Meteorological ground disaster monitoring and early warning system based on big data AI analysis

Abstract

The invention discloses a meteorological ground disaster monitoring and early warning system based on big data AI analysis, and the system comprises a multi-source heterogeneous data collection and fusion module which collects the multi-source heterogeneous data of an affected area, and generates a unified fusion data flow; the dynamic weather prediction and disaster chain analysis module is used for generating predicted weather data, driving a coupled disaster chain model based on the predicted weather data, and simulating and outputting a disaster risk space-time distribution diagram; the refined terrain and hydrological analysis module is used for performing three-dimensional terrain feature quantitative extraction and distributed hydrological dynamic simulation to generate terrain complexity parameters and hydrological risk indexes; the disaster influence evaluation and emergency degree grading module is used for performing nonlinear comprehensive calculation through a trained machine learning evaluation model and outputting an emergency degree quantitative score and a corresponding rescue emergency degree grade; and the early warning terminal and decision support module is used for issuing early warning information through a multi-mode channel based on the rescue emergency degree grade, and calling an emergency plan knowledge base to provide a rescue plan.

Description

Technical Field

[0001] This invention relates to the field of meteorological and geological disaster monitoring technology, and in particular to a meteorological and geological disaster monitoring and early warning system based on big data AI analysis. Background Technology

[0002] The current global climate change is exacerbating the frequent occurrence of extreme weather events and geological disasters. Traditional monitoring methods suffer from limitations such as fragmented data acquisition, insufficient real-time performance, single analytical models, and limited accuracy in early warning. These limitations make it difficult to meet the urgent needs of multi-source heterogeneous data fusion, dynamic trend prediction, and cross-regional collaborative early warning in complex geological disaster scenarios. By integrating multi-dimensional monitoring data from satellite remote sensing, ground sensors, and meteorological radar, and combining AI algorithms such as deep learning and machine learning to build intelligent analysis models, we can achieve real-time monitoring, accurate prediction, and early warning of meteorological geological disasters such as rainfall, landslides, debris flows, and earthquakes. The significance lies in significantly improving disaster prevention and mitigation capabilities. This will enhance early identification and response efficiency of disasters, reduce casualties and property losses, safeguard public safety, and promote the transformation of disaster prevention and mitigation from "passive response" to "proactive prevention and control." Simultaneously, it will facilitate the deep integration and innovative application of cutting-edge technologies such as big data, artificial intelligence, and geographic information in the field of public safety, providing scientific, accurate, and efficient technical support for government decision-making, emergency rescue, and public protection. Ultimately, it will build a modern meteorological and geological disaster monitoring and early warning system based on "data-driven, intelligent analysis, and rapid response," thereby improving the overall national disaster prevention and mitigation capabilities and contributing to the dual goals of sustainable socio-economic development and ecological civilization construction.

[0003] Existing systems typically rely on a single, fixed data source, and each functional module operates in isolation, leading to a break in the disaster risk assessment chain and a lack of holistic and forward-looking early warning decisions. Therefore, this paper proposes a meteorological and geological disaster monitoring and early warning system based on big data AI analysis. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a meteorological and geological disaster monitoring and early warning system based on big data AI analysis.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A meteorological and geological disaster monitoring and early warning system based on big data AI analysis includes: Multi-source heterogeneous data acquisition and fusion module: Collects multi-source heterogeneous data of the disaster-stricken area through fixed observation station network, UAV mobile observation group, satellite remote sensing and radar system, Internet of Things sensor network and population dynamic data interface, and performs spatiotemporal alignment, cleaning and fusion of the multi-source heterogeneous data to generate a unified fused data stream; The dynamic meteorological forecasting and disaster chain analysis module receives meteorological data from the fused data stream, uses a deep learning time series model for forecasting, generates high spatiotemporal resolution forecast meteorological data, and drives a coupled disaster chain model based on the forecast meteorological data to simulate and output a spatiotemporal distribution map of disaster risk. More specifically, it continuously collects actual observed disaster data of the target area, compares the actual observed disaster data with the forecast results of the corresponding spatiotemporal locations in the disaster risk spatiotemporal distribution map, calculates the forecast deviation, and when the forecast deviation exceeds a preset threshold, triggers the parameter optimization process of the deep learning time series model and the disaster chain simulation model, and uses incremental learning technology to update the model online. The refined terrain and hydrological analysis module receives high-precision digital elevation model (DEM) data and real-time rainfall data from the fused data stream, performs quantitative extraction of three-dimensional terrain features and distributed hydrological dynamic simulation, and generates terrain complexity parameters and hydrological risk indicators. Disaster impact assessment and urgency level classification module: Receives disaster images, infrastructure status data and dynamic population data from the fused data stream, and combines them with the disaster risk spatiotemporal distribution map, the terrain complexity parameter and the hydrological risk index. It then performs nonlinear comprehensive calculations through a trained machine learning assessment model to output a quantitative score of urgency level and the corresponding level of emergency response. Early warning terminal and decision support module: Based on the level of urgency of the rescue, it releases early warning information through multimodal channels and calls the emergency plan knowledge base to provide decision-makers with intelligent recommendations for rescue plans.

