2213 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 relates to the technical field of municipal pipeline detection, and discloses a sewage pipe network full pipe operation early warning analysis method comprising the following steps: S1, arranging sensors at key nodes of a sewage pipe network, and collecting real-time operation data of the sewage pipe network; s2, acquiring real-time meteorological data including rainfall capacity, rainfall intensity and other related meteorological factors; s3, performing data cleaning and standardization processing on the collected sewage pipe network data and meteorological data; s4, on the basis of multivariable regression analysis, establishing a relation model among the flow, the liquid level and the pipe fullness risk; and S5, performing time sequence prediction by using a long short-term memory network, and predicting future flow and liquid level trend. Time sequence prediction is carried out through the long-short-term memory network, the early warning threshold value is dynamically adjusted in combination with the self-adaptive algorithm, the running state change of the sewage pipe network is responded in real time, the future flow and liquid level trend is accurately predicted, and the early warning accuracy and the response speed to the sudden pipe fullness risk are improved.

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 discloses a power distribution network operation safety risk assessment method considering typhoon and rainstorm composite disasters, and the method comprises the following steps: generating different typhoon paths as main scenes based on the typhoon and rainstorm disaster forecast data, and endowing each main scene with a weight according to the occurrence probability; the influence of microtopography and rainfall intensity on the wind speed V (r) in the main scene is considered, the corrected wind speed is calculated, the total failure probability of the distribution line between the node i and the node j is calculated according to the fault probability of the distribution line between the node i and the node j and the failure probability caused by foreign matter hanging, and the power distribution equipment failure probability is calculated according to the water accumulation amount of the power distribution network area; simulating and generating N fault scenes s through a Monte Carlo method to form a fault scene set S, and endowing each fault scene with a weight; and taking the minimum expected total load loss amount in all fault scenes as an objective function, constructing a risk assessment model, and solving the risk assessment model in combination with constraint conditions.

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 discloses a collapse geological disaster vulnerability model and risk assessment method and system, and relates to the technical field of geological disaster risk assessment, and the method comprises the steps: collecting and preprocessing target rock mass information; collecting monitoring data and long-term rainfall data of the three-axis acceleration sensor; the acceleration fluctuation characteristics are analyzed, and the risk of rock mass instability is recognized in combination with rainfall data; the instability risk of the rock mass is quantified based on a Monte Carlo method; predicting a motion path and a stacking range of the collapsed rockfall by utilizing numerical simulation, and evaluating threats of the collapsed rockfall to surrounding electric power facilities; and performing level-to-level management on the surrounding area of the transformer substation through the risk level model. Real-time monitoring, probability calculation and numerical simulation technologies are adopted, and the problem that in the prior art, power facilities lack dynamic monitoring, accurate evaluation and graded protection is solved. According to the overall scheme, real-time and automatic monitoring and risk grading of collapse geological disasters are achieved, and the safety and reliability of electric power facilities under the disaster condition are effectively improved.

The present disclosure relates to the technical field of landslide hazard model tests, and in particular to a landslide model test method and device for realizing multi-variable synchronous monitoring. In the test device, a soil slope simulation unit is configured to pile up a soil slope and simulate different inclinations of the slope. A rainfall simulation unit is configured to simulate rainfall of different intensities and induce a landslide. A runoff-seepage separation unit is configured to realize separation of runoff from seepage of the slope under the effect of rainfall. A monitoring module is configured to monitor runoff and seepage flow rates of the slope, changes in water content and pore water pressure within the slope, and a morphological change of the slope under the effect of rainfall, to obtain a mechanism of rainfall-induced landslide deformation and damage based on changes in the flow rate.

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 relates to a rainfall kinetic energy space-time prediction method based on dual-polarization weather radar observation parameters, and the method comprises the steps: firstly processing minute-resolution raindrop spectrometer data, calculating raindrop diameter distribution, and calibrating the data of a rain gauge; secondly, dual-polarization radar parameters (ZH, ZDR and KDP) are calculated based on a physical formula, rainfall kinetic energy KE is calculated in combination with micro-physical characteristics, and rainfall types are marked; and then, after atmospheric field variables are extracted and physical normalization is carried out, a bidirectional time series kinetic energy prediction network (BTKE-Net) is constructed, and rainfall kinetic energy is accurately predicted through BiLSTM and a deep regression network. And finally, after attenuation and offset correction, inputting the data into the trained BTKE-Net model, and carrying out real-time space-time prediction. The innovation of the invention lies in that the application potential of the dual-polarization weather radar in the water environment field is excavated, large-range high-precision rainfall kinetic energy space-time prediction is realized, and more accurate scientific basis and decision support are provided for water and soil conservation and meteorological disaster prevention and control.

The invention discloses an aircraft cold cloud precipitation enhancement quantitative evaluation method based on radar echo enhancement, and belongs to the technical field of disaster prevention and reduction. The method comprises the following steps: acquiring to-be-processed radar echo data and to-be-processed ground raindrop spectrometer data; performing separation processing on the radar echo data to be processed to obtain rainfall enhancement influence echoes and natural rainfall echoes; according to the to-be-processed ground raindrop spectrograph data, the rainfall enhancement influence echo and the natural rainfall echo, establishing a Z-I relationship between the rainfall enhancement influence echo intensity Z of the near-ground layer and the rainfall intensity I; and according to the Z-I relation, the rainfall enhancement influence echo and the natural rainfall echo, calculating to obtain the total rainfall enhancement amount. According to the invention, the rainfall area with clear boundary generated by artificial rainfall enhancement can be separated, the natural precipitation can be identified, and the airplane operation rainfall increment can be quantitatively estimated by using radar data.

