390 results about "Natural disaster" patented technology

The invention belongs to the technical field of emergency management, and discloses a natural disaster emergency rescue system based on multi-source sensing information fusion. The system is composed of a multi-source sensing data acquisition module, a data preprocessing and space-time registration module, a cross-modal feature extraction module, a multi-modal information fusion and conflict resolution module, a disaster type identification and grade discrimination module, a disaster influence range prediction and diffusion modeling module, and a dynamic emergency path planning and response plan generation module. A rescue scheduling and command control module; and an emergency feedback and closed loop dynamic correction module. Through multi-source sensing data fusion, cross-modal feature extraction and deep information fusion technologies, a full-space-time and full-process natural disaster emergency rescue system is constructed, comprehensive sensing, accurate recognition and dynamic plan generation of a disaster site are realized, the rescue response speed and decision scientificity are remarkably improved, and the intelligent level of emergency rescue is comprehensively improved.

The invention provides a disaster chain prediction method and system based on infrastructure cascade failure, and the method comprises the steps: S1, carrying out the screening and concrete of extreme natural disasters, and constructing an extreme natural disaster scene which accords with the characteristics of a research region; s2, constructing a single-layer network topology structure of various infrastructures and a cascade failure mechanism based on coupling characteristics of various infrastructures, and constructing an infrastructure multi-layer coupling network conforming to spatial distribution characteristics and actual operation logic of a research area based on the single-layer network topology structure and the cascade failure mechanism; and S3, superposing the constructed extreme natural disaster scene on the infrastructure multi-layer coupling network, and predicting a disaster chain spatio-temporal evolution process under the impact of the extreme natural disaster by taking the infrastructure multi-layer coupling network as a disaster propagation carrier. According to the invention, the disaster chain can be predicted.

The invention provides an electric power system natural disaster early warning and response method based on real-time monitoring, and relates to the technical field of electric power early warning response, and the method comprises the following steps: data analysis and processing: carrying out the analysis and processing of real-time monitoring data in an electric power system through combining a long short-term memory network with a Bayesian network; generating a scheduling scheme: updating the state of the power system in real time and generating an optimal scheduling scheme based on a multi-source data fusion and optimization method combining a Kalman filter and a genetic algorithm; generating an emergency response strategy: simulating the influence of natural disasters on the power system in real time in the virtual model through a digital twin technology and a self-adaptive dynamic planning method, and providing the emergency response strategy; and anomaly detection and self-healing: carrying out anomaly detection and self-healing on the operation state of the power system by combining big data analysis with edge calculation.

The invention provides a hydropower station natural disaster intelligent early warning and emergency decision-making method and system based on multi-source data fusion and dynamic threshold optimization, and the method comprises the steps: collecting the water regimen data of a hydropower station and a drainage basin in real time through the deployment of a water level gauge, a rain gauge, a wind speed sensor and a thunder and lightning monitoring device, and combining weather forecast data and historical hydrological data, thereby achieving the intelligent early warning and emergency decision-making of the hydropower station. And dynamically calculating a disaster threshold value, and generating a graded early warning signal. The system comprises a data acquisition module, a dynamic threshold matching module, an early warning generation module, a case retrieval module, an information issuing module and a response tracking module. The early warning threshold is dynamically adjusted through multi-source data fusion and a machine learning technology, and the early warning accuracy is improved; a historical case library is constructed by using a knowledge graph technology, and intelligent recommendation of a disaster disposal scheme is realized; and early warning information and disposal suggestions are directionally pushed through multiple channels, so that the emergency response efficiency is improved. The conversion from passive response to active early warning is realized, and the influence of natural disasters on the operation of the hydropower station is obviously reduced.

The invention discloses an ice lake feedback monitoring and early warning method based on artificial intelligence and multi-source data fusion, and belongs to the technical field of natural disaster monitoring, and the method specifically comprises the steps: extracting ice lake state feature vectors of multiple spatio-temporal data through cross-modal self-supervised learning, and constructing a three-dimensional virtual ice lake model; based on the feature vectors, constructing a glacier three-dimensional stress field model by using a finite element method, constructing a seepage channel graph model by using a graph neural network, and fusing the features of the glacier three-dimensional stress field model and the seepage channel graph model to form mechanical-seepage coupling state vectors; constructing a pulse neural network to simulate glacier fracture extension and seepage mutation pulse events; constructing a heterogeneous graph federated architecture and outputting a federated weight matrix in combination with a dynamic weighted aggregation strategy; the federal learning weight and the three-dimensional model output result are fused, the outburst posterior probability is calculated through a Bayesian neural network, and a fourth-level early warning signal is generated; according to the invention, the multi-dimensional characterization and coupling process simulation of the state of the ice lake is realized, and the early warning accuracy and timeliness are improved.