[0006] The above technical solution further includes: Furthermore, the multi-source heterogeneous data acquisition and fusion module specifically includes: Fixed observation station network: collects basic meteorological element data from fixed locations in the disaster-stricken area; Mobile drone observation swarm: In response to the predicted meteorological data or the spatiotemporal distribution map of disaster risk, it flies under control to the monitoring blind area or high-risk area to collect three-dimensional meteorological data and visible light or multispectral images of the disaster-stricken area; Satellite and radar data access unit: Accesses satellite remote sensing images and weather radar data, and retrieves large-scale meteorological elements through image recognition neural networks; IoT sensor subnet: Deployed at geological disaster hazard sites, used to collect and upload soil and environmental monitoring data via low-power wide area network protocol; Edge computing nodes: deployed near the fixed observation station, the drone, or the IoT sensor subnet, used to clean and compress the raw collected data.

[0007] Furthermore, the dynamic meteorological forecasting and disaster chain analysis module extracts historical and real-time meteorological data sequences, satellite remote sensing data, and numerical weather prediction initial field data from the fused data stream, and performs spatiotemporal scale normalization and outlier correction on the data to form a standardized model input dataset; The model is input into historical and real-time meteorological data sequences and satellite remote sensing data sequences in the dataset, and then input into a pre-trained deep learning time series model to generate the first meteorological prediction result. The numerical weather prediction initial field data in the input dataset of the model are subjected to dynamic downscaling to generate a second meteorological prediction result. An adaptive weighted fusion algorithm is used to fuse the first meteorological forecast result and the second meteorological forecast result to generate high spatiotemporal resolution forecast meteorological data for the target area.

[0008] Furthermore, the deep learning time series model is an LSTM-Transformer hybrid architecture model.

[0009] Furthermore, the predicted meteorological data of the target area is used as the driving input and sequentially fed into a pre-constructed and physically coupled disaster chain simulation model, which is a sequentially coupled hydrological runoff sub-model and a geological stability assessment sub-model. Based on the output results of the disaster chain simulation model, calculate the estimated probability and intensity of disaster occurrence for each spatial grid cell in the target area at different future time slices; Based on the probability and intensity estimates, and combined with the geographic information system layer, a spatiotemporal distribution map of disaster risk with time dimension and spatial location information is generated.

[0010] Furthermore, the refined topographic and hydrological analysis module specifically performs the following: Receive fused data stream from the data fusion module, and extract high-precision digital elevation model (DEM) data and real-time rainfall data from the fused data stream; Based on the high-precision digital elevation model (DEM) data, a set of multi-dimensional three-dimensional terrain feature parameters, including slope, aspect, topographic relief, and confluence accumulation, is calculated and generated in batches using spatial analysis algorithms. The high-precision digital elevation model (DEM) data or associated remote sensing images are analyzed using a pre-trained image recognition neural network model to automatically identify and label the special geomorphic units and their spatial distribution of cliffs, landslides and alluvial fans in the disaster-stricken area. The real-time rainfall data is used as input to drive the distributed hydrological model, which sequentially performs runoff calculation and confluence simulation to dynamically deduce the formation and evolution of floods in the disaster area and generate hydrological risk indicators with spatiotemporal attributes. The three-dimensional terrain feature parameter set, the annotation information of the special landform units, and the hydrological risk indicators are integrated and formatted, and output to the disaster impact assessment and urgency level classification module and the UAV intelligent scheduling and mobile observation module of the system. More specifically, the UAV intelligent scheduling and mobile observation module receives early warning signals from the dynamic meteorological forecast and disaster chain analysis module and three-dimensional terrain constraints from the refined terrain and hydrological analysis module. Based on the path planning algorithm, it assigns monitoring tasks to the UAV swarm, plans safe flight paths, and controls them to reach the target area to perform data collection. The collected mobile observation data is transmitted back to the multi-source heterogeneous data acquisition and fusion module in real time.

