Meteorological big data intelligent diagnosis and early warning system and method

Abstract

The application belongs to the technical field of meteorological information, and specifically discloses a meteorological big data intelligent diagnosis and early warning system and method. The system comprises a multi-source meteorological data acquisition device, an extreme event feature extraction unit, a tail correlation modeling module, a risk fluctuation quantification engine and a dynamic early warning response device. By fusing satellite, radar and ground observation data, high-order features of extreme weather are extracted, the nonlinear dependence relationship of multiple elements in the distribution tail is modeled by using a Copula function, the meteorological risk is quantified by introducing the financial option volatility rate idea, and a dynamic risk fluctuation index is generated. When the index exceeds the grading threshold, a conventional attention, high alert or emergency fuse type emergency response is triggered. Through the above technical scheme, the identification sensitivity and early warning timeliness of the "black swan" type extreme weather are improved, and high-precision and low-delay intelligent disaster prevention decision support is realized.

Description

Technical Field

[0001] This invention belongs to the field of meteorological information technology, specifically relating to a meteorological big data intelligent diagnosis and early warning system and method. Background Technology

[0002] With the deep integration of meteorological observation technology and big data analysis methods, meteorological big data diagnosis and early warning systems have become an important cornerstone of the modern public safety assurance system. By processing massive amounts of multidimensional data collected from satellite remote sensing, ground radar, and automatic weather stations in real time, weather forecasting has shifted from traditional qualitative analysis to quantitative calculation with high spatiotemporal resolution, providing crucial support for ensuring social production and the safety of life and property.

[0003] Intelligent diagnosis and early warning of sudden and extreme weather events is a core direction of current meteorological research. This type of technology aims to use deep learning and advanced statistical models to extract anomalous signal features from the complex atmospheric evolution process. Through coupled analysis of multiple factors such as air pressure, humidity, and wind field, it establishes a dynamic response mechanism for severe weather, thereby achieving accurate identification and risk assessment of non-stationary states in atmospheric circulation.

[0004] Current technologies still have limitations in handling extreme and rare weather events. Traditional numerical weather prediction models are prone to smoothing effects during complex nonlinear calculations, leading to predictions that overly resemble historical averages and fail to accurately reflect the instantaneous intensity of extreme disasters. Furthermore, meteorological data exhibits a long-tailed distribution, and existing training frameworks are insufficient in capturing extreme, low-probability samples, making it difficult to analyze the tail correlations of multiple meteorological elements under extreme conditions. In addition, the lack of risk fluctuation quantification and dynamic response mechanisms results in underestimation or lag in early warning signals when facing sudden torrential rains or rare thunderstorms and strong winds, failing to meet the stringent requirements of modern disaster prevention and control for high-frequency fluctuation perception and extreme risk prediction. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent diagnosis and early warning system for meteorological big data, thereby solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a meteorological big data intelligent diagnosis and early warning system, comprising: A multi-source meteorological data acquisition device is configured to acquire multi-dimensional meteorological observation data from multiple observation platforms in real time. The multi-dimensional meteorological observation data includes pressure field distribution, humidity gradient, wind speed vector, temperature profile, and precipitation intensity sequence. An extreme event feature extraction unit is connected to the multi-source meteorological data acquisition device and is configured to identify abnormal signals and detect non-stationary states in the acquired multi-dimensional meteorological observation data, thereby extracting high-order statistical features and instantaneous change indicators that characterize extreme weather events. The tail correlation modeling module is connected to the extreme event feature extraction unit. It is configured to construct a joint probability distribution model of multiple meteorological elements under extreme conditions based on extreme value theory, and to use a connection function to characterize the nonlinear dependency structure of different meteorological variables in the distribution tail region, so as to capture the extreme co-evolution pattern among different meteorological elements. The risk volatility quantification engine, connected to the tail correlation modeling module, is configured to introduce option pricing concepts and high-frequency trading volatility modeling methods to transform the probability of meteorological disasters and their potential impact intensity into risk volatility indicators, and dynamically assess the tendency of extreme events in the atmospheric system based on the risk volatility indicators. The dynamic early warning response device is connected to the risk volatility quantification engine and is configured to trigger a graded early warning mechanism based on the risk volatility index. When the risk volatility index exceeds a preset threshold, it automatically generates and pushes early warning information and simultaneously initiates an emergency response process similar to a stock market circuit breaker.

[0007] Preferably, the multi-source meteorological data acquisition device includes: The satellite data receiving submodule is equipped with a high-gain satellite receiving antenna and a real-time demodulation processor, and is used to acquire multi-channel remote sensing image data provided by geostationary orbit satellites and polar orbit satellites. The multi-channel remote sensing image data includes infrared cloud images, water vapor channel images, visible light cloud images, and atmospheric vertical detection profiles. The radar echo detection submodule is connected to the ground-based meteorological radar network and is configured to periodically acquire Doppler radar echo intensity, radial velocity, and spectral width data according to a preset scanning strategy, in order to detect the evolution trajectory of small- and medium-scale strong convective systems. The ground station network observation submodule includes an automatic weather station cluster deployed on a geographic grid, used to report air pressure, temperature, humidity, wind direction, wind speed and minute-level precipitation in real time; The upper-air sounding submodule is connected to sounding rockets, meteorological drones, and stratospheric sounding balloons to acquire physical quantity field information on different isobaric surfaces; The heterogeneous data fusion submodule is equipped with a high-performance data exchange backplane, which is used to perform unified standardization processing on raw meteorological observation data from different observation platforms with different temporal resolutions, spatial resolutions and data formats, so as to achieve strict alignment of spatiotemporal coordinate systems and data quality control.