The invention discloses a geological disaster risk assessment method and system, and particularly relates to the field of natural disaster risk management, and the method comprises the steps: S101, collecting geological disaster data through a remote sensing technology, ground exploration, meteorological monitoring and a public database, and carrying out the feature extraction and fusion of the collected geological disaster data; s102, correlating different types of geological disaster information with geographical environment and climate factors, and constructing a simulation model to simulate geological disasters; according to the method, the risk of data leakage is avoided, the generalization ability of the whole model is improved, consumption of computing resources is remarkably reduced, the operation efficiency of the model on edge equipment is improved, the reasoning process of the model is accelerated, and a geological disaster department can be helped to efficiently and timely make a rescue plan; the method can improve the decision-making efficiency and reduce the decision-making cost, can provide flexible and effective disaster prevention and reduction measures for decision makers, maximizes the resource utilization rate, and guarantees the continuous effectiveness of the disaster prevention and reduction measures.

The invention discloses a group drought and flood sudden change event identification method and system, and relates to the technical field of natural disaster risk assessment, and the method comprises the following steps: S1, collecting day-by-day rainfall data of a certain time period; s2, calculating an early-stage rainfall index (IAP) based on the day-by-day rainfall data, and standardizing the early-stage rainfall index (IAP) to obtain a standardized early-stage rainfall index (IsAP); s3, based on a run-length theory, respectively screening out a drought event and a flood event; s4, identifying a single-station drought and flood sudden change event, and calculating drought and flood sudden change intensity and sudden change point parameters; and S5, on the basis of the single-station drought and flood sudden change event, identifying a group-occurring drought and flood sudden change event, defining the comprehensive strength of the group-occurring drought and flood sudden change event, and performing grade judgment. The method is clear in physical significance, high in operability and capable of accurately recognizing the group drought and flood sudden turning events.

The invention discloses a distribution network-microgrid toughness improvement robust planning method and device considering natural disasters, and the method comprises the steps: constructing operation models of a distribution network and a microgrid under the natural disasters respectively, and carrying out the integration to obtain a distribution network-microgrid collaborative optimization operation model considering the influence of the natural disasters; generating a random interruption scene of a distribution line, a micro-grid line and a traffic road under the natural disaster; searching a scene which enables the operation cost of the power distribution network to be maximum, and obtaining a most serious natural disaster scene; considering that the distribution network-micro-grid encounters the most serious natural disaster, a distribution network-micro-grid toughness improvement robust planning model is constructed by taking minimization of the total investment cost of distribution network planning as a target, and an optimal toughness improvement scheme is solved. According to the method, on the premise of considering economy, a pre-disaster toughness improvement scheme can be planned for the distribution network-microgrid, and the load shedding amount of the distribution network under natural disasters is effectively reduced.

Along with increasingly important global marine resources, the problems of marine pollution, ecological damage, natural disasters and the like are increasingly severe, which proposes higher requirements for monitoring and managing the marine environment. A traditional marine monitoring method has a certain effect in practice, but the coverage range is limited, data acquisition is difficult, the processing efficiency is low, and comprehensive monitoring and timely response to a dynamic marine environment cannot be realized. In order to solve the problem, the invention provides the ocean multi-modal data processing method, device and equipment for ocean monitoring and the storage medium, the satellite remote sensing technology, the underwater sound telemetering technology, the buoy observation technology and the shipborne observation technology are integrated, and the advanced big data processing and heterogeneous data fusion method is combined, so that the multi-modal data processing of the ocean is realized. And efficiency is provided for comprehensive monitoring and early warning of the marine environment.

The invention discloses a landslide disaster risk dynamic assessment method and system based on an improvement project progress, and relates to the technical field of natural disaster treatment. According to the invention, through integration of multi-source heterogeneous data, real-time dynamic assessment and a machine learning model, accurate and dynamic assessment of landslide disaster risks is realized, assessment precision and stability are improved, and timeliness and pertinence of decision making are enhanced; a feature disturbance stability mechanism is introduced into the SPY algorithm, and a pseudo-negative sample screening strategy with higher robustness and discriminability is constructed; an information entropy reduction amount is introduced, an acquisition function is improved, the efficiency of hyper-parameter optimization is improved, and a stacking generalization integration model is effectively trained; the influence of the regulation situation progress in the landslide disaster area is considered, the susceptibility correction coefficient is set, the landslide disaster susceptibility evaluation result is corrected, it is ensured that the landslide disaster susceptibility evaluation result can reflect the actual situation in time, and powerful technical support is provided for prevention and management of landslide disasters.