[0011] Furthermore, the machine learning evaluation model in the disaster impact assessment and urgency level classification module is a gradient boosting decision tree model or a deep neural network model; The input features of the disaster impact assessment and urgency level classification module include the number and area of ​​damaged buildings, the length of road interruption, disaster keywords extracted from social media through natural language processing technology, dynamic population heat map data, and derived risk indicators from the disaster chain analysis module and the topography and hydrology analysis module, and output a quantitative score of urgency level within a preset range (e.g., 0 to 100). Based on the urgency level quantification score, combined with the current disaster type (such as floods, landslides) and its evolution stage (such as warning period, disaster period), the urgency level quantification score is dynamically matched with the preset or real-time generated level classification threshold range through the rule engine, and mapped to the corresponding emergency level, such as "Level 1 (Extremely Urgent)", "Level 2 (Severe Urgent)", "Level 3 (General Urgent)", etc. More specifically, this level classification process is not static, and its threshold can be adaptively adjusted based on real-time feedback disaster evolution data and regional specific defense capabilities.

[0012] Furthermore, the emergency response plan knowledge base is constructed in the form of a knowledge graph, with nodes including historical disaster cases, rescue force entities, material and equipment entities, transportation network nodes, and terrain constraints, and edges representing the relationships and linkage rules between entities; Based on the current disaster type and level, similar historical case subgraphs are matched in the knowledge graph, and their successful handling paths are extracted as basic recommendation schemes; The feasibility of the basic recommended solution is verified and conflict is detected by using the real-time resource status, which includes the real-time location and status of the rescue team, the amount of material inventory, and the road traffic capacity. The verified schemes are sorted and optimized by a constraint-based recommendation algorithm to generate a set of recommended schemes, which includes the composition of recommended rescue forces, suggested dispatch routes, priority guarantee material lists and key task time nodes. After each early warning response operation is completed, the system automatically collects complete process data of the operation, the final rescue plan implemented, and an evaluation of its actual effectiveness. Natural language processing technology is used to extract key decision points and handling logic from action summary reports and transform them into structured new knowledge; The new knowledge is integrated into the knowledge graph in the form of nodes and edges, or used to correct entity attributes and rule weights in the existing graph, thereby enabling the self-evolution and optimization of the knowledge base.

[0013] The present invention has the following beneficial effects: In this invention, a deep learning time series model is used to fuse numerical forecast products, effectively learning and predicting the nonlinear evolution of meteorological elements. This significantly improves the accuracy and lead time of forecasts for severe convection, short-duration heavy rainfall, and other weather events. Combined with a disaster chain model, it enables direct and dynamic extrapolation from meteorological conditions to specific geological disaster risks, significantly enhancing the scientific nature of early warnings. Attached Figure Description

[0014] Figure 1 This is a system block diagram of a meteorological and geological disaster monitoring and early warning system based on big data AI analysis proposed in this invention. Detailed Implementation

[0015] 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.

[0016] Please see Figure 1 As shown, this invention is a meteorological and geological disaster monitoring and early warning system based on big data AI analysis, comprising: Multi-source heterogeneous data acquisition and fusion module: Collects multi-source heterogeneous data of the disaster-stricken area through fixed observation station network, UAV mobile observation group, satellite remote sensing and radar system, Internet of Things sensor network and population dynamic data interface, and performs spatiotemporal alignment, cleaning and fusion of the multi-source heterogeneous data to generate a unified fused data stream; The dynamic meteorological forecasting and disaster chain analysis module receives meteorological data from the fused data stream, uses a deep learning time series model for forecasting, generates high spatiotemporal resolution forecast meteorological data, and drives a coupled disaster chain model based on the forecast meteorological data to simulate and output a spatiotemporal distribution map of disaster risk. More specifically, it continuously collects actual observed disaster data of the target area, compares the actual observed disaster data with the forecast results of the corresponding spatiotemporal locations in the disaster risk spatiotemporal distribution map, calculates the forecast deviation, and when the forecast deviation exceeds a preset threshold, triggers the parameter optimization process of the deep learning time series model and the disaster chain simulation model, and uses incremental learning technology to update the model online. The refined terrain and hydrological analysis module receives high-precision digital elevation model (DEM) data and real-time rainfall data from the fused data stream, performs quantitative extraction of three-dimensional terrain features and distributed hydrological dynamic simulation, and generates terrain complexity parameters and hydrological risk indicators. Disaster impact assessment and urgency level classification module: Receives disaster images, infrastructure status data and dynamic population data from the fused data stream, and combines them with the disaster risk spatiotemporal distribution map, the terrain complexity parameter and the hydrological risk index. It then performs nonlinear comprehensive calculations through a trained machine learning assessment model to output a quantitative score of urgency level and the corresponding level of emergency response. Early warning terminal and decision support module: Based on the level of urgency of the rescue, it releases early warning information through multimodal channels and calls the emergency plan knowledge base to provide decision-makers with intelligent recommendations for rescue plans.

