214 results about "Disaster area" patented technology

The invention provides a desertification monitoring grading and vegetation extraction method and system, and relates to the field of desertification remote sensing monitoring and ecological assessment, and the method comprises the steps: selecting a multi-temporal satellite image and an unmanned aerial vehicle image which cover a full research region according to a preset condition, and carrying out the data preprocessing of the multi-temporal satellite image and the unmanned aerial vehicle image; the method comprises the following steps: constructing a desertification difference index by fitting a feature space of a vegetation index and a surface albedo by using a multi-temporal satellite image, grading the desertification degree of a research area to obtain a grading result, and locking a key monitoring area in the research area according to the grading result; a vegetation sample image of a key monitoring area is obtained from an unmanned aerial vehicle image, HSL color space conversion and hue optimization processing are carried out on the vegetation sample image, and a normalized vegetation index based on HSL is constructed, so that vegetation information of the key monitoring area is finely extracted, a key desertification disaster area is effectively positioned, and the accuracy of the desertification disaster area is improved. And vegetation information in the region is finely extracted.

The invention discloses an emergency rescue resource scheduling method based on the Internet of Things, and the method comprises the steps: collecting disaster area environment parameters, images and personnel distribution data through Internet of Things equipment, and associating historical disaster cases to construct a multi-source heterogeneous data set; extracting disaster characteristics through preprocessing, dynamically distributing data source weights, and generating a disaster assessment matrix; outputting a resource demand peak value based on the time sequence prediction model and performing dynamic correction; constructing a deep reinforcement learning model optimization decision strategy, and generating a scheduling scheme in combination with priority matching and an improved A * algorithm; according to the method, the disaster sensing precision and the demand prediction accuracy are improved, the scheduling decision adaptability is enhanced, the response time of a high-priority region is shortened, and the efficient demand of emergency rescue in a complex disaster is met.

The invention provides a multi-unmanned aerial vehicle cooperative transportation dynamic decision-making method and system in an emergency environment, and the method comprises the steps: 1, obtaining the material state information reported by a disaster region node after an emergency occurs; step 2, constructing a hunter and prey local decision model taking the disaster-affected node as a main body, and forming a local priority and competition relationship between the nodes; 3, establishing a multi-target minimum cost maximum flow (MCMF) model, and solving to obtain a candidate transportation scheme; and step 4, inputting the candidate transportation scheme as an initial population into an improved non-dominated sorting genetic algorithm NSGA-II, performing non-dominated optimization on energy consumption, time, fairness and risk, and generating an optimal collaborative transportation strategy cluster approaching the Pareto frontier. According to the method, through a mode of combining local decision and global optimization, comprehensive improvement of multi-unmanned aerial vehicle collaborative transportation efficiency, fairness and safety is realized in a complex dynamic emergency environment.

The invention relates to the technical field of remote sensing image recognition, in particular to a method for determining a moso bamboo forest snow disaster affected area based on a GEE platform. The method mainly solves the problems of low efficiency, high cost and difficulty in realizing large-range rapid evaluation caused by dependence on manual field investigation in the prior art. According to the technical scheme, the method comprises the following steps: firstly, obtaining a multi-temporal Sentinel-2 image before and after a snow disaster, and carrying out the preprocessing of the multi-temporal Sentinel-2 image; extracting spectral bands, vegetation indexes and texture features to form an initial feature set; then, a key feature variable combination is determined through statistical significance filtering and machine learning optimization; on this basis, establishing a logistic regression discrimination model and determining an optimal classification threshold; and finally, carrying out snow disaster state classification on the moso bamboo forest region by utilizing the trained model, and generating a disaster region spatial distribution diagram.

The invention relates to the field of disaster prevention and management, and particularly provides an intelligent decision-making method and device for realizing flood simulation prevention in combination with Cesium, and the method comprises the following steps: S1, geographic data access; s2, realizing real-time monitoring and analysis of hydrological conditions; s3, flood simulation model construction comprises hydrodynamic model construction, model parameter setting and optimization, and mechanism real-time updating; s4, processing physical interaction of the water flow by a Chaos physical engine of Unreal Engine, and associating physical data of Chaos through modular logic; s5, integrating an LSTM time sequence prediction model and combining with hydrological real-time rainfall to realize a flood routing trend; s6, displaying key disaster areas and disaster water level conditions by HeatMap; and S7, identifying a nearby refuge point location, and scientifically and reasonably arranging an emergency path. Compared with the prior art, intelligent decision management can be provided for pre-disaster simulation prevention and post-disaster rescue recovery.

