101 results about "Disaster assessment" patented technology

A disaster assessment system is presented. The disaster assessment system generates one or more impact reports detailing the nature of how a disaster has impacted a building site based on sensor data associated with the building site that is collected after the disaster. According to one aspect of the inventive subject matter, the disaster assessment system includes a sensor platform, a pre-disaster database, a historic disaster database, and an impact assessment engine. The sensor platform obtains sensor data reflecting a building site before, during, and/or after an event. The impact assessment engine derives a pre-event 3D model based on sensor data collected by the sensor platform before the event, and derives a post-event 3D model based on sensor data collected by the sensor platform after the event. The impact assessment engine then generates the impact reports based on a difference between the pre-event 3D model and the post-event 3D model.

The invention relates to a tropical cyclone full path simulation method facing disaster risk assessment. The tropical cyclone full path simulation method comprises concrete steps: a first step of establishing a start point model, and taking samples to simulate yearly occurrence frequency of tropical cyclones and generate start point information; a second step of establishing a marching model, and simulating the moving speed and orientation of the tropical cyclones to form a tropical cyclone path; a third step of establishing strength models including an ocean surface strength development model and a land strength attenuation model, and selecting the corresponding strength model in dependence on the latitude and longitude of the next point in the tropical cyclone path; a fourth step of examining the path simulation result, and carrying out statistical result examination and statistical rule analysis on the randomly-generated tropical cyclone path and the strength large sample; and a fifth step of establishing a typhoon wind field engineering model and a boundary layer model, calibrating wind field model key parameters, calculating the typhoon wind speed on the simulated point, and carrying out typhoon risk disaster assessment. The tropical cyclone full path simulation method facing disaster risk assessment can achieve refined evaluation, and is accurate and reliable in result, high in calculating efficiency and wide in application scope.

The invention provides an image change detection method based on a depth-separable convolution network, which is used for solving the technical problem of low detection accuracy existing in the conventional image change detection methods. The implementation steps include: constructing a training sample set, a verifying sample set and a test sample set; using a variant U-Net of a full convolution network as a basic network to establish a depth-separable convolution network; constructing a loss function of the trained depth-separable convolution network; training, testing and verifying the depth-separable convolution network; and using the verified finally-trained depth-separable convolution network to perform test to obtain a change detection result graph. The invention has rich image feature semantics and structure information extracted by the depth-separable convolution network, has strong image expression ability and discrimination, can improve the change detection accuracy, and canbe used in the technical fields of land cover detection, disaster assessment, video monitoring and the like.

The invention discloses an unmanned aerial vehicle reconnaissance system based on a hybrid control technology. The system comprises a signal acquisition module, a ground station and an unmanned aerial vehicle, the signal acquisition module obtains an electroencephalogram signal, an electromyographic signal, and head motion data of a manipulator, the thought, facial expression, and head motion category of the manipulator are recognized through the ground station and mapped as an unmanned aerial vehicle flight command, the unmanned aerial vehicle is controlled to cruise a target region, a reconnaissance image is downloaded to the ground station for real-time display, the state of the manipulator is influenced through the reconnaissance image and visual stimulation generated by the flight state of the unmanned aerial vehicle, a new subsequent flight command is generated, and a reconnaissance task is accomplished. According to the system, aiming at the requirements of disaster assessment, traffic monitoring, and anti-terrorism reconnaissance etc., the brain thought, the head motion, and the facial expression are applied to the control of the unmanned aerial vehicle, autonomous configuration with command mapping is supported, controllers including the conventional mouse and the remote controller are eliminated, the control is simple and convenient, and higher humanization, reliability and accuracy can be achieved.