[0017] In one embodiment, the multi-source heterogeneous data acquisition and fusion module specifically includes: Fixed observation station network: collects basic meteorological element data from fixed locations in the disaster-stricken area; Mobile drone observation swarm: In response to the predicted meteorological data or the spatiotemporal distribution map of disaster risk, it flies under control to the monitoring blind area or high-risk area to collect three-dimensional meteorological data and visible light or multispectral images of the disaster-stricken area; Satellite and radar data access unit: Accesses satellite remote sensing images and weather radar data, and retrieves large-scale meteorological elements through image recognition neural networks; IoT sensor subnet: Deployed at geological disaster hazard sites, used to collect and upload soil and environmental monitoring data via low-power wide area network protocol; Edge computing nodes: deployed near the fixed observation station, the drone, or the IoT sensor subnet, used to clean and compress the raw collected data.

[0018] It should be noted that the specific analysis process for multi-source heterogeneous data acquisition and fusion is as follows: Coordinated triggering and data acquisition of multi-source monitoring tasks: In response to the early warning signal output by the dynamic meteorological forecasting and disaster chain analysis module in the meteorological and geological disaster monitoring and early warning system, or according to the preset periodic monitoring plan, the routine data acquisition tasks of the fixed observation station network, satellite and radar data access units are triggered synchronously, and instructions for mobile observation tasks targeting specific monitoring blind spots or high-risk areas are issued to the UAV intelligent scheduling and mobile observation module, while activating the continuous monitoring data stream of the Internet of Things sensor subnet. Parallel reception and edge preprocessing of heterogeneous raw data: Raw data streams from different data sources are received in parallel through a distributed message middleware; The raw data stream includes at least: time-series data of basic meteorological elements from a first preset frequency from a fixed observation station network; stereo meteorological data packets and image data from a UAV mobile observation swarm; remote sensing images and reflectivity raster data from satellite and radar data access units; soil and environmental parameter sequences from an IoT sensor subnet; and population heat map data from a population dynamics data interface. Edge computing nodes deployed at the data access points perform on-site preprocessing of the raw data stream. Data standardization and alignment based on a unified spatiotemporal reference: assigning a unified spatiotemporal reference label to all accessed heterogeneous data; Using the Network Time Protocol and Geographic Information System coordinate transformation services, the timestamp of each data unit is aligned to standard Coordinated Universal Time, and its spatial location information is uniformly transformed to the preset geodetic coordinate system; for non-point source raster and image data, georegistration and resampling are performed to align it with the system's basic geographic grid framework. Multi-level data quality verification and cleaning: Perform multi-level quality verification on spatiotemporally aligned data. The first level performs threshold filtering based on physical rationality to remove outliers that exceed the sensor's range or clearly violate natural laws. The second level performs outlier detection based on statistics, using a sliding window and interquartile range method to identify and label transient interference data; The third level performs consistency checks based on cross-validation of data sources, comparing the consistency of observation results from different sources for the same area at similar time periods, and weighting and labeling contradictory data based on their credibility. Feature extraction and structured data generation: Features are extracted from the cleaned multi-source data and transformed into structured feature vectors or standard data objects; Specifically, this includes: extracting statistical features from fixed station time series data, retrieving vertical profile parameters from UAV stereo data, retrieving surface meteorological element distribution maps from satellite remote sensing images using convolutional neural networks, extracting trend features from IoT sequence data, and converting population heat map raster data into regional statistical tables. Data fusion and interpolation completion based on adaptive weights: Construct a dynamically weighted data fusion model to fuse structured data from different sources in the same geographical region; A dynamic credibility weight is assigned to each data source, which is dynamically calculated based on the data quality verification results, the historical accuracy of sensor calibration, and the spatiotemporal resolution of the data. A spatial interpolation algorithm based on terrain constraints is used to fuse discrete point observation data with area remote sensing inversion data to generate a spatially continuous and void-free fused data field. Generate and publish a standardized fused data stream: The fused data field is encapsulated into a fused data stream in a standard format. The fused data stream uses time slices and spatial grids as basic organizational units. Each unit contains the optimal estimated value of multiple elements after fusion and its uncertainty measure. The fused data stream is pushed in real time to the downstream modules of the meteorological and geological disaster monitoring and early warning system through a publish-subscribe model, including the dynamic meteorological forecasting and disaster chain analysis module and the refined topographic and hydrological analysis module.

