1652 results about "Rainout" patented technology

The invention provides an urban intelligent drainage management system based on big data analysis, and relates to the technical field of urban drainage management, and the system comprises a multi-source data collection module which collects historical climate changes, rainfall curves, satellite meteorology, pipe network node water level flow and topological structure data in real time; the dynamic three-dimensional modeling module constructs a pipe network three-dimensional model according to a fluid mechanics principle, and binds real-time sensor data by a spatio-temporal data fusion algorithm; the drainage limit analysis module calculates the maximum drainage threshold value and the failure probability of the node under extreme rainfall; the intelligent early warning module fuses LSTM and ARIMA models, generates a drainage pressure thermodynamic diagram in the future 72 hours in combination with satellite meteorological data and triggers early warning; the health assessment module generates a node health index and a maintenance priority order; the resource scheduling engine generates a dynamic scheduling scheme. The urban drainage problem is effectively solved, the ability of a drainage system to deal with extreme weather and pipe network aging problems is improved, and accurate early warning and efficient resource allocation are achieved.

The invention discloses a geological disaster early warning method and system based on big data analysis, and particularly relates to the technical field of geological disaster early warning. Geological deformation time series data of a target area are obtained through satellite remote sensing monitoring, feature importance is evaluated through a random forest algorithm, and regional geological risk grades are divided; meanwhile, wavelet transformation and long-short-term memory network modeling are carried out on rainfall spatio-temporal data of the meteorological monitoring station, and the potential influence of rainfall is evaluated; high-risk areas are screened, an underground water level change rate and rock-soil body strength correlation model is constructed, and a support vector machine algorithm is adopted to evaluate the landslide probability; and finally, analyzing a geological risk grading result, the potential influence degree of rainfall on the target area and the landslide occurrence probability by adopting an analytic hierarchy process, calculating a comprehensive risk index, comparing the comprehensive risk index with a comprehensive risk threshold value, and judging whether to trigger grading early warning, thereby realizing data fusion and intelligent analysis, early warning in advance, and improving the early warning efficiency. And effective help is provided for disaster prevention and reduction.

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.

A regional extreme rainfall forecasting and early warning method based on a conditional generative adversarial network (cGAN) comprises the following steps: preprocessing multi-source meteorological data, including standardization and space consistency alignment, so as to ensure the availability of the data; and through a coding module of the cGAN, extracting and fusing interannual daily average features and horizontal features, constructing a multi-scale feature pyramid, and enhancing the characterization capability of a rainfall spatial mode. Forecasting data are generated through a cGAN generator and a condition discriminator, rainfall collaborative factors are introduced into the discriminator to serve as physical constraint conditions, and network parameters are optimized through an adversarial loss function and physical consistency loss. The forecasting data is post-processed through a graph neural network (GNN), and spatial dependency modeling and optimization correction are carried out by taking a meteorological station observation value as a true value, so that the forecasting precision and reliability of an extreme rainfall event are remarkably improved.

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 relates to the technical field of hydrological forecasting, in particular to a deep learning-based short-term rainfall and flood forecasting method and system, and the method comprises the following steps: constructing a deep learning-based radar echo epitaxial prediction model and a rainfall and flood forecasting model, training and testing the two constructed models by adopting the first data set and the second data set respectively; inputting the rainfall radar echo data of the target watershed in a certain period of time into the radar echo epitaxial prediction model, and predicting the change trend of the rainfall radar echo in a future period of time; and inputting the change trend of the rainfall radar echo in the future period of time into the rainfall and flood forecasting model, and forecasting the rainfall and runoff of the target drainage basin in the future period of time. According to the method, the precipitation and the runoff of the target drainage basin can be predicted more accurately in a short time, and the prediction capability and the decision level of regional water resource management are effectively improved.

The invention relates to the technical field of geological disaster model tests, in particular to a cold region soil engineering collapse monitoring device, system and method.The cold region soil engineering collapse monitoring device comprises an outer box, an inner box and a side slope model, the side slope model is arranged in the inner box, and the outer box is used for containing the inner box; the rainfall assembly is movably mounted on the inner box and is used for controllably and quantitatively spraying water or mist towards the inner box so as to simulate a rainfall or plum rain state; the vibration assembly is arranged at the bottom of the side slope model, the side slope model is adjustably installed on the vibration assembly, and the vibration assembly is used for enabling the side slope model to generate vibration so as to simulate the vibration state; the device further comprises an electromagnetic force loading assembly, and the electromagnetic force loading assembly is arranged on the outer side of the side slope model and used for applying loads in at least one direction to the side slope model. The device is used for simulating the slope stability of the cold region soil engineering under the multi-factor coupling effect.

