959 results about "Rainout" patented technology

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 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 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 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 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 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 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 relates to the technical field of data processing, in particular to a river pollution monitoring management method fused with environmental information perception. According to the method, monitoring points are classified according to information collected in a non-rainfall period, and a plurality of monitoring point clusters are obtained. In the target rainfall period, the rainfall influence degree of the monitoring points is determined by analyzing pollutant concentration data changes before and after analysis and rainfall information, and then the target monitoring points needing data correction are screened out. A time correction method is adopted, reference time periods are selected in the non-rainfall time periods, the reference weight of each reference time period is determined by analyzing the time sequence position of the reference time periods and the rainfall influence degree of the latest rainfall time period, and then time correction is conducted; and further performing space correction by using other monitoring points in the monitoring point cluster obtained by classification to finally obtain corrected pollutant concentration data. According to the method, the influence of rainfall on pollutant concentration data is eliminated, and the data reference is increased while the data coherence is ensured.

The invention provides a multi-source rainfall prediction method and device fusing GNSS and communication satellites, and the method comprises the steps: building a plurality of satellite-ground microwave links which are used for transmitting GNSS data and communication satellite data; gNSS data and communication satellite data on the multiple satellite-ground microwave links are acquired; performing atmospheric rainfall inversion on the plurality of satellite-ground microwave links based on GNSS data, and constructing a two-dimensional water vapor field according to an inversion result; performing rainfall intensity inversion on the plurality of satellite-ground microwave links based on communication satellite data, and constructing a two-dimensional rainfall field according to an inversion result; and fusing the two-dimensional water vapor field and the two-dimensional rainfall field by adopting Bayesian-maximum posteriori estimation to obtain a fusion field, and converting the fusion field to obtain a rainfall prediction field. According to the method, the rainfall field and the water vapor field are fused for rainfall prediction, so that the whole rainfall process can be accurately predicted.

The invention discloses a rainfall intensity rapid identification method and system based on multi-sensor data fusion, and relates to the technical field of hydro-meteorological monitoring and urban drainage scheduling, and the method comprises the steps: obtaining a historical rainfall data set, drawing a peak curve, and extracting an evolution sequence feature before a peak value; correlation analysis is carried out on the data and complete rainfall process characteristics to establish a historical rule model; rainfall intensity data are collected in real time through multiple sensors, and space-time dimension features are fused; matching the fusion features with historical features, and screening target historical events; constructing a rainfall reproduction prediction model to reconstruct a current rainfall evolution process; and finally, calculating an inflow load based on a reconstruction result, and generating a dynamic drainage scheduling scheme. According to the invention, accurate prediction of the rainfall process and intelligent scheduling of the drainage system are realized, and the urban waterlogging prevention capability is improved.

The invention provides an extreme rainfall prediction method and device based on deep learning and electronic equipment, and the method comprises the steps: collecting multi-source data, carrying out the preprocessing of the multi-source data, and constructing a multi-source heterogeneous fusion data set; inputting the multi-source heterogeneous fusion data set into a multi-scale feature extraction network, and extracting space-time evolution features of the rainfall process; generating a synthetic extreme rainfall sample through a generative adversarial network, carrying out mixed training on the synthetic extreme rainfall sample and a real sample, and strengthening extreme rainfall feature learning through a weighted loss function; a channel attention and space attention mechanism is introduced, the feature weight is dynamically adjusted, and the time sequence dependency relationship of the rainfall process is captured. According to the invention, accurate prediction and early warning of the extreme rainfall event are realized, and the problem of poor extreme rainfall prediction accuracy in the prior art is solved.

