125 results about "Natural hazard" patented technology

The invention discloses an urban inland inundation risk multi-level prediction method and device based on time-space diagram learning, a storage medium and a computer program product, and relates to the technical field of natural disaster risk prediction, and the method comprises the steps: collecting multi-modal urban hydrological data; performing hierarchical time modeling on the multi-modal urban hydrological data, and extracting a time embedding vector; constructing a heterogeneous graph based on the time embedding vector, and performing spatial feature aggregation calculation on the heterogeneous graph to obtain spatial embedding representation; and performing multi-level prediction according to the spatial embedding representation to obtain a multi-granularity waterlogging risk index. Through multi-modal data acquisition and preprocessing, layered time modeling, heterogeneous graph construction, spatial feature aggregation calculation and multi-level prediction, multi-modal urban hydrological data are effectively fused, and comprehensive and accurate urban inland inundation risk prediction is realized.

The invention relates to the technical field of electric power meteorological prediction, and aims to solve the problem that a current power grid natural disaster early warning system has defects in feature extraction and cannot fully utilize knowledge in related fields to enhance the prediction capability of a model. The invention discloses a power grid natural disaster early warning method based on a knowledge graph, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: S1, constructing a power meteorological knowledge graph based on meteorological data, geographic information and power equipment data corresponding to a power grid natural disaster; s2, performing feature enhancement on the meteorological data according to semantic features in the knowledge graph to form fusion features; s3, according to the fusion features, constructing a disaster weather prediction model based on a graph convolutional neural network and a self-attention mechanism; and S4, according to the disaster weather prediction model, performing early warning on the natural disasters of the power grid in the target weather. By adopting the method, the accuracy of power grid natural disaster prediction can be improved.

The invention relates to a BIM-based building structure intelligent management method. The method comprises the following steps: step 1, dynamic monitoring factor mining and multi-modal sensing: collecting meteorological hydrology, geological conditions, microenvironment and natural disaster historical data; 2, neural operator-driven virtual-real fusion dynamic analysis: converting the format of the collected data, mapping the data into a BIM model, integrating a virtual-real synchronization engine, and pre-training a Fourier neural operator through real-time displacement and strain data reverse calibration model boundary conditions in a construction stage, and establishing a direct mapping relationship between an external load and a structural response; 3, multi-modal data collaboration and hierarchical permission interaction: forming a multi-dimensional feature matrix through association of timestamps and space coordinates; 4, multi-modal graph convolutional network damage identification: learning dynamic relevance between nodes through an adaptive graph convolutional layer, and extracting damage sensitive features; and step 5, dynamic life early warning of digital twinning drive: predicting the remaining service period of the structure.

The invention relates to an underground assembly structure optimization method, equipment and a medium, and relates to underground space structure optimization. The method comprises the following steps: acquiring urban stratigraphic property related data; according to the urban stratum property related data and the use requirements of underground assemblies, on the basis of meeting preset design conditions and constraint conditions, traversing structural systems, component types and layouts in a pre-storage range, and constructing various underground assembly type structure design schemes; determining comprehensive anti-seismic capacity values of different underground assembly type structure design schemes; and determining an optimal underground assembly structure design scheme by adopting a preset multi-objective optimization algorithm according to the various underground assembly structure design schemes and the multi-objective optimization model. According to the method, the influences of stratum properties, use requirements and dynamic loads can be comprehensively considered, the reliability performance of the design scheme is accurately evaluated through systematic reliability analysis, and the safety risk is reduced; and the disaster resistance of the underground assembly in natural disasters such as earthquakes and the like is improved.

The embodiment of the invention relates to the field of natural disaster risk prediction, and discloses a green land optimization method based on an urban inland inundation risk assessment model, parameters in a maximum entropy model are optimized, and the maximum entropy model after parameter optimization is used as the urban inland inundation risk assessment model, so that model parameters suitable for a city can be optimized; compared with a default parameter method adopted in the prior art, the urban inland inundation risk assessment method has the advantage that the assessment precision can be effectively improved. Meanwhile, according to the method, the obtained urban environment variable test data is pre-evaluated to obtain the urban environment variable evaluation data, so that the operation precision and evaluation accuracy of the urban waterlogging risk model can be improved. In addition, according to the urban inland inundation risk probability distribution diagram and the urban supplementary greenbelt rate output by the urban inland inundation risk assessment model, the greenbelt optimization scheme is determined, and the urban inland inundation risk probability can be effectively reduced.

