1388 results about "Rainout" patented technology

The invention relates to the technical field of data processing, and discloses a geological disaster meteorological risk early warning method and system based on machine learning. The method comprises the following steps: acquiring rainfall intensity, soil saturation, underground water level change and slope runoff coefficient by a multi-source sensor, and constructing a geological disaster meteorological data set; performing sensitivity weight distribution on the meteorological factors according to geological conditions to obtain a weight matrix; carrying out weighted fusion on the weight matrix and meteorological time series data, and extracting features through a geological constraint long-short-term memory network to obtain a risk probability vector; dynamically adjusting an early warning threshold value based on the safety coefficient change rate; and carrying out Bayesian fusion on the risk probability vector and an adaptive early warning threshold to obtain a graded early warning result. The technical problem that an existing geological disaster early warning technology lacks a multivariate meteorological factor intelligent weight distribution and geological condition adaptive threshold adjustment mechanism is solved.

The invention provides an urban inland inundation risk management method and system based on multi-model coupling, and the method comprises the steps: constructing a digital twin holographic base, constructing an urban rainfall flood analysis model through the deep coupling of a ponding recognition intelligent model and a physical mechanism drive type rainfall flood simulation engine, and carrying out the construction of an urban inland inundation risk management model. A high-precision waterlogging risk thermodynamic diagram and a dynamic forecast threshold value are generated, and the risk situation is broadcasted in real time through a voice interaction module; constructing a rainstorm-runoff-pipe network three-dimensional coupling simulation system by means of a digital twin holographic base, updating a deduction result at a minute-level frequency, and transmitting rainfall space-time distribution data to an urban rainfall flood analysis model in real time; and establishing a multi-target collaborative decision engine, inputting the accumulated water diffusion rate to the multi-target collaborative decision engine, and generating a pump station scheduling scheme, a traffic control strategy and a material delivery path. The system comprises a model construction module, a data analysis module and a collaborative decision module. According to the invention, a complete urban inland inundation risk management system is constructed.

The invention provides a rainfall and earthquake coupled slope simulation method and system. The method comprises the following steps: constructing a coupling numerical model through multi-physics field data of a slope; the dynamic coupling effect of rainfall and earthquake is simulated by initializing a seepage field and loading earthquake power; the stability of the simulation result is comprehensively evaluated through iteration of a strength reduction method, and whole-process coupling from seepage to dynamic response of the slope is achieved. SeismoSignal is used for processing seismic waves, and the problem that the value of kinematics parameters is not zero when the seismic action is finished is avoided; the allowable maximum negative pore water pressure boundary is assigned to the slope surface of the side slope, the actual steady-state seepage field of the side slope is restored, and the seepage field in the side slope under different rainfall conditions is accurately calculated; by adopting an unsaturated soil dynamic seepage model for a seepage field, Rayleigh damping is introduced and an earthquake load is applied, so that cross-time scale coupling is realized. The method is simple and convenient to operate, high in calculation efficiency and accurate in calculation result, and has practical significance and value in research of the problem in the rock-soil field.

The invention discloses a river flow missing data reconstruction method, which comprises the following steps of S1, constructing a river network topological structure, and quantifying hydraulic correlation; s2, spatio-temporal feature fusion and multi-source information extraction; s3, performing multi-task cooperative flow reconstruction and confidence coefficient prediction; s4, dynamic weighting and result correction of meteorological factors; and S5, performing anomaly detection and model dynamic updating. According to the method, a river flow missing data reconstruction method is set, the steps are mutually fused and used, and topology-aware space-time diagram convolution is performed: a river network topology structure is encoded into a weighted adjacency matrix for the first time, space correlation features are extracted through a diagram convolution network, and the problem that a traditional method ignores hydraulic connection is solved; a multi-task collaborative learning mechanism: synchronously outputting a flow reconstruction value and confidence, combining topological smooth constraints, and realizing reconstruction reliability quantification while ensuring precision; and meteorological dynamic weighted correction: dynamically adjusting the node weight based on the real-time rainfall intensity, and accurately adapting to the nonlinear response of the water flow in the heavy rainfall period.

