241 results about "Disaster assessment" patented technology

The invention belongs to the technical field of earthquake disaster scene recognition, and discloses an earthquake disaster scene recognition method based on deep learning. The method comprises the following specific steps: S1, data acquisition and preprocessing; S1.1, multi-source heterogeneous data acquisition and establishment of a comprehensive database containing seismic waveform data, surface deformation data, building structure data, geographic information data and historical disaster record data; through fusion of a 3D convolutional network, a graph attention mechanism, a space-time LSTM and an adaptive cross-modal attention fusion technology, combined modeling of a seismic waveform space-time evolution law, an earth surface deformation space distribution characteristic, a building group topology vulnerability and disaster chain time sequence association is realized, the characterization capability of a complex nonlinear disaster mode is effectively improved, and the method has the advantages of high adaptability and high reliability. And disaster assessment response time is shortened to a sub-second level through mixed precision quantification and edge computing deployment, and high recognition accuracy is still kept in a scene with strong noise and data missing in combination with a multi-task classifier and a physical constraint verification mechanism.

The invention discloses a forest fire monitoring system based on satellite remote sensing, particularly relates to the field of forest fire monitoring, and is used for solving the problems of incomplete data coverage, inaccurate boundary recognition, difficulty in sheltered area restoration and the like in a forest fire monitoring process, and accurately capturing comprehensive coverage of a fire area through fusion of multi-source remote sensing data. And full space-time monitoring under complex weather and topographic conditions is realized. Dynamic changes of spectrums and vegetation before and after a fire disaster are continuously tracked, and continuous basic data are provided for evaluating the influence of the fire disaster. And in combination with the analysis of spectrum range and thermal signal propagation, the fine identification capability of a fire high-temperature area and an edge area is enhanced, and particularly, the boundary identification of a low-disturbance area is effectively optimized. According to the method, the space-time reconstruction and interpolation algorithm is adopted, data missing caused by smog and cloud shielding is comprehensively restored, the integrity and continuity of the image are ensured, and the accuracy of disaster assessment and decision support is improved.

The invention provides a multi-modal fusion sensing robot dog inspection slope disaster risk assessment method and device and a storage medium, and relates to the field of slope disaster assessment, and the method comprises the steps: obtaining multi-modal sensing data; performing space-time alignment processing on the multi-modal sensing data and then converting the multi-modal sensing data into a voxel coordinate system; performing feature extraction on the multi-modal sensing data after time-space alignment, and mapping each extracted feature to a unified voxel unit to form a three-dimensional voxel structure containing each extracted feature; constructing a three-dimensional multi-modal data fusion model; identifying disaster types based on the three-dimensional multi-modal data fusion model, wherein the disaster types comprise the ground surface crack length, the underground cavity volume, the water seepage point number, the local collapse and bulging area, the vegetation degradation area and the slope gradient; and calculating a risk index based on the identified disaster type in combination with the association degree of the disaster type. By adopting the evaluation method provided by the invention, rapid and efficient evaluation of slope disasters can be realized, and the method has relatively good accuracy.

The invention discloses an extreme rainstorm cascade disaster emergency decision-making method and system fused with multi-source data, and the method comprises the steps: constructing a historical event knowledge graph and a current event dynamic evolution knowledge graph through integrating the multi-source data and using the integrated multi-source data; searching current and subsequent disaster risks and corresponding emergency decision-making schemes in the historical event knowledge graph according to static disaster characteristics of high similarity in attributes of the historical event knowledge graph and the current event dynamic evolution knowledge graph; and dynamically adjusting the emergency decision scheme and the historical event knowledge graph according to feedback information executed by the decision scheme. According to the method, the accuracy and the real-time performance of extreme rainstorm cascade disaster assessment and emergency decision making are improved, and meanwhile, the prediction capability and the adaptive capability are improved.

The invention discloses a regional traffic network dynamic risk assessment method under sudden earthquake influence, which belongs to the technical field of earthquake disaster assessment and comprises the steps of collecting data and preprocessing, constructing a traffic network model, constructing an earthquake vulnerability model and constructing a risk dynamic assessment system. According to the method, seismic parameters, social economy, a regional traffic network and real-time detection data after an earthquake are comprehensively considered when the earthquake occurs, the risk of the regional traffic network under the earthquake can be evaluated more comprehensively, a Monte Carlo simulation method is applied, an earthquake vulnerability model is combined, a damage scene of the earthquake to the traffic network is simulated, and the risk of the regional traffic network under the earthquake can be evaluated more comprehensively. Evaluating the damage probability and the function loss of the traffic facilities; calculating the connectivity of the traffic network model, and dynamically calculating the performance change of the traffic network after the earthquake based on the connectivity index and weight of the traffic network; and evaluating the toughness of the traffic network according to a network performance recovery curve under the recovery strategy, so that the performance change of the traffic network in the earthquake can be dynamically reflected in real time.