The invention provides a subway tunnel water disaster risk evaluation method under extreme rainfall based on multi-source data, and belongs to the technical field of urban rail transit disaster prevention. The method comprises the following steps: establishing a grading evaluation system on the basis of field rainstorm monitoring data, hydrogeological parameters, field geological exploration, rock-soil physical and mechanical parameters, monitoring data of a tunnel lining structure and material strength parameters of a tunnel site area in combination with a plurality of developed tests, dividing corresponding grades and determining an influence index-; and S4, respectively carrying out weight calculation on the influence index-by adopting an analytic hierarchy process and an entropy weight method, then calculating a dynamic weight of the influence index-, correcting an abnormal dynamic weight, generating a comprehensive score, dividing risk grades, matching graded prevention and control measures, and optimizing and adjusting the prevention and control measures in real time through a parameter risk grade dynamic judgment and construction method matching mechanism. According to the method, minute-level accurate interpretation of a disaster evolution mechanism and dynamic optimization adaptation of a prevention and control scheme are realized.

The invention discloses a mountain tourism route planning method based on weather and natural disasters, and the method comprises the steps: 1, building a Markov game model based on POMDP, combining Mama multi-modal feature compression and RAG semantic enhancement, designing a dynamic risk matrix integrating geological disaster probability, weather trend and ecological sensitivity, and providing partial observable environment state perception; 2, establishing a Nash equilibrium constraint optimization model, sharing an extreme weather strategy through federal learning, introducing an NSGA-II-Actor-Critic architecture to optimize unmanned aerial vehicle inspection and path adjustment, and triggering dynamic communication when rainfall exceeds a threshold value; 3, fusing a security and experience income design reward function, processing continuous action planning by using improved DDPG, and accelerating multi-target Pareto frontier convergence in combination with NSGA-II; 4, constructing a user weather sensitivity portrait, and dynamically adjusting weight balance income and AR simulation preview preference; and 5, based on a GIS closed-loop decision engine, performing agent and environment feature complementation, recording an emergency log by a block chain, and constructing a disaster case library by meta-learning.

The invention discloses an urban low-altitude gridding management process control method and system based on unmanned aerial vehicle analysis, and belongs to the technical field of unmanned aerial vehicle flight management.The urban low-altitude gridding management process control method comprises the steps that urban three-dimensional data are obtained, an urban area is divided into multiple grid units, meteorological data are obtained, and a building interference evaluation model is constructed; importing the open angle and the building surrounding rate of each grid unit into a building interference evaluation model to calculate the building intensity, evaluating the reflection interference on the unmanned aerial vehicle through the number and intensity of reflection paths, constructing a meteorological interference model, obtaining the cloud cluster humidity distribution, raindrop particle size and wind parameters of the grid units, and calculating the building intensity. The method comprises the following steps: importing meteorological data into a meteorological interference model to evaluate a rainfall interference condition and a wind disturbance condition, constructing a flight risk evaluation model, and importing building density, a multi-path reflection error index, a rainfall interference index and wind disturbance intensity into the flight risk evaluation model to evaluate the flight risk degree of the unmanned aerial vehicle. And the safety of the flight path of the unmanned aerial vehicle is improved.

Three-dimensional target detection in complex weather is a key problem in intelligent driving, and extreme weather is easy to interfere with a sensor, so that the detection precision is reduced. The invention provides a three-dimensional target detection method in complex weather based on millimeter wave radar and vision fusion, and designs an image generation module for simulating complex environment images such as rainfall, snowfall and the like aiming at the problem of scarcity of bad weather image data in a public data set. And by adopting a self-distillation mechanism, multi-modal knowledge learned by the teacher model in normal weather is migrated to the student model trained in bad weather, so that the detection performance in a complex environment is improved. Besides, in order to enhance the multi-modal information fusion capability, gating fusion modules are introduced into the teacher model and the student model, and a learnable gating fusion mechanism is utilized to dynamically adjust weight distribution of multi-modal feature fusion. According to the method, the robustness and the detection precision of the model under the complex weather condition are effectively improved.

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

The invention relates to an urban non-point source pollution identification method and system based on rainfall events. The method comprises the following steps: step S100, decomposing rainfall data of a continuous time sequence into independent rainfall events and snowfall events; step S200, dividing grid units, and performing runoff calculation in each unit so as to obtain runoff information of spatial distribution; wherein the rainfall event runoff is estimated by adopting a runoff curve hydrological model method; calculating snow melting runoff by adopting a temperature index snow melting model; s300, estimating the average event concentration of the rainfall event by adopting an accumulation and scouring formula, and estimating the average event concentration of the snow melting event by using average observation values in different land utilization types; and step S400, realizing refined identification and accurate estimation of urban non-point source pollution load by integrating urban functional area characteristics, surface runoff simulation and event average concentration simulation of pollutants. The method is suitable for estimating the rainfall runoff pollution load in a large-scale region with relatively limited water quality monitoring data.