[0008] Preferably, the extreme event feature extraction unit includes: The abnormal signal identification subunit integrates a deep residual neural network model, which contains multiple cascaded residual learning blocks. Each residual learning block contains two consecutive convolutional layers and cross-layer identity mapping connections. The convolutional layer is configured to extract local correlation features from meteorological data, and the identity mapping connection is configured to directly transmit the input signal to the output of the residual learning block and perform addition operations to preserve the original signal structure and suppress gradient decay. The non-stationary state detection subunit is equipped with a wavelet packet decomposition processor, which is used to decompose the non-stationary meteorological time series signal at multiple scales and separate the physically meaningful low-frequency background field signal and high-frequency abrupt pulse signal from the high-noise background. The high-order feature mapping subunit is equipped with a self-attention mechanism module, which is used to calculate the weight distribution of meteorological elements in different spatiotemporal regions. This enhances the ability to extract precursor indicators that characterize sudden torrential rain and rare thunderstorms and strong winds. The generated feature vector is input to the tail correlation modeling module.

[0009] Preferably, the tail correlation modeling module includes: The distribution boundary estimation unit is configured to perform parameterized fitting of the tail distribution of each meteorological element based on the generalized Pareto distribution in extreme value theory. By setting a preset threshold, sample points that are higher than the preset threshold are identified as extreme event observations. The extreme value dependency analysis unit is configured to use Archimedes-type connection functions to characterize the nonlinear dependency structure of multiple meteorological elements in the distribution tail region. The extreme value dependency analysis unit calls the Gamble join function to capture the upper tail correlation between meteorological elements, that is, when the first meteorological element has an extreme value, the second meteorological element also tends to have an extreme value. The extreme value dependency analysis unit calls the Clayton join function to model the lower tail correlation between meteorological elements; The model parameter dynamic update unit is connected to a historical extreme event sample library and periodically corrects the correlation parameters of the Archimedes-like connection function through an online learning algorithm to adapt to the long-term drift of the statistical characteristics of extreme weather events caused by climate change.

[0010] Preferably, the risk volatility quantification engine includes: The option pricing simulation unit is configured to build a meteorological derivative pricing model, using the observed values ​​of meteorological elements as the price of the underlying asset and the disaster warning threshold as the strike price. The implicit volatility inversion unit is configured to calculate the uncertainty of the atmospheric system by simulating the dispersion of weather forecast set members under different initial disturbance conditions, and to convert the path distribution dispersion of the weather forecast set members into implicit volatility, which represents the sensitivity of the atmospheric system to future extreme disturbances. The risk index mapping unit is configured to perform a composite operation on the implied volatility and the probability of extreme events to generate a continuously distributed risk volatility index that characterizes the strength of meteorological risk. The risk volatility index is converted to a fixed numerical range through a preset normalization algorithm.

[0011] Preferably, the dynamic early warning response device includes: The graded threshold monitoring module has three preset response thresholds, which include a first preset threshold corresponding to the normal attention state, a second preset threshold corresponding to the high alert state, and a third preset threshold corresponding to the emergency circuit breaker state. The circuit breaker mechanism trigger module is configured to automatically interrupt the regular forecast generation process and enter the highest level emergency response procedure when the risk volatility index climbs within a predetermined time window and reaches the third-level preset threshold. A multi-channel information distribution module is configured to send the generated early warning information to relevant responsible parties via satellite broadcasting, mobile base station push, emergency radio, and Internet interactive interface; In the emergency circuit breaker state, the dynamic early warning response device is configured to automatically take over the automatic controller of public infrastructure in the disaster area and perform disaster avoidance operations, including turning on drainage pumping stations, shutting down vulnerable industrial equipment, and adjusting traffic lights.

[0012] Preferably, the system is deployed using a cloud-edge collaborative architecture, and the system includes: An edge processing cluster, deployed at edge observation nodes, includes some front-end components of the multi-source meteorological data acquisition device and a preprocessing module of the extreme event feature extraction unit; The edge processing cluster integrates a neural network processor, which is used to receive the original signal nearby and perform the first stage of abnormal signal identification and wavelet packet decomposition, and upload the filtered feature vector to the central cloud platform. The core computing array, deployed on the central cloud platform, carries the tail correlation modeling module, the risk volatility quantification engine, and the dynamic early warning response device. The central cloud platform is equipped with a global meteorological topology map storage device, which is used to store the spatiotemporal correlation of meteorological elements within the observation area; The edge processing cluster is connected to the central cloud platform using a dynamic link allocation strategy. When entering the emergency circuit breaker state, the system automatically activates redundant backup links and enables software-defined network slicing technology to allocate the highest priority transmission bandwidth to the meteorological early warning data stream.

[0013] Preferably, the extreme event feature extraction unit adopts a heterogeneous computing resource pool at the hardware level, which includes a general-purpose processor, a graphics processor, and a customized tensor processing unit. The feature extraction unit is equipped with a resource scheduler, which is configured to dynamically allocate computing power according to the current meteorological data processing load. When faced with sudden, widespread severe weather and an exponential increase in data volume, the resource scheduler is configured to automatically strip away non-core background tasks and concentrate computing power to prioritize feature mapping operations in key spatiotemporal regions. The extreme event feature extraction unit is also equipped with real-time feature importance monitoring logic, configured to identify the dominant physical quantity under the current atmospheric condition by calculating the sensitivity coefficient of the contribution of the input meteorological elements to the output features, and automatically increase the sampling density and feature processing depth of the dominant physical quantity.

[0014] Preferably, the risk volatility quantification engine also includes a liquidity risk assessment submodule, configured to calculate the emergency response flow resistance during a disaster based on the traffic network density, population distribution intensity, and location of key energy facilities within the warning area; When the emergency response flow resistance exceeds the preset safety boundary, the risk index mapping unit adds a penalty term to the generated risk volatility index to raise the warning level; The risk volatility quantification engine is also configured to perform risk spread topology analysis, which models the risk propagation path between different geographical grid points using graph neural networks. When the risk volatility of the first grid point increases, the risk volatility quantification engine assesses the radiation effect on downstream grid points, and when the risk exhibits chain transmission characteristics, it upgrades the early warning response range from point-based early warning to area-based early warning.