The invention relates to the technical field of intelligent construction. The invention provides a rainwater garden construction control method and system based on BIM dynamic rainfall flood simulation. The method comprises the following steps: integrating target area data and real-time weather forecast, and constructing a fusion model; importing the fusion model into a coupling engine, simulating an extreme rainfall scene, and generating a dynamic hydrological load map; according to the dynamic hydrological load map, reverse iterative calculation is carried out, and a three-dimensional parameterized design model is output; decomposing the three-dimensional parameterized design model into a layered construction instruction set, and constructing a real-time twinborn body on a BIM platform; field data are collected and compared with real-time twinborn bodies, and when the soil layer permeability coefficient deviation is larger than a preset threshold value or the process conflict risk, a re-optimization instruction is triggered, and the mechanical operation sequence is adjusted. The problems that due to the fact that existing rainwater garden static design and experience construction cannot dynamically respond to the complex hydrological environment and construction disturbance, facility efficiency is low, failure is prone to occurring, and the construction rework rate is high are solved.

The invention discloses a loess slope stability intelligent monitoring method and system, and the method comprises the steps: carrying out the regional differentiation modeling of multi-source data of a target region, and outputting slope change data; according to the period-by-period displacement time sequence data, a slope deformation trend is obtained through prediction based on an LSTM algorithm; and inputting the slope change data, the slope deformation trend and the meteorological rainfall data into a slope stability evaluation model to predict a safety coefficient, and if the safety coefficient is lower than a preset threshold value, issuing early warning information. According to the loess slope stability intelligent monitoring method and system provided by the invention, data such as gradient change and deformation trend are accurately extracted, multi-source data and meteorological rainfall data are fused by means of cross-modal attention, a random forest model is used for learning a multi-factor coupling rule, and finally multi-factor data prediction is integrated to obtain a safety coefficient for early warning. The problem of low early warning reliability caused by difficulty in accurately evaluating the slope stability by fusing multiple factors such as gradient, deformation and meteorological rainfall can be solved.

The invention discloses an urban area runoff pollutant transportation process simulation forecasting method, which is characterized in that a building area runoff production calculation method under the influence of a wind field is provided, and the building area runoff production calculation method is coupled with an SWMM rainfall flood model; secondly, constructing a two-dimensional hydrodynamic model based on an FVCOM and a Lagrange particle model, coupling the two-dimensional hydrodynamic model with the SWMM rainfall flood model, and driving the two-dimensional hydrodynamic model by taking the overflow flow and the pollutant concentration simulated by the SWMM rainfall flood model as boundary conditions; simulating a transportation process of pollutants along with evolution of surface ponding by establishing a'concentration-quantity 'conversion relation between the pollutants and particles at grid units and nodes, and obtaining a time sequence pollutant concentration data set; and training and establishing a pollutant concentration prediction model based on the obtained data, and then predicting the pollutant concentration of each prediction point after specific time in an actual rainfall event. According to the method, the runoff pollutant concentration can be predicted, and the urban non-point source pollution risk under the rainstorm weather is reduced.

The invention discloses a landslide risk assessment method based on an extreme rainfall and geology coupling model, and relates to the technical field of geological disasters. Comprising the following steps: S1, constructing a three-dimensional probability density field of a fracture network and a non-Gaussian random field model of a permeability coefficient tensor; s2, setting a physical kernel layer according to the non-Gaussian random field model, setting a data driving layer through space-time Transform coding, and constructing a graph attention network model; s3, generating an adversarial network through physical information, constructing extreme rainfall coupling data, and updating the non-Gaussian permeability coefficient random field model according to the graph attention network model; and S4, acquiring an entropy generation rate according to the mechanical field data, the seepage field data and the temperature field data, and determining a risk level. Physical interpretability grading early warning of landslide risks is realized, and meanwhile, risk space distribution can be visually displayed through a sliding surface probability cloud picture, so that accurate decision support is provided for disaster prevention and control.