[0019] In one embodiment, the dynamic weather forecasting and disaster chain analysis module extracts historical and real-time meteorological data sequences, satellite remote sensing data, and numerical weather prediction initial field data from the fused data stream, and performs spatiotemporal scale normalization and outlier correction on the data to form a standardized model input dataset. The model is input into historical and real-time meteorological data sequences and satellite remote sensing data sequences in the dataset, and then input into a pre-trained deep learning time series model to generate the first meteorological prediction result. The numerical weather prediction initial field data in the input dataset of the model are subjected to dynamic downscaling to generate a second meteorological prediction result. An adaptive weighted fusion algorithm is used to fuse the first meteorological forecast result and the second meteorological forecast result to generate high spatiotemporal resolution forecast meteorological data for the target area.

[0020] In one embodiment, the deep learning time series model is an LSTM-Transformer hybrid architecture model.

[0021] In one embodiment, the predicted meteorological data of the target area is used as the driving input and sequentially fed into a pre-constructed and physically coupled disaster chain simulation model, wherein the disaster chain model is a sequentially coupled hydrological runoff sub-model and a geological stability assessment sub-model. Based on the output results of the disaster chain simulation model, calculate the estimated probability and intensity of disaster occurrence for each spatial grid cell in the target area at different future time slices; Based on the probability and intensity estimates, and combined with the geographic information system layer, a spatiotemporal distribution map of disaster risk with time dimension and spatial location information is generated.

[0022] It should be noted that the specific analysis process of disaster chain coupling simulation is as follows: The rainfall forecast data in the predicted meteorological data of the target area is input into the hydrological runoff sub-model. The hydrological runoff sub-model calculates the runoff of each sub-basin based on the SCS-CN method and performs runoff calculation using kinematic wave or diffuse wave equations. It outputs the flow process line of each river network section in the target area and the inundation depth and range of the preset grid unit. The soil moisture content, groundwater level change, or surface runoff scour intensity data output from the hydrological process simulation sub-step, along with the predicted meteorological data for the target area and the preset geotechnical mechanics parameters, are input into the geological stability assessment sub-model. The geological stability assessment sub-model uses an infinite slope stability model or a more complex mechanical model to calculate the safety factor or instability probability of each slope unit. The dynamic change information of the surface soil saturation zone calculated in the hydrological process simulation sub-step is fed back in real time as the key boundary condition for pore water pressure calculation in the geological stability assessment sub-model, thereby realizing the dynamic coupling of the two sub-models in the physical process.