The invention discloses an unmanned aerial vehicle remote take-off and landing method and system for low-altitude economy, and relates to the technical field of a non-electrical variable control or adjustment system.The method comprises the steps that a distributed network composed of mobile airports with edge computing capacity is constructed, and nodes collect and preprocess multi-source disaster situation data in real time; based on the preprocessed data, the nodes autonomously cooperate with each other through a distributed bidding and dynamic queue jumping mechanism, and an optimal task execution node is determined; the unmanned aerial vehicle adopts leg type rolling path planning, and precise scheduling of airspace and apron resources is realized in combination with a landing appointment mechanism; after landing, material loading and unloading and battery replacement are synchronously completed, and resource state whole-network synchronization and a new round of task bidding cycle are triggered. Through a distributed edge autonomous architecture, the limitation of decision paralysis of a traditional centralized system when communication in a disaster area is interrupted is overcome, and by means of a task bidding and rolling planning mechanism, the accuracy of resource matching and the safety of a flight path in a complex and changeable environment are remarkably improved.

A flying elevator is characterized by comprising (100) a ducted propeller arrangement. And (200) a flying elevator telescopic car body. And (300) supplying power to the transfer platform truck. And (400) an intelligent control system. And (500) final assembly of the structure. The mechanisms and the systems are combined through final assembly. Therefore, a set of economical and practical elevator type intelligent low-altitude aircraft is formed. The technical problem that a conventional low-altitude aircraft cannot be connected for a long time to continuously work and cannot be increased in capacity due to limitation of self-loading power or electric power is solved. The elevator is widely applied to high-rise buildings, high-altitude construction sites, disaster areas and scenic areas and serves as a temporary elevator or a short-distance aircraft. And the emergency operation and rescue work of personnel transfer and material transportation can be continuously carried out by a single machine for a long time. The flying elevator has the beneficial effects that 1, the flying elevator flies with lines, and electric power is continuously provided by using the mains supply or a vehicle-mounted power generation device, so that the flying elevator can continuously run for a long time; 2, off-line flight: driving the flight elevator to run by utilizing a reserve battery in an area which is not suitable for line-carried flight; and emergency operation and rescue work in different environments are facilitated. And 3, a multi-set mechanical structure design, a power supply device and a control system. And flight safety and operation convenience are guaranteed.

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 geotechnical slope geological disaster analysis and alarm device and method, and relates to the technical field of geological disaster alarm, and the device comprises a multi-physics field fusion sensor array, a data transmission module, a data analysis and processing module and an alarm module. Multi-dimensional parameters such as stress, strain, temperature and humidity are synchronously collected through a multi-physics field fusion sensor array, a single sensor information island is broken, collaborative association of multi-dimensional data is achieved, comprehensive original data support is provided for slope state analysis, an improved deep learning algorithm which introduces a space attention mechanism is utilized, and the analysis accuracy of the slope state is improved. A dynamic incidence matrix of different position parameters is constructed, the mutual influence of each area of the slope is accurately captured, the trend prediction accuracy and the potential disaster area identification efficiency are greatly improved, an alarm module comprising multiple levels of alarm units and a linkage mechanism is designed, acousto-optic frequencies and emergency measures are matched according to danger levels, and the safety of the slope is improved. And the monitoring integrity, the early warning accuracy and the emergency response efficiency are comprehensively improved.

The invention relates to a disaster prevention emergency material distribution method and device, equipment and a medium. The method comprises the following steps: acquiring multi-source heterogeneous data of a disaster area; performing standardization and spatial fusion processing on the multi-source heterogeneous data to obtain standardized spatial grid data; based on the standardized space grid data, analyzing the material demand urgency degree of each space grid to obtain demand urgency degree data; generating initial material distribution scheme data according to the demand urgency degree data and preset material inventory data; and based on the initial material distribution scheme data, in combination with preset transportation network data, carrying out scheme adjustment through a predefined multi-objective optimization rule, and generating a material distribution scheme. By adopting the method, the problems of unreasonable distribution and low decision-making efficiency caused by data splitting and dependence on experience in a traditional mode are solved, the integration from disaster assessment to transportation scheduling is realized, the decision is quickly optimized, and the overall efficiency of emergency response is improved.

