187 results about "Disaster assessment" patented technology

The invention discloses a goaf collapse risk assessment data fusion system based on big data processing, and particularly relates to the technical field of geological disaster assessment, and the system comprises three core modules: a multi-source data adaptive weighted fusion module which establishes a unified space-time coordinate system, converts non-raster data into a continuous field through Kriging interpolation, and performs data fusion on the continuous field; combining the information entropy and the correlation coefficient to dynamically distribute weights, and generating an enhanced feature field through self-supervised pre-training; the physical-space-time neural network dynamic prediction module is integrated with elastic-plastic mechanical constraint loss and multi-task learning, and outputs a future multi-time step risk probability field and a deformation prediction field through a space-time convolution-memory network; and the risk field three-dimensional subdivision and emergency response module is used for clustering three-dimensional voxels in a high-risk area, automatically calculating risk body parameters, generating an emergency scheme in combination with DEM data and an A * algorithm, and improving evaluation accuracy and emergency scheme practical operability through digital twinborn deduction evaluation.

The invention discloses an intelligent building fire identification and simulation early warning method and system, belongs to the technical field of building informatization and disaster prevention and control, and aims to solve the technical problems of how to realize fire early identification, fire intelligent prediction and evacuation path dynamic planning, improve fire identification accuracy and response speed, and improve the safety and reliability of a building. According to the technical scheme, the method comprises the steps of BIM modeling, wherein a high-precision three-dimensional space model is established based on building information modeling, and the high-precision three-dimensional space model is deeply coupled with an FDS fire numerical simulation engine; multi-modal data acquisition: performing data acquisition by adopting a visual, environmental and spatial multi-modal sensor, and performing feature fusion on the acquired data by using a multi-channel deep convolutional network to generate unified space-time fire characterization; a video-sensing-geometric data collaborative sensing network is constructed on the basis of space-time fire characterization, so that the recognition robustness in a complex environment is improved; intelligent identification and decision making; performing early warning control linkage; and post-disaster assessment feedback.

The invention discloses a disaster risk assessment early warning method and system based on multi-source heterogeneous data, and relates to the technical field of data fusion and processing, and the method comprises the steps: obtaining a multi-source heterogeneous disaster risk data stream; constructing a normalized disaster feature matrix; generating a multi-source feature incidence matrix; generating a disaster risk index set according to the disaster risk prediction model; generating a regional disaster risk distribution thermodynamic diagram; constructing a disaster risk time sequence model; and generating a disaster research and judgment report, and performing visual early warning on the disaster research and judgment report through a three-dimensional simulation technology. The technical problem that traditional disaster risk assessment early warning depends on a single or few data sources, data are one-sided and lack of real-time performance, and consequently assessment early warning is inaccurate is solved, comprehensive real-time analysis based on multi-source heterogeneous data is achieved, the accuracy of disaster risk assessment and the timeliness of early warning are improved, and the risk assessment early warning efficiency is improved. And a reliable basis is provided for disaster emergency decision making.

The invention discloses a fault co-seismic sliding surface inversion method and system based on Beidou and artificial intelligence. The method comprises the steps of collecting Beidou data and seismic waveform data of a target area in real time, and performing preprocessing to extract co-seismic displacement and waveform features; fusing the Beidou deformation features and the seismic wave features to form a multi-modal feature vector; a fault prediction model is constructed based on a graph neural network, forward modeling and historical earthquake example data training are utilized, and multi-modal features are input to obtain preliminary fault sliding distribution prediction; rapid optimization under physical constraint is carried out through the elastic dislocation model, and a final fault sliding model conforming to the geophysical law is obtained; finally, uncertainty quantification is carried out, and a visual product of the dynamic process including sliding distribution, seismic moments and fault deformation is generated. According to the system, full-process automatic processing is achieved through cooperation of all the units, the inversion speed, precision and physical credibility are improved, and reliable support is provided for earthquake emergency response and disaster assessment.