The invention discloses a chaos annealing ant colony-based adversarial blocking timeliness emergency material transportation toughness intelligent decision-making method, and relates to the technical field of intelligent emergency scheduling and optimization. The method comprises the steps of 1, extracting scene data and converting the scene data into model parameters; step 2, constructing a mathematical model for survivability optimization; 3, initializing algorithm parameters and generating an initial path; 4, dynamically adjusting the temperature and the pheromone volatilization rate; 5, generating an adversarial blocking perception path and implementing detour repair; 6, updating pheromone distribution based on an annealing criterion; and step 7, locking the critical path and outputting an optimal transportation scheme. According to the method, timely delivery of materials can be guaranteed in a complex road network blocking and emergency demand scene, the timeliness and toughness of an emergency transportation system are improved through a chaos annealing mechanism and an adversarial blocking simulation technology, the method can be widely applied to the fields of natural disaster rescue, public health event response and the like, and a scientific basis is provided for emergency decision making.

The invention provides an emergency rescue path planning method and system for adaptive risk avoidance. The method comprises the following steps: establishing an emergency rescue road network structure; the method comprises the following steps: before an actual natural disaster comes, carrying out risk entropy driven adaptive Monte Carlo simulation analysis, carrying out path information statistical analysis on a Monte Carlo simulation result, and establishing a pre-planning path library; when an actual natural disaster comes, prior risk distribution of each planned path in the pre-planned path library is established; performing dynamic correction on the prior risk distribution of each planned path in the emergency rescue process by adopting sequential Bayesian reasoning; and carrying out emergency rescue path dynamic planning based on the path posterior risk mean value of each planning path after sequential Bayesian reasoning, selecting an optimal rescue path, and executing an emergency rescue task until the emergency rescue activity is finished. According to the invention, through a path dynamic adjustment strategy, a decentralized transportation strategy, a path pre-planning strategy and a network-free communication strategy, rescue resources are ensured to efficiently and safely arrive at a target location in natural disaster scenes such as flood and typhoon.

The invention discloses a spatial-temporal probability-based regional debris flow risk dynamic assessment method, and belongs to the technical field of geological disaster prediction.The spatial-temporal probability-based regional debris flow risk dynamic assessment method comprises the steps of establishing a dynamic probability model of a time dimension and a distributed probability model of a spatial dimension by integrating data such as meteorological data and historical disaster records; in particular, the method can solve the problem of result deviation caused by neglecting time dynamic characteristics in a traditional method, achieves the accurate prediction of the debris flow risk, breaks through the limitation of the traditional method in the aspect of data utilization, and improves the accuracy of debris flow risk prediction. Through fusion of multi-source information, the precision and applicability of the model are improved, and the assessment and decision support capability for complex natural disasters is significantly enhanced.

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

A deformation monitoring device for geological landslide relates to the technical field of natural disaster monitoring and early warning, and comprises two monitoring pier bodies, a pull rope, a fixed seat, a support, a stay wire mechanism and an energy storage mechanism, the two monitoring pier bodies are correspondingly arranged on two sides of a ground crack, the pull rope is arranged between the two monitoring pier bodies in a tightened state, and the fixed seat is arranged on the support. The number of the fixing seat is one, the fixing seat is fixedly arranged between the two monitoring pier bodies in the front-back linear direction, the support is installed on the upper surface of the fixing seat, and the pull rope mechanism and the energy storage mechanism are arranged in the middle of the pull rope, so that the problem that the pull rope is interfered by external sundries is effectively solved; the motor drives the screw rod threaded block to reciprocate front and back, the wire hooking groove in the connecting plate pulls the pull rope to the edge of the support and loosens the pull rope, the check block of the energy storage mechanism blocks the rebounded pull rope to store energy, and when the threaded block drives the connecting plate to reset, the energy storage pull rope is released, and sundries are shaken off through resilience force.

