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

The invention discloses 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.

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 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 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 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 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.

The invention discloses a rainstorm disaster loss prediction method and system and a medium, and belongs to the technical field of natural disaster loss prediction. Comprising the following steps: acquiring disaster-inducing factors causing rainstorm disasters, disaster-pregnancy environmental factors breeding the rainstorm disasters and factors reflecting exposure and vulnerability of disaster-bearing bodies in the rainstorm disasters; through Min-Max standardization, scale differences of disaster-inducing factors, disaster-pregnancy environment factors and exposure and vulnerability factors are eliminated; adjusting the sequence of the disaster-inducing factors, the disaster-pregnancy environment factors and the exposure and vulnerability factors by using a random sampling mode; integrating a plurality of machine learning algorithms after hyper-parameter combination optimization, and constructing a multi-model rainstorm disaster loss prediction system; inputting the processed disaster-inducing factors, disaster-pregnancy environment factors and exposure and vulnerability factors into a multi-model prediction system, and outputting economic loss of the rainstorm disaster; according to the method, the economic loss of the rainstorm disaster is effectively predicted.

We provide the system. [Solution] A method that uses generative models to predict future natural disasters by analyzing environmental data collected from diverse sources, A means for providing an appropriate evacuation route to a user terminal based on the predictions of the generation model, A means of sharing community information among users in real time and promoting its use in disaster prevention, A method for using unmanned aerial vehicles to acquire information on the situation in disaster-stricken areas and then re-proposing the optimal evacuation route based on that information, A system that includes this.

The invention relates to the technical field of data analysis, in particular to a natural disaster engineering rescue data platform construction and intelligent analysis system, which comprises an edge intelligent sensing module for generating a field three-dimensional live-action grid through laser radar point cloud and visible light video fusion and extracting disaster feature vectors; the cloud deep analysis module adopts a graph neural network to aggregate multi-source data, and generates a global disaster evolution graph composed of disaster grade distribution and infrastructure vulnerability indexes; the dynamic decision generation module is used for generating a space-time cooperative rescue instruction set composed of an equipment scheduling path and an engineering disposal sequence in combination with a rescue resource library and engineering constraints including an equipment mechanical arm operation radius and an aftershock influence coefficient; and the closed-loop optimization module is used for collecting operation feedback logs, correcting extraction weights of disaster feature vectors through a difference comparison engine, and dynamically updating an engineering constraint library. According to the invention, unified cooperation of disaster sensing intelligence, task allocation optimization and response process closed loop is realized.

The invention relates to the technical field of electric power, and discloses an electric power natural disaster emergency resource allocation method and system based on multi-data fusion, and the key points of the technical scheme are as follows: multi-source data acquisition and fusion, resource demand prediction, dynamic allocation scheme generation, and real-time monitoring and adjustment. By constructing a multi-source data fusion system, establishing a high-precision demand prediction model and dynamically generating an optimal allocation scheme in combination with traffic accessibility, accurate and efficient overall planning of emergency resources is realized, the resource utilization rate is improved, and the power recovery time after disasters is shortened.

Techniques for calculating or determining risk scores for certain natural disasters perils based on machine learning model outputs are discussed herein. 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 relates to the field of natural disaster early warning, and discloses an intelligent early-warning mountain torrent disaster monitoring system, which comprises a data acquisition unit used for acquiring multi-source dynamic monitoring data and static geographic data; the risk prediction unit is connected with the data acquisition unit and is used for generating probabilistic prediction of the mountain torrent disaster state in a specific time window in the future based on the multi-source dynamic monitoring data and the static geographic data; and the dynamic utility evaluation unit is used for quantifying the comprehensive utility of different early warning decisions in different disaster scenes in combination with a preset decision maker risk preference model and a public trust degree dynamic evolution model. By fusing multivariate heterogeneous data and applying a hybrid prediction model of a dynamic Bayesian network and Gaussian process regression, the accuracy and reliability of mountain torrent risk prediction are significantly improved. Compared with an early warning mode depending on a single data source or a simple model in the prior art, the method has the advantages that the defects of insufficient data utilization and difficulty in quantification of uncertainty are effectively overcome.

The invention provides a flood disaster range monitoring method and system based on satellite remote sensing images, and belongs to the technical field of natural disaster monitoring and artificial intelligence crossing. The method comprises the steps that S1, a large number of historical SAR satellite remote sensing images are acquired to construct a data set; s2, training the flood segmentation model through the data set, and optimizing model parameters through a joint loss function in the training process; s3, deploying the trained flood segmentation model; s4, acquiring a target SAR satellite remote sensing image, inputting the target SAR satellite remote sensing image into the flood segmentation model, and reasoning to obtain a water body distribution diagram; s5, performing differential operation on the adjacent water body distribution diagrams to obtain a newly added flood disaster range; and S6, generating a time sequence flood inundation range map based on each flood disaster range, calculating flood characteristic parameters, and carrying out dynamic monitoring and early warning based on the time sequence flood inundation range map and the flood characteristic parameters. The method has the advantages that the accuracy and timeliness of flood disaster range monitoring are greatly improved.