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 carbon sink dynamic prediction method, device and equipment based on multi-source remote sensing space-time fusion and three-dimensional point cloud deep learning and a storage medium, and relates to the technical field of remote sensing information processing and ecological environment monitoring, and the method comprises the steps: obtaining space-time fusion data, a vegetation point cloud inversion algorithm and a carbon sink prediction hybrid model; extracting three-dimensional point cloud biomass based on the vegetation point cloud inversion algorithm and the space-time fusion data, and determining vegetation parameter information; and predicting a carbon sink change trend based on the vegetation parameter information and the carbon sink prediction hybrid model, determining a carbon sink prediction result, and controlling a system to complete disaster assessment and risk early warning based on the carbon sink prediction result. According to the method, multi-source remote sensing space-time fusion is carried out to eliminate the space-time resolution difference, the three-dimensional point cloud biomass is extracted, the carbon sink change trend is predicted to realize disaster assessment and risk early warning, the multi-source data fusion precision and the vegetation parameter inversion accuracy are effectively improved, and the method has remarkable environmental benefits and social values.

The invention provides a lodging area determination method and system based on a lodging monitoring spectral index image, and relates to the field of crop form prediction.The method comprises the steps that firstly, a target farmland remote sensing image and a digital surface model are obtained, radiation and geometric correction are completed in combination with GNSS positioning data and meteorological data, and a standardized orthoimage is generated; calculating a vegetation index on the image and carrying out difference to obtain change information; executing opening and closing operation by adopting a structural element adaptive to the image resolution and the crop row spacing, filtering noise according to the area of a connected domain or a pixel number threshold value, and extracting a spectral index abnormal region and a boundary thereof; and then fusing the boundary and the digital surface model under a unified coordinate reference, accumulating the area according to the pixel resolution, and outputting the area of the abnormal region. The method has parameter self-adaption and multi-source data fusion capabilities, can stably obtain a consistent abnormal region area in a multi-resolution and complex field environment, and provides reliable technical support for agricultural condition monitoring and disaster assessment.

The invention is suitable for the technical field of disaster monitoring, and provides a forest fire sample library generation method based on remote sensing inversion, which comprises the steps of image data collection, data preprocessing, overfire area extraction, overfire area post-processing, sample library generation and the like. According to the invention, inversion extraction is carried out on the earth surface change in the remote sensing image, and a whole-process intelligent and automatic solution from fire information acquisition to sample data set production is automatically generated. According to the method, a large-scale fire sample data set can be automatically produced, the time and cost of manual labeling are greatly reduced, and efficient and accurate data support is provided for training and optimization of a fire monitoring and disaster evaluation model. According to the data set generated by the technology, the accuracy, the response speed and the adaptive capacity of a fire monitoring model are remarkably improved.

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 a Mama-based edge-refined remote sensing image semantic change detection method, and belongs to the technical field of remote sensing image change detection. In order to solve the problem of rough prediction edge caused by insufficient optimization of boundary region details in the feature extraction and fusion process of the existing method, the invention provides the following technical scheme: firstly, extracting multi-level features of a dual-temporal remote sensing image by using a twin Mama encoder backbone network; secondly, cross-time-phase feature interaction and difference feature extraction are carried out through a difference module based on Mamba; then, an edge-refined visual state space decoder is adopted, and expansion and corrosion operation and an attention mechanism are fused to reinforce edge information; meanwhile, the learning ability of the model to edge details is improved by combining a loss function strategy of depth boundary supervision and change region supervision. Experiments are verified based on a SECOND data set, the method is superior to an existing mainstream method in the aspects of precision, intersection-to-union ratio, F1 score and other indexes, the boundary precision and semantic segmentation effect of change detection are remarkably improved, and the method is suitable for urban planning, disaster assessment and other high-precision demand scenes.

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 flood recovery method based on a distributed hydrological-hydrodynamic coupling mode. The method comprises the following steps: reorganizing a data set of historical mountain torrent disasters, determining a basin topological structure relationship, and drawing up key nodes of a corresponding basin; on the basis of the key nodes, constructing a hydrological model in the drainage basin above the key nodes, constructing N calculation units based on the data set and according to the topological relation of the drainage basin, and adjusting parameters and calibrating to obtain an optimal flood simulation result and a parameter result; constructing a hydrodynamic model in the flooding area drainage basin below the key node, and constructing the hydrodynamic model to perform simulation analysis on flooding area partition application and flood routing based on the optimal flood simulation result and parameter result driving; the generation, confluence and evolution process of the flood in the drainage basin is reflected more accurately, the application effect of the method in the aspects of flood simulation, early warning and coping is improved, the actual situation of the flood disaster can be restored more accurately in the flood recovery process, and a reliable basis is provided for post-disaster evaluation, reconstruction and planning.