The invention discloses an offshore wind power plant disaster risk evaluation method based on an analytic hierarchy process. The method comprises the following steps: establishing a three-level risk evaluation index system comprising a target layer, a criterion layer and an index layer; constructing a comprehensive judgment matrix among the indexes based on an analytic hierarchy process, and calculating index weights; historical typhoon data and wind power plant data are collected, and an index historical data set is constructed; based on the index weight and the index historical data set, performing normalized score assignment on the index, and calculating a risk score of the disaster of the offshore wind plant under the typhoon effect; dividing the risk scores to obtain a risk grade division table, comparing the risk scores with the risk grade division table to obtain the disaster risk grade of the offshore wind plant under the typhoon effect, and completing risk evaluation. The method solves the problems that in the prior art, research objects are wide, index selection is extensive, and the method is difficult to apply to a specific disaster-bearing body, namely an offshore wind power plant, in offshore natural disaster-causing risk evaluation research.

The embodiment of the invention provides a power grid natural disaster early warning method and system based on artificial intelligence, and belongs to the technical field of artificial intelligence. The method comprises the following steps of: firstly, constructing a multi-source power grid and disaster information evolution association set which comprises power grid equipment operation dynamic information and natural disaster occurrence dynamic information of a corresponding region and is associated in real time through a region identifier and annotated with an information evolution trend, and then carrying out sequential risk conduction analysis on the multi-source power grid and disaster information evolution association set; then calling a pre-trained power grid disaster intelligent hierarchical prediction model to carry out multi-stage feedback prediction processing on the result, generating a power grid natural disaster hierarchical prediction result, executing early warning strategy dynamic iteration optimization according to the result, generating early warning strategy dynamic iteration adjustment parameters, and carrying out early warning strategy dynamic iteration adjustment. And finally, generating a power grid natural disaster accurate early-warning instruction based on the early-warning strategy dynamic iteration adjustment parameters, and sending the power grid natural disaster accurate early-warning instruction to a power grid monitoring center terminal. The power grid natural disaster early-warning method can comprehensively, accurately and dynamically perform early warning on the power grid natural disaster.

The invention relates to the technical field of optical signal processing and analysis, and particularly provides a submarine optical fiber vibration event detection method based on credibility weighting, and the method comprises the steps: obtaining an optical pulse echo matrix according to a received signal, and carrying out the decomposition of the signal through a low-rank and sparse decomposition model. A credibility coefficient is introduced to measure the reliability of the data, an optimization model is established, and the credibility coefficient is set to be a tide credibility coefficient or a natural disaster credibility coefficient according to the marine environment. Solving the optimization model to obtain a sparse item estimation matrix corresponding to the sparse item; and detecting a natural disaster event and a vibration event in the received signal according to the sparse term estimation matrix. According to the method, reliability evaluation of the signals can be dynamically adjusted according to different marine environments, noise and real vibration events are effectively distinguished, high robustness is kept in the complex and multi-interference marine environment, the method is not prone to being affected by interference sources, and event detection precision and accuracy are good.

The invention relates to an optimal maintenance position determination method and device for power grid fault prediction, belongs to the technical field of data preprocessing and recognition, and solves the problem of how to determine an optimal maintenance position after a power grid fault under natural disasters. The method comprises the following steps: acquiring historical fault data and related influence factor data, and performing interpolation processing through an adversarial network; selecting a plurality of key influence factors; judging the correlation degree between the power grid fault and each key influence factor by adopting an improved anti-entropy-grey correlation analysis method so as to select part of key influence factors as influence indexes; constructing and training a neural network to obtain a prediction model, taking the geological disaster probability and the influence index as input parameters of the prediction model, and constructing a geological disaster early warning model to obtain the geological disaster probability; and an optimal maintenance position optimization model is constructed based on the predicted power grid fault, and an optimal maintenance position is obtained by combining an objective function and a constraint condition. And accurately predicting the maintenance demand by using the geological disaster probability and the key influence index.

The invention relates to the field of natural disaster early warning, in particular to a road traffic natural disaster early warning, prevention and control system based on AIOT. The technical problems are that a road traffic natural disaster early warning prevention and control system in the prior art often depends on manual judgment or simple threshold setting, is not intelligent enough, mostly depends on a single data source for analysis, and ignores other important factors such as meteorology and geography; according to the technical scheme, the road traffic natural disaster early warning, prevention and control system based on the AIOT comprises an edge layer module, a transmission layer module, a data layer module, a supporting layer module and an application layer module; multi-dimensional data such as temperature and humidity, wind power and wind direction, rainfall, PM 2.5-10 and video images are fused, deep learning and mining are carried out through the AI intelligent analysis unit, the accuracy and comprehensiveness of natural disaster early warning are improved, automatic intelligent early warning and alarming are achieved through logical judgment and an AI algorithm, man-made misjudgment is reduced, and the working efficiency is improved. And the timeliness and accuracy of early warning are improved.

