340 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 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.

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

The invention discloses a 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 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 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 discloses an emergency management method and system based on multimodal transport, and relates to the technical field of emergency management, and the method comprises the following steps: S1, obtaining the dynamic geographic information of a disaster area in real time through a satellite remote sensing technology; according to the method, a dynamic cross-modal cooperative scheduling mechanism is constructed by fusing satellite remote sensing data, real-time mastering of geographic information of a disaster area is achieved, the problem that scheduling lacks real-time geographic information support is solved, connection nodes and transfer processes of different transportation modes are automatically adjusted through a model according to real-time road conditions, and the scheduling efficiency is improved. The conditions of transportation mode adjustment lagging and too long transfer waiting time are avoided, the timeliness of emergency material transportation is improved, information sharing and collaborative cooperation among the transportation modes are promoted by establishing cross-modal collaborative scheduling, an efficient transportation chain is formed, the emergency demand of emergency management for material transportation is met, and the emergency management efficiency is improved. Dynamic adaptation and efficient scheduling of multimodal transport of emergency materials are realized, and timeliness and effectiveness of emergency response are guaranteed.

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 provides a tree damage range intelligent identification method and system based on a satellite remote sensing image, and the method comprises the steps: determining a disaster cut image based on a satellite remote sensing image of a disaster area; fusing different spectral band data with the disaster cut image by adopting a multi-spectral band fusion technology to obtain a fused image; on the basis of the fused image and in combination with multiple indexes, a color mapping technology and a clustering algorithm are adopted for combined comparative analysis, and a post-disaster damage area preliminary screening image is obtained; inputting the post-disaster damage area preliminary screening image into a pre-constructed refined segmentation model to obtain a post-disaster tree damage detection result; performing spatial overlay analysis on the post-disaster tree damage detection result and power grid geographic data to obtain a risk identification result of a tree damage range; wherein the refined segmentation model comprises an encoder part adopting a visual architecture based on a shift window mechanism. The damage condition of trees around the power transmission line can be rapidly obtained after a disaster occurs, and the line patrol time is shortened.

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.

The invention discloses a natural disaster rescue-oriented satellite communication terminal rapid deployment and directional locking method, which comprises the steps of acquiring and preprocessing sensor data, performing magnetic field interference mode classification, attitude estimation and filtering-clustering double-loop feedback control, generating a compensation parameter set and feeding the compensation parameter set back to the magnetic field interference mode classification and attitude estimation. Circularly executing until a precision threshold value is met; and executing satellite direction positioning and locking based on the terminal attitude estimation value meeting the precision threshold and the satellite position information, and outputting a satellite pointing angle and a pointing control instruction. According to the method, the technical problems of large attitude estimation error, discontinuous parameter adjustment, lack of differential compensation and the like of a traditional method in a magnetic field interference environment are solved, and the deployment efficiency and the communication reliability of the satellite communication terminal in a disaster area environment are 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 relates to the technical field of satellite positioning, and discloses an emergency rescue method and system based on satellite positioning, and the method comprises the steps: obtaining the real-time information of a region and the function information of an emergency unit, dispatching a satellite to carry out image collection and signal linkage, analyzing a satellite image and signal data, and constructing an emergency rescue real-time model; a multi-search scheme is formulated according to the model and issued to each emergency rescue unit, search work is executed, the rescue scheme is dynamically adjusted according to feedback information, and a cyclic execution mechanism is formed. According to the method, real-time and accurate data support is provided through the satellite technology, the efficiency, accuracy and strain capacity of emergency rescue are improved, and the rescue efficiency is improved. Rapid and effective disaster area rescue is ensured, and the problem of poor linkage between a satellite and a ground unit in emergency rescue in the prior art is solved.

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

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.