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 relates to the technical field of data processing, in particular to a flood disaster-oriented wireless sensor network multi-modal feature fusion system and method, and provides the following scheme: acquiring multi-dimensional sensing data through a wireless sensor network, constructing a multi-modal feature matrix, and inputting a preset intelligent AI model for disaster assessment. The rainfall intensity change trend is dynamically captured through a self-adaptively adjusted time window and a sliding step length in combination with an LSTM prediction model, and the disaster prediction precision is optimized. The problems of multi-source data fusion, poor real-time performance and low precision in a traditional flood disaster early warning system are solved, and efficient and accurate flood disaster prediction and risk assessment are realized.

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 an earthquake intensity field disaster situation acquisition system, and discloses an earthquake intensity field disaster situation acquisition system, which comprises the following modules: an unmanned aerial vehicle hardware platform, a disaster situation data acquisition module, an intelligent disaster situation analysis module, a disaster situation prediction and decision support module and a disaster situation return and dynamic response module. The unmanned aerial vehicle carries a high-definition camera, a thermal imager, a laser radar sensor and a meteorological sensor, collects image data, thermal imaging data, three-dimensional point cloud data and meteorological data of a disaster area in real time, and carries out disaster identification and evaluation through a deep convolutional neural network. In combination with multi-sensor data, the system can automatically identify disaster information such as building damage degree, fire areas, casualties and the like, intelligently optimizes resource allocation according to disaster assessment results, and ensures that rescue resources can be timely and accurately allocated to most required places of disaster areas.

To provide a measuring pole coordinate axis installation stand that facilitates quickening an examination of a disaster assessment, serves as a coordinate system for preparing a cross-section diagram of a disaster site by photograph point group measuring, and achieves an origin, direction, and contraction scale of the coordinate system with a measuring pole 1.SOLUTION: A coordinate axis support arm for achieving a two-dimensional or three-dimensional coordinate system is imparted a contraction scale by mounting a measuring pole insertion hole or measuring pole fitting groove measuring pole provided in the coordinate axis support arm. In the coordinate axis support arm, a calibration window is provided that allows for visibly recognizing coincidence of a length of the coordinate axis support arm with a boundary of a white and red pattern having white and red separately painted in the measuring pole, and the boundary of the pattern. A measuring pole coordinate axis installation stand includes a magnet for making a direction of the measuring pole coordinate axis installation stand coincide with an azimuth thereof, and a bubble level that makes a tilt of the measuring pole coordinate axis installation stand flush with a horizontal line.SELECTED DRAWING: Figure 2

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 discloses a disaster comprehensive management method and system and a computer program. The method comprises the following steps: acquiring a historical natural information data set; constructing a training decision tree to identify disaster types, and obtaining a disaster type identification model; constructing and training a full-connection neural network to predict a disaster assessment score, and obtaining a disaster assessment score prediction model; natural information data are monitored and acquired in real time, standardization processing and feature extraction are performed, analysis is performed through the trained disaster type identification model and the disaster assessment score prediction model, and disaster types and disaster assessment scores are acquired; acquiring a disaster severity level; and processing by adopting a preset strategy. According to the technical scheme provided by the invention, a plurality of functions are integrated together, so that the management efficiency and the response speed are improved; real-time data acquisition and processing are realized, disaster risks can be found in time, and corresponding early warning and countermeasures can be taken; the artificial intelligence algorithm is used for analyzing disaster data, decision making is assisted, and the accuracy and efficiency of dealing with disasters are improved.

The invention relates to an intelligent identification and evaluation technology for tornado ground disasters, and the technology comprises a data collection and preprocessing module which collects image data and text data of tornado ground disasters from a plurality of data sources, carries out the preprocessing and cleaning of the data, and generates a structured data set; the multi-modal disaster assessment model is based on a VLLM and an advanced visual identification technology, adopts a double-flow network structure and comprises an image feature extraction branch and a text processing branch, the image feature extraction branch extracts key disaster features from a high-resolution image, and the text processing branch deeply analyzes text information related to disaster grade assessment; and the interactive learning mechanism allows the multi-modal disaster assessment model to request feedback to a user after preliminary analysis, and performs self-adjustment and optimization according to the feedback. The invention further provides a map display method. According to the method, the problems of data scarcity, image diversity, low damage evaluation efficiency, rapid processing requirements and model generalization ability can be effectively solved.

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.