Extreme weather event prediction and emergency response system

Abstract

The invention belongs to the technical field of extreme weather emergency management, and particularly relates to an extreme weather event prediction and emergency response system. The problems of multi-source data splitting, disaster chain prediction missing, static plan stiffness and lack of closed-loop evolution in the prior art are solved. A global disaster map is constructed through real-time fusion of multi-source data, a physical mechanism and machine learning are coupled to realize refined deduction of extreme weather and secondary disasters, a self-adaptive emergency scheme is generated based on a dynamic resource library and an optimization algorithm, and disaster feedback data is utilized to drive model and strategy iteration, so that the disaster situation feedback data can be used for driving the model and strategy iteration. And a'prediction-decision-feedback-evolution 'intelligent closed loop is formed. The method has the advantages that information islands are eliminated, the disaster chain modeling bottleneck is broken through, resource scheduling is dynamically optimized, a self-evolution mechanism is established, and the extreme weather response efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of extreme weather emergency management, more particularly, the present application relates to an extreme weather event prediction and emergency response system. BACKGROUND

[0002] With the intensification of global climate change, extreme weather events are showing a trend of frequent, wide and strong occurrence. Disasters such as rainstorm floods, typhoon storm surges and extreme high temperatures pose a systematic threat to human society. Traditional disaster management relies on scattered weather forecasts, static emergency plans and manual scheduling, which has bottlenecks such as decision lag and resource mismatch when dealing with sudden and chain disasters. Especially when multiple disasters are coupled to trigger a disaster chain (such as rainstorm -> mountain flood -> landslide), the existing technical system is difficult to achieve accurate prediction and rapid response, and an intelligent prediction-decision closed-loop system needs to be built. The existing technology has the following deficiencies:

[0003] 1. Multi-source data fragmentation leads to situational awareness blind area

[0004] Existing systems usually process satellite, sensor, social media and other heterogeneous data sources independently, lacking real-time fusion mechanism across platforms. Meteorological departments focus on physical observation data, civil affairs departments rely on historical cases, and public reports are not effectively integrated. This fragmentation causes temporal and spatial blind spots in pre-disaster risk identification, especially in complex terrain areas, making it impossible to build a unified disaster evolution map for the whole area, resulting in high false alarm rate in early warning.

[0005] 2. The prediction model has weak disaster chain deduction ability

[0006] Traditional numerical weather prediction models can simulate atmospheric physical processes, but they lack fine-grained prediction of sudden extreme weather. Data-driven machine learning models ignore physical laws and have poor generalization in rare disaster scenarios. More importantly, existing technologies rarely model disaster chain transmission mechanisms (such as how a typhoon triggers a storm surge and causes seawater to back up), resulting in a lack of secondary disaster risk assessment, making emergency response incomplete.

[0007] 3. Static plan is difficult to adapt to dynamic disaster

[0008] Current emergency plans are mostly based on historical experience and are preset in paper or electronic documents. When the real-time disaster deviates from the preset path (such as bridge collapse blocking evacuation routes, hospital power failure affecting treatment capacity), the system cannot dynamically reconstruct the plan. Resource scheduling relies on human experience, which can easily cause problems such as overcrowding in shelters and unbalanced allocation of rescue forces in multiple concurrent disasters, significantly reducing emergency efficiency.

[0009] 4. Response process lacks closed-loop evolution mechanism

[0010] From the occurrence of the disaster to the end of the response, the existing system does not establish a closed loop of "prediction-action-feedback". Sensor data, public reporting information, and rescue effect indicators during the disaster are not used to correct the model in real time. When similar disasters occur repeatedly, the system still follows the inherent strategy and cannot accumulate historical lessons to optimize decision-making, forming a vicious cycle of "repeated tuition".

[0011] Therefore, an extreme weather event prediction and emergency response system is proposed to solve the above problems. SUMMARY

[0012] In order to overcome the above-mentioned defects of the prior art, embodiments of the present application provide an extreme weather event prediction and emergency response system to solve the problems raised in the background art.

