398 results about "Disaster area" patented technology

The invention provides a disaster area unmanned aerial vehicle cluster dynamic task allocation and cooperative control method and system. The method comprises the steps that S1, original data of a disaster area scene are collected in real time through ground IoT equipment; s2, performing dynamic task priority marking on the original data, and integrating multi-dimensional information of the unmanned aerial vehicle; s3, tasks are allocated to the unmanned aerial vehicle based on a hybrid allocation strategy, and an initial obstacle avoidance path is generated; s4, when the unmanned aerial vehicle executes the task, the flight path is updated in real time, temporary obstacles are avoided, path conflicts are coordinated through a conflict detection algorithm, and task redistribution or degradation is carried out if necessary; s5, periodically, globally and dynamically adjusting task priorities or correcting paths; and S6, constructing a self-adaptive closed-loop control mechanism, and tracking multi-dimensional information and task attributes of the unmanned aerial vehicle in real time. According to the method, the global resource utilization rate is maximized while the second-level response of the high-priority task is guaranteed, and finally, the robustness and the adaptive ability of the unmanned aerial vehicle cluster in a complex scene are realized.

The invention relates to a geological disaster automatic identification method and system based on multi-source remote sensing data, and belongs to the technical field of geological disaster monitoring, and the method comprises the steps: collecting the multi-source remote sensing data of a to-be-detected region, carrying out the cross-modal registration, and generating a registered multi-source data set; carrying out multi-modal feature extraction based on the multi-source data set and carrying out space-time correlation analysis to obtain a multi-modal feature map; performing dynamic weight distribution on the multi-modal feature map, generating a fusion feature vector, inputting the fusion feature vector into a pre-trained geological disaster prediction model, and outputting a geological disaster probability map; performing binarization segmentation on the geological disaster probability graph to obtain a potential disaster area mask; and according to the geological disaster probability map and the potential disaster area mask, based on a preset joint determination rule of the surface deformation rate and the gradient characteristics, carrying out risk grade division on the potential disaster area to obtain a risk grade distribution map of the to-be-detected area. The disaster prediction precision can be improved, and the emergency response capability is enhanced.

The invention discloses an emergency resource dynamic scheduling method and system based on artificial intelligence, and the method comprises the steps: building a dynamic road network topology through the fusion processing of multi-source disaster area state data, achieving the dynamic evolution modeling of a road network state based on a space-time attention map network model, and generating a path passing weight coefficient and a disaster diffusion simulation result; a multi-objective optimization model with material transportation timeliness, a path risk coefficient and disaster area demand urgency degree as optimization objectives is established, and space-time constraint conditions and a dynamic weight adjustment mechanism are introduced, so that a material distribution scheme with a space-time attenuation factor is solved; and finally, generating an emergency resource scheduling strategy in combination with path priority planning and a space-time matching algorithm. According to the invention, the timeliness of emergency resource scheduling in a complex environment can be improved.

The invention provides an emergency disaster dynamic decision-making method and system based on a multi-modal AI large model, and the method comprises the steps: firstly obtaining a real-time disaster monitoring data set of a target disaster area, covering disaster image collection, environment sensing, historical response records and geographical distribution topological data, carrying out the feature extraction of the real-time disaster monitoring data set, and carrying out the feature extraction of the real-time disaster monitoring data set; then calling a pre-trained multi-modal dynamic decision-making model, analyzing the disaster scene matching degree of the multi-modal disaster feature set, generating a matching degree score and disaster response optimization strategy set, and screening a target optimization strategy based on the matching degree score, and generating an emergency decision instruction set and sending the emergency decision instruction set to a disaster response terminal, finally obtaining decision execution feedback data of the disaster response terminal, and performing incremental parameter optimization processing on the multi-modal dynamic decision model to realize more accurate and effective emergency disaster dynamic decision.

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 relates to a short-time heavy rainfall region collaborative early warning method based on multi-source fusion data. The method comprises the following steps: firstly, acquiring multi-source heterogeneous data of a ground rainfall station, a radar, a satellite and the like, and converting the multi-source heterogeneous data into a rainfall monitoring data set by adopting multi-scale interpolation and standardization processing; secondly, preprocessing the data set to extract precipitation space features, and combining wind field and air pressure field data to input a time sequence prediction model to generate a precipitation path prediction sequence; and then, processing the prediction sequence by using a graph structure segmentation algorithm, and generating a high-precision early warning region boundary sequence. And finally, carrying out space overlapping degree calculation on the early warning region boundary sequence and a historical disaster region, and when a preset threshold value is exceeded, comprehensively analyzing parameters such as a disaster type and an influence range by adopting a probability model to generate a graded early warning sequence. By adopting the method, the integrity and accuracy of the rainfall monitoring data set can be improved, and the timeliness and reliability of short-time heavy rainfall early warning can be effectively improved.