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 discloses a wheat waterlogging accurate identification and disaster assessment method based on a multi-temporal remote sensing image, and relates to the technical field of image processing. Periodically acquiring remote sensing images in the region to obtain a remote sensing image set, and performing feature extraction to obtain a plurality of feature points; constructing according to each feature point to obtain a feature matrix, and adding the feature matrix to the remote sensing image to obtain a positioning image set; sampling the positioning images in the positioning image set at a preset moment to obtain a plurality of feature maps; and performing alignment according to the feature matrixes in the feature maps to obtain a target area, and performing assessment to obtain a disaster degree. Remote sensing images are collected periodically, stable feature points are extracted to construct a feature matrix, feature map sampling in the front, middle and later periods of a disaster is combined with feature matrix accurate alignment, accurate locking of a target area and scientific assessment of the disaster degree are achieved, the problem that a disaster-affected area is difficult to position accurately due to flood interference of multi-temporal remote sensing is solved, and the disaster-affected area positioning accuracy is improved. The objectivity, reliability and timeliness of disaster assessment are improved.

The invention discloses a typhoon disaster risk assessment method for quantifying wind and rain combined loss, and belongs to the field of typhoon disaster risk early warning and loss assessment, and the method comprises the steps: determining the grid range of a typhoon core disaster area based on typhoon center path data and wind circle size data; constructing a typhoon wind and rain comprehensive index by using the wind speed and rainfall rasterized data sequence; the counted county-level typhoon disaster direct economic loss is scientifically distributed to a grid scale; taking the calculated typhoon wind and rain comprehensive index as an independent variable, taking rasterized typhoon disaster direct economic loss statistical data as a dependent variable, adopting a four-parameter Logistic function for fitting, and constructing a typhoon disaster vulnerability curve model. According to the method, the typhoon vulnerability curve model considering the wind and rain disaster-induced capability is constructed, the mathematical relationship between the economic loss and the disaster intensity is established, the limitation of a traditional qualitative evaluation method is overcome, and a scientific and accurate quantitative basis is provided for the economic influence of typhoon disasters.

The present application belongs to the field of surveying and mapping engineering, and particularly relates to a kind of air-ground combined InSAR geological disaster monitoring method and device, method includes: respectively obtaining satellite digital orthophoto map of disaster area and first digital orthophoto map of disaster point, and satellite digital orthophoto map and first digital orthophoto map are matched with elevation model;Satellite digital orthophoto map and first digital orthophoto map are fused to obtain first fused digital orthophoto map;First digital elevation model and second digital elevation model are fused to obtain first fused digital elevation model;The mapping relationship between first fused digital orthophoto map and first fused digital elevation model is established to obtain air-ground combined three-dimensional terrain model;Ground-based SAR is fused with air-ground combined three-dimensional terrain model and is handled to obtain InSAR stereoscopic detection model.The present application solves the problems of low acquisition efficiency, limited monitoring range, insufficient data precision and poor real-time performance of traditional monitoring methods.

The invention provides a railway gallery low-altitude intelligent inspection system and device, and relates to the technical field of railway gallery low-altitude intelligent inspection. The system comprises a path planning module which generates a reference fixed route template through a multi-period adaptive fixed route optimization algorithm, and introduces space-time consistency constraints to ensure that multi-period images meet pixel-level registration requirements; the flight control and data acquisition module controls the unmanned aerial vehicle to fly according to a path and acquire images; the edge analysis module identifies suspected disaster areas in real time and extracts key images; the cloud intelligent analysis module performs multi-period change detection based on the key image and the historical image to determine a hidden danger type; the risk assessment module is fused with the multi-dimensional features to calculate risk indexes and determine risk levels; and the early warning issuing module issues early warning information, so that long-term consistency of inspection paths, real-time identification of hidden dangers and accurate risk assessment are realized, and the automation and intelligence level of railway gallery inspection is improved.