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 a slope stability prediction method based on deformation monitoring, and relates to the field of slope monitoring, and the method comprises the steps: obtaining a time sequence deformation field of a target slope through employing an InSAR; generating a time sequence Dn through differential interference processing; collecting rainfall data; generating a time sequence representing regional rainfall conditions; grey correlation analysis is adopted, and the correlation degree of Dn and Pn is calculated; constructing a slope slip prediction model based on InSAR displacement and rainfall factors; if the displacement time sequence has an acceleration trend or fluctuation, marking the corresponding slope unit as an early warning analysis object; obtaining rainfall forecast data in a future period of time, and calculating a displacement predicted value di '(N + 1) of the marked side slope unit at the (N + 1) th time point in the future; and comparing the displacement predicted value di '(N + 1) of the (N + 1) th time point with a threshold value, and if di' (N + 1) exceeds the threshold value, giving a slippage early warning level. Aiming at low precision of a rainfall-induced slope slippage early warning model in the prior art, the precision is improved.

The invention discloses an artificial precipitation data acquisition system and evaluation method based on a remote sensing image, and relates to the technical field of ecological environment restoration and meteorological science. Comprising the following steps: acquiring remote sensing image data and rainfall data of meteorological stations in a region, performing geometric correction and radiation correction on the remote sensing image data based on ENVI software to obtain corrected remote sensing data, and calculating the rainfall data of the meteorological stations on the basis of the corrected remote sensing data and regional historical rainfall data. The rainfall change data and the vegetation change data in different time periods before and after artificial rainfall enhancement are respectively obtained through a regression analysis method and a pixel dichotomy method, the rainfall change data and the vegetation change data in different time periods before and after artificial rainfall enhancement are compared, the influence of rainfall enhancement on vegetation recovery is quantitatively analyzed, and the rainfall enhancement effect is analyzed by utilizing spatial-temporal dynamic change. According to the method, the influence of rainfall enhancement on the ecological environment can be evaluated more comprehensively and accurately, and a new scientific basis is provided for ecological restoration of arid regions.

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.

The invention relates to the technical field of mine safety, and discloses a mine extreme weather emergency defense and up-and-down pre-alarm system, which comprises a ground surface monitoring module used for monitoring the water level, rainfall and environment humidity of a mine wellhead and a ground surface low-lying area in real time and generating a dynamic water level pre-alarm signal; the wellhead flood control module comprises an automatic emergency flood control door and is used for receiving the early warning signal of the earth surface monitoring module and automatically closing the flood control door when the water level reaches a warning line so as to prevent surface accumulated water from flowing backwards to the underground; and the underground early warning and evacuation module comprises a sound-light alarm device and a wireless communication system. By adopting the technical scheme of multi-sensor fusion and water level trend prediction, through multi-dimensional real-time monitoring of the wellhead water level, the rainfall intensity and the environment humidity, the pre-closing state of the flood gate is triggered in advance in combination with dynamic data analysis and an intelligent algorithm, and the technical effects of timely responding to rapid rising of the water level and reducing the operation pressure of equipment are achieved.

The invention relates to a method for measuring the toughness of an urban public transport network in extreme rainstorm weather. The method comprises the following steps: acquiring station and line data of the urban public transport network, rainstorm disaster information and passenger flow data; a bus-subway double-layer traffic network is constructed; based on the constructed bus-subway double-layer traffic network, establishing a linear relation between rainstorm rainfall intensity and passenger flow change, and calculating disturbance values of the rainstorm to stations and connecting edges in the network; constructing a bus-subway double-layer traffic network cascade failure model considering passenger flow, and representing a dynamic evolution process of an urban public traffic network in extreme rainstorm weather; and determining a network performance measurement index by using the constructed bus-subway double-layer traffic network cascade failure model, and measuring the toughness level of the urban public traffic network in extreme rainstorm weather. According to the method, the urban public transport network toughness can be accurately measured, and the coping capacity of an urban public transport system in extreme rainstorm weather is improved.

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 discloses a rainfall prediction method and system for the Beijing, Tianjin and Hebei region, and relates to the technical field of rainfall prediction, and the method comprises the steps: obtaining multi-source data, carrying out the preprocessing of the data, extracting the core characteristics of the data, carrying out the dynamic rainfall modeling according to the core characteristics of the data, and simulating a rainfall evolution path; and constructing a rainfall prediction model, performing rainfall intensity prediction based on the rainfall evolution path, performing extreme weather early warning based on a prediction result, and generating a rainfall distribution diagram of a high-risk area. Through fusion of multi-source data, the generative adversarial network and spectral analysis, the spatial resolution of the data and the rainfall evolution prediction capability are improved, the real-time performance and the accuracy of rainfall prediction are improved in combination with dynamic rainfall modeling, a fractional order differential equation and a multiple regression model, and the prediction accuracy is improved. The rainfall prediction challenge under complex weather conditions can be effectively dealt with, and accurate prediction of rainfall intensity and extreme weather events is realized.