The invention discloses a disaster area surveying and mapping and deep learning analysis system based on cooperation of a low-altitude unmanned aerial vehicle and satellite remote sensing, and belongs to the field of surveying and mapping and deep learning, and the system comprises the steps: carrying out the preliminary cruise scanning of a suspected disaster area through a low-altitude unmanned aerial vehicle platform, and obtaining high-resolution local image data from the suspected disaster area; marking abnormal features identified in the image to generate preliminary disaster distribution information; according to the real-time change information, in combination with time sequence image data of satellite remote sensing, calculating the dynamic evolution trend of the disaster hotspot by applying a long-short term memory network time sequence analysis algorithm, and determining the evolution direction and speed; and constructing a disaster diffusion prediction model based on data driving according to the evolution direction and speed data, adjusting an unmanned aerial vehicle cruising route and a satellite monitoring key area according to a possible diffusion path identified in a prediction path distribution diagram, and continuously tracking key nodes on the path to obtain updated monitoring data.

The invention discloses a meteorological disaster risk assessment method and system based on disaster risk grading, and belongs to the technical field of meteorological disaster assessment, and the method specifically comprises the steps: collecting the meteorological and disaster-bearing body data and geographic information of a disaster area, and carrying out the preprocessing through a spatial interpolation method; setting meteorological disaster-inducing factors, disaster-pregnant environments and disaster-bearing body vulnerability indexes, and constructing a judgment matrix and distributing index weights by using an analytic hierarchy process; constructing a disaster risk grading model according to the risk indexes and the corresponding weights, inputting data of the to-be-evaluated region, calculating a risk value, judging a risk grade, and generating a report; establishing a real-time monitoring system, updating a risk assessment result, and issuing early warning information when a risk value reaches an early warning threshold value; according to the invention, through comprehensive analysis and real-time monitoring, the accuracy and timeliness of meteorological disaster risk assessment are improved.

The invention provides a disaster site satellite communication blind area image acquisition and transmission method of a low-altitude unmanned aerial vehicle relay. The method comprises the following steps: acquiring disaster area information and executing adaptive multi-modal image acquisition; establishing a low-altitude unmanned aerial vehicle relay network; performing feature extraction and semantic analysis on the marked image data set, generating non-uniform image fragments and calculating a transmission priority; and monitoring a communication environment state, and dynamically adjusting a transmission strategy through a dual-cycle feedback mechanism. According to the method, semantic integrity is ensured, transmission window perception is realized, parameter adjustment is stabilized through a double-loop feedback mechanism, and the problem of efficient transmission of the satellite communication blind area edge image in a disaster site is solved.

A disaster relief system using AI-powered intelligent antennas in drone swarms, consisting of: a plurality of drones configured to collect environmental data in real time, with the drones being deployed in a disaster area environment; a smart antenna system mounted on each drone, each smart antenna system configured to perform adaptive beamforming to maintain communication links between drones; an artificial intelligence (AI) processing unit configured to receive real-time signal data, including obstacle information, drone positions, and interference patterns from the disaster environment, analyze the received signal data using reinforcement learning algorithms, generate optimized beamforming parameters based on the analysis, and transmit the optimized beamforming parameters to the intelligent antenna systems; a swarm communication protocol module configured to establish communication links between the plurality of drones using the optimized beamforming parameters, coordinate task assignments within the drone swarm, and maintain network integrity during operation; and a central control system configured to monitor all swarm operations, process environmental data, and adapt swarm behavior based on disaster relief requirements.

The invention relates to the technical field of disaster relief path planning, in particular to a disaster relief path planning and mode optimization method and system based on a large model, and the method comprises the following steps: collecting and preprocessing data; constructing a prediction model; planning and optimizing a path; a decision support system; real-time adjustment and feedback are carried out; the method has the beneficial effects that the disaster condition and the influence thereof can be quickly analyzed by using big data and a machine learning technology, so that the time required by traditional rescue path planning is greatly shortened, and the emergency response speed is improved. Within golden 72 hours of post-disaster rescue, each second is crucial. The system can provide an optimal rescue path and a resource allocation scheme in an extremely short time, help rescue workers to quickly arrive at a disaster area, and save more lives.