[0015] This invention also provides a method for intelligent diagnosis and early warning of meteorological big data, which uses the aforementioned intelligent diagnosis and early warning system for meteorological big data to achieve intelligent diagnosis and early warning of meteorological big data.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The meteorological big data intelligent diagnosis and early warning system provided by this invention solves the problem of smoothing in the prediction of extreme and rare events in traditional numerical weather forecasts by introducing extreme value theory and high-frequency trading fluctuation modeling ideas from the field of financial engineering.

[0017] 2. The system utilizes the Copula function to accurately characterize the complex dependencies of multiple meteorological elements at the tail of the distribution, improving the sensitivity of identifying "black swan" type extreme weather events; by constructing a risk volatility quantification mechanism similar to option pricing, the abstract disaster probability is transformed into an operable early warning decision indicator, and combined with a circuit breaker-like dynamic response strategy, a closed-loop linkage from risk perception to emergency action is realized.

[0018] 3. This system not only solves the problem of prediction gaps caused by insufficient training of long-tailed distributed data, but also significantly reduces the lag and underestimation risk of extreme event warnings, providing intelligent support for the modern meteorological disaster prevention and mitigation system. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram of the core principle framework of tail correlation modeling based on the Copula function in this invention; Figure 3 This is a flowchart illustrating the logical process of extreme weather feature extraction and physical precursor signal separation in this invention. Figure 4 This is a flowchart illustrating the logical process of quantifying meteorological risk fluctuations and inverting implied volatility of options in this invention. Figure 5 This is a schematic diagram of the multi-level interaction relationship and data flow between graded early warning triggering and circuit breaker-like emergency response in this invention. Detailed Implementation

[0020] Example 1: Reference Figures 1 to 5 The meteorological big data intelligent diagnosis and early warning system includes a multi-source meteorological data acquisition device, an extreme event feature extraction unit, a tail correlation modeling module, a risk fluctuation quantification engine, and a dynamic early warning response device. The multi-source meteorological data acquisition device is used to acquire raw meteorological observation information of the target area and its surrounding related areas from all directions and dimensions. The extreme event feature extraction unit is connected to the multi-source meteorological data acquisition device and is used to perform noise reduction processing, spatiotemporal alignment, and capture of key physical precursor signals on massive amounts of raw meteorological information. The tail correlation modeling module is connected to the extreme event feature extraction unit and is used to analyze the nonlinear collaborative evolution mechanism of multidimensional meteorological elements under extreme distribution states. The risk fluctuation quantification engine is connected to the tail correlation modeling module and is used to map complex meteorological evolution probabilities into measurable risk fluctuation indicators. The dynamic early warning response device is connected to the risk fluctuation quantification engine and is used to execute tiered early warning issuance and emergency intervention logic.

[0021] The multi-source meteorological data acquisition device includes a satellite data receiving submodule, a radar echo detection submodule, a ground station network observation submodule, an upper-air detection submodule, and a heterogeneous data fusion submodule.

[0022] The satellite data receiving submodule is equipped with a high-gain satellite receiving antenna and a real-time demodulation processor, used to acquire multi-channel remote sensing image data provided by geostationary orbit satellites and polar orbit satellites, including but not limited to infrared cloud images, water vapor channel images, visible light cloud images, and atmospheric vertical detection profiles.

[0023] The radar echo detection submodule is connected to the ground-based meteorological radar network and is configured to periodically acquire Doppler radar echo intensity, radial velocity, and spectral width data using a preset scanning strategy, in order to detect the evolution trajectory of small- and medium-scale strong convective systems.

[0024] The ground station network observation submodule includes an automatic weather station cluster deployed on a geographic grid, used to report basic meteorological elements such as air pressure, temperature, humidity, wind direction, wind speed, and minute-level precipitation in real time.

[0025] The high-altitude sounding submodule is connected to a sounding rocket, a meteorological drone, and a stratospheric sounding balloon to acquire physical quantity field information on different isobaric surfaces.

[0026] The heterogeneous data fusion submodule is equipped with a high-performance data exchange backplane, which is used to standardize the raw information from different observation platforms with different time resolutions, spatial resolutions and data formats, so as to achieve strict alignment of spatiotemporal coordinate systems and data quality control.

[0027] The extreme event feature extraction unit includes an abnormal signal recognition subunit, a non-stationary state detection subunit, and a high-order feature mapping subunit.

[0028] The abnormal signal recognition subunit integrates a deep residual neural network model, which contains multiple cascaded residual learning blocks. Each residual block contains two consecutive convolutional layers and cross-layer identity mapping connections. The convolutional layers are configured to extract local correlation features from meteorological data, while the identity mapping connections are used to directly transmit the input signal to the output and perform addition operations, preserving the structural information of the original signal and suppressing the gradient decay problem during the training process of the deep network.

[0029] The non-stationary state detection subunit is equipped with a wavelet packet decomposition processor, which is used to perform fine decomposition of non-stationary meteorological time series signals on multiple scales, and separate low-frequency background field signals and high-frequency abrupt pulse signals with specific physical meaning from high-noise background.

[0030] The higher-order feature mapping subunit adopts a self-attention mechanism to calculate the weight distribution of meteorological elements in different spatiotemporal regions, which enhances the ability to extract precursor indicators that characterize extreme weather events such as sudden torrential rain and rare thunderstorms and strong winds. The generated feature vector is input into the subsequent modeling process.