The invention discloses an emergency rescue resource scheduling method based on the Internet of Things, and the method comprises the steps: collecting disaster area environment parameters, images and personnel distribution data through Internet of Things equipment, and associating historical disaster cases to construct a multi-source heterogeneous data set; extracting disaster characteristics through preprocessing, dynamically distributing data source weights, and generating a disaster assessment matrix; outputting a resource demand peak value based on the time sequence prediction model and performing dynamic correction; constructing a deep reinforcement learning model optimization decision strategy, and generating a scheduling scheme in combination with priority matching and an improved A * algorithm; according to the method, the disaster sensing precision and the demand prediction accuracy are improved, the scheduling decision adaptability is enhanced, the response time of a high-priority region is shortened, and the efficient demand of emergency rescue in a complex disaster is met.

The invention discloses a transmission tower vulnerability analysis method and system based on seismic simulation, and belongs to the technical field of electric power facility disaster prevention, and the method comprises the steps: correcting bedrock seismic oscillation parameters based on equivalent shear wave velocity, and generating a high-precision earth surface PGA distribution diagram; automatically positioning a key node layer through eigenvalue decomposition, reducing the order of the three-dimensional model of the power transmission tower into a two-dimensional series multi-degree-of-freedom model, and outputting a layer mass vector, a condensation stiffness matrix and rod piece bending stiffness; performing time-history analysis by using the corrected PGA-driven reduced-order model to generate an earthquake damage probability; the multi-source remote sensing data and the PGA are fused, the landslide space probability is output through a deep learning model, and the impact strength and the landslide kinetic energy are calculated to judge the landslide damage probability; and based on the dual-threshold condition triggering probability union set, calculating a comprehensive damage probability, and generating a three-dimensional vulnerable curved surface. According to the method, the problems of high missing report rate and low calculation efficiency of single disaster assessment are solved, and minute-level accurate early warning of the composite disaster risk of the power transmission tower is realized.

The invention discloses a stochastic finite fault source model establishment method, which comprises the following steps of S1, determining a geometrical shape of a seismic fault based on regional geological data and seismic monitoring data, and estimating a non-planar fault parameter and a fault plane average sliding amount in combination with historical data and an empirical formula; s2, estimating distribution and sliding strength of concave-convex bodies and obstacles on the fault surface according to historical seismic inversion data, and obtaining the sliding amount of each sub-fault by randomly disturbing the average sliding amount; s3, the discrete fault plane serves as a sub-fault unit, and after an initial fracture point and the average fracture speed are determined, random fracture initial time conforming to normal or power law distribution is generated based on a random number method; s4, calculating the seismic oscillation time history of each sub-fault by adopting a random point source method, superposing contributions, introducing the influence of a shallow velocity structure V30, and generating a three-dimensional seismic oscillation field; and S5, comparing actual strong earthquake record adjustment parameters, and outputting a multi-risk-level earthquake motion parameter evaluation result after iterative optimization. According to the method, by fusing randomness and empirical data, the simulation precision of the seismic source model on the uncertainty of the seismic process is improved, the influence of the potential maximum-magnitude earthquake possibly occurring in the active fault zone in the future can be effectively estimated, the seismic oscillation simulation precision and the calculation efficiency can be improved, and the method is suitable for engineering structure aseismic design and seismic disaster evaluation.

The invention provides a disaster situation dynamic assessment method and system based on multi-source data fusion, and relates to the technical field of disaster assessment, and the method comprises the steps: collecting disaster multi-source parameters according to a plurality of data sources, and obtaining a first disaster data set; performing time scale unification by adopting a time alignment algorithm to obtain a second disaster data set; mapping the second disaster data set to a grid space framework by adopting a spatial interpolation algorithm to obtain a third disaster data set; performing multi-source data correction fusion to obtain a disaster fusion data set; inputting the disaster fusion data set into a disaster situation dynamic evaluation model to obtain a disaster situation dynamic evaluation result; and constructing a disaster situation map. According to the disaster situation assessment method and device, the technical problem that the accuracy of disaster situation assessment is poor due to the fact that different types of data are different and multi-source heterogeneous data are difficult to integrate is solved, and the accuracy and timeliness of disaster situation assessment are improved by fusing the multi-source data and data correction.