The invention discloses a mountain torrent disaster early warning method and system. The method comprises the following steps: acquiring cloud layer image data; processing the cloud layer image data based on a pre-trained deep learning model to obtain a predicted rainfall sequence; a hydrological and hydrodynamic model is constructed, scene simulation is carried out, and disaster threshold rainfall is delimited; and performing early warning in combination with the predicted rainfall sequence and the disaster threshold rainfall. The system comprises an image acquisition module, a rainfall prediction module, a threshold determination module and an early warning module. According to the invention, mountain torrent disaster early warning can be carried out in advance, important disaster relief and risk avoiding time can be won for mountain torrent disasters, and the rainfall prediction module is combined with air pressure observation and a cloud layer rainfall estimation method based on deep learning, so that the prediction precision is well guaranteed. The method can be widely applied to the field of natural disaster early warning.

The invention relates to the technical field of power system management, and provides an urban power grid scene probability risk assessment method and system under the influence of natural disasters. The method comprises the following steps: carrying out operation trend analysis on collected power grid data, and identifying and extracting key factors of urban power grid risks; according to the key factors, qualitatively building a PICE model for urban power grid risk assessment; and according to the collected power grid data, on the basis of a PICE model for urban power grid risk assessment, carrying out quantitative analysis on the situation evolution of the urban power grid under the influence of natural disasters, and carrying out risk prediction on the urban power grid according to an analysis result. Therefore, through quantitative analysis of the PICE model, main problems and main event evolution possibly faced by the urban power grid under the influence of various natural disasters are predicted, the key aspects of power grid operation are covered, the overall consideration of the stability of the urban power grid is combined, and it is ensured that the potential influence of the natural disasters on the urban power grid can be accurately reflected.

The invention discloses a risk management and control method, system and equipment based on a water conservancy project, and relates to the technical field of risk management and control. A sensor data drift abnormal index is calculated by obtaining multi-source monitoring data of a target water conservancy project after an earthquake, and an engineering structure change abnormal index is calculated based on a preset structure health model and epicenter parameters; and calculating an earthquake propagation influence index by combining the seismic source distance, calculating an earthquake influence coefficient by combining the three factors, and judging whether an original risk early warning evaluation system needs to be updated according to the earthquake influence coefficient. According to the method, after a natural disaster event (such as a slight earthquake) is encountered, whether the risk early warning evaluation system needs to be updated or not can be judged according to the actual disaster degree; it is ensured that early warning is not delayed or invalid, and the possibility of causing larger potential safety hazards is reduced; meanwhile, whether the risk early warning evaluation system is updated or not can be reasonably judged according to the influence of the actual earthquake on the construction of the hydraulic engineering project, and unnecessary waste of computing resources is reduced.

The invention discloses a satellite precipitation product improvement method, device and system based on a dual-time scale minimum error, and the method comprises the steps: carrying out the improvement of a first satellite precipitation product, a second satellite precipitation product and an interpolation precipitation product according to the obtained first satellite precipitation product, second satellite precipitation product and interpolation precipitation product; sequentially adopting a dual tool variable algorithm, a least square fusion algorithm and an inverse error weighting algorithm to respectively calculate first fusion rainfall products under a daily scale and a monthly scale; and fusing the first fused precipitation products under the daily scale and the monthly scale to generate a second fused precipitation product. Ground observation data are not needed, daily scale and monthly scale rainfall errors can be minimized at the same time, the thought of data fusion is incorporated, the performance of satellite rainfall products is enhanced, and detailed data support can be provided for natural disaster monitoring and early warning and regional water resource management.

