130 results about "Disaster assessment" patented technology

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 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 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 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 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 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.

The invention relates to an autonomous data acquisition method and system based on an unmanned aerial vehicle, and belongs to the field of data processing. The method comprises the following steps that: the unmanned aerial vehicle performs macroscopic scanning and candidate area discovery, and outputs a candidate area set; performing causal reasoning and target confirmation based on the candidate region set to obtain a target region set; performing adaptive fine acquisition based on the target region set to obtain a fine data set; performing three-dimensional semantic reconstruction based on the refined data set, and outputting a final semantic tag and a three-dimensional semantic scene model; and carrying out risk deduction and decision report based on the final semantic label and the three-dimensional semantic scene model. According to the method, an autonomous data acquisition method fusing geometric and semantic significance analysis, causal reasoning, information gain optimization and three-dimensional semantic reconstruction is realized, and an efficient and reliable technical means is provided for disaster assessment and decision support.

The invention relates to the technical field of geological monitoring, in particular to a flood disaster geological monitoring method based on satellite images. By fusing optical images, SAR images and open source geographic data and combining deep learning and semantic information, high-precision automatic registration of multi-modal images, accurate identification of flood ranges, extraction of core disaster-bearing bodies and assessment of disaster damage levels are realized, a whole-process automatic monitoring chain is formed, and the monitoring efficiency is improved. And efficient and accurate technical support is provided for flood disaster emergency response and disaster situation evaluation.

The application discloses a kind of based on spatial intelligence's active fault three-dimensional model automatic construction method, belong to geological exploration and earthquake engineering technical field;Method includes: collection data, analysis and extract minimum complete subdirectory;Through adaptive threshold hierarchical clustering combined with improved RANSAC algorithm, the exclusive small earthquake cluster of each fault is automatically identified;Based on local weighted regression, three-dimensional automatic slice is made to small earthquake cluster, and each profile fault interpretation line is fitted by moving least square method;Finally, active fault three-dimensional fine model is constructed;Test model rationality and output model file;The application realizes the automation, quantification of fault modeling whole process, can be fused multi-source data and realize multi-element constraint modeling, adapt complex fault zone modeling, and the fine three-dimensional model constructed can provide key data support for fault present-day deformation inversion, three-dimensional potential source research, earthquake geological disaster assessment, with higher engineering application value.

This invention relates to a flood inundation monitoring method and apparatus based on multi-level cross-modal fusion of optical and SAR images. The method first acquires and preprocesses pre-disaster optical images and post-disaster SAR images to construct training samples. Through three core modules—cross-modal adaptive interactive fusion, frequency-domain adaptive dual-stream fusion, and hierarchical multi-interactive fusion—it achieves complementary deep features and modeling of optical and SAR images, improving feature robustness and boundary accuracy. The cross-modal adaptive interactive fusion module enhances inter-modal complementarity, the frequency-domain adaptive dual-stream fusion module balances consistency and boundary accuracy through high- and low-frequency modeling, and the hierarchical multi-interactive fusion module considers both global semantics and local details, further improving the model's adaptability to different scenarios. This invention can effectively improve the accuracy of flood monitoring, enhance extraction performance in complex scenarios, and provide reliable methodological support for flood inundation monitoring, emergency response, and disaster assessment, with broad application prospects.

The invention discloses a flood influence range extraction method and system based on a convolution and state space model, and belongs to the technical field of remote sensing image processing and disaster monitoring. Comprising the following steps: acquiring remote sensing image data of a to-be-detected area, and performing preprocessing; inputting the preprocessed remote sensing image into a multi-stage and multi-branch hierarchical feature extraction encoder; a channel attention and space attention mechanism of an encoder is extracted by using hierarchical features, and dynamic weighting and fusion are carried out on local and global features of different scales; and sending the fused multi-scale feature map into a decoder for up-sampling, and finally outputting a pixel-level binary segmentation mask of the flood influence range. The method has the advantages of being high in calculation efficiency, accurate in edge positioning and high in anti-interference capacity, and is suitable for actual services such as emergency monitoring, disaster situation evaluation and post-disaster reconstruction.

The present application belongs to the technical field of tunnel engineering disaster assessment, and provides a sanding dolomite tunnel disaster risk intelligent assessment method and system, which comprises the following steps: performing risk grade jump analysis through historical disaster risk assessment data, identifying risk grade jump assessment, and statistically judging whether the current model has a jump phenomenon; if so, performing nonlinear coupling analysis on the variables, determining whether the risk jump is caused by nonlinear coupling of the variables, and constructing a nonlinear variable set; and judging the nonlinear relationship identification ability of the current model through a nonlinear identification index; if the identification ability is weak, a coupling-superposition double module model is constructed with coupling increment as the link; and finally, according to the comparison result of the current variables and the nonlinear variable set, the corresponding model is dynamically called to complete real-time risk assessment, solving the problems of ignoring variable nonlinear coupling, frequent risk grade jumps and low assessment accuracy of the existing model.