The invention relates to the technical field of intelligent disaster assessment, in particular to a multi-source heterogeneous disaster investigation and assessment model elastic integration method and system, and the method comprises the following steps: generating an assessment reference layer based on multi-source heterogeneous data, recognizing abnormal fluctuation and influence factor deviation, screening key nodes and combinations, and extracting a propagation trajectory and a dynamic path. And analyzing trend and efficiency superposition characteristics, and outputting a disaster behavior linkage response control instruction set. According to the method, a space-time identification mechanism is introduced through structured recombination of multi-source heterogeneous data, so that multi-dimensional unified expression and time sequence continuity of disaster information are realized, abnormal intervals and factor deviations are identified, interference data are effectively eliminated, disaster identification precision and evaluation consistency are improved, and positioning capability is enhanced; the disaster diffusion direction is determined through area offset and trajectory recognition, recovery efficiency information is superposed to analyze response association, a control instruction set with spatial-temporal characteristics is output, and closed-loop regulation and control of disaster assessment, prediction and response are achieved.

The invention discloses a crop rainstorm disaster quantitative risk assessment method based on a CNN-LSTM hybrid neural network, and the method comprises the steps: obtaining multi-source data, carrying out the standardization processing, and carrying out the time-space superposition to construct a three-dimensional feature matrix; based on a CNN-LSTM hybrid neural network model, respectively extracting a spatial feature vector and a time sequence feature vector of the disaster situation data, completing fusion to obtain a fused feature vector, and outputting a risk probability and loss intensity; and continuing to comprehensively consider the crop loss caused by the risk probability and the loss intensity, completing the construction of a rainstorm disaster-crop coupling loss function, and finally generating the crop rainstorm disaster risk probability, the main crop loss intensity and the rainstorm disaster-crop coupling total loss based on the intelligent grid rainfall and wind speed forecast data. According to the method, the quantitative loss of crops is estimated by integrating rainstorm disaster-crop coupling dynamic response, and effective transformation from static disaster assessment to dynamic multi-source fusion response assessment is realized.

The invention discloses an earthquake service integrated management method, and the method can achieve the deep mining of a complex internal relation between historical earthquake service associated data and the final number of casualty population through the powerful nonlinear fitting capability of deep learning. The prediction precision of the casualty population prediction model trained through a large amount of historical earthquake service associated data and the corresponding casualty population quantity is far higher than that of a traditional method, a reliable basis is provided for scientific decision making, unified data assets with an earthquake event as a core are constructed through associative storage of a prediction result and original service data, and the prediction accuracy of the casualty population prediction model is improved. Therefore, information islands are broken, disaster assessment results are deeply integrated into the whole earthquake service process, and a unified and authoritative data entry is provided for subsequent work such as emergency command, resource scheduling, information issuing and post-disaster assessment.

The invention discloses a landslide-debris flow disaster chain assessment method based on a graph neural network, and the method comprises the steps: generating a composite ground feature unit through the watershed segmentation of a composite curvature field and hydrological analysis, abstracting the composite ground feature unit as a geographic node, optimizing a multi-source environment factor through mutual information screening, constructing a directed weighted graph in combination with spatial similarity, and carrying out the reconstruction of a landslide-debris flow disaster chain. An improved GraphSAGE model which introduces neighbor weight, self-loop and residual error information and cancels random sampling is adopted, a sample set adaptive to a less-data area is constructed through a unit splitting-feature matching-terrain fitting mechanism, and model training and risk prediction are completed. The method solves the problems of single disaster assessment, strong sample dependence and high missed judgment rate in the traditional technology, has excellent performance indexes, effectively reduces the missed judgment risk of a high-risk area, and provides accurate and efficient technical support for disaster chain risk assessment of a small watershed in a small-data mountainous area.