The invention provides an unsupervised SAR image change detection method which is used for solving the technical problem existing in the prior art that detection accuracy and calculation efficiency are relatively low. The implementation steps include: filtering registered SAR images of two time phases; generating an initial difference diagram; performing saliency detection on the initial difference diagram to obtain a saliency difference diagram; utilizing a fuzzy C-means clustering algorithm to pre-classify the saliency difference diagram to obtain candidate training samples and uncertain samples; performing equalization processing on the candidate training sample set to obtain training samples; utilizing a PCA filter to extract characteristics of the training samples and characteristicsof the uncertain samples; and utilizing the characteristics of the training samples to train a support vector machine, and utilizing the trained support vector machine to classify the uncertain samples to obtain a change detection result. The unsupervised SAR image change detection method provided by the invention can reduce operation time while improving detection accuracy, and can be used for disaster assessment, disaster development tendency prediction, target detection, land covering and utilization monitoring and the like.

The invention belongs to the technical field of remotely sensed image processing, and provides a snow passive microwave mixed pixel decomposition method based on the classified information of five types of ground features. According to five types of typical underlying surfaces affecting the radiation characteristic of snow, the method acquires the classified data of the ground features with high special resolution in an observed region, utilizes the different characteristics of the microwave radiation of snow on the different underlying surfaces and selects a microwave antenna gain function and a sampling rate to build a snow passive microwave mixed pixel decomposition model, and adopts the least square method with constraint conditions to iteratively solve an underdetermined system of equations, thus realizing snow passive microwave mixed pixel decomposition. The method can effectively solve the problem of the mixed pixels of passive microwaves of snow, improves the precision of snowparameter inversion, and has a significant application value in fields such as climate and hydrological research and snow disaster assessment.

The invention relates to a disaster information cloud computing system and a disaster information cloud computing method. The computing system comprises a disaster information cloud computing platform, a user terminal and a data transmission network. The disaster information cloud computing platform is designed into a six-layer architecture, and is configured to call a geological model and an analysis model according to disaster information and geological information to complete a disaster information computing and processing, disaster simulation, disaster assessment, warning and forecasting and disaster demonstration process. The six-layer architecture is composed of a hardware resource layer, a uniform resource pool layer, a geological model and analysis model resource pool layer, a disaster simulation model resource pool layer, a disaster visual simulation expression layer, and a management scheduling layer. The system and the method enable users to enjoy professional and fast disaster information processing service, and time, money, manpower and other costs can be saved. The system and the method can be widely applied to all kinds of natural disasters such as landslide, debris flow, collapse, earthquake, flash flood and avalanche.

The invention provides a super-high/ultra-high voltage power transmission line radar remote sensing monitoring method. A monitoring system employed by the method comprises the following three units: a data preprocessing unit, a lead and iron tower object characteristic detection unit and a line safety state assessment unit. Data acquired by a high resolution SAR (Synthetic Aperture Radar) satellite goes through the data preprocessing unit, the lead and iron tower object characteristic detection unit and the line safety state assessment unit in order, thus line safety state assessment information is generated. According to the invention, by utilizing all-weather and all-time information expression force of high resolution SAR satellite remote sensing data in wide area space, and specific reflection characteristics of a power transmission line and an iron tower in the high resolution SAR data, safety state of the power transmission line is monitored. Through fusion processing on lead and iron tower reflection speckle information and object characteristic detection data, line safety state assessment data is generated, and a scientific basis for electrical network power transmission line safety state early warning and disaster assessment is provided.

The invention provides a crop disaster loss rapid assessment method, which is based on a crop model of a DSSAT system and a GLUE parameter estimation tool, utilizes historical data of crops in a disaster area, obtains leaf area indexes and yields of the crops under various scenes based on crop model simulation, and further constructs a regression equation. And then based on a satellite image of Google Enth (GEE), substituting the leaf area index of each pixel on the satellite image into a linear regression equation to obtain the yield of each pixel. And finally, comparing the yield of the disaster year of the disaster area with the yield of the last year to obtain the relative yield loss. According to the method, a large amount of ground observation data is not needed, only a small amountof experimental data is needed for calibration, the evaluation efficiency is improved, the time cost of disaster loss evaluation is reduced, and a guarantee is provided for disaster prevention and reduction. According to the method, quantitative assessment of disaster loss is achieved, the yield reduction rates of different spatial scales can be reliably estimated, and the quality of disaster assessment is improved.