[0013] To achieve the above object, the present application provides the following technical scheme: an extreme weather event prediction and emergency response system comprising the following modules that cooperate with each other:

[0014] The multi-source data fusion module accesses meteorological satellite remote sensing data, ground meteorological station observation data, ocean buoy sensor data, meteorological radar scanning data, social media public opinion text, and historical disaster database in real time, eliminates noise through data cleaning, matches coordinate reference through space-time alignment, extracts key meteorological indicators through a feature extraction engine, and forms a standardized space-time fusion data set;

[0015] The dynamic prediction analysis module receives the fusion data set, runs an artificial intelligence hybrid model coupling numerical weather prediction physical equations and space-time graph neural networks, generates extreme weather type, intensity probability distribution, influence range raster map, and secondary disaster chain risk assessment matrix;

[0016] The emergency resource dynamic database stores and updates in real time the geographic coordinates and capacity of shelters, the distribution of material reserves, the location and state identification of rescue teams, the real-time traffic speed of the road network, the population density thermal model, and the vulnerability classification data of key infrastructure;

[0017] The dynamic emergency scheme generator optimizes the evacuation path planning, material scheduling scheme, and early warning classification strategy based on the prediction results and resource data using a multi-objective evolutionary algorithm, and outputs an emergency response instruction set that minimizes evacuation time, maximizes resource coverage, and meets the needs of special groups;

[0018] The visualization command platform integrates a geographic information system engine, superimposes a predicted risk layer, resource distribution hotspots, and a three-dimensional dynamic deduction of emergency solutions, and provides a manual intervention interface to adjust and optimize the weight parameters; the closed-loop feedback optimization module accesses real-time water level and wind speed data from Internet of Things disaster sensors, unmanned aerial vehicle aerial image recognition results, and public mobile terminal disaster reporting information, compares the prediction error and response effect, and drives the online learning of model parameters and the adaptive iteration of solution generation strategies.

[0019] Further, the multi-source data fusion module dynamically weights the contribution of different data sources using an attention mechanism, specifically including assigning meteorological evolution feature weights to satellite cloud images, performing sentiment analysis and geographic location extraction on social media text, matching similar spatiotemporal patterns in historical disaster data, and filling in missing observation data in the missing areas through spatiotemporal interpolation algorithms.

[0020] Further, the hybrid model in the dynamic prediction analysis module outputs numerical patterns as physical constraints, and constructs a spatiotemporal graph neural network node containing atmospheric pressure, humidity, and temperature three-dimensional grid points, with edge weights representing meteorological element propagation relationships. The output layer generates kilometer-level resolution probabilistic predictions of future 72-hour rainstorms, hurricanes, heatwaves, and mountain flood and landslide derived risk indices through a Transformer architecture.

[0021] Further, the optimization process of the dynamic emergency solution generator includes four objective functions: objective one calculates the minimum total travel time of evacuation paths and avoids flooded areas, objective two maximizes the proportion of population covered by material distribution, objective three minimizes the disruption loss of key facilities such as hospitals and power plants, and objective four sets up exclusive evacuation routes and resource quotas for the elderly, the young, and the disabled, and solves the Pareto optimal solution set through a non-dominated sorting genetic algorithm.

[0022] Further, the disaster data sources of the closed-loop feedback optimization module include water level sensors and soil moisture probes installed on rivers and dams, building damage identification images taken by unmanned aerial vehicle groups, and location markers, on-site videos, and help texts uploaded by the public through mobile applications. After spatial clustering and severity grading, the above data generates a real-time disaster situation map.

[0023] Further, the system is deployed in a distributed stream processing architecture, where the multi-source data fusion module uses an Apache Flink real-time stream engine to perform second-level data processing, the dynamic prediction analysis module uses GPU clusters for parallel acceleration of model inference, and the dynamic emergency solution generator uses in-memory computing technology to output optimized solutions within 5 minutes, meeting the response timeliness requirements of extreme weather.

[0024] Furthermore, the hybrid model integrates an online learning mechanism. When the closed-loop feedback optimization module detects that the predicted wind speed error exceeds 15% or the disaster range underreporting rate is greater than 10%, it automatically triggers the incremental training process: injecting the latest disaster data into the training set, using an elastic weight solidification algorithm to update the neural network parameters, and retaining the memory weights of historical disaster patterns.

[0025] Furthermore, the dynamic emergency response plan generator embeds a reinforcement learning decision framework. Its state space is defined as weather forecast level, resource inventory vector, and traffic congestion matrix. The action space includes evacuation route adjustment and material vehicle dispatch instructions. The reward function integrates historical response effect data, and its specific expression is: response speed coefficient multiplied by 0.6, resource utilization coefficient multiplied by 0.3, and casualty avoidance coefficient multiplied by 1.0. The strategy network is iteratively optimized through Q-learning algorithm.

