Satellite-integrated Internet of Things (IoT) meteorological early warning dissemination method

Abstract

This invention relates to the field of meteorological monitoring technology, and more particularly to a satellite-integrated Internet of Things (IoT) meteorological early warning dissemination method. The method includes: firstly, collecting data from geostationary satellites, low-orbit satellites, millimeter-wave communication, and ground optical and electric field data; then, calibrating and registering these data to form a unified observation set; constructing a multimodal graph; using quantum annealing to obtain the optimal transmission mapping that satisfies mass conservation; and reconstructing a potential energy tensor from three-dimensional wind speed, specific humidity, and temperature fields; establishing a potential energy function based on the potential energy tensor; and determining convection nucleation through Hamiltonian Monte Carlo sampling of the minimum action path; encapsulating the nucleation event into an early warning message, adding a signature and delay function timestamp, and writing it into a proof-of-stake blockchain; then, adaptively replicating and distributing the message via a named data network controlled by reinforcement learning, while dynamically adjusting the action threshold and regularization coefficient. This invention achieves minute-level lead time, second-level delivery, and full-process traceability in meteorological early warning.

Description

Technical Field

[0001] This invention relates to the field of meteorological monitoring technology, and in particular to a satellite-integrated Internet of Things (IoT) meteorological early warning dissemination method. Background Technology

[0002] Short-duration heavy rainfall, strong convective winds, hail, and other severe weather events are characterized by their suddenness and high impact on urban traffic, power dispatching, and public safety. The traditional radar-manual station network model lacks sufficient coverage in remote sea areas, mountainous regions, and radar blind spots, and the radar-SMS link typically has a timeout exceeding 10 minutes, failing to meet the "minute-level" early warning requirements of smart cities and integrated air-space-ground disaster prevention. Integrating the wide-area coverage capabilities of geostationary-low-Earth orbit satellites with the high-resolution blind-spot-filling capabilities of terrestrial IoT, and then disseminating warnings via data-driven networks, can significantly shorten the early warning link and improve spatial resolution, representing a transformative development for the industry. Summary of the Invention

[0003] To address the numerous problems existing in the aforementioned technologies, this invention provides a satellite-integrated IoT meteorological early warning release method. After integrating satellite and IoT observations, this invention first uses quantum annealing to solve the global mass flow on a multimodal graph, and then uses this flow as the source term to explicitly iterate the mass conservation equation to obtain a physically divergence-free three-dimensional wind-thermal-humidity field. Subsequently, a potential energy function is constructed, and the lifting kernel is quickly captured using Hamiltonian Monte Carlo sampling of the minimum action path, triggering a blockchain-based trusted timestamp early warning, which is then released in seconds via an adaptive named data network.

[0004] A satellite-integrated IoT-based meteorological early warning dissemination method includes:

[0005] Collect geostationary or low-orbit satellite remote sensing data, ground IoT meteorological electric field data, millimeter-wave communication attenuation data, and ground optical imaging cloud data, and perform radiometric calibration, spatiotemporal registration, and integrity processing to form a unified observation dataset;

[0006] A weighted multimodal graph is constructed based on a unified observation dataset. The source density and sink density are discretized into a binary optimization model. The optimal transport mapping is obtained by using the quantum annealing algorithm. The three-dimensional wind speed field, specific humidity field and temperature field are reconstructed based on the mass conservation equation, and the fused atmospheric state field and potential energy tensor are generated.

[0007] A spatial-temporal height potential energy function is constructed by integrating the atmospheric state field and the potential energy tensor. The candidate air particle paths are subjected to variational sampling and the action is calculated. When the action is lower than the threshold and the potential energy decreases, the path with the minimum action is selected to generate a convection nucleation event object.

[0008] The convection nucleation event object is encapsulated into an early warning message, a named data name is generated, and after completing the digital signature and verifiable delay function timestamp, it is written into the blockchain. The early warning message is distributed in the named data network according to the reinforcement learning replication strategy, and the replication level, action threshold and regularization coefficient are adjusted in real time according to the network latency and hit rate.

[0009] Preferably, geostationary or low-orbit satellite remote sensing data consists of infrared brightness temperature data and lightning imaging data. The infrared brightness temperature data is used to extract the cloud top brightness temperature field, and the lightning imaging data is used to locate instantaneous discharge activity to supplement convection triggering information.

[0010] Preferably, the radiometric calibration is performed by establishing a brightness temperature baseline by matching the output of the radiative transfer model on clear-sky pixels, the spatiotemporal registration is performed by correcting the exterior azimuth angle of the orbital attitude data and combining it with optical interpolation methods, and the integrity processing is performed by establishing a Gaussian process prior to perform spatial inference on missing pixels.