The invention relates to a sensor assembly (100) for continuous monitoring in the region of natural hazards and / or for detecting natural hazard events, in particular gravimetric mass movements, preferably avalanches, landslides and rockslides, the assembly comprising: - a housing (1) for contacting, in particular partially and / or entirely, a mass to be monitored for the occurrence of a gravimetric mass movement, in particular a snow mass, soil mass or rock mass, which housing can be cast off by means of an aircraft; - at least one position sensor which is designed to determine the position of the housing, in particular in the form of geographic coordinates; - at least one acceleration / orientation sensor (Acc), in particular for detecting orientation, translational movement, acceleration and / or vibrations; - at least one temperature sensor; - a wireless communication device and / or a data memory; - an energy supply device; and - a control unit, - wherein the at least one position sensor, the at least one acceleration / orientation sensor (Acc), the at least one temperature sensor, the control unit, the communication device and the power supply device are each disposed in and / or on the housing, - wherein the control unit is connected to the position sensor, to the acceleration / orientation sensor (Acc) and to the temperature sensor and is designed to control the sensors in order to measure sensor measurement values, and - wherein the control unit is connected to the communication device and / or to the data memory and to the energy supply device and is designed to transmit sensor measurement values determined by the sensors by means of the communication device and / or to store said sensor measurement values in the data memory.

The invention provides an overhead transmission line fault loss assessment method based on spherical fuzzy and entropy weight methods, and relates to the technical field of transmission line fault loss assessment. Comprising the following steps: S1, acquiring natural disaster data of a to-be-evaluated line area; s2, dividing the fault loss into a plurality of evaluation dimensions, and constructing a hierarchical fault loss evaluation system by taking natural disasters as influence factors; s3, based on the disaster data, calculating fault loss cost of each evaluation dimension under different natural disasters; s4, using a fuzzy set theory to quantify the influence degree of the natural disaster on each evaluation dimension, and calculating a subjective weight; s5, calculating the objective weight of each evaluation dimension by adopting an entropy weight method; and S6, determining a comprehensive weight through the subjective and objective weight fusion model, and evaluating the fault loss of the overhead transmission line based on the comprehensive weight. In the whole life cycle cost calculation, fault loss evaluation is added, accurate quantification of multi-dimensional loss is realized, and the reliability of the line is improved in the later line design process.

The invention relates to the technical field of intelligent driving, and discloses an intelligent risk avoiding method and system for a vehicle, and the method comprises the steps: obtaining disaster early warning information from a natural disaster early warning platform; acquiring sensing data of the surrounding environment of the vehicle; according to the disaster early warning information and the sensing data, determining that the vehicle has a risk avoiding demand; and controlling the vehicle to execute risk avoiding operation according to the type of the disaster. In combination with disaster early warning information of the natural disaster early warning platform and sensing data of the vehicle to the surrounding environment, risk avoiding control is carried out on the vehicle, and the driving safety is improved.

The invention discloses an elasticity evaluation and improvement method for a power transmission system under the action of debris flow, is used for solving the technical problem of single fault rate consideration factor in the prior art, and belongs to the technical field of evaluation of anti-disaster capability and recovery capability of the power transmission system under extreme natural disasters (such as debris flow). The method comprises the following specific steps: step 1, constructing a debris flow model considering an impact force attenuation effect; s11, describing the influence area of the debris flow by using the three parameters; s12, carrying out stress analysis on the tower impacted by the debris flow; 2, constructing a fault model considering the accumulated impact process of debris flow on the power transmission element; s21, calculating a tower failure probability; s22, calculating the failure probability of the power transmission line; s23, calculating the overall failure probability of the power transmission line; 3, sampling at a constant time interval delta t based on the solved failure probability of the power transmission line within the debris flow duration delta T; 4, drawing an expected power supply power curve of the power transmission system; and 5, calculating an elastic index.

The invention discloses a temporary reinforcing device and method suitable for a land wind turbine generator. The temporary reinforcing device comprises a mooring unit, an anchoring unit and an auxiliary lifting unit. The cable tying unit comprises a main cable, a hook-and-loop fastener, an auxiliary cable, a tying cable and an auxiliary lifting cable. The two ends of the main cable can be wound into a telescopic rope loop in a slipknot tying mode. One end of each mooring rope is tied to the main mooring rope, and the other end of each mooring rope is tied to the corresponding anchoring unit; one end of each auxiliary lifting cable is tied to the main cable, and the other end of each auxiliary lifting cable bypasses the corresponding auxiliary lifting unit; the secondary piece of the hook-and-loop fastener is fixed on the main cable, and the primary piece of the hook-and-loop fastener is fixed on the secondary cable; and the other end of the auxiliary cable is connected with the other end of the main cable through the master slice of the magic tape. According to the design, convenience of quick mounting and dismounting and reliability and effectiveness under the extreme weather condition are considered, and the emergency reinforcement requirement of the wind power plant in the face of natural disasters such as typhoon is met.