[0026] The technical effects and advantages of this invention are as follows:

[0027] Compared with existing technologies, this invention achieves full-chain collaborative operation by constructing a real-time fusion engine for multi-source heterogeneous data, a dynamic prediction model coupling physical mechanisms and machine learning, an emergency response plan generator based on optimization algorithms, and a closed-loop feedback optimization module. First, it integrates cross-platform data streams from satellite remote sensing, ground sensors, and social media, eliminating data silos in traditional technologies and constructing a unified disaster evolution map across the entire domain. Second, it employs a spatiotemporal graph neural network constrained by physical equations to model the disaster chain transmission mechanism, overcoming the limitations of single numerical forecasts or purely data-driven models, and enabling refined simulations of extreme weather and its secondary disasters. Furthermore, relying on a dynamic resource database and multi-objective optimization algorithms, it generates emergency instructions in real time that balance evacuation efficiency, resource coverage, and the needs of special groups, overcoming the rigidity of static plans. Finally, it drives online learning of model parameters and adaptive iteration of plan strategies through disaster feedback data reported by IoT devices, drones, and the public, forming an intelligent closed loop of "prediction-decision-feedback-evolution." Functionally, this system completely transforms the traditional emergency response model, shifting from passive response to a new paradigm of disaster governance characterized by proactive defense, dynamic optimization, and continuous evolution. Attached Figure Description

[0028] Figure 1 This is a system framework diagram of the present invention.

[0029] Figure 2 This is a flowchart of the physical-AI coupling prediction process of the present invention.

[0030] Figure 3 This is a diagram of the closed-loop optimization working mechanism of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Embodiment 1: Prediction and Response Process for Flash Floods Caused by Heavy Rain Scenario Description: A city in the Yangtze River Basin experiences heavy rainfall. The system predicts the risk of flash floods and generates an evacuation plan. Step 1: Multi-source data fusion (time < 2 minutes)

[0032] Real-time system access:

[0033] Infrared cloud image from Fengyun-4 satellite (1km resolution);

[0034] Rainfall per minute at 87 surface rain gauge stations across the city;

[0035] Location information based on social media keywords "heavy rain" and "flooding" (after filtering out rumors through sentiment analysis);

[0036] Similar cases in the historical flash flood database (flood patterns in the same region in 2016).

[0037] Fusion Algorithm:

[0038] Employs a dynamic attention weighting mechanism:

[0039] Satellite data weight = 0.7 × cloud top brightness temperature gradient + 0.3 × water vapor channel difference.

[0040] Ground data weight = real-time rainfall / historical maximum rainfall.

[0041] Social media weight = 0.5 × geographic location density + 0.5 × emotional crisis value.

[0042] By filling in the monitoring blind spots in mountainous areas through spatiotemporal interpolation, a 1km×1km gridded precipitation intensity map is generated.

[0043] Step 2: Flash Flood Disaster Chain Prediction (Time < 3 minutes)

[0044] Predictive model execution flow:

[0045] First, multi-source data is input into the physical-AI dual-mode architecture. The physical layer runs a simplified SWMM hydrological model to calculate surface runoff in real time (formula: runoff = precipitation intensity × surface permeability coefficient × topographic slope factor). Simultaneously, a spatiotemporal graph neural network (STGNN) is deployed in the AI ​​layer—using a 500m grid as nodes (node ​​features include elevation, soil type, and vegetation cover). Dynamic edge weights are generated based on the water flow direction matrix (simulating the hydraulic conduction intensity between grids). A spatiotemporal convolutional gating mechanism outputs a 3-hour flash flood probability distribution map (500m × 500m resolution). The secondary disaster module then initiates a landslide risk assessment (landslide risk index = 0.6 × soil saturation + 0.4 × slope angle), ultimately identifying three townships as red high-risk areas (probability > 85%), achieving precise early warning of the "meteorological-hydrological-geological" three-dimensional disaster chain. Step 3: Dynamic emergency plan generation (time < 4 minutes)

[0046] Execution of multi-objective optimization algorithms:

[0047] In disaster emergency evacuation scenarios, the multi-objective optimization algorithm executes the following intelligent decision-making process based on flash flood warnings: First, it integrates population heat maps of high-risk areas (mobile phone signaling density distribution), shelter capacity constraints (2000 people in Hall A / 1500 people in School B), and real-time traffic conditions (40% congestion rate on G328 National Highway) to construct a three-dimensional optimization objective:

[0048] 1. Minimize total evacuation time: Objective function = Σ(path length / dynamic traffic speed).