The utility model relates to the technical field of model tests, and discloses a landslide damage simulation test device which comprises a model test box, a rainfall frame, a hydraulic assembly, a plurality of spraying assemblies, a water tank assembly and a constant temperature system. The angle of the landslide model box can be adjusted through the hydraulic machine so as to change the slope of a model test landslide soil body; the multiple spraying assemblies are arranged at the top of the rainfall frame and located over the model test box, the output ends of the spraying assemblies aim at a test soil body in the model test box, the influence of rainfall on landslide slippage is simulated by adjusting the spraying size, and the constant temperature system is arranged at the top of the model test box and used for regulating and controlling the temperature of the test soil body. The constant temperature system is used for measuring the influence of the dry-wet cycle effect on the landslide soil body.

The present disclosure discloses an early warning method for a shallow soil landslide based on rainfall and soil moisture content, relating to the field of soil landslides prevention and control. The early warning method comprises the following steps: a. determining whether a location is a humid region; b. calculating soil moisture content of the humid region in a rainy season; c. monitoring rainfall amounts in real time by sensors; d. calculating the soil moisture content S by step b; e. calculating rainfall duration on an hourly basis, calculating a total hourly rainfall amount by superposition from an initial 1-hour rainfall amount, and dividing the total hourly rainfall amount by the rainfall duration to obtain hourly rainfall intensity; f. calculating a critical value Cr of a shallow soil landslide; and g. determining a landslide grade and giving an early warning signal.

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 computer simulation, in particular to an earth surface two-dimensional calculation method based on a cellular automaton, which comprises the following steps of: analyzing topographic data of a plurality of areas based on topographic data acquisition records, recording elevation and gradient, processing the topographic data by using the cellular automaton, constructing a topographic model, and calculating the topographic model by using the cellular automaton. And obtaining regional terrain information. According to the method, through terrain data acquisition and terrain model construction, the basic data quality of the model is improved, accurate input data is provided for simulation, a key position is identified, the size and distribution of cells are adjusted, the simulation resolution of a key area is optimized, the local precision of simulation is improved, and the consumption of computing resources is reduced; the rainfall simulation parameters are adjusted according to the actual meteorological data, the prediction reliability is enhanced, the erosion effect and anti-erosion ability of various areas are evaluated by simulating soil erosion and surface runoff, and the prediction accuracy and practicability of surface two-dimensional calculation are 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 belongs to the technical field of meteorological disaster prediction, and discloses a flood early warning method based on rainfall multilayer perception. Comprising the steps of collecting meteorological parameter data, terrain parameter data, hydrological soil data and human activity data; preprocessing the collected meteorological parameter data, topographic parameter data, hydrological soil data and artificial activity data to obtain a meteorological feature data set, a topographic feature data set, a hydrological soil feature data set and an artificial activity feature data set; fusing the meteorological feature data set, the topographic feature data set, the hydrological soil feature data set and the human activity feature data set to obtain a comprehensive feature data set; training according to the comprehensive feature data set to obtain a flood risk prediction model, and predicting through the flood risk prediction model to obtain a flood risk index; the predicted flood risk index is compared with a preset flood risk index threshold value, and whether flood early warning needs to be carried out or not is judged; and a worker is helped to accurately make flood control measures.

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.

A hot-humid climatic wind tunnel and a multi-field coupling control system therefor. A fan (18), a variable temperature device, a variable humidity device, a salt fog generator (8), a sky background radiation board (11), a solar radiation lamp array (12) and a rain generator (13) are mounted in a wind tunnel body (29). A wind speed sensor, a temperature sensor, a humidity sensor, a salt fog concentration sensor, a background radiation temperature sensor, a hemispherical radiometer and a rain gauge are mounted in a test section; the sky background radiation board (11) is mounted on the top of the test section of the wind tunnel, and the solar radiation lamp array (12) and the rain generator (13) are mounted on the sky background radiation board (11); the sky background radiation board (11) comprises heat conduction boards (11-2), stainless steel boards (11-3) and copper tubes (11-1); and the plurality of heat conduction boards (11-2) are divided into multiple rows and mounted on the stainless steel boards (11-3), and the copper tubes (11-1) are laid on each row of heat conduction boards (11-2). The copper tubes (11-1) are connected to an external cold and hot water device. The present invention can achieve simulation of fields of seven parameters, i.e. wind speed, temperature, humidity, solar radiation illuminance, sky effective temperature, rainfall, and salt fog in outdoor natural climate.

The invention provides a goaf tunnel lining structure safety evaluation method and system under heavy rainfall, and relates to the technical field of underground and tunnel engineering.The method comprises the steps that a tunnel evaluation index is generated by obtaining rainfall intensity, rainfall duration, geological characteristics, soil permeability and other data; the method comprises the following steps: analyzing interaction of rainfall moisture on lining water pressure and surrounding soil deformation, calculating surrounding rock stress, calculating water pressure generated after rainfall permeation, quantifying dynamic influence of the water pressure on the lining water pressure, and calculating a safety coefficient of a lining structure by integrating a tunnel evaluation index, the surrounding rock stress and the water pressure after permeation. And obtaining lining displacement, crack development speed and water flow permeation data, generating a risk coefficient, comparing the risk coefficient with a safety coefficient, and judging whether to give out early warning or not. According to the method, related data of the tunnel lining are comprehensively analyzed, a tunnel safety evaluation and early warning mechanism is established, and the safety of the goaf tunnel lining structure under the heavy rainfall condition is ensured.

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.