The invention discloses a multi-disaster-bearing body marine disaster risk assessment method based on a sensitive matrix, and relates to the technical field of marine science, the method comprises the following steps: defining a marine disaster assessment area, and determining multiple disaster-inducing factor types in the assessment area; and sorting the distribution characteristics of various disaster-bearing bodies in the evaluation area, and determining an evaluation object. According to the multi-disaster-bearing body marine disaster risk assessment method based on the sensitive matrix, the sensitive matrix of the disaster-inducing factors and the disaster-bearing bodies is constructed, the sensitivity difference of different disaster-bearing bodies to the disaster-inducing factors is quantified, the loss difference of different disaster-bearing bodies under similar disasters can be accurately described, the limitation of a traditional single disaster assessment method is avoided, and the risk assessment accuracy is improved. The method comprehensively analyzes the potential threats of disasters to different disaster-bearing bodies, improves the overall precision of risk assessment, and guarantees that the vulnerability degrees of the different disaster-bearing bodies are accurately reflected when the different disaster-bearing bodies face specific disaster-inducing factors through sensitive matrix normalization.

The invention relates to the technical field of unmanned aerial vehicle remote sensing, in particular to a geological disaster monitoring method and system for unmanned aerial vehicle surveying and mapping, and the method comprises the following steps: extracting a terrain anomaly data set based on monitoring unit point cloud and texture, screening a behavior deviation region according to gradient and texture, and recognizing and matching an anomaly unit in combination with stress and slope direction. And calling a risk table comparison response to preferentially screen a monitoring unit needing to be adjusted according to the evaluation deviation calibration abnormity level, and outputting a geological monitoring intelligent collaborative scheduling instruction set. According to the method, a linkage identification path is constructed by extracting point cloud density sudden change and texture anomaly information in a monitoring unit on a space axis, the capture capability of fine earth surface changes is enhanced, the space identification accuracy of an abnormal area is improved, risk grade judgment is refined according to a terrain stability curve deviation trend, and the reliability of disaster assessment is enhanced. Corresponding calibration of the monitoring unit and the geological function diagram is achieved, the response range adjusting efficiency is improved, and it is ensured that the dispatching instruction has higher pertinence.

The invention specifically discloses a high-precision remote sensing image semantic segmentation method based on a pyramid decoder and a multi-scale feature interactive attention module. The method comprises the following steps: firstly, extracting three complementary level features of low-level details, high-level semantics and an original image through a lightweight backbone network; and then inputting the multi-scale features into a network taking an encoder-decoder structure as a core, introducing a pyramid residual context module in a decoding stage, and explicitly enhancing high-level semantics by using pyramid pooling and residual nonlinear transformation to suppress redundant information. The multi-scale feature interactive attention module adaptively calculates space-channel weights of low-level details, high-level semantics and original features, so that differential fusion is realized, and feature conflicts are reduced. And after feature fusion is completed, compensating coding compression loss by optimizing jump connection, and finally outputting a 1024 * 1024 pixel-level semantic segmentation result. The method can be widely applied to high-resolution remote sensing scenes such as urban planning, disaster assessment and environment monitoring.

The invention discloses a fire monitoring-oriented SAR-to-optical image generation method and device, and aims to solve the problems that an SAR image is difficult to interpret and optical remote sensing is shielded in fire monitoring, and an existing generation method is unstable in training, high in calculation overhead, discontinuous in structure and the like. According to the method, an SAR image is acquired and preprocessed, and a pseudo RGB image is constructed; extracting near-infrared and short-wave infrared bands of the multispectral image, calculating an NBR index to generate a fire mask, and constructing a spectrum guide loss function; taking DDPM as a generation trunk, embedding a cross-domain translator and combining a hierarchical structure guide mechanism to perform weighted fusion on global and local reconstruction results; and outputting the optical style image. The method can generate optical images with clear details, consistent spectrums and coherent structures under the shielding of smoke and clouds, balances the generation quality and efficiency, improves the accuracy of fire monitoring and post-disaster evaluation, and is suitable for multi-scene fire monitoring.

The invention relates to a disaster prevention emergency material distribution method and device, equipment and a medium. The method comprises the following steps: acquiring multi-source heterogeneous data of a disaster area; performing standardization and spatial fusion processing on the multi-source heterogeneous data to obtain standardized spatial grid data; based on the standardized space grid data, analyzing the material demand urgency degree of each space grid to obtain demand urgency degree data; generating initial material distribution scheme data according to the demand urgency degree data and preset material inventory data; and based on the initial material distribution scheme data, in combination with preset transportation network data, carrying out scheme adjustment through a predefined multi-objective optimization rule, and generating a material distribution scheme. By adopting the method, the problems of unreasonable distribution and low decision-making efficiency caused by data splitting and dependence on experience in a traditional mode are solved, the integration from disaster assessment to transportation scheduling is realized, the decision is quickly optimized, and the overall efficiency of emergency response is improved.