[0049] 2. Maximize resource coverage: Objective function = Σ(Shelter resource inventory ÷ Estimated affected population).

[0050] 3. Special population protection: The priority of evacuation routes for nursing homes / schools is increased by 1.5 times.

[0051] The algorithm is implemented using a non-dominated sorting genetic architecture:

[0052] Initialization: Randomly generate 50 path schemes (node ​​granularity down to the township level).

[0053] Fitness assessment: Simultaneously calculate the values ​​of the three objective functions (time + resources + special groups).

[0054] Optimization iteration: Perform crossover and mutation (mutation rate of 15% for swapped nodes), screen Pareto front solutions, and finally output differentiated evacuation schemes:

[0055] Severely affected township X: Detour via county road Y203 (avoid landslide area) → Hall A (12km / 25min).

[0056] Township Y (moderately dangerous): Via S7 Expressway (currently clear) → School B (18km / 20min).

[0057] Townships with mild disasters (Z): Community diversion strategy (prioritizing transfer to nursing homes).

[0058] Technological breakthrough: Dynamic path weighting (penalty coefficient for landslide-risk sections +300%) increases the success rate of the plan by 82% (compared to the traditional Dijkstra algorithm), ensuring optimal evacuation efficiency and resource matching for 13,000 people.

[0059] Step 4: Closed-loop feedback and optimization (continuous operation during disaster)

[0060] The disaster emergency response system receives real-time feedback from multiple sources: the riverbank IoT sensor detects that the water level exceeds the warning line by 1.2m (confidence interval ±0.15m); the drone identifies and confirms that a road in a certain village is interrupted (visual confidence level 99%); and the villager's APP reports "bridge on the west side collapsed" with GPS location (coordinate error <5m), triggering the dynamic response mechanism.

[0061] The plan was reconstructed in seconds: the route generator recalculated the detour route on County Road X205 (avoiding interruption points and improving dynamic traffic capacity by 35%), and the weights of the shelters were updated simultaneously (the capacity of Hall A was expanded to 2,300 people);

[0062] Online model learning: The measured water level error (ΔH = +0.4m) is injected into the training data, and the hydrological model parameters (saturated hydraulic conductivity coefficient × 1.12) are adjusted according to the correction amount = 0.8 × 0.4 - 0.2 × 0.15 = 0.29m.

[0063] Performance closed-loop verification: The evacuation completion time was reduced by 22% compared to the plan (actual time 83 minutes vs. original estimate 106 minutes), 13,520 people were safely transferred with zero casualties, and the material coverage rate reached 98% (optimization target exceeded).

[0064] Example 2: Hurricane Emergency Response in Coastal Cities

[0065] Scenario description: A super typhoon is approaching, and the system predicts storm surge and allocates rescue resources.

[0066] Step 1: Multi-source fusion (emphasizing marine data)

[0067] The typhoon forecasting system innovatively integrates multi-source data: it accesses ensemble forecasts of typhoon paths from the European Centre for Medium-Range Weather Forecasts (ECMWF), real-time wave height data from buoy arrays (maximum wave height 8.5 meters), port vessel AIS location information, and a historical storm surge inundation database. The core technological breakthrough lies in the use of a three-dimensional convolutional neural network—the first layer of 5×5 convolutional kernels accurately identifies the typhoon eyewall structure (temperature gradient ≥ 3℃ / km characteristic area), and the second layer of 3×3 convolutional kernels extracts the water vapor transport intensity of the spiral rainband (cloud top brightness temperature < -70℃ marker area). Through spatiotemporal feature fusion, the typhoon center positioning error is compressed to < 2km (60% higher accuracy than traditional methods). Combined with a wave-tide coupling model, the system achieves a 24-hour storm surge inundation range prediction error rate of < 8%.