The invention provides a protection method, device and equipment for power grid facilities under natural disasters and a medium. The method comprises the following steps: firstly, acquiring an initial data set, wherein the initial data set comprises a rainfall data set, a normalized vegetation index, land utilization data, road network data, soil type data, river network density data, elevation data, slope data, rock data and a surface deformation rate; and then, based on the initial data set, simulating a position point of a landslide barrier dam generated in a river after landslide is generated by rainfall, and geometric parameters of the landslide barrier dam. A first evolution law of outburst flood generated by the landslide barrier dam after outburst is then simulated based on the location points and the geometric parameters. And finally, based on the first evolution rule of the outburst flood, determining emergency protection measures of power grid facilities at the downstream of the landslide barrier dam. The protection accuracy of power grid facilities under natural disasters in the prior art is improved.

The invention relates to the technical field of disaster emergency management, and discloses a disaster emergency management method based on LSTM time sequence analysis. The method comprises the following steps: acquiring natural disaster situation data which is reported by a management node where a disaster situation is located and contains multi-dimensional time sequence characteristics; according to the multi-dimensional time sequence characteristics and a preset disaster situation type knowledge base, determining a target disaster situation type matched with the multi-dimensional time sequence characteristics; calling historical disaster situation data corresponding to the target disaster situation type, and performing time sequence analysis on the historical disaster situation data through an LSTM model to generate a disaster situation characteristic evolution sequence; and dividing a disaster response priority region and a secondary response region based on the disaster feature evolution sequence and the emergency measure database. According to the method, the disaster situation type can be accurately determined, the disaster situation evolution trend can be accurately analyzed, the response area can be scientifically divided, the emergency resource distribution can be optimized, the disaster situation emergency management efficiency can be improved, and the method is suitable for emergency management work of various natural disaster situations.

Systems and methods for natural disaster damage analysis. Aerial images are converted (710) into a set of property images. The property images are then clustered (720) using vector embedding. Clustering the property images includes classifying the property images into separate categories, determining a symmetric similarity between the property images in each category, and clustering the property images based on the similarity. A first artificial intelligence agent extracts (730) damage information from a set of representative members of the clustered property images, where the damage information includes damage level information and damage reasoning information. The damage information is then stored (740) in a datastore with at least two databases, where at least one of the databases stores damage reasoning information and at least one other one of the databases stores damage level information.

A routing optimization method for truck-drone integrated rescue vehicles in forest firefighting includes the following steps: collecting the data of the disaster sites caused by forest fires, calculating transportation time on road and flight time according to the data of the disaster sites, and getting the transportation time data of post-disaster relief; based on the disaster site data, transportation time data, and an amount of relief supplies that can be allocated to disaster relief sites, constructing a mathematical model of the truck-drone based vehicle routing problem; solving the mathematical model of the truck-drone based vehicle routing problem by the optimization algorithm, and specifying a post-disaster rescue route of the truck-drone integrated rescue vehicle according to the solution results. The model based on the truck-drone based vehicle routing problem method can reduce the impact of sudden natural disasters, and give full play to the advantages of truck-drone integrated rescue vehicles.

This pipeline damage prediction system uses a computer to predict pipeline damage due to a natural disaster, the pipeline damage predication system comprising: a basic data storage unit that stores pipeline attribute data indicating an attribute of a pipeline, stores ground characteristic data which indicates a characteristic of the ground in which the pipeline is buried and which includes at least liquefaction data, ground boundary data, or terrain modification data in units of polygons in a map information system GIS, and stores damage data indicating the degree of damage due to a natural disaster occurring in the past; and a damage prediction calculation unit that generates damage prediction information in which damage occurring in the pipeline is predicted on the basis of the pipeline attribute data, the ground characteristic data, and the damage data stored in the basic data storage unit.