The invention discloses a flood water depth estimation method based on deep learning and an FwDET model. The method is used for solving the problems that in a traditional method, multi-source data consistency is poor, change detection precision is low, water depth estimation is complex, and a structured result is lacked. According to the method, high-precision flood recognition is achieved through double-time-phase same-source SAR remote sensing images, the depth of the flood is rapidly estimated in combination with DEM data, an automatic processing flow is constructed, efficient, accurate and extensible flood disaster monitoring is achieved from pixel-level recognition to vectorization expression, and reliable data support is provided for disaster assessment and emergency response.

The invention discloses a flood disaster assessment method and system based on satellite remote sensing data, and relates to the technical field of flood assessment, and the method comprises the steps: distinguishing different earth surface types of a monitoring region, collecting remote sensing data, and obtaining hydrological information, historical flood data and terrain elevation information; and dynamically updating the data updating frequency and precision of the monitoring area according to the flood risk of the area. According to the method, the difference value between the layers is calculated through the layered pyramid data, high-frequency detail information of the data can be effectively extracted and reserved, the space complexity of the data of each layer is quantized, the fusion weight of the data of each layer is dynamically adjusted, the multi-source data is reasonably fused under different scales and feature complexity, and the fusion efficiency is improved. The actual feature complexity of the data is reflected by introducing the data variability, the limitation of a simple weight distribution method is avoided, highly-adaptive multi-scale data fusion is realized, and the change of the water depth and the water velocity in different directions can be comprehensively considered through a three-dimensional shallow water equation model.

The invention provides a dynamic disaster assessment method and system based on iron tower data, and relates to the technical field of GIS data processing, and the method comprises the steps: obtaining the iron tower state data of a target region, collecting multi-dimensional basic information, determining a plurality of critical regions based on the target region, carrying out the classification according to disaster levels, adding disaster level identifiers, and carrying out the clustering analysis. The method comprises the following steps: drawing up a disaster aggregation map, carrying out risk correction on the disaster aggregation map through iron tower state data and multi-dimensional basic information, determining a disaster influence level and identifying a key risk area, and carrying out resource allocation management in the disaster aggregation map according to the disaster influence level and the key risk area. The technical problems of low resource allocation decision accuracy and low rescue efficiency caused by inaccurate disaster assessment results in the prior art are solved. The technical effects of improving disaster assessment accuracy, ensuring scientificity and pertinence of resource allocation work and further improving rescue efficiency are achieved.

The invention belongs to the technical field of building boundary segmentation, and provides a remote sensing image building boundary segmentation method based on a U-net network model, and the method comprises the steps: extracting a gradient magnitude image through a Sobel operator, generating a binary boundary mask, dynamically adjusting the weight of a convolution kernel in combination with a gradient direction, and enhancing the feature extraction capability of a boundary region; boundary artifacts output by cavity convolution are suppressed through a high-frequency residual boundary mask, an artifact area is restored in combination with gradient direction constraint, and original detail features are reserved; calculating complexity scores based on boundary density and tortuosity, adaptively selecting voidage, and balancing global context and local detail capture capability; extracting a high-frequency component through a Laplace operator, marking an artifact point by combining a dynamic threshold value, and repairing an abnormal region by utilizing a neighborhood gradient direction; the method solves the performance bottleneck of a traditional method in a boundary fuzzy, artifact interference and receptive field fixed scene, and is suitable for remote sensing image analysis tasks such as urban planning and disaster assessment.