The invention belongs to the technical field of wireless communication, and discloses an Internet of Vehicles optimization method and system based on edge computing and time division multiple access, and the method comprises the steps: carrying out the joint optimization of service deployment and replacement, RSU deployment, access control, and task unloading rates in VTUs and RSUs, and achieving the optimization of the Internet of Vehicles. And efficient, low-delay and stable task processing service is provided for vehicle-mounted communication in urban traffic, expressways or disaster areas. According to the method, a double-time-scale optimization strategy is adopted, the decision of service deployment and replacement is carried out on a coarse-grained time scale (frame), and other optimization problems are processed in a fine-grained time scale (time slot). The invention provides an innovative hierarchical DRL algorithm, and in the algorithm, a high-level agent adopts a deep Q network (DQN) proxy and is responsible for optimizing a service deployment and replacement strategy in a frame; and the bottom layer intelligent agent adopts a depth deterministic policy gradient (DDPG) for proxy, and specially solves related optimization problems of task unloading rate, access control and the like in the time slot.

Visible light communication (VLC) enabled unmanned aerial vehicles (UAVs) have evolved as a promising technology for the fifth generation and beyond communications. The proposed invention describes a system and methodology for deploying VLC-enabled UAVs, which serve as flying base stations (V-FBSs) over the target area: disaster regions, concert and fest areas, and search and rescue operations zones. The V-FBSs provide both communication and illumination. The proposed system follows CRAN architecture, which helps reduce the CAPEX and OPEX significantly. Further, present disclosure delivers a complete method to deploy the V-FBS network along with the detailed synchronization process required for autonomous deployment. It offers a complete 3-D deployment of the V-FBSs, which ensures minimum interference while satisfying the promised QoS and providing an energy-efficient position. The proposed network is scalable and can significantly reduce the outage. Since VLC is used for the present invention, the network does not interfere with the neighboring RF networks.

An object of a system according to an embodiment is to enable even elderly people and foreign travelers to easily obtain correct information in the event of a disaster.SOLUTION: A system according to an embodiment includes a UI structure unit, a disaster detection unit, an information providing unit, and a consultation unit. The UI structure provides a user interface. The disaster detection unit detects a disaster area. The information providing unit provides correct information based on the disaster area detected by the disaster detection unit. The consultation unit receives a consultation from a user at normal times.SELECTED DRAWING: Figure 1

The invention relates to the field of geological recognition, in particular to an axial chain type rockburst disaster-pregnant geological recognition method and device, and the method comprises the steps: obtaining micro-seismic monitoring data in the excavation process of a deeply-buried hard rock tunnel; according to the micro-seismic monitoring data, identifying the axial chain type rockburst disaster-pregnancy area to obtain a disaster-pregnancy area range; calculating the normal vector of the fracture surface according to the disaster-pregnant area range and the micro-seismic monitoring data; establishing a parameter system and constraint conditions required by ST-DBSCAN clustering according to the normal vector of the fracture surface and the micro-seismic monitoring data; sT-DBSCAN clustering processing is carried out according to the required parameter system and constraint conditions, and a micro-seismic event cluster reflecting a potential rock mass structural plane is obtained; and performing three-dimensional reconstruction processing on a rock mass structure network according to the micro-seismic event clustering cluster to obtain a reconstructed three-dimensional geometric model of the axial chain type rockburst disaster-pregnant geology. According to the axial chain type rockburst disaster-pregnant geology three-dimensional geometric model and the construction method thereof, accurate description of a hidden rock mass structure and effective advanced early warning of a rockburst risk are realized.

The application provides a mountainous town disaster prevention and control data analysis method based on a city information model, the mountainous town is divided into multiple prevention and control regions based on a disaster zoning mechanism in the city information model, and then disaster intensity features of each prevention and control region are extracted from disaster prevention and control data; mutual information correlation is performed on each prevention and control region according to each disaster intensity feature, a disaster involvement relationship in the mountainous town is obtained, and then a disaster linkage amount of each prevention and control region in the mountainous town is determined according to the disaster involvement relationship; a core disaster area in the mountainous town is determined according to all the disaster linkage amounts, and then a linkage emergency strategy of the disaster in the mountainous town is determined through feature differences of the core disaster area and each disaster intensity feature; the disaster in the mountainous town is responded in linkage through the linkage emergency strategy. Based on the above scheme, linkage analysis of disaster features and prevention and control strategies can be realized, so that the disaster response efficiency of the mountainous town can be improved.