[0031] The tail correlation modeling module includes a distribution boundary estimation unit, an extreme value dependency analysis unit, and a model parameter dynamic update unit.

[0032] The distribution boundary estimation unit performs parameterized fitting of the tail distribution of each meteorological element based on the generalized Pareto distribution in extreme value theory. By setting a preset threshold, sample points above the threshold are identified as extreme event observations.

[0033] The extreme value dependency analysis unit is configured to use a family of connection functions to characterize the nonlinear dependency structure of multiple meteorological elements in the distribution tail region; the extreme value dependency analysis unit uses Archimedes-like connection functions, such as Gamble connection function or Clayton connection function, to model the co-evolution relationship of key element combinations such as heavy precipitation and low-level jet stream, thunderstorm wind and vertical wind shear.

[0034] The Gamble join function is configured to capture the upper-tail correlation between features, that is, the probability that when one feature has an extreme value, another feature will also have an extreme value; the Clayton join function is used to model the lower-tail correlation between features.

[0035] The model parameter dynamic update unit is connected to a historical extreme event sample database and periodically corrects the correlation parameters of the connection function through an online learning algorithm to adapt to the long-term drift of the statistical characteristics of extreme weather events caused by global climate change.

[0036] The risk volatility quantification engine includes an option pricing simulation unit, an implied volatility inversion unit, and a risk index mapping unit.

[0037] The core of the risk volatility quantification engine lies in treating the occurrence of meteorological disasters as a kind of financial asset volatility, and quantifying the instantaneous change intensity of the atmospheric system by drawing on high-frequency trading modeling methods in financial engineering.

[0038] The option pricing simulation unit is configured to construct a meteorological derivative pricing model, using the observed values ​​of meteorological elements as the price of the underlying asset and the disaster warning threshold as the strike price.

[0039] The implied volatility inversion unit calculates the uncertainty of the current atmospheric state by the dispersion of the weather forecast set members; the more dispersed the path distribution of the weather forecast set members, the higher their corresponding implied volatility, which represents the stronger sensitivity of the atmospheric system to initial disturbances.

[0040] The risk index mapping unit performs a composite operation on the calculated volatility parameter and the probability of extreme events to generate a continuously distributed volatility index that can characterize the strength of meteorological risk. The volatility index is then converted to a fixed numerical range through a preset normalization algorithm.

[0041] The dynamic early warning response device includes a graded threshold monitoring module, a circuit breaker mechanism triggering module, and a multi-channel information distribution module. The graded threshold monitoring module has three preset response thresholds: the first threshold corresponds to the normal attention state, the second threshold corresponds to the high alert state, and the third threshold corresponds to the emergency circuit breaker state.

[0042] The graded threshold monitoring module receives the volatility index from the risk volatility quantification engine in real time and continuously compares it with the preset threshold.

[0043] When the volatility index exceeds the first-level preset threshold for the first time, the system automatically increases the sampling frequency of the multi-source meteorological data acquisition device and starts the encrypted observation mode.

[0044] When the index continues to climb and exceeds the second-level preset threshold, the system triggers a high-alert logic, automatically retrieves the affected geographical area, and generates a refined short-term warning document.

[0045] If the index rises sharply within a predetermined short time window and touches the third preset threshold, it is determined that the atmospheric system has entered an extremely unstable "black swan" critical state. At this time, the circuit breaker mechanism trigger module is immediately activated, mimicking the logic of stock market circuit breakers, forcibly interrupting the normal forecast generation process, and instead entering the highest level emergency response procedure, directly linking the automatic execution terminal of the emergency management department.

[0046] The multi-channel information distribution module is responsible for sending early warning information to relevant responsible entities through satellite broadcasting, mobile base station push, emergency radio, and Internet interactive interface.

[0047] In the extreme event feature extraction unit, for the nonlinear processing of massive heterogeneous data, the system employs a hardware-accelerated field-programmable gate array (FPGA). The hardware accelerator internally deploys a parallel array of multiply-accumulate operation units, capable of performing convolution operations in deep residual networks with nanosecond-level latency. To ensure robust data transmission under extreme weather conditions, the multi-source meteorological data acquisition device and the extreme event feature extraction unit are interconnected via redundant dual-path 10 Gigabit fiber optic Ethernet, supporting remote direct memory access protocols to reduce the load on the central processing unit and ensure efficient throughput of high-frequency data streams within the system.

[0048] In the tail correlation modeling module, the selection logic of the connection function is an automatic optimization process based on the distribution characteristics of historical data. The module is configured to perform a goodness-of-fit test on the sample data within the current observation window before the start of each calculation cycle, automatically evaluating the applicability of various connection functions to the current meteorological situation. If a thick-tailed characteristic is detected in the precipitation intensity distribution, the weight of the Gamble connection function is automatically increased; if a coordinated contraction characteristic of a low-pressure cyclone is observed, the Clayton connection function is preferentially called. This dynamic selection mechanism ensures that the system can accurately capture the physical correlation patterns behind different types of extreme weather events, avoiding the underestimation of extreme risks caused by neglecting tail correlation in traditional linear correlation analysis.

[0049] The risk volatility quantification engine incorporates a time-weighted average volatility algorithm when calculating the volatility index. This algorithm is configured to assign higher weights to observations more recent to the current time and lower weights to samples from more distant historical times, enabling the risk index to sensitively reflect instantaneous, pulse-like changes in atmospheric circulation. Furthermore, the implied volatility inversion unit possesses self-learning capabilities. By comparing the intensity of actual extreme weather events with the volatility levels predicted by the system in the past, it uses posterior analysis to correct calculation biases in implied volatility, giving the quantification model a closed-loop evolutionary capability that continuously optimizes with data accumulation.