The invention discloses a sudden natural disaster fault scene generation and influence identification method and system, and belongs to the technical field of power system disaster risk assessment and toughness modeling, and the method comprises the steps: building an interdependence relation matrix based on a node set of a three-network coupling network model; calculating the damage probability of each node in combination with the geographic coordinates of the nodes, and generating an initial fault set by using a Monte Carlo sampling method; defining a node state vector, and performing fault cascade propagation simulation until the node state vector reaches a stable state; respectively identifying a power failure area, a communication out-of-control area and a traffic blocking area; and outputting the initial fault set, the stable state and the three regions together as a simulated disaster scene sample to finish identification. According to the method, the problem that multi-network cascade fault propagation and system interruption identification are difficult under the influence of complex disasters is solved, automatic generation of samples and accurate identification of disaster influence areas are supported, data support is provided for follow-up, and the method can be used for evaluation and resource scheduling of multi-network integration.

Examples described herein include methods and computing systems which may include examples of calculating risk scores for certain natural disasters perils based on machine learning model outputs. For example, a machine learning model may weight each of the pixels of a map in accordance with the set of weights associated with a structure, to calculate a risk score for a particular natural disaster peril associated with that structure. A plurality of risk selections may be provided to a user computing device for selection by a user, with those risk selections being associated with that risk score. Advantageously, the computing system facilitates the interaction of datasets with different measurement parameters in a machine learning model. In normalizing datasets before providing the datasets to input nodes of a machine learning model, a computing system may efficiently provide hazard and vulnerability outputs of the machine learning model.

The invention discloses an optimization system for photovoltaic array positioning, and the system comprises a sensor module which is used for monitoring the environment condition and the operation state of a photovoltaic array in real time, and providing various data needed by system optimization; the positioning and positioning precision module is used for determining the accurate position of the photovoltaic array; the driving and executing module is used for adjusting the layout and the angle of the photovoltaic array according to the output of the optimization algorithm; the support and the structure of the photovoltaic array can be adjusted to be in the form of a temporary shelter, and due to the arrangement of the emergency shelter building module, the photovoltaic array can be rapidly converted into the temporary shelter when natural disasters occur, and shelter and electric power support is provided for personnel in a disaster area; the integrated energy storage unit can store redundant electric energy collected in the daytime, and stable electric power supply is ensured after disasters or under emergency conditions; the communication module is linked with a local emergency communication network and is integrated with an emergency rescue system, so that personnel in a disaster area can obtain rescue information in time.

The invention relates to the technical field of natural disaster risk assessment, in particular to a multi-disaster disaster-inducing factor internal correlation analysis method, which comprises four steps of data preprocessing, Bayesian network correlation model construction, space-time dynamic evolution analysis and risk early warning threshold model construction. And deep coupling analysis of disaster-inducing factors of multiple disasters such as mountain torrent-debris flow and the like is realized. The method comprises the following steps: firstly, collecting 17 disaster-inducing factor data such as terrain and rainfall, performing dimensionality reduction through kernel principal component analysis, and calculating a dynamic weight by adopting an entropy evaluation method; then constructing a Bayesian network model to quantify conditional probability association; introducing a space-time attention mechanism to optimize a multi-scale coupling weight; and finally, establishing a coupling risk early warning threshold value and outputting a relevance intensity matrix. According to the method, the problem that a traditional single-disaster analysis method cannot capture the space-time linkage effect of disaster-inducing factors is solved, the risk early warning accuracy of multiple disasters is improved, and scientific decision support is provided for regional disaster prevention and reduction.

The invention belongs to the technical field of natural disasters, and provides a space gridding snow disaster early warning method and device, equipment and a storage medium. The method comprises the following steps: completing analysis of daily snow melting characteristics and laws by using hour-by-hour snow depth data, proposing accumulated snow melting indexes on this basis, screening sensitive factors and sensitive time periods of accumulated snow melting through analysis of a collaborative change relationship between snow depth and air temperature, ground temperature and the like, determining index factors of snow melting prediction, establishing a snow melting prediction model, and predicting the snow melting. Performing regression calculation on the snow depth of each grid point in the target area by using the snow melting prediction model based on the historical snow depth of accumulated snow and the gradual time meteorological data, extracting the unique slope and intercept parameter of each grid point, and constructing a space gridding model; and based on the space gridding model, snow disaster early warning is realized. The method provided by the invention solves the problems of inaccurate prediction and low spatial quantification degree caused by snow melting deficiency in the current snow disaster early warning process.