[0023] In one embodiment, the refined topographic and hydrological analysis module specifically performs the following: Receive fused data stream from the data fusion module, and extract high-precision digital elevation model (DEM) data and real-time rainfall data from the fused data stream; Based on the high-precision digital elevation model (DEM) data, a set of multi-dimensional three-dimensional terrain feature parameters, including slope, aspect, topographic relief, and confluence accumulation, is calculated and generated in batches using spatial analysis algorithms. It should be noted that the specific analysis process for quantitative extraction of three-dimensional terrain features is as follows: Based on the rasterized high-precision digital elevation model (DEM) data, the surface slope and aspect of each location in the disaster-stricken area are calculated pixel by pixel using the moving window analysis method. By setting neighborhood analysis windows of different sizes, the elevation standard deviation between each cell and the surrounding area is calculated, thereby quantifying and generating a topographic relief parameter layer that reflects the degree of drastic change in local terrain. Hydrological analysis tools were used to fill depressions, calculate water flow direction and runoff accumulation in the high-precision digital elevation model (DEM) data, and generate a runoff accumulation parameter layer characterizing the surface runoff convergence capacity. The high-precision digital elevation model (DEM) data or associated remote sensing images are analyzed using a pre-trained image recognition neural network model to automatically identify and label the special geomorphic units and their spatial distribution of cliffs, landslides and alluvial fans in the disaster-stricken area. It should be noted that the pre-trained image recognition neural network model is a convolutional neural network (CNN), which is trained on a DEM dataset containing a large number of labeled samples of the special landform units. It can automatically identify and delineate the boundaries of cliffs, ancient landslides and riverbank alluvial fans based on topographic elevation, texture and morphological features. The real-time rainfall data is used as input to drive the distributed hydrological model, which sequentially performs runoff calculation and confluence simulation to dynamically deduce the formation and evolution of floods in the disaster area and generate hydrological risk indicators with spatiotemporal attributes. It should be noted that the specific analysis process of distributed hydrological dynamic simulation is as follows: The SCS-CN runoff curve number method is used, combined with land use type and soil hydrological grouping data of the disaster-stricken area, to calculate the net rainfall or direct runoff depth generated by the real-time rainfall data in each sub-basin of the disaster-stricken area. Using the runoff calculation sub-step as input, based on the confluence theory of kinematic waves or diffused waves, the digital water system network is used to simulate flood evolution and calculate the changes in water level, flow rate and inundation range of different sections in the river over time. The real-time soil moisture data obtained from the fused data stream is used as the initial soil moisture condition correction parameter for the SCS-CN runoff curve number method to dynamically adjust the initial state of the runoff generation calculation model and improve the simulation accuracy. The three-dimensional terrain feature parameter set, the annotation information of the special landform units, and the hydrological risk indicators are integrated and formatted, and output to the disaster impact assessment and urgency level classification module and the UAV intelligent scheduling and mobile observation module of the system. More specifically, the UAV intelligent scheduling and mobile observation module receives early warning signals from the dynamic meteorological forecast and disaster chain analysis module and three-dimensional terrain constraints from the refined terrain and hydrological analysis module. Based on the path planning algorithm, it assigns monitoring tasks to the UAV swarm, plans safe flight paths, and controls them to reach the target area to perform data collection. The collected mobile observation data is transmitted back to the multi-source heterogeneous data acquisition and fusion module in real time.

[0024] In one embodiment, the machine learning evaluation model in the disaster impact assessment and urgency level classification module is a gradient boosting decision tree model or a deep neural network model; The input features of the disaster impact assessment and urgency level classification module include the number and area of ​​damaged buildings, the length of road interruption, disaster keywords extracted from social media through natural language processing technology, dynamic population heat map data, and derived risk indicators from the disaster chain analysis module and the topography and hydrology analysis module, and output a quantitative score of urgency level within a preset range (e.g., 0 to 100). Based on the urgency level quantification score, combined with the current disaster type (such as floods, landslides) and its evolution stage (such as warning period, disaster period), the urgency level quantification score is dynamically matched with the preset or real-time generated level classification threshold range through the rule engine, and mapped to the corresponding emergency level, such as "Level 1 (Extremely Urgent)", "Level 2 (Severe Urgent)", "Level 3 (General Urgent)", etc. More specifically, this level classification process is not static, and its threshold can be adaptively adjusted based on real-time feedback disaster evolution data and regional specific defense capabilities.

[0025] It should be noted that the specific analytical process for assessing the severity of the disaster's impact and the urgency of the relief efforts is as follows: Receive real-time and near real-time data from the fused data stream, and perform standardization and feature extraction, specifically including: The image recognition engine processes disaster images to automatically identify and quantify the number of damaged buildings, the length of road interruptions, and the condition of damaged bridges. Analyze infrastructure status data to obtain the extent of power outages and the offline status of communication base stations; integrate dynamic population data to generate population heat maps of the affected areas to calculate the real-time exposed population; Access the spatiotemporal distribution map of disaster risk from the disaster chain analysis module, as well as the terrain complexity parameters and hydrological risk indicators from the terrain and hydrological analysis module; The extracted quantitative indicators are organized and coded according to the preset disaster assessment dimensions to construct a unified, structured multi-dimensional assessment feature vector. The multi-dimensional assessment feature vector covers at least the dimensions of physical damage to the disaster-bearing body, impact of lifeline engineering, personnel exposure risk, hazard of disaster-causing factors, and vulnerability of the environmental background. The specific feature values ​​under each dimension are normalized to eliminate the influence of dimensions. The constructed evaluation feature vector is input into a pre-trained machine learning evaluation model for nonlinear comprehensive calculation. This model uses algorithms such as gradient boosting decision tree (XGBoost) or deep neural network. It has been trained with a large amount of historical disaster case data to learn the complex nonlinear mapping relationship between each feature and the actual disaster loss and urgency. After receiving the multi-dimensional features of the current disaster, the model performs forward propagation and inference, and finally outputs a scalar value within a preset range (e.g., 0 to 100), which is the urgency quantification score. The severity of the disaster is quantified and scored, and the current disaster type and stage of evolution are combined to dynamically match the preset or real-time threshold ranges for the classification of the level. The urgency level is quantitatively scored and linked to the urgency level of the rescue operation to form a structured assessment result report. This result is output to the early warning terminal and decision support module in real time, driving the issuance of early warnings and the generation of rescue plans.