[0050] The circuit breaker mechanism in the dynamic early warning response device is both mandatory and immediate. In an emergency circuit breaker state, the system is configured to automatically take over the public address system and critical infrastructure controllers in parts of the disaster-stricken area. For example, it automatically starts drainage pumping stations, shuts down high-precision industrial equipment susceptible to lightning strikes, and adjusts intelligent traffic lights to guide evacuation traffic. The core purpose of this circuit breaker mechanism is to minimize the damage caused by extreme weather events by quickly cutting off potential disaster-causing chains.

[0051] Example 2: As a supplement to Example 1 and another hardware implementation scheme, this example provides a meteorological big data intelligent diagnosis and early warning system based on a cloud-edge collaborative architecture. The various functional modules of the system exhibit distributed characteristics in spatial deployment to meet the needs of large-scale, cross-regional meteorological collaborative monitoring.

[0052] The meteorological big data intelligent diagnosis and early warning system in this embodiment includes an edge processing cluster deployed at edge observation nodes and a core computing array deployed on a central cloud platform. The edge processing cluster includes some front-end components of the multi-source meteorological data acquisition device described in Embodiment 1 and a preprocessing module of the extreme event feature extraction unit. The core computing array carries the tail correlation modeling module, the risk fluctuation quantification engine, and the decision core of the dynamic early warning response device.

[0053] At the edge observation nodes, the edge processing cluster is configured to receive raw signals from nearby automatic weather stations, mobile radar vehicles, and micro-meteorological sensors. The edge processing cluster integrates a low-power neural network processor unit specifically for performing the first stage of anomaly signal identification. By performing wavelet packet decomposition and preliminary non-stationary state detection at the edge, the system can significantly filter out irrelevant and redundant noise data, uploading only summary information and key feature vectors with extreme precursor characteristics to the central cloud platform. This design solves the bandwidth congestion and transmission lag problems caused by the backhaul of massive amounts of raw meteorological data in a large-scale station network environment.

[0054] The central cloud platform consists of a cluster of multiple high-performance servers, which interact internally via a high-speed interconnect backplane. The central cloud platform is equipped with a global meteorological topology map storage system to store the spatiotemporal correlations of meteorological elements throughout the entire observation area. The tail correlation modeling module performs global extreme value theory analysis on the central cloud platform, using a distributed parallel computing framework to jointly model feature vectors from hundreds or thousands of edge nodes. In this embodiment, the connectivity function modeling is no longer limited to single-point element combinations but extends to spatial correlation descriptions. The extreme value dependency analysis unit is configured to construct a spatial connectivity function model to assess the propagation probability of extreme disasters between adjacent areas.

[0055] The risk volatility quantification engine combines global output field data from numerical weather prediction models with localized implied volatility inversion results in the cloud. The risk index mapping unit employs a hierarchical risk assessment logic: first, it calculates the micro-volatility of each local region, and then generates a regional-level macro-risk volatility map through a global aggregation algorithm. This quantification approach, moving from the local to the global, more clearly demonstrates the occurrence patterns and evolution trends of extreme weather events.

[0056] In the early warning response phase, the dynamic early warning response device is scheduled through a unified coordination center in the cloud. When the central cloud platform issues an emergency circuit breaker command, this command is synchronously sent to all edge processing clusters in the affected area via a low-latency industrial IoT protocol, and each edge node is responsible for executing specific localized response actions. Furthermore, the dynamic early warning response device in this embodiment also introduces a blockchain-based early warning evidence storage submodule. This submodule is configured to store the original indicator data, threshold comparison results, and response execution records of each early warning trigger in the form of encrypted blocks. The technical purpose of this is to establish an immutable disaster liability tracing mechanism, providing authoritative technical support for subsequent disaster assessment and insurance claims.

[0057] The communication connection between the edge processing cluster and the central cloud platform employs a dynamic link allocation strategy. When the system is under normal monitoring, the communication link primarily uses low-frequency heartbeat detection to minimize data synchronization overhead. Once a high-alert or emergency circuit breaker state is entered, the system automatically activates redundant backup links and enables software-defined network slicing technology to allocate the highest priority transmission bandwidth to the meteorological warning data stream. This communication architecture ensures that even if extreme weather conditions damage some network infrastructure, core warning commands can still be successfully issued via residual links or satellite links.

[0058] In the edge processing cluster, to improve the accuracy of anomaly signal identification, each edge node is also equipped with a local environment adaptive learning unit. This unit is configured to monitor local geographic environmental parameters in real time, such as terrain elevation, underlying surface features, and water distribution. Since extreme weather manifests differently under varying terrain conditions, the learning unit adjusts the feature weight coefficients of the deep residual neural network to ensure that the feature extraction process fully considers the modulation effect of local terrain on atmospheric circulation. For example, in mountainous nodes, the system automatically enhances its sensitivity to localized heavy precipitation signals caused by terrain uplift.

[0059] The tail correlation modeling module employs an interpolation algorithm based on Gaussian process regression to fill in the observation gaps between edge nodes using joint probability distributions when processing massive spatial node data. The model parameter dynamic update unit optimizes the parameter search space of the connection function in the cloud using deep reinforcement learning algorithms, finding the optimal extreme value dependency description parameters under different climatic backgrounds by simulating millions of meteorological evolution scenarios.

[0060] The risk volatility quantification engine also includes a liquidity risk assessment submodule. This submodule borrows the concept of liquidity from finance to assess the impact of meteorological disasters on the flow of social production factors. This submodule is configured to calculate the emergency response flow resistance during a disaster based on the density of the road network, population distribution intensity, and the location of key energy facilities within the warning area. If the emergency response flow resistance exceeds a preset safety boundary, the system will automatically add a penalty term to the volatility index, raising the warning level to compensate for potential response lag.