The invention discloses a rainstorm disaster loss assessment method and system based on machine learning, and the method comprises the steps: taking data of a rainstorm disaster data source in a preset region in a specified time period as to-be-analyzed data; dividing the preset area according to the to-be-analyzed data based on the rainfall, taking the preset area greater than a rainfall threshold as a disaster area, otherwise, taking the preset area as an uncertain area, and calculating the destructive power of the uncertain area according to a hydrological mechanism and a drainage system. Adding the uncertain area greater than a destructive power threshold value into the disaster area; performing comprehensive risk analysis on the to-be-analyzed data of the disaster area to obtain a comprehensive risk index, calculating a damage degree index of the disaster area, and performing real-time dynamic evaluation on the disaster area according to the damage degree index and the comprehensive risk index to obtain a loss index; and constructing a rainstorm disaster loss evaluation model according to the loss index, and outputting an evaluation result.

The invention discloses a crop flood disaster large-area quantitative evaluation method and system, and the method comprises the steps: obtaining flood disaster data and SAR time sequence remote sensing data, obtaining a flood disaster submerging space range based on a deep learning image semantic segmentation model according to an SAR remote sensing image, and obtaining a crop flood disaster flood area based on landform and river water level observation data. Estimating the flood submerging depth of the DEM pixel scale by using a submerging depth model; recognizing a flood disaster area by combining SAR (Synthetic Aperture Radar) time sequence remote sensing data, the precipitation observed on the ground and the water level change of a river channel; core evaluation indexes of crop flood submerging range, submerging duration and submerging depth are calculated, flood disaster events are associated with crop periods, and a flood hazard degree equation is constructed by taking flood stress sensitivity as a basis. According to the method, local crop characteristics of a research area are fused, a flood disaster event is associated with a crop period, and the flood hazard degree equation is constructed by taking the flood stress sensitivity as one of parameters, so that quantitative calculation of the flood hazard degree is realized.

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

The invention discloses an emergency command system and method based on satellite communication, and particularly relates to the technical field of radio transmission. On-site information of a disaster area is obtained in real time, the information is transmitted to an emergency command center through a satellite communication channel, dynamic command decision and resource optimization are achieved, in the signal transmission process of the system, the phase noise and the inter-carrier interference ratio of satellite signals are monitored and analyzed in real time, whether the signals are distorted or not is judged, and when the signals are distorted, the emergency command center is started. The system is automatically switched to a standby satellite communication channel to ensure that information transmission is not interrupted; besides, the satellite communication link monitoring system can adjust the signal quality in real time and carry out fault-tolerant processing, continuous operation of the command system is ensured, emergency response failure caused by communication interruption is avoided, the stability of satellite signals in various environments is effectively improved, and efficient operation of the emergency command system is ensured.

The invention discloses a disaster area robot operation path and energy consumption optimization method and system, and the method comprises the steps: constructing a multi-factor coupled unit path energy consumption prediction model, fusing the terrain, real-time wind resistance and battery attenuation factors of a disaster area, and generating a precise energy consumption map; a hierarchical reinforcement learning path scheduling framework is established, an upper-layer strategy network generates a global path by taking energy consumption and time optimization as double targets, a lower-layer execution network processes sensor data in real time based on MPC, and path stability and low energy consumption are considered during obstacle avoidance; designing task plug-in mechanism optimization scheduling triggered by an event; and compressing an upper-layer strategy network by adopting knowledge distillation, and deploying to a robot edge end. According to the method, the endurance and task completion rate are improved through the precise energy consumption model, global-local control disjunction is cracked through the layered architecture, the high-real-time requirement is met through the plug-in mechanism, the edge deployment adapts to the weak network / broken network environment, and the method is suitable for high-real-time scenes such as post-disaster inspection and material transportation.

The invention discloses a forest fire fighting robot path planning method and system based on ground-air cooperation, and the method comprises the steps: carrying out the air scanning of a disaster area through employing an unmanned plane to carry a laser radar and infrared and visible light cameras, and obtaining the space information and fire intensity of a fire point in real time; the unmanned aerial vehicle is in communication connection with the wind power fire extinguishing robot, shares the three-dimensional point cloud model and performs environment proofreading and positioning; an improved A * algorithm is used for path planning, and a path planning mathematical model with the minimum disaster degree as the target is constructed; through fusion of a global satellite navigation system and an inertial navigation system, the working path of the wind power fire extinguishing robot is dynamically updated and planned; after a target fire point is reached, the wind power fire extinguishing robot identifies a fire source, calculates a safe fire extinguishing distance by using a flame detection model result, and dynamically adjusts an air duct angle according to a flame state to perform fire extinguishing operation. Through cooperative work between the unmanned aerial vehicle and the fire extinguishing robot, the efficiency and the intelligent degree of fire extinguishing operation are improved.