[0026] In one embodiment, the emergency response plan knowledge base is constructed in the form of a knowledge graph, where nodes include historical disaster cases, rescue force entities, material and equipment entities, transportation network nodes, and terrain constraints, and edges represent the relationships and linkage rules between entities. Based on the current disaster type and level, similar historical case subgraphs are matched in the knowledge graph, and their successful handling paths are extracted as basic recommendation schemes; The feasibility of the basic recommended solution is verified and conflict is detected by using the real-time resource status, which includes the real-time location and status of the rescue team, the amount of material inventory, and the road traffic capacity. The verified schemes are sorted and optimized by a constraint-based recommendation algorithm to generate a set of recommended schemes, which includes the composition of recommended rescue forces, suggested dispatch routes, priority guarantee material lists and key task time nodes. After each early warning response operation is completed, the system automatically collects complete process data of the operation, the final rescue plan implemented, and an evaluation of its actual effectiveness. Natural language processing technology is used to extract key decision points and handling logic from action summary reports and transform them into structured new knowledge; The new knowledge is integrated into the knowledge graph in the form of nodes and edges, or used to correct entity attributes and rule weights in the existing graph, thereby enabling the self-evolution and optimization of the knowledge base.

[0027] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A meteorological and geological disaster monitoring and early warning system based on big data AI analysis, characterized in that, The application relates to a disaster risk prediction and rescue decision support system. The system comprises: a multi-source heterogeneous data acquisition and fusion module: through a fixed observation station network, an unmanned aerial vehicle mobile observation group, a satellite remote sensing and radar system, an Internet of Things sensing network and a population dynamic data interface, multi-source heterogeneous data of a disaster area are collected, and the multi-source heterogeneous data are subjected to time-space alignment, cleaning and fusion to generate unified fusion data flow; a dynamic weather prediction and disaster chain analysis module: receiving meteorological data in the fusion data flow, using a deep learning time series model for prediction to generate predicted meteorological data, and driving a coupled disaster chain model based on the predicted meteorological data to simulate and output a disaster risk space-time distribution map; a refined terrain and hydrological analysis module: receiving digital elevation model data and real-time rainfall data in the fusion data flow, performing three-dimensional terrain feature quantitative extraction and distributed hydrological dynamic simulation to generate terrain complexity parameters and hydrological risk indicators; a disaster impact assessment and emergency level classification module: receiving disaster image data, infrastructure state data and dynamic population data in the fusion data flow, combining the disaster risk space-time distribution map, the terrain complexity parameters and the hydrological risk indicators, and performing nonlinear comprehensive calculation through a trained machine learning evaluation model to output an emergency level quantitative score and a corresponding rescue emergency level; 2.The weather and disaster monitoring and early warning system based on big data AI analysis of claim 1, wherein, a warning terminal and decision support module: based on the rescue emergency level, issuing warning information through a multi-modal channel and calling an emergency plan knowledge base to provide a rescue scheme. The multi-source heterogeneous data acquisition and fusion module specifically comprises: a fixed observation station network: collecting basic meteorological element data of fixed points in the disaster area; an unmanned aerial vehicle mobile observation group: flying to monitor blind areas or high-risk areas in response to the predicted meteorological data or the disaster risk space-time distribution map, collecting stereoscopic meteorological data and visible light or multispectral images of the disaster area; a satellite and radar data access unit: accessing satellite remote sensing images and weather radar data, and inversely calculating large-scale meteorological elements through an image recognition neural network; an Internet of Things sensing subnetwork: arranged at geological disaster hidden danger points, used for collecting and uploading soil and environmental monitoring data through a low-power wide-area network protocol; 3.The weather and disaster monitoring and early warning system based on big data AI analysis of claim 1, wherein, an edge computing node: arranged near the fixed observation station, the unmanned aerial vehicle or the Internet of Things sensing subnetwork, used for cleaning and compressing preprocessing of original collected data. The dynamic weather prediction and disaster chain analysis module extracts historical and real-time meteorological data sequences, satellite remote sensing data and numerical weather prediction initial field data from the fusion data flow, and performs time-space scale normalization and outlier correction processing on the data to form a standardized model input data set; the historical and real-time meteorological data sequences and satellite remote sensing data sequences in the model input data set are input into a pre-trained deep learning time series model to generate a first meteorological prediction result; the numerical weather prediction initial field data in the model input data set is subjected to dynamic downscaling processing to generate a second meteorological prediction result; an adaptive weight fusion algorithm is used to fuse the first meteorological prediction result and the second meteorological prediction result to generate target area predicted meteorological data.