[0061] In this embodiment, the circuit breaker triggering logic of the dynamic early warning response device is further refined into hard circuit breaker and soft circuit breaker. Hard circuit breaker directly cuts off the associated power or communication link for high-risk risk avoidance operations; soft circuit breaker guides the social system to operate in an orderly degraded manner through methods such as current limiting, speed limiting, and service diversion. This layered response strategy improves the system's flexibility in responding to extreme "black swan" events.

[0062] Example 3: As a further variation of Examples 1 and 2, this example focuses on describing a meteorological big data intelligent diagnosis and early warning system architecture with high robustness and self-healing capabilities. In this example, the system is configured to maintain the continuity of core early warning functions even in the event of partial component failure or external interference.

[0063] The meteorological big data intelligent diagnosis and early warning system in this embodiment adopts a containerized microservice architecture, with each functional module encapsulated in an independent virtual computing unit. The multi-source meteorological data acquisition device includes a distributed virtual sensing gateway, which can automatically discover and connect to newly deployed temporary observation nodes. When a satellite receiving link or radar data source is interrupted, the sensing gateway is configured to automatically switch to the backup data acquisition path and start a data completion algorithm to predict and fill in the missing data using observation information from surrounding stations.

[0064] The extreme event feature extraction unit employs a heterogeneous computing resource pool at the hardware level, with pooled resources including general-purpose processors, graphics processors, and customized tensor processing units. Internally, the feature extraction unit includes a resource scheduler responsible for dynamically allocating computing power based on the current meteorological data processing load. In the face of sudden, widespread severe weather events, where the data volume increases exponentially, the resource scheduler can automatically offload non-core background tasks, concentrating computing power to prioritize feature mapping operations for key spatiotemporal regions, ensuring the real-time extraction of extreme signals.

[0065] The tail correlation modeling module incorporates an ensemble learning mechanism. Internally, this module runs multiple connection function models with different structures in parallel, including logarithmic connection functions, Jo connection functions, and hybrid Archimedean connection functions. The module is equipped with a model fusion processor that uses a Bayesian weighting-based allocation logic to weight and aggregate the outputs of multiple models. When a single model exhibits prediction bias under a specific weather scenario, the model fusion processor can maintain the stability of the overall output by reducing its weight. This redundant design of multiple parallel models enhances the system's robustness in capturing rare weather events.

[0066] The risk volatility quantification engine incorporates an uncertainty propagation quantification unit. This unit is configured to cumulatively calculate measurement errors in the input data, parameter errors in model building, and the inherent randomness in future atmospheric evolution, generating a confidence interval for the volatility index. A high-level warning is triggered only when both the volatility index and its corresponding lower confidence limit exceed a preset threshold. This dual-confirmation mechanism reduces the false alarm rate caused by data noise or single model failure, improving the scientific rigor of warning decisions.

[0067] In this embodiment, the dynamic early warning response device integrates a simulation prediction sandbox. This sandbox is configured to simulate the social response effect after the early warning command is issued within milliseconds of the early warning triggering time using a rapid numerical simulation method. If the simulation results show that the current response strategy may lead to secondary disasters or social panic, the circuit breaker mechanism triggering module will automatically adjust the response intensity, achieving a balance between risk suppression and social stability by fine-tuning the rhythm and content of the early warning release.

[0068] In the multi-source meteorological data acquisition device, to address the issue of detection failure under extreme environments, the system is equipped with a cluster of self-organizing unmanned reconnaissance drones. When the Doppler radar is damaged due to power outages or extreme winds, the virtual sensing gateway automatically releases the self-organizing unmanned reconnaissance drone cluster. The drones, equipped with miniaturized millimeter-wave radar and temperature and humidity sensors, form a temporary mobile observation grid in the disaster center area. The heterogeneous data fusion submodule rapidly fuses these temporary observation data with historical background data to reconstruct the physical field distribution of the disaster site, ensuring that the early warning system does not experience "observation blind spots" due to hardware damage.

[0069] Within the extreme event feature extraction unit, a real-time feature importance monitoring logic is also designed. This logic automatically identifies the dominant physical quantity under the current atmospheric conditions by calculating the sensitivity coefficient of the input element's contribution to the output feature. For example, during the evolution of a heavy precipitation event, if the system detects an abnormal jump in the contribution of low-altitude water vapor flux, it will automatically increase the sampling density and feature processing depth of the element. This dynamic allocation of attention mechanism is similar to the attention allocation of the human visual system, enabling the system to devote limited computing resources to the analysis of the most threatening disaster-causing elements.

[0070] In this embodiment, the tail correlation modeling module also possesses autonomous evolution capabilities. The model parameter dynamic update unit periodically performs deep reflection and learning on historical failure cases to identify fitting failure points of existing connection functions in extreme tail regions. When the system determines that the existing function structure cannot accurately describe new extreme weather, it automatically reconstructs new correlation descriptors from the function library using a genetic algorithm, and automatically deploys them online after offline testing. This ability to "evolve through learning" is the core technological guarantee for this system to cope with future climate uncertainties.

[0071] In this embodiment, the risk volatility quantification engine incorporates "risk contagion topology" analysis. The engine calculates not only the risk volatility at a single point but also the mutual driving force of risk volatility between different geographical grid points. The risk index mapping unit uses a graph neural network to model the risk propagation path. When the risk volatility of a certain grid point increases, the system automatically assesses its radiation effect on downstream grid points. If the risk exhibits obvious chain-like transmission characteristics, the early warning response level will automatically upgrade from "point-based early warning" to "area-based early warning," achieving proactive defense against the spread of disasters.

[0072] The dynamic early warning response device also includes an adaptive feedback loop. During the early warning issuance process, the system uses social media data streams, real-time traffic monitoring videos, and changes in pedestrian flow at operator base stations to perceive the intensity of public response to the early warning information in real time. If the evacuation speed within the warning area is detected to be slower than a predetermined threshold, or the efficiency of emergency resource allocation is lower than expected, the circuit breaker mechanism trigger module will automatically increase the intensity of the early warning push, and may even forcibly execute the response logic by directly intervening in the automated public facilities within the area.