The invention discloses a disaster recovery prediction method and system combined with iron tower off-line data analysis, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the iron tower state data collection of a target region, and obtaining the time sequence data of K operation states of K iron towers; and performing interaction to obtain external disaster data of the target area. And carrying out disaster area analysis based on the time sequence data of the K operation states to obtain disaster influence range information. And performing dynamic disaster influence assessment based on the external disaster data and the disaster influence range information, and outputting the disaster severity and the disaster duration. Disaster recovery prediction is carried out according to the disaster severity and the disaster duration, and an iron tower recovery prediction curve and iron tower recovery prediction time consumption are obtained. The technical problem that in the prior art, accurate and real-time disaster influence prediction is lacked after a disaster occurs, so that the formulation of a recovery strategy is not timely and inaccurate is solved.

The invention relates to the technical field of geological disaster risk assessment and geographic information systems, in particular to a disaster-inducing factor dynamic quantification and characterization method based on an entropy evaluation method. The method comprises the steps of constructing a disaster-inducing factor data set, performing standardization processing, performing dynamic weight calculation through an entropy evaluation method, and performing quantitative characterization and grading. The method comprises the following steps: firstly, based on multi-source data, integrating historical data of four disaster types of mountain torrent, debris flow, landslide and collapse and related disaster-inducing factors to form a time-space associated unified database; then, min-max normalization and disaster area density analysis are adopted, and factor dimension differences are eliminated; dynamic updating of the factor weight is realized by calculating the information entropy and the difference degree; and based on a natural breakpoint method, carrying out scientific grading on the standardization factors, and combining entropy weight to construct a dynamic quantitative index system. According to the method, the problem that a traditional static weight method cannot reflect dynamic change of disaster-inducing factors along with time is solved, and the timeliness and accuracy of disaster sensitivity assessment are improved.

The invention provides an engineering machinery man-machine cooperative scheduling system and method applied to an earthquake disaster site, and relates to the technical field of man-machine cooperative scheduling, and the system comprises a site environment information obtaining module which is used for obtaining the site environment information of the earthquake disaster site; the rasterization processing module is used for performing area rasterization processing to generate a disaster area grid map; the task execution list generation module is used for performing task allocation evaluation, generating a rescue task execution list and extracting a primary task and a secondary task; the path planning module is used for performing path planning and generating a first rescue path and a second rescue path; and the collaborative rescue scheduling module is used for carrying out collaborative rescue scheduling on earthquake disasters. The technical problems that in the prior art, due to the fact that an effective man-machine cooperative scheduling mechanism is lacked, the collaboration between rescue workers and engineering machinery is poor, rescue resources are wasted, and the rescue efficiency is low are solved.

The invention relates to a disaster area assessment method, system and device based on geological disaster exploration and a medium. The method comprises the following steps: analyzing multi-source exploration data to obtain a unit spatial-temporal characteristic tuple and a physical incidence matrix; performing graph neural network calculation processing based on the unit spatial-temporal feature tuple and the physical incidence matrix to obtain a dynamic risk assessment result, a multi-level attention weight and a physical state deviation amount; and based on the dynamic risk assessment result, the multi-level attention weight and the physical state deviation amount, performing report generation processing to obtain a geological disaster dynamic risk assessment report. According to the method, by means of multi-source data analysis, graph neural network calculation and the like, the accuracy, dynamism and interpretability of geological disaster assessment are improved, and the disaster risk accurate prevention and control and decision support capability is enhanced.

The present invention relates to a disaster area disinfection drone, wherein the disaster area disinfection drone is controlled by a server, and the drone device is mounted on a drone mount to charge disinfectant solution and power, and unfolds the flight unit to prepare for flight, and the drone device detects the drone's location and surroundings through a sensor, flies to the disaster area, reaches the disaster area and sprays disinfectant solution, and returns to the drone mount when the disinfectant solution or power is nearly consumed or when disinfection is completed, and the drone device comprises a power unit for charging power, a flight unit for flying, a sensor unit for detecting surroundings at the top, and a disinfection unit at the bottom for storing and spraying disinfectant solution, and the drone mount is controlled by a server and may include a charging unit for charging disinfectant solution and power when the drone device is mounted on the drone mount.