[0068] Step 2: Storm Surge Inundation Prediction

[0069] Storm surge prediction models are implemented through a dual-drive approach of physics and AI: the physical layer solves shallow water equations in real time. Where h is the water depth and u / v are the velocity components, the storm surge propagation process is simulated; the AI ​​layer learns historical error patterns through an LSTM network—inputting wind speed, pressure gradient and tidal phase data, outputting the water level correction of the physical model (the residual learning strategy reduces system error by about 38%), and finally generating a 100-meter resolution storm surge overtopping probability map (the accuracy is improved by 52% compared to the pure physical model). Its coupling logic provides a dynamic skeleton for the physical model, and the AI ​​compensates for the uncertainty of boundary conditions in real time (the mean square error is verified to be ≤0.23m by actual measurement).

[0070] Step 3: Optimize the allocation of rescue resources

[0071] In the typhoon disaster emergency decision-making reinforcement learning system, the state space is dynamically integrated [typhoon level (e.g., Category 14), shelter occupancy rate (82%), number of medical teams on standby (15 teams)], and the action space includes {sending helicopter A to an isolated island (carrying 40 people), dispatching assault boats to low-lying areas (transferring 200 people / hour), and reinforcing hospital B (sending 5 additional teams)}. The reward function adopts a multi-objective weighted approach: reward value = 1.0 × number of people transferred - 0.5 × resource idle rate + 2.0 × number of critically ill patients rescued. Real-time iteration is achieved through Q-learning: new Q value = old Q value + 0.2 × (immediate reward + 0.9 × maximum Q value of the next state - old Q value), ultimately generating a priority decision—firstly, dispatching helicopter A (action value Q = 9.7) to transfer island fishermen (expected survival rate increased by 35%). After 200 training cycles, the system's global resource utilization rate is optimized by 37% (compared to the rule-driven baseline model).

[0072] Output: Prioritize relocating island fishermen (expected survival rate increase of 35%)

[0073] Step 4: Post-disaster model evolution

[0074] The disaster model, based on the measured inundation depth deviation (+0.8 meters), traced the source of the impact of the newly constructed dam breach and immediately initiated incremental training: an infrastructure integrity parameter β (β = 1 - breach width / total dam length, β = 0.87 in this example) was added to the physical shallow water equation, and the AI ​​corrector network weights were updated simultaneously.

[0075] Physical layer correction: (Q represents the gap flow). AI layer weight adjustment: LSTM weight matrix update: W_new=W_old×(1-0.3×γ)(γ=gap width influence factor=0.8).

[0076] After 17 minutes of incremental training, the model coupled output correction results showed that the prediction error was reduced to ±0.15 meters (convergence speed improved by 2 times), and 3 similar dam risk points were identified (confidence probability > 90%). Example 3: Description of extreme heat wave health risk prevention and control scenario: North China experiences continuous high temperatures of 40°C. The system issues early warnings of heatstroke risk and allocates medical resources.

[0077] Step 1: Integrate urban microclimate data

[0078] Real-time access to temperature data from 100 meteorological stations (highest temperature in urban area: 42.1℃), peak power grid load (150% higher than usual), social media keyword density for "heatstroke / power outage" (>5 posts / km²·min), and population age database (23% of the population is over 65 years old) were used to quantify the urban heat island effect using an innovative model: Urban temperature increase ΔT = 0.8 × building density index + 0.5 × green space loss rate - 0.3 × water coverage rate (coefficient validated by regression analysis of historical data from 132 cities, R...). 2 =0.91), outputting a level 3 risk decision:

[0079] Medical alert: Target elderly communities (such as communities with 23% elderly residents) and push "cooling and heatstroke prevention guide" to the target users' apps;

[0080] Power grid control: Dynamically switch backup power sources based on areas of load surge (load transfer rate > 40%);

[0081] Emergency Response: When ΔT>5℃ and the keyword "heatstroke" suddenly increases, the subway cooling points will be automatically opened (covering 150,000 people / day).

[0082] Step 2: Health Risk Prediction

[0083] Medical-Meteorological Cross Model:

[0084] The system calculates the heatstroke risk index in real time:

[0085] Risk index = 0.4 × WBGT + 0.3 × number of consecutive high-temperature days + 0.2 × elderly population density + 0.1 × (1 - air conditioning penetration rate).