The invention relates to the field of urban governance and management, and discloses an urban systematic risk prediction method, which comprises the steps of determining a first prediction value according to natural disaster information, environmental pollution information or emergency event information; when the first prediction value is greater than a preset first prediction value, first alarm information is sent out; when the first prediction value is smaller than or equal to a preset first prediction value, determining a second prediction value according to the public event information; when the second prediction value is larger than a preset second prediction value, second alarm information is sent out; wherein the second alarm information is used for prompting an advanced response to the public event information; when the second prediction value is smaller than or equal to a preset second prediction value, determining a third prediction value according to the economic fluctuation information; and when the third predicted value is greater than a preset third predicted value, third alarm information is sent out. According to the invention, systematic risk prediction in urban governance can be realized.

The present invention discloses a method and system for the site selection of disaster relief material reserve warehouses based on earthquakes of magnitude 7 or above, relating to the technical field of the site selection of disaster relief material reserve warehouses, including: determining an intensity area where the earthquake magnitude is 7 or above and the earthquake intensity is 8 or above according to the earthquake literature materials and earthquake intensity attenuation models of the research area; using remote sensing building recognition technology and eight-connected mode to process the intensity area to obtain the disaster-affected area; determining the farthest safe flight distance when helicopters transport disaster relief materials and determining the candidate areas for the site selection of disaster relief material reserve warehouses within the research area; screening the candidate areas for the site selection according to the constraint relationship between the farthest safe flight distance and the disaster-affected area, as well as natural disasters, terrain indices, traffic conditions, urban planning, etc. of the research area, so as to determine the optimal site selection area. The address area selected by the present invention can achieve the purpose of practical feasibility.

The invention belongs to the field of ionosphere detection, artificial intelligence and deep learning, and particularly relates to a lightning electromagnetic pulse-based large-area ionosphere D layer height measurement method, system and equipment. The problems that a traditional detection method is high in cost, limited in coverage range and prone to being interfered by natural disasters and strong electromagnetic pulses are solved. The method comprises the following steps: acquiring lightning event information based on an electromagnetic pulse detection station; calculating a ground wave and sky wave arrival time difference based on a pre-trained deep learning model, and calculating to obtain the ionosphere D layer height of a single position; performing gridding processing on the obtained ionosphere D layer height data at different positions according to certain time and spatial resolution, performing interpolation null value filling on the gridded data, and performing smoothing processing; and outputting a calculation result of the D layer height of the large-area ionized layer. According to the invention, wide-area low-cost ionosphere detection is realized by adopting the natural lightning radiation source, and the measurement precision is improved by optimizing the model for different lightning types.

The invention discloses a mountain disaster susceptibility prediction method and device considering a composite terrain distance, and relates to the field of natural disaster risk assessment, and the method comprises the steps: obtaining a mountain disaster sample and a multi-source environment factor in a research area range; terrain factors are screened from the multi-source environment factors, and the weight of each terrain factor is calculated based on an entropy weight method; on the basis of the Euclidean space distance, a weighted terrain difference distance is introduced, and a composite terrain distance is constructed through fusion; sorting and screening the mountain disaster sample data according to the composite terrain distance, and determining a local reference sample set and a geographic weighted weight corresponding to each local reference sample; adopting a Bayesian optimization method to perform joint optimization on the bandwidth parameter of the geographical weighted weight, the weight parameter of the composite terrain distance and the structure parameter of the random forest model to obtain an optimal parameter combination; and under the constraint of the optimal parameter combination, carrying out point-by-point prediction on the research area. According to the invention, the precision of mountain disaster susceptibility prediction can be improved.

The present application relates to the technical field of power meteorological prediction. In order to solve the problem that the current power grid natural disaster warning system has deficiencies in feature extraction and fails to make full use of the knowledge in related fields to enhance the prediction ability of the model, a power grid natural disaster warning method, a computer device, a computer-readable storage medium, and a computer program product based on a knowledge graph are disclosed. The method includes step S1 of constructing a power meteorological knowledge graph based on meteorological data, geographic information, and power equipment data corresponding to the occurrence of power grid natural disasters; step S2 of enhancing the features of the meteorological data according to the semantic features in the knowledge graph to form fused features; step S3 of constructing a disaster weather prediction model based on a graph convolutional neural network and a self-attention mechanism according to the fused features; and step S4 of warning of power grid natural disasters under target weather according to the disaster weather prediction model. Using this method can improve the accuracy of power grid natural disaster prediction.