The invention discloses a flood disaster risk intelligent early warning method fusing multi-source information, and relates to the technical field of flood disaster prediction.The method comprises the steps that multi-source data of meteorology, hydrology, terrain, remote sensing, ground monitoring terminals and the like are obtained, and a unified data set is constructed through time synchronization, space resampling, abnormal value elimination and missing data filling; constructing multi-dimensional spatio-temporal features based on factors such as rainfall process, topographic features, surface coverage, drainage capacity, river network structure and soil humidity; inputting the spatial-temporal characteristics into a multi-source information fusion model, and predicting a flood occurrence probability in a future time period; flood risk indexes are generated according to the prediction probability, real-time monitoring and historical samples, and regional risks are divided into different grades; early warning information is automatically generated and issued according to the risk level; and the early warning result is fed back and corrected through field monitoring and patrol data, so that self-adaptive updating of early warning parameters and a model structure is realized.

A digital twin-based intelligent slope monitoring method, system, and storage medium are provided. The method includes obtaining point cloud data of a surface of a slope, creating a digital twin model; dividing the digital twin model to generate a plurality of first grids, calculating a displacement change value of each first grid during a target duration; defining the first grid with a displacement change value greater than a first threshold as a first region, dividing the target duration into a plurality of first durations with different lengths; setting a plurality of rainfall patterns, determining an optimal pattern for each first duration, calculating representative rainfall, constructing a prediction model, predicting representative rainfall for a future duration based on the optimal pattern and the representative rainfall of the first duration; generating a safety factor of the first region during the future duration based on the representative rainfall and a pore water pressure.

The invention relates to the technical field of rainfall slope evaluation, in particular to a rainfall slope stability evaluation method based on a physical certainty coupling model. The method comprises the following steps: acquiring digital elevation model data, engineering geological survey data and historical rainfall data of a target area; extracting topographic features from the digital elevation model data to obtain basic topographic parameters; the method comprises the following steps: carrying out longitudinal layering division on a rock-soil body based on engineering geological survey data, and carrying out physical and mechanical parameter setting on each layered rock-soil body to obtain a rock-soil body layering parameter set; and performing frequency analysis on the historical rainfall data to obtain an extreme rainfall intensity sequence. According to the method, by fusing the multi-source data and the three-dimensional physical coupling model, dynamic and accurate rainfall induced slope stability evaluation and risk early warning are realized, and the prediction precision is effectively improved.

The invention provides a satellite scatterometer Ku wave band observation value rainfall influence correction method, and relates to the field of sea surface wind inversion methods, and the method comprises the steps: obtaining Ku wave band observation data, C wave band data and rainfall rate data of a satellite scatterometer, carrying out wind field matching and wind speed correction under a rain-free condition, so as to obtain an NRCS reference value corresponding to a sea surface real wind speed, and carrying out the calculation of the NRCS reference value; constructing to obtain a scatterometer rainfall matching data set; according to the scatterometer rainfall matching data set, the influence of rainfall on the scatterometer observation value is analyzed, and a Ku waveband rainfall influence conceptualization model is constructed; and calculating rainfall influence conceptualization model parameters and generating a deviation lookup table. According to the invention, on the basis of a classic scatterometer wind field inversion process, the problem of large inversion wind field deviation caused by the influence of rainfall on the observation value of the Ku wave band scatterometer in a medium-low rainfall rate range is solved.

The invention relates to the technical field of geotechnical engineering disaster monitoring and early warning, and discloses a stratum rainfall seepage deformation coupling numerical simulation method and system and a storage medium, and the method comprises the steps: building a three-dimensional geologic model reflecting soft and hard rock interbed characteristics; converting the on-site rainfall data into hydraulic boundary conditions in real time; calculating a rock mass shear expansion volume change rate, determining a chemical damage acceleration factor, and updating a total damage variable; reconstructing a permeation tensor representing the dominant flow channel according to the space gradient of the damage variable; solving a fluid-solid coupling equation based on the permeability tensor, obtaining pore water pressure and calculating a displacement field; building a likelihood function by using field monitoring data to perform inversion correction on model parameters; and evaluating the stability based on the corrected calculation result and outputting an early warning signal. By constructing a mechanical coupling mechanism and a dominant flow channel model, precise simulation of a landslide evolution process in a complex geological environment is realized.