[0086] The WBGT (Wet Bulb Temperature Gradient) is dynamically generated hourly (formula: WBGT = 0.7 × Wet Bulb Temperature + 0.2 × Black Bulb Temperature + 0.1 × Dry Bulb Temperature). The model integrates real-time temperature and humidity data from weather stations, the distribution of the elderly population in the community (proportion of those over 65 years old), and residential air conditioning coverage data, outputting a 500-meter grid-level risk map through spatiotemporal interpolation algorithms. When a community's index exceeds 85 (risk threshold), a Level 3 response is automatically executed.

[0087] Targeted early warning: Send time-sharing heat avoidance guidelines (APP + SMS) to high-risk communities.

[0088] Resource allocation: Medical mobile clinics are allocated according to the density of the elderly population (1 vehicle per 1,000 people).

[0089] Facility intervention: When there are ≥3 consecutive days of high temperature, air-raid shelters and cooling points will be forcibly opened (with an occupancy rate >80%).

[0090] Step 3: Dynamic allocation of medical resources

[0091] The system uses a mixed-integer programming algorithm to find the optimal solution:

[0092] Objective function: Minimize (Σ material demand gap in community i + 0.3 × total ambulance mileage).

[0093] Double constraint:

[0094] Each hospital has dispatched ambulances ≤ available vehicles at the moment (e.g., Hospital A has ≤ 8 vehicles).

[0095] Community i receives supplies equal to 0.9 times the number of people at high risk of heatstroke (ensuring 90% of basic needs).

[0096] Intelligent solution process:

[0097] Initial risk-oriented approach: Sort communities in descending order of heatstroke risk value (e.g., priority is given to communities with an index > 85), and allocate supplies and ambulances using a greedy algorithm;

[0098] Route depth optimization: The branch-bound method is used to adjust ambulance routes (such as merging adjacent community service circles), saving up to 35% in mileage costs;

[0099] Gap compensation mechanism: For communities that do not meet the standards (gap > 10%), a drone delivery plan will be activated (response time < 15 minutes).

[0100] Step 4: Adaptive Strategy Optimization

[0101] Feedback data:

[0102] The number of patients with heatstroke in the hospital's emergency department was 15% higher than predicted.

[0103] A power outage caused a surge in risk in a certain community.

[0104] Model update:

[0105] 1. Risk Index Restructuring: A new "Power Stability Weighting Item" (8% weighting) has been added, and the calculation formula has been adjusted as follows:

[0106] Risk index = original formula + 0.08 × (1 - power supply stability rate in the past 24 hours).

[0107] (Power supply stability rate in communities experiencing power outages = 0 → This contribution + 0.08 risk value)

[0108] 2. Gap compensation algorithm update:

[0109] New material shortage = Original predicted shortage × [1 + 0.5 × min(power outage duration / 6, 1.0)].

[0110] (Power outages lasting more than 6 hours are calculated using a full coefficient of 0.5, resulting in a 25% increase in demand for stimulus supplies).

[0111] 3. Resource reallocation mechanism:

[0112] The power failure community is automatically upgraded to the highest priority (surpassing the original risk value ranking).

[0113] Ambulance dispatch coefficient = power outage duration × 0.3 (e.g., 4-hour power outage → 1.2 times the dispatch capacity).

[0114] Finally, the following points should be noted: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installation", "connection", and "linkage" should be interpreted broadly, and can be mechanical or electrical connections, or internal connections between two components, or direct connections. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may change.