The invention discloses a multi-defense-line cooperative mountain torrent early warning method and system, and relates to the technical field of disaster early warning, and the method comprises the steps: obtaining multi-source heterogeneous meteorological data of an area where a to-be-evaluated observation station is located, and carrying out the preprocessing, and obtaining the preprocessed multi-source heterogeneous meteorological data; mutual feedback verification is carried out on the multi-source heterogeneous meteorology, and dynamic weights are distributed; performing data fusion on the preprocessed multi-source heterogeneous meteorological data based on a dynamic weight to obtain fused meteorological data; judging whether the rainfall capacity of the to-be-evaluated observation station is abnormal or not based on the rainfall abnormal station identification model in combination with the fused meteorological data; and if the rainfall of the to-be-evaluated observation station is abnormal, determining a mountain torrent disaster dynamic early warning level of the to-be-evaluated observation station based on the dynamic early warning model and the fused meteorological data. According to the invention, the technical problems of inaccurate local heavy rainfall measurement and calculation delay in the prior art are solved.

The invention discloses a rainstorm disaster early warning and alarming method, which comprises the following steps: carrying out multi-source data acquisition, and carrying out cleaning, standardization and fusion processing on the acquired multi-source data; based on the basic geographic information data and the historical disaster data, constructing a regional disaster bearing capability evaluation model, and respectively setting differentiated early warning thresholds for regions of different risk levels; based on the preprocessed multi-source data, constructing a rainstorm trend prediction model, and predicting rainfall intensity, cumulative rainfall and rainfall duration in a future preset time period; starting a corresponding multi-channel collaborative alarm mechanism according to the characteristics of the early warning level and the early warning area; in the early warning period, real-time monitoring data are continuously collected, and the rainstorm trend prediction result is updated. According to the method, the problems of inconsistency of multi-source data and low fusion precision are effectively solved, meanwhile, the situation that the prediction advance of secondary disasters such as excessive release of emergency resources and landslide is increased by 1-2 hours is avoided through the temporary high-risk labels, and comprehensive early warning of the secondary disasters is achieved.

The invention discloses an intelligent rainfall forecasting method and system based on real-time assimilation driving of sky-air-ground multi-mode sensing data, and relates to the field of machine learning, and the method comprises the steps: carrying out the coordinate alignment of an obtained standardized multi-source meteorological data set, distributing a weight according to the time-space credibility of a data source, and carrying out the calculation of the time-space credibility of the data source; generating an enhanced radar image feature set implying multi-modal information, and inputting the enhanced radar image feature set into the double-branch-UNet-GAN collaborative optimization model for training to obtain an optimal model adaptive to multi-modal data; based on severe convective weather characteristics and business requirements, precision correction and lightweight processing are performed on a forecast result after test set adversarial optimization through a regional attention mechanism, visual display is realized through an APP and a webpage terminal, positioning query and early warning push functions are synchronously provided, and a closed loop from data to service is completed. According to the method, full-chain innovation is formed from data fusion, model performance, aging precision, resource adaptation to service landing, and the scientificity and application value of intelligent rainfall forecasting are remarkably improved.

The invention discloses a flood inundation range dynamic simulation method and system based on multi-source data fusion. The method comprises the following steps: S1, collecting multi-source original data of a to-be-simulated target drainage basin; s2, extracting an initial submerging range grid according to the multi-source original data; s3, according to the initial submerging range grid, generating a river channel-beach area coupled section terrain file; s4, obtaining the dynamic water level process of each downstream section according to the section topographic file and the weather forecast rainfall field; and S5, obtaining a dynamic simulation result of the flood inundation range according to the dynamic water level process. According to the invention, through continuous driving of the distributed hydrological model and the one-dimensional hydrodynamic model, full-chain simulation from rainfall forecast to submerging dynamic state is realized, so that the submerging range can be updated in real time along with meteorological conditions and riverway water levels.