The present invention provides a typhoon disaster assessment system. The system comprises: a disaster event simulation and intensity calculation module, used for simulating generation of a random typhoon disaster event and calculating a disaster intensity of the random typhoon disaster event; an engineering module, used for establishing a plurality of vulnerability functions targeted at various disaster types and building structure systems and calculating a total loss corresponding to the random typhoon disaster event; and a loss contemplation module, used for converting the total loss corresponding to the random typhoon disaster event into an insurance loss in a preset insurance calculation manner. The system provided by the present invention provides a scientific, practical, complete and secure huge disaster loss contemplation model for huge disaster risk management, and it is especially of important reality significance to make a proposal of performing a quantified pre-assessment on the direct economical loss caused by the typhoon and storm surge.

The invention provides a corn waterlogging disaster assessment method and system based on an unmanned aerial vehicle laser radar. The method comprises the steps that through LIDAR point cloud data, which are acquired in advance, of a corn planting area, the corn point cloud data of the corn planting area and ground point echo point cloud data are acquired; the canopy height information of the corn planting area is acquired according to the corn point cloud data and the ground point echo point cloud data; waterlogging disaster grading is carried out on the canopy height information of the corn planting area to acquire the waterlogging disaster assessment result of the corn planting area. The system comprises a point cloud data acquisition module, a canopy height information calculation module and a waterlogging disaster assessment module. According to the technical scheme of the invention, the occurrence range and the degree of a corn waterlogging disaster can be objectively and quickly monitored, which provides a technical support for the assessment and after-disaster recovery of the corn waterlogging disaster.

The invention discloses a rapid assessment method for earthquake landslide disasters, which is characterized in that it comprises the following steps: within 2 hours after the earthquake, implement the first rapid assessment, that is, a rough assessment; within 3 to 6 hours after the earthquake, implement the second quick assessment The second rapid assessment is the preliminary assessment; within 8 to 12 hours after the earthquake, the third rapid assessment is based on the implementation of the key assessment; within 24 hours after the earthquake, the fourth rapid assessment is the fine assessment; Within 48 hours, implement the fifth rapid assessment, namely the revised assessment; within 72 hours after the earthquake, complete the sixth rapid assessment, namely the quantitative assessment. The present invention aims at the problems of time-consuming, low precision and large errors in rapid assessment of earthquake-induced landslide disasters. The assessment process fully considers the completeness of information, from rough assessment in the case of incomplete information to quantitative assessment in the case of relatively complete information. All disaster assessment work was completed within the "golden 72 hours" after the disaster.

The invention discloses a disaster area population distribution statistical method based on a mobile phone base station and relates to a rapid disaster assessment technology and a mobile phone base station communication technology. The disaster area population distribution statistical method based on the mobile phone base station aims at overcoming the defects in the prior art. The method is characterized in that the mobile phone base station performs summary statistics on the number of mobile phones having access to the mobile phone base station at regular intervals and transmits data to a disaster rapid reporting system in time; due to the fact that the proportion of the number of the mobile phones nearby the base station to the population of the coverage area of the base station is a determined value in a certain region within a certain period of time, and the disaster area population distribution can be estimated according to the number of the mobile phones having access to the base station and the proportion of the number of the mobile phones to the population, and therefore, the disaster area personnel casualty can be estimated rapidly according to the affected degree distribution situation obtained by the disaster rapid reporting system, the method is beneficial to effective and reasonable rescue scheme making of the government, and more lives are saved.

The invention provides a water and soil coupling landslide simulation method based on a high-order double-set double-phase material point method, and the method comprises the steps: employing two setsof material point division to disperse solid soil particles and pore fluid, and employing a regular background grid to realize the solving of a control equation; under a continuous medium mechanicalframework, establishing a solid-liquid two-phase substance point method control equation for dividing two sets of substances; realizing interaction between solid-liquid biphase material points and background grid nodes through a high-order B spline primary function; and based on the basic function of the B spline, updating the acceleration, speed, position and other information of the solid-liquidtwo-phase material point, mapping the calculated node information to the material point, updating the material information of the latest solid-liquid two-phase material point, and finally outputtingsimulation information. The method can accurately simulate the large deformation damage process and the water-soil coupling mechanism of the water-soil coupling soil landslide, and provides data support for disaster assessment and prevention of the water-soil coupling soil landslide problem.