The invention relates to the technical field of microwave communication, and discloses a microwave communication data transmission method, which comprises the following steps: S1, sensing real-time rainfall intensity and atmospheric turbulence intensity parameters through combination of a multi-band radar and a meteorological sensor; s2, establishing a fractional order random channel model of a rainfall and turbulence coupling effect; s3, generating a dynamic coded modulation control strategy based on deep fractional order reinforcement learning; s4, optimizing a programmable metasurface reflection coefficient matrix according to the dynamic coded modulation control strategy and a channel state to reconstruct an electromagnetic propagation path; s5, executing power-codebook joint optimization of space-time coding and non-orthogonal multiple access; and S6, the system stability is monitored on line, and a degradation fault-tolerant mechanism is triggered. According to the invention, a Ku / W dual-band radar and meteorological sensor variational Kalman fusion technical scheme is adopted, and compared with a single-band radar or sparse meteorological station isolated observation scheme in the prior art, the problem of signal sudden drop misjudgment in a rainstorm scene is solved.

The invention provides an experimental system of a hot water force coupling landslide model, which is characterized in that on the basis of a large freezing and thawing environment test box, an artificially manufactured slope model is placed in the large freezing and thawing environment test box, and meanwhile, a rainfall and snowfall system, a slide rail system, a water level control system, a servo loading system, a medium strain rate disturbance system and a data acquisition system are integrated; a complete slope landslide model indoor experiment device under the combined action of freeze thawing-load-seepage is formed and used for simulating slope landslide caused by rainfall and snowfall, blasting vibration, slope body water level change and other conditions, and multi-source heterogeneous data of the whole process of the landslide from the unstable factor accumulation stage, the temporary sliding stage, the sliding stage and the stable stage are obtained.

The invention provides a rainfall inversion method integrating GNSS (Global Navigation Satellite System) water vapor chromatography and a water conservancy rain-measuring radar. The rainfall inversion method comprises the following steps: collecting GNSS data, water conservancy rain-measuring radar data, meteorological data and geographic information; estimating the atmospheric water vapor content SWV on the GNSS oblique signal path; performing three-dimensional water vapor chromatography to obtain a grid water vapor density value; identifying an inversion factor; and training the neural network and carrying out rainfall inversion. According to the rainfall inversion method established by the invention, GNSS water vapor chromatography and water conservancy rain measurement radar data are effectively utilized, the correlation between space-time gaseous water and liquid water is comprehensively considered, the rainfall inversion process is optimized, and a new thought is provided for refined inversion of rainfall intensity and spatial distribution.

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 rainfall type loess side slope instability early warning methods, and discloses a rainfall type loess side slope instability early warning method and system based on multi-source data fusion, and the method comprises the steps: S1, arranging a sensor network on a side slope, and collecting rainfall capacity, surface displacement and soil pressure data in real time; s2, preprocessing the collected multi-source data, including data cleaning, missing value filling and data standardization; and S3, extracting characteristic indexes capable of reflecting the stability state of the slope from the preprocessed data. Environmental data of the loess slope, such as rainfall, evaporation capacity, temperature and humidity, are acquired by using a sensor network; the deformation data comprises earth surface displacement, deep displacement and crack width; the mechanical data comprises soil pressure, pore water pressure and soil moisture content; image data: a slope surface image and a crack development image are collected, an early warning model is constructed based on a machine learning algorithm, and data features can be automatically learned.

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 relates to the field of agricultural water resource management, in particular to a method, device and equipment for estimating irrigation water consumption and canal system water utilization efficiency, and the method comprises the steps: screening extreme meteorological and hydrological data meeting extreme rainfall scene conditions from historical meteorological data and hydrological data, calculating label data representing moisture storage and discharge capacity of an underground water change zone by combining net exchange flux at a deep soil boundary in an extreme rainfall scene, and constructing a space-time variable specific yield estimation model by combining a machine learning algorithm based on dynamic nonlinear influence and spatial heterogeneity of the extreme rainfall scene on soil moisture; training a specific yield estimation model by using the data set carrying the label data; and based on the dynamic similar characteristics of the soil moisture in the extreme rainfall scene and the irrigation scene, migrating the trained specific yield estimation model to the irrigation scene so as to estimate the irrigation water consumption and the canal system water utilization efficiency. Therefore, the problem of how to improve the parameter accuracy of the arid region is solved.

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.