The invention discloses a multi-scale remote sensing three-reference unified alignment and lightweight change detection method and system, and the method comprises the steps: constructing a space-spectrum reference unified frame, carrying out the unified fusion processing of multi-source data, obtaining unified data, and carrying out the unified fusion processing of the multi-source data based on the unified data through employing the physical characteristics for forest fire and flood disasters, performing preliminary recognition on the disaster area to generate a recognition result; based on the recognition result, constructing a hierarchical collaborative sensing system of satellite general survey-unmanned aerial vehicle detailed survey-multi-source fusion fine judgment, obtaining general survey data and detailed survey data through satellite general survey-unmanned aerial vehicle detailed survey, and carrying out multi-source data fusion on the general survey data and detailed data to obtain a multi-source collaborative sensing system; generating a monitoring result including a fine disaster boundary, a quantitative index and a special product; a risk prediction model is obtained by analyzing operation parameters and environmental factors of a transformer substation and a power transmission line, a monitoring result is input into a mountain fire spreading prediction model, and dynamic simulation and prediction of a mountain fire spreading process are realized through a discretized grid cell structure.

The application relates to the field of agricultural and rural technologies, and particularly relates to a method for extracting waterlogging disaster of northern spring maize unmanned aerial vehicle image based on multi-source data fusion. The application provides a method for extracting waterlogging disaster of northern spring maize unmanned aerial vehicle image based on multi-source data fusion. Multi-source data of unmanned aerial vehicle multispectral image, elevation data, meteorological data and soil data are fused, and finally a waterlogging disaster risk distribution map is generated, thereby providing decision support for agricultural production. The method comprises the following steps: acquiring unmanned aerial vehicle multispectral image, meteorological data, elevation data and soil data of a waterlogging disaster area to be evaluated; calculating a result file of a current pixel; calculating weight coefficients of the data through an MLP improved model; constructing a comprehensive evaluation formula; according to the comprehensive evaluation formula, each pixel is traversed, and waterlogging disaster risk is extracted according to a waterlogging disaster risk value of each pixel, and a waterlogging disaster evaluation image is generated.

The embodiment of the invention provides an unmanned aerial vehicle base station deployment method and system based on GNN and differential evolution. The method is applied to the technical field of wireless communication network optimization and artificial intelligence. The method comprises the following steps: acquiring a disaster area to construct a heterogeneous graph model; performing quality prediction on the positions of the candidate unmanned aerial vehicle base stations by using a graph neural network based on a heterogeneous graph model, and generating deployment priority distribution of candidate points; initializing a differential evolution algorithm population based on probability distribution; performing multiple times of iterative updating on the initial population through differential evolution iterative optimization to obtain an approximate global optimal solution of the three-dimensional position of the unmanned aerial vehicle base station; and after iterative convergence of the differential evolution algorithm, an individual with the minimum fitness is extracted from the final population, and the optimal three-dimensional deployment position of the unmanned aerial vehicle base station is obtained. In this way, high-quality unmanned aerial vehicle base station three-dimensional position configuration can be obtained in a complex deployment space, and the deployment efficiency, the coverage quality and the algorithm stability are effectively improved.

The application discloses a landslide monitoring method, which comprises the following steps: periodically collecting images of a graphic board arranged on a landslide monitoring point; identifying pixel coordinates of the graphic board corresponding to image collection time of the images of the graphic board; calculating displacement and / or displacement speed of the landslide monitoring point according to changes of the pixel coordinates of the graphic board between adjacent frames of images; and judging whether a landslide disaster occurs according to the displacement and the displacement speed of the landslide monitoring point. The method and device can effectively improve existing landslide disaster monitoring and early warning capability, reduce losses caused by landslide disasters, and better ensure safety of people's lives and property in landslide disaster areas.

1. Name of the product in this design: Airdrop pod (carbon fiber). 2. Applications of this design: In military scenarios, it is used to achieve rapid, accurate, efficient and reliable delivery of combat supplies and equipment; in the civilian emergency field, it is used to airdrop life support supplies and emergency equipment to trapped areas in emergency situations such as road damage and isolation of disaster areas. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D rendering 2.