The invention relates to the technical field of disaster assessment, in particular to a mountain torrent disaster risk assessment method based on DEM. The method comprises the following steps: S1, selecting an evaluation area; S2, obtaining a digital elevation model of the evaluation area; S3, making hydrological analysis on the evaluation area digital elevation model obtained in the step 2, and watershed division of the evaluation area is obtained; S4, performing feature extraction on the watershed division obtained in the step 3 to obtain the watershed area, the maximum elevation, the minimum elevation and the watershed length of each watershed; S5, calculating the shape coefficient and the fluctuation degree of each drainage basin, and S6, obtaining a mountain torrent disaster evaluation index according to the shape coefficient and the fluctuation degree of each watershed obtained in the step 5. The evaluation method is high in evaluation result accuracy.

The invention discloses a two-dimensional drought disaster evaluation method under hydrological cycle variation driving. The method comprises the following steps: firstly, collecting basin meteorological and hydrological data; based on the M global climate modes, acquiring a future meteorological sequence through a quantile deviation correction method, and considering the influence of global climate changes on the future drought; adopting a Budyko equation to consider the hydrothermal balance of basin water circulation, taking the characteristic parameters of a Fourier formula as covariables,and considering the influence of human underlying surface activity. A two-variable drought event joint probability distribution function under a non-consistency condition is constructed, and a self-adaptive non-consistency two-variable drought disaster assessment method based on hydro-thermal coupling balance under the comprehensive driving of climate change and underlying surface human activities is provided based on a most possible combination mode of drought duration and intensity. Underlying surface conditions and non-consistency characteristics of hydrological series are considered, andan important reference basis with high operability can be provided for watershed water resource management and planning.

The invention discloses a constrained DelaunayTIN interpolation algorithm-based surface model building method, and relates to the technical field of computer software development, in particular to three-dimensional visualization, geological modeling, numerical calculation and geological disaster assessment. According to a system, input point line data, a data processing unit, a surface model generation unit, a surface model display unit and an output surface model interact in sequence; and the surface model generation unit interacts with the output surface model. The method comprises the steps of (1) processing data for a three-dimensional multi-line segment DXF file; (2) generating a surface model based on a constrained DelaunayTIN interpolation algorithm; and (3) displaying the surface model based on vtkpython. According to the method, the phenomenon that a slender and narrow pathological triangular patch occurs locally is effectively eliminated; the locally generated distortion of evaluation is effectively eliminated; and the surface model is relatively high in accuracy and relatively good in reasonability, and makes up for the deficiency of an existing method for building an earth surface model in a constraint condition.

The invention proposes a ground operation control network system for space-based global real-time continuous monitoring. The ground operation control network system comprises a satellite operation control node, a task scheduling node, an image processing node, a product distribution node and a virtual satellite node. According to the invention, satellite-ground task allocation is configured againbased on work characteristics, so that the research and development efficiency is improved effectively and the cost is lowered while the system performance and timeliness are improved substantially. The ground operation control network system can be used for building a low-cost satellite network system for global multi-point real-time video surveillance and high-value moving target tracking. A sensitive area can be monitored continuously; a high-resolution video can be transmitted to the ground in real time; the moving path of the target can be tracked dynamically; and the disaster evaluationis carried out in real time. Moreover, business intelligence information as well as accurate weather forecasting information of a cell can be obtained in real time; and disaster assessment, emergencyrescue, and exploration assistance can be carried out.