The invention discloses an intelligent alarm method for driving intervention after a disaster, and the method comprises the steps: collecting a visible light image and an infrared thermal imaging image, and scanning the coordinate data of a three-dimensional space at the same time; inputting the visible light image into a convolutional neural network model to perform road obstacle type identification, and generating three-dimensional contour data of the obstacle in combination with the three-dimensional space coordinate data; when the height of the obstacle exceeds a height threshold value and the distance between the obstacle and the vehicle is smaller than a distance threshold value, uploading a feature data set containing the GPS coordinates, the three-dimensional contour data and the obstacle type to a cloud server through a cellular network; the cloud server matches the feature data set with a pre-stored disaster area digital elevation model based on the three-dimensional space coordinate data, and calculates the ratio of the projection area of the obstacle on the current lane cross section to the lane width; and when the ratio exceeds a ratio threshold value, generating an alarm instruction. According to the invention, the accident risk caused by misjudgment of obstacles or improper path planning after disasters can be effectively reduced.

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

The invention provides a multi-unmanned aerial vehicle communication sensing cooperation method in a power grid fault scene, and the method combines unmanned aerial vehicle dynamic topology reconstruction and a multi-stage optimization strategy, and achieves efficient information acquisition for emergency scenes of damaged ground base stations and large-area power failure in disaster areas after a power grid fault. Power flow entropy is introduced to analyze power grid node vulnerability, communication unmanned aerial vehicle deployment is optimized, and an optimal communication coverage strategy is solved based on a multi-target grey wolf optimization algorithm; meanwhile, a graph theory method and a shortest path search algorithm Dijkstra are used for optimizing the inspection path of the unmanned aerial vehicle, so that the fault diagnosis efficiency is improved; under a two-stage optimization framework, in the first stage, important load nodes are preferentially covered and routing inspection is executed, in the second stage, supplementary optimization is carried out on an uncovered area, and finally a global optimal solution considering unmanned aerial vehicle resource efficiency, communication quality and information acquisition is obtained. The method improves the recovery efficiency of a post-disaster power grid, and is suitable for the emergency response of a power system in an extreme disaster environment.

The invention discloses a disaster emergency material distribution method based on multi-agent collaborative optimization, and the method comprises the following steps: S1, obtaining the real-time data of a disaster area, and generating a multi-dimensional data set; s2, constructing a collaborative optimization model based on the game theory, and generating a multi-level risk assessment model; s3, demand prediction is carried out by using the space-time convolutional neural network; s4, optimizing a material distribution path and resource scheduling; s5, automatically adjusting a scheduling strategy through a strategy gradient algorithm; s6, performing global optimization through particle swarm optimization, and adjusting a transportation path; and S7, dynamically adjusting a material distribution scheme and path planning. According to the invention, through the multi-level risk assessment model and the optimization algorithm, the material distribution path and the scheduling strategy are adjusted in real time, and the efficiency, the accuracy and the emergency response capability of disaster emergency material distribution are improved.

The invention discloses a path optimization and resource intelligent scheduling method and system based on digital twinning. The method comprises the following steps: constructing a digital twinning three-dimensional model of disaster area infrastructure to realize three-dimensional scene reconstruction; constructing a first-aid repair path optimization model on the basis of a multi-objective function to optimize paths among the sites in the digital twinborn three-dimensional model; a job resource multi-target scheduling model is established, and intelligent resource scheduling of the digital twin three-dimensional model is realized; setting constraint conditions of scheduling and execution; updating the multi-target path optimization model and the job resource multi-target scheduling model once every other scheduling cycle by adopting a model prediction control idea; according to the method, collaborative optimization of the first-aid repair path and resource scheduling can be realized, an optimal scheme is generated by combining terrain and risk constraints, and the first-aid repair operation efficiency is improved.