The invention discloses an ARM (Advanced RISC Machines) architecture-oriented CGFDM (China General Microbiological Fused Deposition Modeling) seismic wave solver for hybrid precision and vectorization collaborative optimization and a simulation method, and aims to break through the bottleneck of memory bandwidth and calculation efficiency of ARM platform seismic simulation. According to the method, firstly, an elastic wave equation is reconstructed in a dimensionless mode, key physical quantities are made to adapt to the FP16 range, the mixed precision strategy of FP16 storage and FP32 calculation is adopted, and on the premise that precision is guaranteed, about 50% of memory consumption is reduced. And secondly, for ARM scalable vector expansion (SVE), the CGFDM differential template is mapped to a vector register, SIMD parallel computing is realized, and the throughput is remarkably improved. Finally, the FP16-SVE solver realizes data rearrangement and conversion between the FP16 and the FP32, the problems of alignment and efficiency in mixed precision operation are solved, and synchronous improvement of storage and calculation efficiency is realized. Tests show that the method has the advantages that while the precision is maintained, the memory is halved, nearly three times of speed-up ratio is obtained, and an efficient scheme is provided for large-scale earthquake simulation and real-time disaster assessment of an ARM platform.

The invention discloses a method for identifying and delineating a slope debris flow risk prevention area of a steep slope, and the method comprises the steps: collecting topographic data, geological data, meteorological data and historical disaster data of a needed area, and carrying out the arrangement and field rechecking of the collected data; based on the collected data, slope debris flow dynamic parameters are determined and analyzed, and the parameters comprise the gradient, the flow depth, the density, the flow velocity and the volume; based on data and parameter analysis, a peak impact pressure model is constructed, impact damage resistance thresholds of different types of disaster-bearing bodies are determined, and a disaster-causing range is quantified; based on the impact pressure obtained through calculation and the determined shock resistance threshold value of the disaster-bearing body, a high-risk area, a medium-risk area and a low-risk area are divided in combination with the quantified disaster-causing range, the method constructs a model suitable for the debris flow impact pressure of the steep slope surface, determines the shock resistance threshold value of the disaster-bearing body and quantifies the single disaster-causing range, and the risk prevention area is divided; and a basis is provided for disaster assessment.

The invention discloses a multi-disaster concurrent ocean disaster risk assessment method based on a complementary set superposition algorithm, relates to the technical field of ocean disaster risk management, and aims to clarify an ocean area to be researched, determine a plurality of disaster-inducing factors and disaster-bearing bodies, collect data of the disaster-bearing bodies and calculate the storage proportion of the data. According to the multi-disaster concurrent marine disaster risk assessment method based on the complement set superposition algorithm, the complement set superposition algorithm is introduced, so that the composite influence of multiple disaster-inducing factors on different disaster-bearing bodies can be comprehensively considered, the limitation of a traditional single disaster assessment mode is avoided, the risk value under the single disaster-inducing factor is calculated, and the risk value under the single disaster-inducing factor is calculated; and the superimposed effect under different disaster combinations is quantified through the sensitivity matrix, so that the actual risk state under a multi-disaster concurrent scene is reflected more accurately, more reasonable support is provided for disaster early warning and emergency response in coastal regions, and the accuracy and reliability of risk assessment are effectively improved.

The invention provides an explosion discharge disaster intelligent evaluation method based on a convolutional neural network, and relates to the technical field of combustible gas explosion protection. The method comprises the following steps: acquiring an explosion discharge disaster assessment multi-dimensional data set; according to the obtained multi-dimensional data set, determining an explosion discharge disaster evaluation model based on a convolutional neural network model; wherein the convolutional neural network model is used for learning and internalizing a high-dimensional mapping relationship between a multi-source working condition parameter and a disaster intensity field in an explosion discharge physical process; acquiring multi-source working condition parameters of a to-be-evaluated environment; and inputting the multi-source working condition parameters of the environment to be evaluated into the explosion discharge disaster evaluation model to obtain the on-way distribution of the explosion discharge peak overpressure, the peak temperature and the peak wind speed, thereby realizing the efficient and accurate prediction of the disaster peak at any position on the whole propagation path after the explosion discharge.

The invention discloses a remote sensing image unsupervised change detection method and system based on structure perception and noise enhancement. According to the method, shared feature representation is extracted from a dual-temporal remote sensing image, a structure perception contrast learning mechanism is introduced to enhance the perception capability of a model for real geographic structure change, noise disturbance consistency constraint is designed to avoid an optimization shortcut problem, and a frequency attention decoding mechanism is adopted to finely depict a change region boundary. The system comprises a preprocessing module, a feature coding module, a structure sensing module, a noise disturbance module, a frequency attention decoding module and an unsupervised optimization module. According to the method, under the condition that manual labeling is not needed, the problems that an existing unsupervised change detection method is short in optimization, insufficient in semantic representation capacity, not fine in boundary description and the like are effectively solved, the accuracy and robustness of change detection are improved, and the method is suitable for the fields of urban expansion monitoring, disaster assessment, environment change analysis and the like.