[0073] To ensure the network security of the entire system, all inter-module communication is encrypted and decrypted using a customized quantum encryption chip. The system's control command flow employs a multi-signature authorization mechanism; any high-level warning trigger action must undergo a three-party consensus verification process involving the risk volatility quantification engine, the tail correlation modeling module, and a pre-set legal manual review logic (automatically executed if no intervention occurs within a specific time window). This security architecture prevents false warning triggers caused by external hacker attacks or internal misoperations, ensuring the system's technical authority and social credibility.

[0074] The system's storage layer design employs a non-consistent storage access architecture, physically isolating the hot data required for real-time computation, the temperature data needed for recent analysis, and the historical archived cold data. Hot data is stored in a fast cache based on non-volatile memory, with read / write latency controlled to the microsecond level, supporting the real-time requirements of the high-frequency fluctuation quantization engine. Cold data is stored in a distributed object storage cluster for online learning of long-cycle connection function parameters and model evolution. This hierarchical storage strategy ensures system response speed while also addressing the long-term governance needs of massive meteorological big data.

[0075] Through the synergistic cooperation of the three embodiments described above, this invention provides a fully closed-loop, all-round, and highly intelligent meteorological early warning technology solution encompassing perception, modeling, quantification, and response. The system breaks through the limitations of linear thinking in traditional weather forecasting, achieving precise capture of "black swan" events in atmospheric systems by introducing the high-frequency fluctuation perspective of financial engineering and the scientific framework of extreme value theory.

[0076] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0077] 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 alterations 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 big data intelligent diagnosis and early warning system, characterized in that, include: A multi-source meteorological data acquisition device is configured to acquire multi-dimensional meteorological observation data from multiple observation platforms in real time. The multi-dimensional meteorological observation data includes pressure field distribution, humidity gradient, wind speed vector, temperature profile, and precipitation intensity sequence. An extreme event feature extraction unit is connected to the multi-source meteorological data acquisition device and is configured to identify abnormal signals and detect non-stationary states in the acquired multi-dimensional meteorological observation data, thereby extracting high-order statistical features and instantaneous change indicators that characterize extreme weather events. The tail correlation modeling module is connected to the extreme event feature extraction unit. It is configured to construct a joint probability distribution model of multiple meteorological elements under extreme conditions based on extreme value theory, and to use a connection function to characterize the nonlinear dependency structure of different meteorological variables in the distribution tail region, so as to capture the extreme co-evolution pattern among different meteorological elements. The risk volatility quantification engine, connected to the tail correlation modeling module, is configured to introduce option pricing concepts and high-frequency trading volatility modeling methods to transform the probability of meteorological disasters and their potential impact intensity into risk volatility indicators, and dynamically assess the tendency of extreme events in the atmospheric system based on the risk volatility indicators. The dynamic early warning response device is connected to the risk volatility quantification engine and is configured to trigger a graded early warning mechanism based on the risk volatility index. When the risk volatility index exceeds a preset threshold, it automatically generates and pushes early warning information and simultaneously initiates an emergency response process similar to a stock market circuit breaker.

2. The meteorological big data intelligent diagnosis and early warning system according to claim 1, characterized in that, The multi-source meteorological data acquisition device includes: The satellite data receiving submodule is equipped with a high-gain satellite receiving antenna and a real-time demodulation processor, and is used to acquire multi-channel remote sensing image data provided by geostationary orbit satellites and polar orbit satellites. The multi-channel remote sensing image data includes infrared cloud images, water vapor channel images, visible light cloud images, and atmospheric vertical detection profiles. The radar echo detection submodule is connected to the ground-based meteorological radar network and is configured to periodically acquire Doppler radar echo intensity, radial velocity, and spectral width data according to a preset scanning strategy, in order to detect the evolution trajectory of small- and medium-scale strong convective systems. The ground station network observation submodule includes an automatic weather station cluster deployed on a geographic grid, used to report air pressure, temperature, humidity, wind direction, wind speed and minute-level precipitation in real time; The upper-air sounding submodule is connected to sounding rockets, meteorological drones, and stratospheric sounding balloons to acquire physical quantity field information on different isobaric surfaces; The heterogeneous data fusion submodule is equipped with a high-performance data exchange backplane, which is used to perform unified standardization processing on raw meteorological observation data from different observation platforms with different temporal resolutions, spatial resolutions and data formats, so as to achieve strict alignment of spatiotemporal coordinate systems and data quality control.

3. The meteorological big data intelligent diagnosis and early warning system according to claim 2, characterized in that, The extreme event feature extraction unit includes: The abnormal signal identification subunit integrates a deep residual neural network model, which contains multiple cascaded residual learning blocks. Each residual learning block contains two consecutive convolutional layers and cross-layer identity mapping connections. The convolutional layer is configured to extract local correlation features from meteorological data, and the identity mapping connection is configured to directly transmit the input signal to the output of the residual learning block and perform addition operations to preserve the original signal structure and suppress gradient decay. The non-stationary state detection subunit is equipped with a wavelet packet decomposition processor, which is used to decompose the non-stationary meteorological time series signal at multiple scales and separate the physically meaningful low-frequency background field signal and high-frequency abrupt pulse signal from the high-noise background. The high-order feature mapping subunit is equipped with a self-attention mechanism module, which is used to calculate the weight distribution of meteorological elements in different spatiotemporal regions. This enhances the ability to extract precursor indicators that characterize sudden torrential rain and rare thunderstorms and strong winds. The generated feature vector is input to the tail correlation modeling module.