The invention discloses a landslide risk early warning method, device and equipment based on multi-source data fusion, relates to the technical field of natural disaster monitoring, and aims to solve the problem of low precision and timeliness of landslide risk early warning. The method comprises the following steps: collecting multi-source monitoring data of a target area in real time and constructing a multi-source parameter set, wherein the multi-source parameter set comprises rainfall characteristic data, soil hydrogeological parameters and slope morphological parameters; performing space-time discretization moisture migration simulation based on the multi-source parameter set, and generating space-time distribution data reflecting the dynamic change of the slope moisture field; calculating a slope stability index sequence according to the spatial and temporal distribution data to construct a prediction model reflecting a risk evolution trend; and setting a dynamic grading early warning threshold based on a prediction model output result, and triggering multi-grade early warning information when the risk grade reaches a preset condition.

The invention provides an urban power supply risk assessment method and system under the action of natural disasters. The method comprises the following steps: collecting and verifying power grid basic data in a pre-selected risk assessment area; screening risk assessment model data for urban power supply risk assessment from the power grid basic data according to a preset rule; based on a pre-established urban power grid element outage probability model dominated by disaster-inducing factors under different disasters, taking the risk assessment model data as input to obtain risk assessment index values respectively output by the outage probability model; taking the risk assessment index values, the corresponding disaster-inducing factors and the urban power grid elements which are respectively output by the outage probability model as inputs of a pre-trained incidence relation model, and outputting an urban power supply risk assessment result containing spatial and temporal distribution of the power outage area; wherein a training set of the incidence relation model comprises fusion codes of disaster-inducing factors, urban power grid elements and risk assessment index values and marked power failure area space-time distribution code labels.

The invention relates to the technical field of data acquisition and analysis, in particular to a natural disaster engineering rescue data demand analysis and acquisition method, which comprises the following steps: based on different natural disaster site information, identifying a current engineering rescue task type, constructing a mapping model between the task type and a data demand, and outputting a task feature vector; outputting a data demand priority matrix in combination with the disaster emergency level, the task timeliness requirement and the core component distribution; inputting the data demand priority matrix into an acquisition path planning module, and generating an acquisition task sequence in combination with field reachability analysis and sensing resource states; and executing a structured data acquisition operation according to the guidance of an acquisition task sequence, outputting a feedback correction factor to update a task feature vector, and realizing self-adaptive iteration of a data acquisition scheme. The method has the advantages of high adaptability, high precision, high resource utilization rate and the like, and is suitable for natural disaster engineering rescue scenes with variable task types, complex environments and limited decision windows.

The invention belongs to the technical field of natural disaster monitoring and early warning, and particularly discloses a landslide deformation monitoring device which comprises monitoring piers, the monitoring piers are arranged on the two sides of a crack, a monitoring box is fixedly arranged on the upper side of one monitoring pier, and a rope end fastening mechanism is arranged on the upper side of the other monitoring pier. A winding disc, a displacement monitoring mechanism and a centrifugal driving anti-interference mechanism are arranged in the monitoring box, the winding disc is rotationally arranged on the side wall of the monitoring box in a clamped mode, the displacement monitoring mechanism is located on the rope outlet side of the winding disc, the centrifugal driving anti-interference mechanism and the winding disc are coaxially arranged, and the winding disc is connected with a tension pre-tightening mechanism on the outer side of the monitoring box. A solar power assembly is arranged on the upper side of the monitoring box. According to the invention, a pure mechanical structure is utilized to judge the real reason of the change of the monitoring value, the influence of external interference on the crack width monitoring result is effectively eliminated, so that false alarm is avoided, the requirement of frequently going to the site for troubleshooting is reduced, the installation is simple, convenient and rapid, complex operation is not needed, and the device can be rapidly deployed in various terrain environments.

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) and graph neural network combined earth surface deformation space-time prediction method, which is suitable for space-time prediction of earth surface deformation. Based on DS-InSAR, obtaining radar sight line-to-time sequence earth surface deformation of the high-coherence measuring points; calculating the geographic distance between the measuring points and the mutual information between the deformation sequences corresponding to the points; the method comprises the following steps: judging connectivity between high-coherence measuring points according to a geographic distance between the measuring points and mutual information to generate an adjacent matrix, and organizing an original InSAR deformation monitoring result into graph structure data; and inputting the graph structure data into the LSTM-GCN model to predict and obtain the surface deformation of all the high-coherence measuring points in the whole area. The method is high in prediction precision and wide in application range, and can be effectively applied to the fields of space-time prediction of deformation of earth surfaces and buildings (structures) caused by mine closure, underground resource development, natural disasters and the like.