The invention relates to the technical field of urban inland inundation rapid prediction, and discloses an urban inland inundation rapid prediction method based on deep learning, and the method comprises the following steps: S1, collecting historical meteorological data, landform data, urban drainage system data and historical inland inundation event data; s2, performing data cleaning on the collected data, removing noise, filling missing values, and processing abnormal values; s3, constructing a deep learning model architecture; when urban inland inundation risk prediction is carried out, multi-source heterogeneous data are integrated and standardized, and a unified spatial-temporal characteristic analysis framework is constructed, so that the system can eliminate magnitude differences of weather, terrain and drainage system data, and data comparability of different regions is ensured; and meanwhile, dynamic feature extraction is performed on real-time rainfall data by using a deep learning model, an abnormal fluctuation rule of meteorological elements is identified, the waterlogging risk pre-judgment capability of extreme weather events is improved, and the stability and credibility of a prediction result are enhanced.

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

The invention relates to the technical field of permeable material testing, solves the problem that a more complete material selection basis cannot be provided for engineering design in the prior art, and provides a method, a device and equipment for testing the heat dissipation performance of an evaporative cooling material and a storage medium. Acquiring a first mass change value of the to-be-tested sample in the simulated rainfall process; when the instantaneous moisture content meets a preset condition, rainfall simulation is stopped, and a second mass change value of the to-be-tested sample in a preset duration is obtained; placing the to-be-tested sample in the wind tunnel test platform, and obtaining a third mass change value and a surface temperature change value of the to-be-tested sample and an environment temperature change value, a humidity change value and a wind speed change value of the wind tunnel test platform; and obtaining a heat dissipation performance evaluation result of the to-be-tested sample based on the parameters. According to the method, quantitative, comparable and repeatable evaluation of the cooling capacity of the permeable material is realized, and a scientific basis is provided for material performance judgment and application scene selection.

The invention discloses a rainfall station network layout method, and relates to the technical field of hydrometric station network layout, and the method comprises the steps: obtaining the rainfall data of a historical rainfall station network in a target region, the historical rainfall station network comprising a plurality of rainfall stations; extracting spatio-temporal characteristics of the rainfall data, and obtaining a rainfall prediction value of each spatial unit based on the spatio-temporal characteristics; obtaining prediction precision and a mutual information mean value based on the rainfall prediction value, constructing a fitness function based on the prediction precision and the mutual information mean value, and obtaining an optimal solution based on the fitness function; and obtaining a target rainfall station network layout result based on the optimal solution. The method solves the problems that an existing rainfall station network layout method is insufficient in spatial-temporal feature capture capability, is easy to fall into a local optimal solution due to dependence on a certain heuristic algorithm, and is low in optimization efficiency and cannot effectively balance and collaboratively optimize multiple targets.

The embodiment of the invention provides a disaster risk situation generation method and system based on dynamic grading, and a medium, and relates to the technical field of multi-disaster chain type disaster dynamic early warning. Comprising the following steps: performing rainstorm flood peak flow deduction by taking an infiltration loss parameter array as a constraint condition and combining an instantaneous rainstorm peak time-space field and a persistent rainfall characteristic time-space field to generate a disaster-inducing potential curved surface array; performing grid-level disaster susceptibility evaluation on the area static factors of the disaster monitoring area and outputting a reference susceptibility array; and constructing a dynamic grading-probability coupling array by coupling the disaster-causing potential curved surface array and the reference susceptibility array, executing disaster risk situation prediction based on real-time rainfall data, and outputting disaster chain risk probability distribution. The problems that in the prior art, a static single disaster type early warning system is adopted for split type static early warning, rainstorm-mountain torrent-geology chain type dynamic conduction risks cannot be captured, the dynamic conduction process of chain disaster is difficult to quantify, disaster chain response lags behind, and precision is insufficient are solved.