The invention discloses a wheat waterlogging accurate identification and disaster assessment method based on a multi-temporal remote sensing image, and relates to the technical field of image processing. Periodically acquiring remote sensing images in the region to obtain a remote sensing image set, and performing feature extraction to obtain a plurality of feature points; constructing according to each feature point to obtain a feature matrix, and adding the feature matrix to the remote sensing image to obtain a positioning image set; sampling the positioning images in the positioning image set at a preset moment to obtain a plurality of feature maps; and performing alignment according to the feature matrixes in the feature maps to obtain a target area, and performing assessment to obtain a disaster degree. Remote sensing images are collected periodically, stable feature points are extracted to construct a feature matrix, feature map sampling in the front, middle and later periods of a disaster is combined with feature matrix accurate alignment, accurate locking of a target area and scientific assessment of the disaster degree are achieved, the problem that a disaster-affected area is difficult to position accurately due to flood interference of multi-temporal remote sensing is solved, and the disaster-affected area positioning accuracy is improved. The objectivity, reliability and timeliness of disaster assessment are improved.

The invention relates to a disaster area three-dimensional point cloud modeling method based on unmanned aerial vehicle cluster cooperation. The method comprises the following steps: carrying out regional division on a disaster area based on different terrain types; based on the divided areas, performing task allocation on the unmanned aerial vehicle cluster by adopting a task allocation algorithm to obtain a flight path of each unmanned aerial vehicle; based on the flight path, sensor data of the unmanned aerial vehicle in the flight process is obtained, the integrity of the collected data is detected in real time, and a disaster area data set is obtained; and processing the disaster area data set by adopting a three-dimensional reconstruction algorithm to obtain a disaster area three-dimensional point cloud model. The system can effectively cope with a complex terrain environment, achieves the precise analysis and comprehensive data collection of a disaster area, and provides important support for disaster assessment and rescue decision making.

The invention relates to a semantic change detection method based on text assistance and comparative learning. The method comprises the following steps: 1, acquiring remote sensing images acquired in different time phases in the same area, and inputting a twin multi-scale encoder with shared parameters to extract multi-stage features; 2, a pre-training vision-language model and a text encoder are introduced in the training stage, a text describing changes is generated, high-level visual features are injected, migration features are obtained, and reconstruction constraints based on comparative learning are constructed; 3, constructing a context and channel perception fusion module to carry out adaptive fusion on the multi-scale features; 4, designing a multi-scale decoder to recover the spatial resolution and outputting a semantic change graph; and 5, performing end-to-end optimization on the network by adopting a joint training target consisting of spatial mask supervision loss and reconstruction loss. The method is suitable for scenes such as remote sensing monitoring, disaster assessment and urban dynamic analysis.

The invention discloses a vehicle-mounted micrometeorological monitoring and camping site disaster early warning system and method, and belongs to the technical field of vehicle-mounted meteorological monitoring, and the system comprises a data collection and preprocessing module which is used for obtaining and calibrating multi-source meteorological and geographic data; the microclimate modeling module is used for generating a microclimate grid with a real-time weather predicted value by taking local microclimate data as a benchmark and fusing cloud and geographic data; the disaster risk assessment module performs quantitative risk assessment on camping disasters such as flood, tree smashing, landslide and thunderstorm based on the data of the microclimate grid; and the early warning generation module generates visual early warning information including a disaster icon, a risk level, time information and a risk avoiding instruction according to the quantitative risk assessment result. According to the invention, by optimizing the layout of the sensors and introducing a terrain correction interpolation algorithm and scene disaster assessment, the meteorological monitoring precision in a mobile environment is improved, and timely early warning of camping field micro-scale disasters is realized.

The invention provides a flue-cured tobacco disaster assessment method based on conventional meteorological element calculation, and the method comprises the following steps: S1, collecting meteorological data of a target region, including wind speed, precipitation, environment temperature and air humidity parameters; s2, wind disaster assessment: identifying a wind disaster event by setting a multi-stage wind speed threshold, establishing a continuous wind disaster day number tracking mechanism, triggering basic wind disaster judgment when detecting that the wind speed exceeds the minimum threshold, and determining the wind disaster according to the dynamic association relationship between the wind speed intensity and the continuous day number; when the number of continuous wind disaster days reaches a preset critical value, disaster grade jump calculation is automatically executed; and S3, drought disaster assessment: calculating a crop moisture supply and demand balance index based on an improved evapotranspiration model, setting a dynamic moisture threshold according to different growth periods of the flue-cured tobacco, and activating a progressive disaster upgrading program by monitoring the coupling effect of a moisture deficit index and drought duration in real time when the combination of the moisture deficit index and the drought duration exceeds a staged judgment standard.