Provided in the present invention is a wind-resistant seismic isolation device, the device comprising an upper support and a lower support which are fitted with each other, wherein corresponding replacement core cavities are provided in the center of the upper support and the center of the lower support; a replaceable core is arranged in each replacement core cavity, and an annular groove is formed in the middle of the replaceable core; and a sliding block having a width fitted with the diameter of the replaceable core is provided on a side surface of the lower support. The wind-resistant seismic isolation device for roads and bridges is easily subjected to various natural disasters and is thus damaged, such that a replaceable core is provided to prevent a device main body from being damaged; and an annular groove is provided in the middle of the replaceable core, and is a weak link in the device, such that the quality problem of a road and bridge support structure is prone to occur here, and the replaceable core is used instead of replacing the entire road and bridge.

The invention provides a vehicle insurance claim amount prediction method, system and device under a natural disaster condition and a medium, and the method comprises the steps: obtaining a data set of vehicle insurance claim data; the data set is segmented, the data set is segmented into a training set, a test set and a verification set, and the training set is used as a sample set for model training; a base model and a meta model are constructed, and the base model comprises a time sequence base model and a non-time sequence base model; respectively inputting the training set data into the time sequence base model and the non-time sequence base model for training to obtain a prediction result of the base model; and after the base model training is completed, taking prediction results of the time sequence base model and the non-time sequence base model on the verification set as input, inputting the prediction results into the meta-model, and carrying out prediction model training to obtain a final prediction result. The method can achieve the prediction of the insurance claim amount when a natural disaster occurs in each region.

The present invention discloses a construction external failure fault diagnosis method, device, equipment and storage medium; the method comprises: acquiring fault information, identifying the fault information through a fault identification model to obtain identification result information, and determining the fault type based on the identification result information and first environmental monitoring information; when the fault type belongs to a non-lightning fault, determining whether the fault type belongs to a metallic fault by performing feature analysis on the fault waveform information in the fault information, and determining whether the fault type belongs to a non-natural disaster fault through second environmental monitoring information; when the fault type belongs to a metallic fault and a non-natural disaster fault, determining the probability of the fault being a construction external failure fault based on the fault information and the second environmental monitoring information according to a preset construction external failure fault correlation factor analysis rule, so as to accurately analyze and determine the probability of the occurrence of a construction external failure fault, and improve the accuracy and reliability of line fault diagnosis.

The invention relates to a large-span special-shaped double-layer upright quadrangular pyramid net rack roof and a construction method. A large-span special-shaped double-layer upright quadrangular pyramid net rack roof structure, a large-span special-shaped roof supporting column beam system, a net rack roof sectional assembly adjustable jig frame structure and a special-shaped roof auxiliary installation support auxiliary falling position adjusting device are researched and developed. The double-layer upright quadrangular pyramid grid structure has extremely high overall stability and bearing capacity, can effectively resist the influence of natural disasters such as strong wind and earthquakes, ensures the safety of buildings, and can obtain better technical, economic and social benefits when being applied to actual engineering.

The present invention relates to an integrated management method and system for an island city, comprising the following steps: collecting historical environmental monitoring data, traffic monitoring data and resource data of the island city; constructing a meteorological prediction model and performing meteorological prediction, and calculating various natural disaster coefficients of the island city based on the meteorological prediction results and the historical environmental monitoring data; constructing a road flow model through the traffic monitoring data, and calculating the flow coefficient of each road in the island city through the road flow model; constructing a resource utilization efficiency model based on the resource data, and calculating the resource utilization efficiency coefficient of the island city through the resource utilization efficiency model; constructing an island city management decision model through various natural disaster coefficients of the island city, the flow coefficient of each road and the resource utilization efficiency coefficient, and providing a reference management decision plan for the island city through the island city management decision model.

The invention provides an urban natural disaster risk prevention and control method and system, and belongs to the field of urban disaster risk prevention and control. The urban natural disaster risk prevention and control method comprises the following steps: acquiring monitoring data of each region in a city; analyzing and determining correlation degree data among the monitoring data according to a pre-constructed historical disaster situation database; determining a plurality of monitoring data groups according to the correlation degree data and a clustering algorithm; calculating an overall risk value of each monitoring data group; determining a disaster risk possibility score of each region in the city according to the overall risk value; based on the method, the analysis of the monitoring data of each region in the city can be realized, and the disaster risk possibility of each region is determined based on the monitoring data, so that the risk prevention and control of urban natural disasters are realized.