The invention relates to the technical field of roadbed slope protection, discloses a roadbed slope protection composition based on solid waste-geopolymer and a construction method of the roadbed slope protection composition, and aims to solve the problems of high carbon emission, low solid waste utilization rate, strong ecological destructiveness and weak disaster resistance of a traditional roadbed slope protection material. According to the invention, fly ash, desulfurized gypsum, mineral powder and pretreated red mud are taken as core raw materials, and are matched with a geopolymer activator and a heavy metal stabilizer to form a cement-free flow state system with a high solid waste mixing ratio; during construction, a casing pipe is not needed, through the drilling-layered pouring technology, the solidification body is naturally attached to soil through the fluidity, a multi-deformation solidification structure without rigid cutting is formed, the anti-scouring and anti-collapse capacity of a roadbed slope can be enhanced through the structure, damage of natural disasters such as flood to a roadbed can be effectively resisted, and the construction period is shortened. Meanwhile, the ecological compatibility of the mechanical property and the plant root system penetration rate is considered.

The utility model provides a monitoring and early warning device for a debris flow gully in a canyon fall section, and relates to the technical field of natural disaster early warning. The device comprises a supporting column, a connecting frame, a sliding rail, a debris flow monitoring module and a rainfall monitoring module. Wherein the supporting columns are embedded and fixed on the two sides of a debris flow gully; the connecting frame is fixedly connected to the upper end of the supporting column. The sliding rail is fixed to the lower end of the middle of the connecting frame and extends in the same direction as the debris flow gully. The debris flow monitoring module is slidably connected to the sliding rail and can follow the debris flow state in the debris flow gully when the debris flow disaster occurs. And the rainfall monitoring module is fixed at the upstream position of the debris flow gully. According to the scheme, the debris flow monitoring module can move along the channel for monitoring through the sliding track, dynamic data collection is achieved in combination with the rainfall monitoring module, the problem that traditional fixed-point monitoring cannot adapt to complex canyon terrains is solved, and the accuracy and timeliness of early warning are improved.

The invention discloses a natural disaster emergency response method and system based on power failure monitoring, and the method comprises the steps: obtaining a set of power failure data, which comprises a power failure influence unit, a power failure public and special variable, a power failure cell and an initial power failure influence user number; and a data processing and analysis algorithm is introduced at the rear end to carry out collaborative analysis on a set of the power failure data, so that the total number of the power failure users is accurately predicted, and corresponding natural disaster emergency response is carried out. Therefore, the intelligence of emergency management and response of natural disasters can be realized, so that the disaster response efficiency is improved, and the loss is reduced.

A system is provided.SOLUTION: A system comprising: means for collecting natural disasters in real time; means for training a generative AI model using the collected natural disasters; means for predicting a probability of occurrence of disasters using the generative AI model; means for reflecting the predicted probability of occurrence of disasters in a map service; and means for providing shelter information and information for preparing for disasters.SELECTED DRAWING: Figure 1

The embodiment of the invention provides a natural disaster risk evaluation method, device and equipment and a computer readable storage medium. The method comprises the steps of determining natural disasters possibly involved in a to-be-evaluated region based on geographical environment information of the to-be-evaluated region; determining each natural disaster risk index based on the survey evaluation data of each natural disaster; based on the natural disaster risk indexes, a total risk index and an overlapping risk index are determined, the total risk index is related to the risk of independent occurrence of each natural disaster, and the overlapping risk index is related to the risk of simultaneous occurrence of all natural disasters; and determining a comprehensive risk index of the to-be-evaluated region based on the total risk index and the overlapping risk index. According to the method, the risk superposition influence of various natural disasters is fully considered, so that the obtained risk assessment result is more suitable for areas influenced by various natural disasters, and scientific and comprehensive data support is provided for comprehensive risk assessment of local disasters, disaster control and emergency rescue work.

The application provides a kind of nuclear power plant dry storage spent fuel emergency evaluation method, system, equipment and medium, applied to dry nuclear horizontal modular storage system, the dry nuclear horizontal modular storage system includes concrete module and storage container, the method comprises: obtaining the information of natural disaster;According to the type and risk parameter of the natural disaster information of the natural disaster;In response to the risk parameter being higher than the response threshold, the radiation dose of the concrete module is obtained;According to the radiation dose, the effective multiplication coefficient of the storage container is obtained;In response to the effective multiplication coefficient being higher than the effective multiplication coefficient threshold, the storage container transfer operation is executed, can be integrated together by natural disaster risk assessment, radiation dose monitoring and effective multiplication coefficient control, form a complete emergency evaluation and response system, improve the overall efficiency of nuclear power plant in response to disaster.

Systems and methods for natural disaster damage analysis. Aerial images are converted into a set of property images. The property images are then clustered using vector embedding. Clustering the property images includes classifying the property images into separate categories, determining a symmetric similarity between the property images in each category, and clustering the property images based on the similarity. A first artificial intelligence agent extracts damage information from a set of representative members of the clustered property images, where the damage information includes damage level information and damage reasoning information. The damage information is then stored in a datastore with at least two databases, where at least one of the databases stores damage reasoning information and at least one other one of the databases stores damage level information.