The invention provides a space-based global real-time continuous monitoring network system, comprising a small satellite cluster and a ground server cluster. The small satellite cluster includes an imaging node, a communication node, a standby nods and an offline node, and a relay imaging is used to achieve continuous video surveillance in many regions of the world. The ground server cluster includes a satellite operation and control node, a task scheduling node, an image processing node, a product distribution node and a virtual satellite node. The real-time remote control of the whole network satellite node is realized by using inter-satellite link, and remote sensing data is received, processed and distributed to users in real-time by Internet operation mode. Because the satellite-ground task division is reconfigured according to the work characteristics, the system efficiency and timeliness can be greatly expanded, and the research and development production efficiency can be effectively improved and the cost can be reduced. Continuous monitoring of sensitive areas, real-time transmission of high-resolution video to the ground, tracking the moving path of dynamic targets, and real-time disaster assessment can be achieved.

The invention discloses an image change detection method based on deep neural network structure optimization, and mainly solves the problems of low detection generalization performance and low flexibility in an existing image change detection method. According to the implementation scheme, the method comprises the following steps: reading an image, performing normalization processing, constructinga training sample set, and encoding a deep neural network structure; initializing a population according to a network structure coding mode; based on the training sample set, searching an optimal network structure by using an evolutionary algorithm; and outputting an image change detection result graph by using the optimal network structure. The optimal deep neural network structure of a given task can be efficiently and automatically searched based on a search strategy of an evolutionary algorithm, good effects and robustness are achieved for various complex conditions, the flexibility of change detection is improved, and the method can be used for land cover detection, disaster assessment and video monitoring.

The invention provides a hyperspectral image classification method based on wavelet kernel local Fisher discriminant analysis, and relates to the technical field of hyperspectral images. The method comprises the following steps: firstly, reading hyperspectral data as sample data, and normalizing a sample data set; mapping the data from a low-dimensional original space to a high-dimensional featurespace by adopting a wavelet kernel function; performing feature extraction on the sample data by using a local Fisher discriminant analysis method; dividing the data set after dimension reduction into training data and test data, and inputting the training data into an SVM classifier to obtain an optimal parameter value; inputting the test data into a classifier to obtain a classification result;and performing analysis and precision evaluation on a classification result. According to the hyperspectral image classification method based on wavelet kernel local Fisher discriminant analysis provided by the invention, a very good classification effect is obtained, and the method can be applied to the fields of agricultural monitoring, environmental management, disaster assessment, mineral mapping and the like.

The invention relates to an earthquake casualty evaluation method and system. The method comprises the steps of obtaining an earthquake historical case library; determining the similarity between a current earthquake disaster and each historical case based on a similarity evaluation model of the Manhattan distance; screening historical cases in an earthquake historical case library according to the similarity to obtain an initial historical case set participating in disaster assessment; obtaining the spatial correlation degree between each historical case in the initial historical case set andthe current earthquake disaster; screening the initial historical case set to obtain a final historical case subset participating in disaster assessment; determining the weight of each historical case participating in disaster assessment in the final historical case subset; obtaining a correction coefficient corresponding to each historical case; and evaluating the current earthquake disaster casualty quantity according to the weight of each historical case participating in disaster evaluation in the final historical case subset and the corresponding correction coefficient. The method can improve the precision of earthquake casualty population evaluation.

The invention provides a satellite-ground dual-cluster network system applicable to space-based global real-time continuous monitoring, which comprises a small satellite cluster and a ground server cluster, wherein the small satellite cluster includes imaging nodes, communication nodes, standby nodes and off-line nodes, and realizes uninterrupted continuous video monitoring for many areas at middle and low latitudes all over the world by means of relay imaging; the ground server cluster includes satellite operation control nodes, task scheduling tasks, image processing nodes, product distribution nodes and virtual satellite nodes, performs real-time remote control on satellite nodes in the whole network by using inter-satellite links, receives remote sensing data at the same time, processes the remote sensing data and distributes the processed remote sensing data to users in real time. The satellite-ground dual-cluster network system reconfigures satellite-ground task division according to working characteristics, thereby being capable of effectively improving the development and production efficiency and reducing the cost while greatly expanding the efficiency and timeliness of the system, being capable of realizing continuous monitoring for sensitive areas, downloading high-resolution video to the ground in real time, tracking the moving path of a dynamic target and performing real-time disaster assessment and the like.