[0115] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0116] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An extreme weather event prediction and emergency response system, characterized in that, It includes the following modules that work together: The multi-source data fusion module accesses meteorological satellite remote sensing data, ground meteorological station observation data, ocean buoy sensor data, meteorological radar scanning data, social media public opinion text and historical disaster database in real time. Through data cleaning to remove noise, spatiotemporal alignment to match coordinate benchmarks, and feature extraction engine to extract key meteorological indicators, a standardized spatiotemporal fusion dataset is formed. The dynamic prediction and analysis module receives the fused dataset, runs an artificial intelligence hybrid model that couples the physical equations of numerical weather prediction with a spatiotemporal neural network, and generates a raster map of extreme weather types, intensity probability distributions, impact ranges, and a risk assessment matrix for secondary disaster chains. The emergency resource dynamic database stores and updates in real time the geographical coordinates and capacity of shelters, heat maps of material reserve distribution, location and status indicators of rescue teams, real-time traffic speed of the transportation network, population density heat models, and vulnerability classification data of critical infrastructure. The dynamic emergency response generator uses a multi-objective evolutionary algorithm to simultaneously optimize evacuation route planning, material dispatching schemes, and early warning classification strategies based on prediction results and resource data. It outputs an emergency response instruction set that minimizes evacuation time, maximizes resource coverage, and meets the needs of special populations. The visualization command platform integrates a geographic information system engine, overlaying and displaying predicted risk layers, resource distribution hotspots, and 3D dynamic simulations of emergency plans. It provides a manual intervention interface to adjust and optimize weight parameters. The closed-loop feedback optimization module connects to real-time water level and wind speed data from IoT disaster sensors, drone aerial image recognition results, and disaster reporting information from public mobile terminals. By comparing prediction errors with response effects, it drives online learning of model parameters and adaptive iteration of plan generation strategies.

2. The extreme weather event prediction and emergency response system according to claim 1, characterized in that: The multi-source data fusion module uses an attention mechanism to dynamically weight the contributions of different data sources. Specifically, it assigns meteorological evolution feature weights to satellite cloud images, performs sentiment analysis and geolocation extraction on social media texts, matches historical disaster data with similar spatiotemporal patterns, and fills in the missing observation data through spatiotemporal interpolation algorithms.

3. The extreme weather event prediction and emergency response system according to claim 1, characterized in that: The hybrid model in the dynamic prediction and analysis module uses numerical model output as physical constraints to construct a spatiotemporal graph neural network node containing three-dimensional grid points of atmospheric pressure, humidity, and temperature. The edge weights represent the propagation relationship of meteorological elements. The output layer generates probabilistic predictions of rainstorms, hurricanes, and heat waves with a kilometer-level resolution for the next 72 hours, as well as a risk index for flash floods and landslides.

4. The extreme weather event prediction and emergency response system according to claim 1, characterized in that: The optimization process of the dynamic emergency plan generator includes four objective functions: objective one is to calculate the minimum total travel time of evacuation routes and avoid flooded areas; objective two is to maximize the coverage ratio of material distribution to the population; objective three is to minimize the losses caused by the interruption of key facilities such as hospitals and power plants; and objective four is to set up dedicated refuge channels and resource quotas for the elderly, children, sick and disabled. The Pareto optimal solution set is solved by a non-dominated sorting genetic algorithm.

5. The extreme weather event prediction and emergency response system according to claim 1, characterized in that: The disaster data sources for the closed-loop feedback optimization module include: water level sensors and soil moisture probes deployed on river embankments, building damage identification images captured by drone swarms, and disaster location markers, on-site videos, and help requests uploaded by the public through mobile applications. The above data are spatially clustered and severity-classified to generate a real-time disaster situation map.

6. The extreme weather event prediction and emergency response system according to claim 1, characterized in that: The system is deployed on a distributed stream processing architecture. The multi-source data fusion module uses the Apache Flink real-time stream engine to perform second-level data processing, the dynamic prediction and analysis module accelerates model inference through parallel processing of GPU clusters, and the dynamic emergency solution generator uses in-memory computing technology to ensure the output of optimized solutions within 5 minutes, meeting the timeliness requirements for extreme weather response.

7. The extreme weather event prediction and emergency response system according to claim 3, characterized in that: The hybrid model integrates an online learning mechanism. When the closed-loop feedback optimization module detects that the predicted wind speed error exceeds 15% or the disaster range underreporting rate is greater than 10%, it automatically triggers the incremental training process: injecting the latest disaster data into the training set, using an elastic weight solidification algorithm to update the neural network parameters, and retaining the memory weights of historical disaster patterns.

8. The extreme weather event prediction and emergency response system according to claim 4, characterized in that: The dynamic emergency response plan generator embeds a reinforcement learning decision framework. Its state space is defined as weather forecast level, resource inventory vector and traffic congestion matrix. The action space includes evacuation route adjustment and material vehicle dispatch instructions. The reward function integrates historical response effect data. The specific expression is: response speed coefficient multiplied by 0.6, resource utilization coefficient multiplied by 0.3, and casualty avoidance coefficient multiplied by 1.

0. The policy network is iteratively optimized through Q-learning algorithm.

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