4. The weather and disaster monitoring and early warning system based on big data AI analysis according to claim 1, characterized in that, The deep learning time series model is an LSTM-Transformer hybrid architecture model. 5.The weather and disaster monitoring and early warning system based on big data AI analysis of claim 3, wherein, The target area prediction meteorological data is sequentially fed into a pre-constructed and physically coupled disaster chain simulation model as driving input, and the disaster chain model is a hydrological runoff sub-model and a geological stability evaluation sub-model sequentially coupled. According to the output result deduced by the disaster chain simulation model, the disaster occurrence probability and intensity estimation value of each spatial grid unit in the target area at different future time slices are calculated. Based on the probability and intensity estimation value, a disaster risk space-time distribution map with time dimension and spatial location information is generated in combination with a geographic information system layer.

6. The weather and disaster monitoring and early warning system based on big data AI analysis according to claim 1, characterized in that, The fine terrain and hydrological analysis module specifically performs: Receives the fusion data stream from the data fusion module and extracts digital elevation model data and real-time rainfall data from the fusion data stream; Based on the digital elevation model data, a multi-dimensional three-dimensional terrain feature parameter set including slope, slope direction, terrain relief and convergence accumulation is batch calculated and generated by a spatial analysis algorithm; The pre-trained image recognition neural network model is used to analyze the digital elevation model data or associated remote sensing images, automatically identify and label special landform units such as cliffs, landslide bodies and alluvial fans in the disaster area and their spatial distribution; The real-time rainfall data is input into the distributed hydrological model as driving, and runoff calculation and convergence simulation are sequentially performed to dynamically deduce the formation and evolution process of flood in the disaster area, and generate hydrological risk indicators with space-time attributes; The three-dimensional terrain feature parameter set, the labeled information of the special landform units and the hydrological risk indicators are integrated and formatted, and output to the disaster impact assessment and emergency classification module of the system.

7. The weather and disaster monitoring and early warning system based on big data AI analysis according to claim 1, characterized in that, The machine learning evaluation model in the disaster impact assessment and emergency classification module is a gradient boosting decision tree model or a deep neural network model; The input features of the disaster impact assessment and emergency classification module include the number and area of building damage identified by target detection neural network from unmanned aerial vehicle or satellite image, road interruption length, disaster keywords extracted from social media through natural language processing technology, dynamic population heat map data, and derived risk indicators from the disaster chain analysis module and the terrain and hydrological analysis module, and output emergency level quantitative score; According to the emergency level quantitative score, combined with the type and evolution stage of the current disaster, the preset or real-time generated threshold interval of grade division is dynamically matched, and the emergency level quantitative score is mapped to the corresponding rescue emergency level. 8.The weather and disaster monitoring and early warning system based on big data AI analysis of claim 1, wherein, The system self-optimization module is also included, and the emergency plan knowledge base is constructed in the form of knowledge graph, the nodes include historical disaster cases, rescue force entities, material equipment entities, traffic network nodes and terrain constraint conditions, and the edges represent the relationship and linkage rules between entities; According to the current disaster type and level, similar historical case sub-graphs are matched in the knowledge graph, and their successful disposal paths are extracted as the basic recommended scheme; The real-time resource state includes real-time positions and states of rescue teams, inventory of materials, and road traffic capacity; The recommended algorithm based on the constraint condition is used to sort and optimize the checked scheme, and a recommended scheme set including recommended rescue force composition, suggested dispatch route, priority protection material list, and key task time node is generated.

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