A temperature-rainfall-reservoir water combined testing system for a permafrost landslide model includes a tempered glass case with a permafrost landslide model and reservoir water provided therein; a temperature control system provided on the tempered glass case and including an incubator temperature control module, a landslide mass rear edge water tank temperature control module and an air circulation temperature control module; a seepage control system configured to realize seepage of the permafrost landslide model, and form the reservoir water in front of a landslide mass; a rainfall control system including a water tube, a flow controller and a nozzle, where the nozzle is provided in the tempered glass case, and located above the permafrost landslide model; and a multi-field monitoring system including a temperature sensor, a pore water pressure gauge, a moisture meter and a grating strain gauge that are provided in the permafrost landslide model.

The invention discloses a dam body deformation risk intelligent early warning method and system, and the method comprises the steps: arranging a sensor in a target dam body region, carrying out the real-time monitoring of a dam body based on the sensor, and obtaining the real-time data of the dam body; constructing theoretical deformation curves of the dam body at different water levels and temperatures; performing physical-data dual-drive anomaly detection on the real-time data and the theoretical deformation curve to obtain a deformation early warning condition of the dam body; generating a rainfall deformation threshold value of the dam body based on pre-acquired rainfall data; and constructing a multi-stage early warning linkage response mechanism of the dam body based on the deformation early warning condition and the rainfall deformation threshold, and performing deformation risk intelligent monitoring on the dam body based on the multi-stage early warning linkage response mechanism. According to the invention, the dam body deformation risk intelligent early warning efficiency can be improved.

The invention relates to the technical field of rainstorm early warning, in particular to a deep learning-based rainstorm terminal early warning method and system, which introduces an image perception and semantic recognition mechanism to carry out consistency judgment on an overturning state of a rain gauge and image features such as a rain curtain and a water mark so as to realize effective elimination of unnatural trigger factors (such as manual water pouring and the like). And the reliability of rainfall sensing data is improved. A double-branch deep learning model capable of being deployed locally is adopted, multi-source data such as rainfall, temperature and humidity and topographic factors are fused, feature extraction and recurrence period prediction are performed based on a cross attention mechanism, and a threshold correction coefficient is dynamically generated in combination with historical events, so that self-adaptive adjustment of an early warning standard is realized, and misinformation and missing report risks are effectively reduced. The system supports low-power-consumption local operation, early warning decision is executed, and timeliness and accuracy of local early warning response are effectively improved.

The invention relates to the technical field of strategy optimization, and discloses a regional weather prediction method and system based on a meteorological large model, and the method comprises the steps: obtaining a terrain influence weight matrix; inputting into a spatial distribution feature model, outputting an optimized spatial distribution feature set, and realizing space-time registration through Kriging interpolation to obtain a spatial distribution feature set of local weather; extracting a seasonal change trend, and executing deviation correction to obtain a deviation correction coefficient; updating the weight matrix and performing coupling calculation to generate a coupling parameter set; generating a prediction result according to the coupling parameter set, and if the precision is lower than a threshold value, generating computing resource configuration; combining the resource configuration, the updating weight matrix and the spatial feature set to carry out weather prediction to obtain a prediction result; and finally, generating a rainfall intensity distribution diagram, performing differentiation correction, and outputting a regional weather prediction result. The method can achieve the high-precision prediction of the weather in the complex region.

The invention provides a drought and flood sudden turning event identification method, device and system, and relates to the technical field of water resource management, and the method comprises the steps: determining a research region and time period, collecting rainfall, sunshine duration, wind speed, pressure, humidity and air temperature data, carrying out the linear difference of vacant data, and carrying out the data preprocessing; constructing a rainfall difference comparison index for judging the sudden change degree of the drought and flood sudden change event; calculating a standardized rainfall evapotranspiration index for judging and identifying drought and flood events; establishing a grading standard of drought and flood events, and classifying the severity of the identified drought and flood events; and combining the rainfall difference comparison index with a standardized rainfall evapotranspiration index to construct a drought and flood sudden turning index for analyzing the sudden turning intensity. According to the method, the influence of rainfall intensity change on the flood disaster event is fully considered, and the occurrence process and severity level of the drought and flood sudden change event can be effectively and accurately identified.