4. The meteorological big data intelligent diagnosis and early warning system according to claim 3, characterized in that, The tail correlation modeling module includes: The distribution boundary estimation unit is configured to perform parameterized fitting of the tail distribution of each meteorological element based on the generalized Pareto distribution in extreme value theory. By setting a preset threshold, sample points that are higher than the preset threshold are identified as extreme event observations. The extreme value dependency analysis unit is configured to use Archimedes-type connection functions to characterize the nonlinear dependency structure of multiple meteorological elements in the distribution tail region. The extreme value dependency analysis unit calls the Gamble join function to capture the upper tail correlation between meteorological elements, that is, when the first meteorological element has an extreme value, the second meteorological element also tends to have an extreme value. The extreme value dependency analysis unit calls the Clayton join function to model the lower tail correlation between meteorological elements; The model parameter dynamic update unit is connected to a historical extreme event sample library and periodically corrects the correlation parameters of the Archimedes-like connection function through an online learning algorithm to adapt to the long-term drift of the statistical characteristics of extreme weather events caused by climate change.

5. The meteorological big data intelligent diagnosis and early warning system according to claim 4, characterized in that, The risk volatility quantification engine includes: The option pricing simulation unit is configured to build a meteorological derivative pricing model, using the observed values ​​of meteorological elements as the price of the underlying asset and the disaster warning threshold as the strike price. The implicit volatility inversion unit is configured to calculate the uncertainty of the atmospheric system by simulating the dispersion of weather forecast set members under different initial disturbance conditions, and to convert the path distribution dispersion of the weather forecast set members into implicit volatility, which represents the sensitivity of the atmospheric system to future extreme disturbances. The risk index mapping unit is configured to perform a composite operation on the implied volatility and the probability of extreme events to generate a continuously distributed risk volatility index that characterizes the strength of meteorological risk. The risk volatility index is converted to a fixed numerical range through a preset normalization algorithm.

6. The meteorological big data intelligent diagnosis and early warning system according to claim 5, characterized in that, The dynamic early warning response device includes: The graded threshold monitoring module has three preset response thresholds, which include a first preset threshold corresponding to the normal attention state, a second preset threshold corresponding to the high alert state, and a third preset threshold corresponding to the emergency circuit breaker state. The circuit breaker mechanism trigger module is configured to automatically interrupt the regular forecast generation process and enter the highest level emergency response procedure when the risk volatility index climbs within a predetermined time window and reaches the third-level preset threshold. A multi-channel information distribution module is configured to send the generated early warning information to relevant responsible parties via satellite broadcasting, mobile base station push, emergency radio, and Internet interactive interface; In the emergency circuit breaker state, the dynamic early warning response device is configured to automatically take over the automatic controller of public infrastructure in the disaster area and perform disaster avoidance operations, including turning on drainage pumping stations, shutting down vulnerable industrial equipment, and adjusting traffic lights.

7. The meteorological big data intelligent diagnosis and early warning system according to claim 6, characterized in that, The system is deployed using a cloud-edge collaborative architecture, and the system includes: An edge processing cluster, deployed at edge observation nodes, includes some front-end components of the multi-source meteorological data acquisition device and a preprocessing module of the extreme event feature extraction unit; The edge processing cluster integrates a neural network processor, which is used to receive the original signal nearby and perform the first stage of abnormal signal identification and wavelet packet decomposition, and upload the filtered feature vector to the central cloud platform. The core computing array, deployed on the central cloud platform, carries the tail correlation modeling module, the risk volatility quantification engine, and the dynamic early warning response device. The central cloud platform is equipped with a global meteorological topology map storage device, which is used to store the spatiotemporal correlation of meteorological elements within the observation area; The edge processing cluster is connected to the central cloud platform using a dynamic link allocation strategy. When entering the emergency circuit breaker state, the system automatically activates redundant backup links and enables software-defined network slicing technology to allocate the highest priority transmission bandwidth to the meteorological early warning data stream.

8. The meteorological big data intelligent diagnosis and early warning system according to claim 7, characterized in that, The extreme event feature extraction unit adopts a heterogeneous computing resource pool at the hardware level, which includes a general-purpose processor, a graphics processor, and a customized tensor processing unit. The feature extraction unit is equipped with a resource scheduler, which is configured to dynamically allocate computing power according to the current meteorological data processing load. When faced with sudden, widespread severe weather and an exponential increase in data volume, the resource scheduler is configured to automatically strip away non-core background tasks and concentrate computing power to prioritize feature mapping operations in key spatiotemporal regions. The extreme event feature extraction unit is also equipped with real-time feature importance monitoring logic, configured to identify the dominant physical quantity under the current atmospheric condition by calculating the sensitivity coefficient of the contribution of the input meteorological elements to the output features, and automatically increase the sampling density and feature processing depth of the dominant physical quantity.

9. The meteorological big data intelligent diagnosis and early warning system according to claim 8, characterized in that, The risk volatility quantification engine also includes a liquidity risk assessment submodule, configured to calculate the emergency response flow resistance during a disaster based on the traffic network density, population distribution intensity, and location of key energy facilities within the warning area. When the emergency response flow resistance exceeds the preset safety boundary, the risk index mapping unit adds a penalty term to the generated risk volatility index to raise the warning level; The risk volatility quantification engine is also configured to perform risk spread topology analysis, which models the risk propagation path between different geographical grid points using graph neural networks. When the risk volatility of the first grid point increases, the risk volatility quantification engine assesses the radiation effect on downstream grid points, and when the risk exhibits chain transmission characteristics, it upgrades the early warning response range from point-based early warning to area-based early warning.

10. A method for intelligent diagnosis and early warning based on meteorological big data, characterized in that: The meteorological big data intelligent diagnosis and early warning system described in any one of claims 1 to 9 is used to realize intelligent diagnosis and early warning of meteorological big data.

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