The invention discloses a multi-temporal optical remote sensing image robust registration method based on multi-scale joint similarity measurement, and the method comprises the steps: 1, inputting a dual-temporal image, and obtaining uniformly distributed control point pairs through SIFT extraction and interactive manual auditing; 2, constructing a global dense displacement field by using a thin-plate spline, completing coarse registration and partitioning according to an overlapping strategy; 3, performing three-stage coarse-to-fine template matching on each image block, inheriting an initial displacement value step by step, and realizing radiation difference and geometric deformation synchronous self-adaption by taking a product of normalized mutual information and a normalized cross correlation coefficient as joint similarity measurement; and 4, performing sub-pixel-level deformation correction on the reliable displacement field by using a local thin plate spline, and outputting a registration image and metadata. According to the method, a global sparse-local dense framework and a multi-scale progressive + joint measurement + quality constraint mechanism are coupled, and a high-robustness and high-precision registration solution is provided for quantitative remote sensing application such as change detection and disaster assessment.

The invention discloses a typhoon disaster assessment method based on WRF adaptive nesting and machine learning. The typhoon disaster assessment method comprises the steps that typical typhoon examples in a research area are selected, and weather, disaster loss and exposure data in the typhoon period are collected and preprocessed; evaluating a plurality of candidate thresholds under the constraint of the existing typhoon disaster wind speed threshold, and preferentially determining a disaster-causing threshold; constructing multi-layer WRF nesting to downscale a wind field, recognizing an over-threshold region along a typhoon path according to a determined disaster-causing threshold, performing high-resolution encryption solution in the over-threshold region, checking the over-threshold region, and obtaining the maximum wind speed of the region; matching the over-threshold region with an exposure data space to form an exposure degree, taking the maximum wind speed of the region and the exposure degree as input, and taking a historical loss rate as output, so as to establish a loss rate intelligent prediction model for vulnerability quantification; and calculating the typhoon disaster risk by integrating the disaster intensity, the exposure degree and the vulnerability. According to the invention, the accuracy of typhoon disaster risk assessment can be effectively improved, and a reliable basis is provided for graded early warning and emergency decision making.

The application discloses a kind of quick assessment system and method of the influence of flood disaster on wetland ecosystem, and the system includes, animal image acquisition module, for obtaining animal image data;Animal behavior analysis module, for identifying animal behavior based on animal image data, to obtain animal behavior distribution map and animal behavior timetable;Plant remote sensing module, for obtaining wetland plant distribution map based on wetland remote sensing data;Flood data acquisition module, for obtaining water environment parameters when flood disaster occurs;Influence index evaluation module, for evaluating model to determine ecosystem influence index.The application integrates animal image acquisition and analysis, plant remote sensing identification, real-time acquisition of water environment parameters and other multi-source data, and constructs a unified influence index evaluation model, can quickly generate the comprehensive influence index of wetland animals and plants after flood disaster occurs, so that the assessment of disaster influence is timely and effective.

The application belongs to the technical field of icing disaster early warning, and discloses a power transmission line icing disaster early warning method, a system and a storage medium. A weather prediction model is constructed based on a convolutional neural network, current meteorological data is input into the trained weather prediction model to output meteorological prediction data, extreme weather features are extracted from historical meteorological data, and an extreme weather comprehensive influence factor is extracted and constructed. The icing thickness of the power transmission line is calculated through a physical model, and the icing type and local icing density are obtained through icing shape recognition. The results are input into a preset icing prediction model to output icing prediction data. The icing prediction accuracy density function under each sub-interval of the extreme weather comprehensive influence factor is established, the corresponding icing prediction accuracy under the sub-interval of the extreme weather comprehensive influence factor is obtained, the icing prediction correction value under the sub-interval of the corresponding comprehensive influence factor is obtained, and the icing prediction data is corrected for disaster assessment.