The invention discloses a microwave link-weather radar combined rainfall inversion method based on Z-R relation adaptive adjustment, and the method comprises the steps: defining a related parameter set of microwave link rainfall estimation and weather radar reflectivity measurement in observation time, and carrying out the matching and space-time averaging processing, and obtaining rainfall intensity-radar reflectivity characteristics; fitting a radar reflectivity-rainfall intensity relation within observation time by using a least square method based on the rainfall intensity-radar reflectivity characteristics; according to the radar reflectivity-rainfall intensity relation sample of the known position, calculating the spatial distribution of the radar reflectivity-rainfall intensity relation in the research area by using an inverse distance weighted interpolation method; and according to the spatial distribution of the radar reflectivity-rainfall intensity relationship within the observation time and the average reflectivity factor measured by the radar, calculating to obtain a spatial distribution result of the accumulated rainfall within the observation time. According to the invention, the accuracy of regional rainfall monitoring is effectively 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 invention discloses an urban road rainwater runoff stage treatment system and method, and relates to the technical field of urban rainwater pollution treatment. In order to solve the problems that an existing system is difficult to monitor multi-index water quality in real time, the pollution degree is accurately recognized, and distribution control lacks dynamics, the system is provided with a multi-source spectrum water quality monitor at the tail end of a pipe network, turbidity, COD, TOC and heavy metal concentration are monitored in real time, rainfall intensity data are accessed into a control center, the pollutant load instantaneous rate L is calculated through a weighted summation formula, and the real-time monitoring of the pollution degree is achieved. A controllable flow distribution module in the flow distribution well distributes the rainwater to an enhanced treatment unit, a biological aerated filter system or a horizontal flow sedimentation tank, wherein the enhanced treatment unit comprises a rotational flow sand setting unit, an electrochemical heavy metal degradation unit and a chemical flocculation unit. The system is mainly used for realizing quality-divided efficient treatment and resource recycling of rainwater and guaranteeing the safety of a receiving water body.

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 relates to the technical field of mine environment risks, and discloses a mine environment risk multi-modal analysis and early warning decision-making method, which comprises the following steps of: performing time and space reference alignment on multi-source observation data, constructing a unified spatio-temporal data set, and establishing a troposphere disturbance model to realize observation disturbance coupling. And constructing a physical evolution operator and a rainfall-driven external input operator based on a seepage mechanical relationship, and generating a state evolution mechanism. Semantic direction parameters are generated by mapping semantic query information to a target attention dimension, and a lagging propagation operator is constructed by setting a rainstorm ending moment as a starting point. And determining a non-regular spectrum transition index and a peak lag moment through the non-regularity measurement and the dynamic change rate of each lag propagation operator, and setting a reference threshold value based on the spectrum transition index of a historical non-risk time period. Finally, when the early warning intensity meets the condition, early warning is output, and a query result is generated in combination with the lagging propagation operator and the semantic direction parameters.

The invention discloses a meteorological-based geological disaster risk early warning method, a server and a storage medium, and the method comprises the steps: constructing a two-factor dynamic evaluation framework, scientifically quantifying a disaster-bearing body vulnerability index reflecting a geological background and a disaster-causing inoculation factor reflecting a recent rainfall accumulation effect, and carrying out the inverse coupling of the disaster-bearing body vulnerability index and the disaster-causing inoculation factor, and generating a current rainfall disaster threshold dynamically adjusted in real time for each grid unit. By implementing the method and the device, the problem of insufficient early warning accuracy caused by the fact that traditional early warning cannot adapt to geological environment time-space difference is effectively solved, and accurate, dynamic and differentiated evaluation and early warning of geological disaster risks are realized.

The invention provides an intelligent rainwater regulation and storage method and system, and relates to the technical field of hydraulic engineering, and the method comprises the steps: collecting irrigation area environmental parameters in real time, the environmental parameters comprising rainfall intensity, soil humidity, weather forecast data and crop growth stage identifiers; dividing an irrigation area into a plurality of sub-areas according to the environmental parameters, integrating the sub-areas, and judging a water storage or drainage demand state of the integrated sub-areas; according to the water storage or drainage demand state of the integration sub-region, the dynamic drainage or water storage priority is calculated by combining the terrain gradient, the crop root depth and the real-time humidity value of the integration sub-region, and a water distribution scheme is generated through a multi-objective optimization algorithm; the water conservancy facility is driven to execute regulation and storage operation, the regulation and storage effect of the water distribution scheme in each integration sub-region is simulated in real time through digital twinning, and the regulation and storage strategy is dynamically corrected; by constructing an intelligent closed-loop decision-making system and fusing multi-source data to drive dynamic regulation and storage unit division and multi-objective optimization, the accuracy of water resource scheduling is improved.

