water monitoring space-time data set; a four-water conjugate coupling analysis and prediction model is established, after training is completed based on historical monitoring data, the water level and water quality of each monitoring point are predicted, and pollution transmission paths and pollution lag characteristics among four types of water bodies are identified; and performing over-threshold judgment by combining the monitoring index and the early warning threshold, and outputting graded early warning and intervention regulation and control suggestions. According to the technical scheme of the invention, four-water integrated dynamic monitoring, pollution evolution rule identification and prospective early warning of the closed pit mine are realized, and the water pollution risk management and control precision and response efficiency are improved.

The invention provides a hectometer-level short temporary rainfall forecasting method based on generative adversarial network downscaling and physical constraint, which belongs to the technical field of weather forecast, and is characterized in that S-band radar jigsaw and high-resolution X-band radar observation data are fused through an adaptive rule to generate a fused radar echo combined reflectivity, and the combined reflectivity is used for forecasting the rainfall of the hectometer-level short temporary rainfall. A super-resolution downscaling model is constructed by using a generative adversarial network, a long-sequence high-resolution radar echo training data set is reconstructed, a deep learning forecasting model is constructed, a high-resolution radar echo sequence is used as input, space-time modeling training is performed by using a composite loss function, and a future radar echo forecasting field is output. The Z-R relation is converted into quantitative rainfall forecast, and finally the quantitative rainfall forecast with the spatial resolution reaching the hectometer level is generated. The interpretability of the model is enhanced through physical constraint, the bottleneck of insufficient hectometer-level resolution training data is effectively solved, and the spatial refinement degree of short temporary rainfall forecasting and the forecasting capacity for a severe convection system are remarkably improved.

The invention discloses a rainstorm flood risk early warning method and system based on machine learning and multi-source data fusion, and belongs to the technical field of flood prediction.The method comprises the steps that multi-source sample data is constructed based on flood disaster historical data; the multi-source sample data type comprises rainfall characteristic data, hydrological characteristic data, landform characteristic data, earth surface attribute characteristic data and social economic characteristic data; an XGBoost ensemble learning algorithm is adopted to construct a risk prediction model, and training and evaluation are carried out based on multi-source sample data; historical rainstorm flood event records are taken as labels in the training process; performing quantitative and application verification on the risk prediction model, and calibrating a risk level probability output by the risk prediction model based on a risk level distribution probability of historical disaster situation data; performing real-time estimation based on the optimized risk estimation model; and generating a spatial refined rainstorm flood risk grade early warning map in a future preset time period according to an estimation result in a rolling manner.

The invention provides a rainfall monitoring method and system based on unmanned aerial vehicle multi-mode noise reduction in the technical field of environment monitoring and unmanned aerial vehicle application, and the method comprises the steps: S1, collecting a visible light image and a long-wave infrared image through a multispectral vision module after an unmanned aerial vehicle takes off, and collecting a rainfall audio from an acoustic cabin through a microphone; s2, carrying out noise reduction processing on the visible light image, the long-wave infrared image and the rainfall audio; s3, extracting a raindrop size distribution histogram and a spatial density thermodynamic diagram based on the visible light image and the long-wave infrared image, and extracting acoustic MFCC features based on rainfall audio; s4, performing feature fusion operation based on the raindrop size distribution histogram, the visual density thermodynamic diagram and the acoustic MFCC features to obtain rainfall intensity; and S5, carrying out cumulant dynamic compensation based on the rainfall intensity to obtain the cumulative rainfall. The rainfall monitoring method has the advantages that the precision, robustness and practical level of rainfall monitoring are greatly improved.

The invention discloses a rainfall monitoring method and device based on millimeter wave radar, and relates to the technical field of meteorological monitoring. The method comprises the following steps: controlling a 24 / 60GHz commercial millimeter wave radar to transmit a millimeter wave signal of an adaptive parameter to a preset area; a raindrop echo signal is received, and clutters are removed through Kalman filtering and wavelet noise reduction; extracting a one-dimensional energy feature, a two-dimensional speed feature and a speed-energy two-dimensional distribution matrix; inputting a low-error model trained by a random forest algorithm, calculating a rainfall energy value and mapping the rainfall energy value into a rainfall level; data is output through a LoRa / ZIGBEE network; the device comprises a millimeter wave radar module, a signal processing module, a calculation output module and a wireless communication module. The problems of single monitoring dimension, high cost and difficult deployment in the prior art are solved, non-contact, low-cost, high-real-time and distributed rainfall monitoring is realized, the error is less than or equal to 5%, the response period is less than or equal to 100ms, and the method is suitable for traffic, municipal administration, emergency and other scenes.

A method for identifying a potential supercooled water zone through a satellite-borne multi-parameter ensemble includes: acquiring reference data on potential supercooled water zone identification, collecting parameter data, and pre-processing the reference data and the parameter data; grouping based on distribution features of the parameter data to obtain interval data, and performing feature selection of the parameter data based on the reference data and the interval data according to a cumulative frequency crossover method to obtain an influencing parameter; voting based on the influencing parameter to identify a potential supercooled water zone, and calculating a probability of supercooled water to obtain identification data; establishing a potential supercooled water zone identification model based on the identification data, and optimizing the potential supercooled water zone identification model using the reference data; and inputting data into the potential supercooled water zone identification model to obtain identification results.

The invention discloses a soil water content abnormal data detection method and device and electronic equipment, and relates to the field of agricultural data abnormal value detection. The method comprises the following steps: determining a soil water content change rate and a time-frequency characterization diagram according to actual soil water content data collected by a sensor; according to the soil water content change rate, the standard soil water content data and the rainfall data, determining water content time sequence change characteristics through a multi-scale time sequence residual error convolutional neural network; according to the time-frequency representation diagram, determining water content frequency domain spatial characteristics through frequency dynamic convolution; fusing the water content time sequence change feature and the water content frequency domain spatial feature through an attention mechanism to obtain a fused feature, and performing feature mapping and classification operation on the fused feature through a full connection layer to obtain a soil water content abnormal data identification result. According to the technical scheme, the recognition precision of the abnormal data of the soil water content collected by the sensor is improved.

The invention relates to a ground precipitation intensity estimation method based on phased array radar echoes and geographic factors, which belongs to the field of radio, and comprises the following steps of: mapping and interpolating polar coordinate multivariable radar echoes and a three-dimensional wind field to a unified three-dimensional grid to form a consistent spatio-temporal data basis; then, various rainfall type areas are automatically divided based on local density clustering, a time lag field is constructed according to rainfall types, and adaptive time alignment of radar echoes and ground station observation is achieved; a three-dimensional wind field and a raindrop terminal end velocity are introduced to calculate rainfall falling drift, the position of a grid point matched with a ground station point space is corrected, and accurate space matching of radar echoes and ground station observation is completed; a terrain uplift index is constructed by combining a stratum wind direction, a DEM (Digital Elevation Model), a gradient, a slope direction and a slope position type, and a radar nonlinear precipitation potential vector and a terrain feature are jointly modeled through a physical constraint coupling model, so that quantitative estimation of the ground precipitation intensity is realized, and the reliability and applicability of ground precipitation estimation in a complex environment are improved.