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 safety management, in particular to a city safety emergency management system design scheme based on a three-dimensional map, and the scheme comprises a facility space construction module, a risk identification module, a disaster type area identification module, a linkage early warning marking module and an avoidance path analysis module. According to the invention, risk scoring is carried out on facility node coordinates, function levels, the number of accommodated people and service radiuses in a city three-dimensional map, the risk distribution condition of each facility node is visually reflected, and the node risk levels are displayed in a three-dimensional manner by setting the identification map layer through column height, color marking and flicker frequency. Visual focusing and spatial clustering of risk nodes are realized, a commander is helped to quickly identify a high-risk area, response grading of a disaster situation interaction area is effectively refined by performing composite situation grade evaluation on a composite disaster type area and marking an early warning icon, an avoidance passage path is automatically screened and planned, and traffic congestion and secondary risks are avoided in real time.

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

The invention relates to the technical field of mountain torrent disaster emergency rescue, and particularly discloses a sky-ground collaborative mountain torrent disaster emergency rescue method, which comprises the following steps: deploying and training the following emergency rescue key technologies based on a federal learning framework; the method comprises the following steps: firstly, proposing an unmanned aerial vehicle disaster area cluster wide-area optimal deployment algorithm based on a dual-target multi-arm tiger machine, and balancing the coverage range and communication efficiency of an unmanned aerial vehicle cluster; secondly, developing a space-ground collaborative two-stage semi-definite relaxation disaster area positioning method to ensure that the disaster area is accurately positioned; finally, an emergency rescue method based on unmanned aerial vehicle remote sensing vision and dynamic path optimization is provided, and the emergency rescue and evacuation efficiency is improved.

The invention provides a ground satellite signal distributed cooperative processing method and system, and relates to the technical field of communication, and the method comprises the steps: extracting a user MAC address hash value, an emergency priority code and a spectrum occupancy matrix from a synchronous network, and generating a triple feature vector; transmitting the triple feature vectors through a double-layer Mesh network, selecting two reference target points with fixed geographic coordinates on the ground of the disaster area, obtaining three-dimensional coordinates of the reference target points, and constructing a space reference straight line; according to the real-time position data of each node, calculating a vertical projection distance from each node to the space reference straight line, and generating a node offset parameter; and dynamically generating a refraction correction coefficient of the signal propagation path based on a comparison result of the node offset parameter and a preset threshold value, and when the offset exceeds the threshold value, increasing a correction weight by adopting a piecewise linear interpolation method. According to the invention, high-reliability and low-delay emergency communication coverage can be realized.

The invention belongs to the technical field of mobile crowd sensing. The invention provides a heterogeneous multi-agent collaborative awareness and rescue method based on hierarchical reinforcement learning. According to the embodiment of the invention, the unmanned vehicle is responsible for collecting the sensing data from the sub-task area and completing data processing locally in real time. A processed result is transmitted to a rescuer in real time through a wireless communication technology, efficient action of the rescuer in a disaster area is supported, and then accurate rescue is achieved. The unmanned aerial vehicle can be flexibly dispatched in the disaster area by means of the high maneuverability and the rapid deployment capability of the unmanned aerial vehicle. The unmanned aerial vehicle not only can independently complete data sensing and calculation in a task area which cannot be covered by the unmanned vehicle, but also can directly participate in rescue execution in a high-risk area which cannot be reached by rescuers. When the perception and rescue tasks in the task area are successfully executed, the task is considered to be successful, the overall target is to maximize the task completion rate, and it is ensured that as many tasks as possible can be successfully completed within given time through an efficient task distribution and cooperation mechanism.

The invention discloses a flight path planning method for rapidly covering a key disaster area by an unmanned aerial vehicle cluster. The method comprises the following steps: constructing a disaster-affected area tile map for gradually analyzing disaster-affected conditions in a single tile map area; information obtained by a sensor, such as GPS position and image data of an unmanned aerial vehicle, is transmitted into an image segmentation network based on deep learning, and the type and the influence range of a disaster area are extracted; establishing a disaster degree calculation model in a single tile map, and calculating whether a current block area has a key disaster area or not; the disaster situation distribution condition of the key disaster-affected area is established, a disaster situation estimation model is generated to detect whether there are remaining undetected tile maps in the key area, and a detection route is planned for an unmanned aerial vehicle cluster by using an unmanned aerial vehicle course guidance function based on disaster situation evolution characteristics in subsequent route planning. According to the method, the coverage detection efficiency of the unmanned aerial vehicle cluster on key disaster areas is improved, the complexity of flight path planning of the unmanned aerial vehicle cluster system is reduced, and the method can be widely applied to the fields of unmanned aerial vehicle technology and artificial intelligence.

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.