The present application relates to a kind of fusion lightweight backbone network and remote sensing change detection method of convolution decoding, including using backbone network feature extraction module, for extracting multi-scale features with strong representation ability from input dual-phase remote sensing image;CNN-Decode feature alignment module, using convolutional coding structure is spatially aligned to dual-phase feature, avoid the feature confusion problem of traditional attention mechanism;And change detection module, based on the feature after alignment realizes pixel-level change identification.The present application is optimized by double-path cooperation, while enhancing the feature expression ability of model, it enhances spatial alignment accuracy, effectively improves the integrity and boundary accuracy of change detection under complex scene, under the premise of keeping low computational complexity, significantly improves detection performance, can be widely applied in urban planning, disaster assessment and environmental monitoring and other fields.

The invention belongs to the technical field of hydrological monitoring data processing, and relates to a two-dimensional drought disaster assessment method and system driven by hydrological cycle variation, and the method comprises the following steps: constructing an anomaly propagation map with variables as nodes and a nonlinear relationship as edges based on a simulation sequence; generating an initial state matrix by using the standardized abnormal value of the real-time observation data; a stable coupling abnormal state is obtained by iteratively updating a node phase; calculating a cross-scale system energy index, and decomposing the constructed instantaneous information flux field to generate a dominant propagation phase rate index; and finally, mapping the index to a two-dimensional phase space, and generating an evaluation result based on a drought evolution trajectory. The method solves the problems that in the prior art, due to the fact that effective modeling for abnormal propagation time delay is lacked, an early warning window is shortened, evolution misjudgment is caused due to the fact that the capacity for distinguishing one-way dissipation and positive feedback closed loops in the system is insufficient, multi-source data relevance integration is lacked, and the overall dynamic characteristics of the system are difficult to master.

The invention provides a method and device for estimating vegetation growth vigor change influenced by high temperature-drought composite disasters, and relates to the technical field of disaster assessment, and the method comprises the steps: obtaining historical satellite remote sensing data and historical meteorological station data corresponding to a research area; according to the historical satellite remote sensing data and the historical meteorological station data, identifying a high temperature-drought composite event historically occurring in the research area and corresponding historical risk index data, and determining historical growth period distribution corresponding to the research area; determining a historical growth change based on the high temperature-drought composite event and the historical growth period distribution; and training the machine learning model by taking the historical risk index data as input and the historical growth vigor change as a label to obtain a growth vigor change pre-estimation model which is used for pre-estimating the growth vigor change of the vegetation in the research area. According to the method, the vegetation growth vigor change can be estimated according to disasters.

The invention discloses a disaster emergency response super-computing unmanned aerial vehicle and a use method thereof. The disaster emergency response super-computing unmanned aerial vehicle comprises an unmanned aerial vehicle body, and wings are fixedly connected to the four corners of the unmanned aerial vehicle body. According to the invention, a supercomputing module and a multi-modal intelligent agent platform are deeply integrated, a dynamic computing power distribution strategy of 20% of data preprocessing, 40% of disaster situation evaluation, 30% of resource scheduling and decision assistance and 10% of redundancy is adopted, and the dynamic weight fusion of a Transform model, CNN / U-Net high-precision disaster situation identification and a resource scheduling algorithm with a'rescue strength fatigue coefficient 'are combined. According to the method, efficient processing and accurate decision output of multi-source disaster situation data are realized, the problems that a traditional unmanned aerial vehicle is insufficient in computing power and insufficient in data fusion in a disaster scene and decision suggestions break away from actual rescue requirements are solved, and disaster situation assessment precision and resource scheduling rationality are greatly improved.

The invention discloses a disaster assessment method and device based on a satellite remote sensing image and a storage medium. The method belongs to the technical field of disaster risk assessment. The method comprises the steps that according to a road network topological structure of a target city, an initial connected graph with all areas in the target city as nodes and roads between the areas as edges is constructed to serve as a first graph structure; based on real-time disaster information obtained by satellite remote sensing, identifying roads interrupted due to disasters in the first graph structure, and removing edges corresponding to the interrupted roads from the first graph structure to obtain a second graph structure; determining a graph Laplacian matrix corresponding to the second graph structure, and extracting algebraic connectivity of the graph Laplacian matrix; and inputting the algebraic connectivity into a pre-trained connectivity evaluation model, outputting a connectivity probability value, and determining the overall disaster degree of the target city based on the connectivity probability value. According to the method, dynamic and quantitative analysis is carried out on the communication state of the urban global road network, so that accurate assessment of the disaster degree of the whole city is realized.