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

The invention provides a roof rainwater collection system and method capable of achieving source discarding, and belongs to the technical field of rainwater collection. The system comprises a rainwater collection module arranged on a roof main body, the module comprises a photovoltaic unit, a water collection unit and a protection unit, the water collection unit comprises a water collection tank, a tank cover, a flow guide eave and the like, synchronous control of overturning of the tank cover and sliding of the flow guide eave is achieved through a gear and rack linkage mechanism, and polluted rainwater is automatically discarded at the initial stage of rainfall; in the later period, the photovoltaic panel, the inner surface of the box cover and the three rainwater collecting faces of the protection plate cooperatively collect rainwater, and the rainwater collecting efficiency is effectively improved; the protection plate has functions of normal water collection and extreme weather protection, can resist hail impact, and realizes protection of the photovoltaic panel. According to the scheme, through the three-dimensional design of source discarding, multi-surface water collection and intelligent protection, deep integration of the photovoltaic function and the rainwater collection function is constructed, rainwater cleaning is achieved from the source, and the advantages of efficient water collection, source pollution control and equipment protection are achieved.

The invention provides a radar flowmeter with environment compensation and a compensation method, and relates to the technical field of measurement, the radar flowmeter comprises an environment compensation module and a monitoring module; the environment compensation module is arranged on the upper shell and is used for monitoring rainfall and wind speed; the monitoring module is arranged on the lower shell and used for monitoring the water level and the flow. The upper shell and the lower shell are fastened through bolts. The compensation method comprises the steps of obtaining a rainfall signal and a humidity value; judging whether the current environment is rainfall or local dripping based on the rainfall signal and the humidity value; and when it is judged that rainfall exists, the section flow of the river channel is dynamically compensated. According to the radar flowmeter with the environment compensation function and the compensation method, high-precision and real-time water flow monitoring can still be achieved under the complex weather conditions such as rainfall and strong wind, and the radar flowmeter with the environment compensation function and the compensation method have remarkable technical advantages and application prospects.

The invention discloses a deep learning-based less-data small-watershed water level forecasting method. The method comprises the following steps of: acquiring historical hourly rainfall data of a plurality of upstream rainfall stations of a small watershed and historical hourly water level data of a downstream water level station; preprocessing the acquired data, and screening rainfall sessions with the rainfall capacity of over 15mm per hour within 24 hours and corresponding water level data; the method comprises the following steps: performing data enhancement through a double-generation GAN model, generating simulated rainfall data by a first GAN model by taking white noise as input, generating simulated water level data by a second GAN model by taking rainfall data as input, combining historical data and simulated data, performing normalization, and dividing the data into a training set, a verification set and a test set according to a proportion; constructing a CNN-LSTM hybrid prediction model, training the model by taking a Nash coefficient as an optimization target, and verifying the prediction model by adopting a progressive verification strategy; and deploying the model for real-time water level prediction to obtain a water level prediction result. The method can break through the bottleneck of few data, and achieves the high-precision mountain torrent early warning of the small watershed water level.

The invention discloses a post-raining side slope seepage characteristic testing method based on waveform inversion of nano-iron concentration distribution, and belongs to the field of seepage characteristic determination in foundation pit side slope stability monitoring, the method comprises the following steps: spraying a mixture of nano-iron particles and a surfactant into cumulonimbus to make nano-iron permeate into a side slope soil body along with rainfall; a radio frequency coil is arranged in the orthogonal direction of the selected section of the slope, an electromagnetic field is applied to excite nano iron resonance, and electromagnetic wave signals are collected; dividing the selected section of the slope into a plurality of grid units, and inverting the concentration distribution of the nano-iron in each grid unit by using an iterative optimization algorithm based on the electromagnetic wave signal to obtain an inversion result; and generating a seepage cloud picture based on the inversion result and calculating the seepage speed. According to the method, economic loss caused by large-area laying of nano-iron and interference to follow-up monitoring are avoided, the seepage position and size are determined, and complete and clear information is provided for monitoring seepage changes in slope engineering.