The invention relates to the technical field of grain storage, and discloses a novel heat preservation silo which comprises a silo wall, and the silo wall serves as a foundation of the whole silo and is used for bearing and building other structural parts. The granary top is located at the top of the granary wall, the bottom of the granary top is attached to the top of the granary wall, the granary top is used in cooperation with the granary wall, the granary wall comprises a wall plate, a side face heat preservation layer and a side face lining layer, the side face heat preservation layer is arranged on the inner side of the wall plate, the side face lining layer is arranged on the inner side of the side face heat preservation layer, the wall plate is made of galvanized steel, and the side face lining layer is arranged on the inner side of the side face heat preservation layer. The side face heat preservation layer is made of polyurethane foam, and the side face lining layer is made of reinforced concrete. By adopting the innovative structure that the outer-layer steel plates are matched with the inner-layer reinforced concrete, the design of the barrel-shaped wall plates and the vault accords with the mechanics principle, stress is dispersed, large loads can be borne, natural disasters such as earthquakes and typhoons can be effectively resisted, and compared with a traditional steel silo, the firmness is greatly improved.

The invention provides a flood risk assessment early warning method and system based on big data analysis, and relates to the field of natural disaster risk assessment. The big data analysis-based flood risk assessment and early warning method comprises the following steps of S1, collecting basic information of a regional river through big data; s2, real-time information of a regional river is collected through the Internet of Things; the invention further provides a flood risk assessment early warning system which comprises a big data acquisition module, an online monitoring module, a data processing module, a model construction module, a virtual operation module and a self-learning module. According to the scheme, the water level change condition in a prediction period is finally realized according to the predicted meteorological information, and the prediction, evaluation and early warning of flood risks in an analysis area range are realized; and reliable data support is provided for effective prediction, evaluation and early warning of natural disasters.

The invention belongs to the field of natural disaster monitoring and early warning information processing, and discloses a landslide monitoring system and method based on multi-source data fusion. A data preprocessing module; a data processing and analyzing module; a dynamic early warning module and a remote visualization module; the method comprises the following steps: S1, installing equipment; s2, collecting data; s3, data preprocessing; s4, data fusion analysis; s5, risk judgment and early warning; s6, displaying a result; by adopting the technical scheme, the environmental factors can be monitored in real time, the environmental factors are fused into the basis of early warning judgment, the influence of the induction factors on the stability of the mountain can be sensed in advance, the comprehensiveness of landslide judgment is improved, and the accuracy and sensitivity of mountain early warning are improved; meanwhile, according to the technical scheme, the threshold value is optimized, so that the system judges whether the threshold value is a fixed threshold value or not, the threshold value can be dynamically adjusted according to the monitored environmental factors, and the early warning of the system on the landslide is more accurate.

The invention relates to the technical field of natural disaster risk assessment, in particular to a sandstorm disaster risk zoning method, which comprises the following steps of: performing comprehensive risk assessment by combining a disaster-inducing factor risk index, a disaster-pregnant environment sensitivity index, a disaster-bearing body vulnerability index and a disaster prevention and reduction capability index and adopting a weighted synthesis method to obtain a sandstorm disaster risk zoning map; and then a sandstorm hazard source can be determined through a sandstorm disaster risk area, that is, the wind speed reaches a main area in which the surface sand is generated, so that the degree of influence and harm caused by sandstorm diffusion to the environment, animals, plants and human beings can be correctly evaluated, a scientific basis is provided for formulating targeted risk management measures, and the method is suitable for popularization and application. Therefore, the technical problem that the life and property of people cannot be safely guaranteed due to the fact that no assessment method capable of comprehensively considering the risk of disaster-inducing factors, the vulnerability of disaster-bearing bodies and the risk area prone to sand storm exists at present is effectively solved.

The invention discloses a temporary emergency shelter site selection method and system suitable for a huge disaster scene, and relates to the technical field of natural disaster emergency management. According to the method, disaster data and remote sensing image data of a disaster area are processed, disaster grids and non-disaster grids in the disaster area are identified, the site selection range is narrowed, and the site selection precision and accuracy are further improved; risk evaluation, openness and traffic evaluation are carried out on the non-disaster grids, based on the evaluation result, the grey wolf algorithm is utilized to carry out site selection on the non-disaster grids, a temporary emergency shelter site selection scheme is obtained, through data driving, manual analysis dependence is reduced, the site selection automation degree is improved, the site investigation requirement is reduced, and efficiency is improved. And through integration and rapid analysis of multi-source data, dynamic response to change demands is realized, dynamics is enhanced, and a site selection strategy is updated through real-time data, so that emergency service coverage is ensured to be matched with actual demands.