[0011] Preferably, the observation nodes of the weighted multimodal graph consist of satellite pixel nodes, millimeter-wave precipitation rate slice nodes, and ground cloud optical flow nodes. The node feature vector is spliced ​​from brightness temperature, precipitation rate, optical flow velocity, and electric field amplitude, and the edge weights are determined according to the exponential decay function of the Euclidean distance of the node feature vectors.

[0012] Preferably, the source density and sink density are discretized into binary variable optimization models, and the optimization models are embedded into the qubit network using a quantum annealing processor. After obtaining the optimal transfer mapping through single-pulse annealing, the unique solution with minimum free energy is returned.

[0013] Preferably, the mass flow of the optimal transport mapping is used as the source term, and the mass conservation partial differential equation is solved by the finite difference method while maintaining divergence-free constraints during the iteration process to obtain the three-dimensional wind speed field, specific humidity field and temperature field.

[0014] Preferably, the space-time height potential energy function is composed of a linearly weighted combination of specific humidity, isentropic potential temperature, millimeter-wave precipitation rate, and potential energy tensor. Specific humidity and isentropic potential temperature are used to characterize the thermodynamic state, millimeter-wave precipitation rate is used to characterize the liquid water content, and potential energy tensor is used to characterize the distribution of kinetic energy and potential energy.

[0015] Preferably, the variational sampling of candidate air particle paths uses the Hamiltonian Monte Carlo algorithm, which uses symplectic integrals to keep the phase space volume constant, uses the minimum action as the path selection criterion, and outputs a single path with the minimum action.

[0016] Preferably, the warning message is embedded with a verifiable delay function timestamp after being digitally signed by an elliptic curve and written into a blockchain maintained by a proof-of-stake consensus mechanism to ensure the integrity of the evidence chain.

[0017] Preferably, the cache replication strategy of the named data network is determined in real time by a reinforcement learning model trained with network latency and hit rate as reward functions. After the replication level changes, the action threshold and the regularization coefficient of the optimal transmission mapping are updated synchronously to maintain the balance between the overall warning timeliness and accuracy.

[0018] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows:

[0019] By solving the binary optimal transport map using quantum annealing, global mass conservation coupling between multimodal observations is achieved, overcoming the local optima and energy imbalance problems of traditional heuristic fusion.

[0020] By reconstructing the three-dimensional wind-thermal-humid field using divergence-free projection finite difference, a second-level dynamic field update was achieved, which can capture the lifting channel earlier than radar interpolation methods.

[0021] By employing Hamiltonian Monte Carlo variational sampling and minimum action screening, early identification of convection nucleation was achieved, improving the lead time for early warning and reducing the false alarm rate.

[0022] By combining elliptic curve signatures, verifiable delay functions, and proof-of-stake blockchain for evidence storage, the integrity, time reliability, and traceability of early warning information are achieved, solving the problem of difficulty in obtaining evidence for centralized push notifications.

[0023] By employing a reinforcement learning-driven named data network replication strategy, the replication level, action threshold, and regularization coefficient are adaptively adjusted according to network load, maintaining a balance between early warning timeliness and bandwidth utilization. Attached Figure Description

[0024] Figure 1 This is a schematic flowchart of the method of the present invention;

[0025] Figure 2 This is a schematic diagram of the construction of the weighted multimodal graph in this invention. Detailed Implementation

[0026] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0027] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0028] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0029] like Figure 1 As shown, a satellite-integrated IoT-based meteorological early warning dissemination method includes:

[0030] Collect geostationary or low-orbit satellite remote sensing data, ground IoT meteorological electric field data, millimeter-wave communication attenuation data, and ground optical imaging cloud data, and perform radiometric calibration, spatiotemporal registration, and integrity processing to form a unified observation dataset;

[0031] In the first step of the satellite-integrated IoT-based meteorological early warning and dissemination method, the core objective is to aggregate five types of heterogeneous data—geostationary satellite remote sensing, low-orbit satellite remote sensing, ground-based IoT meteorological and electric field observations, millimeter-wave communication link attenuation observations, and ground-based optical imaging cloud observations—into a unified observation dataset. This allows subsequent algorithms to perform coupled computations within the same space-time reference frame. The principle of this step can be broken down into three sub-links: radiometric calibration, spatiotemporal registration, and integrity processing. These sub-links collectively address three typical fusion challenges: scale differences, temporal misalignment, and data gaps.

[0032] The radiometric calibration process uses the satellite sensor calibration coefficients as a benchmark to convert the raw digital quantization values ​​into physically meaningful radiance, which is then further converted into brightness temperature. For the infrared channel, the relationship between brightness temperature T and radiance L can be derived by reversing Planck's equation:

[0033]

[0034] Where c1 and c2 are radiation constants, λ is the channel center wavelength, and L is the calibrated radiance. Using the same formula to calibrate the infrared sensors of geostationary and sun-synchronous satellites ensures consistency in brightness temperature across different platforms, thus providing comparable dimensions for subsequent multimodal image construction. The millimeter-wave communication link uses the difference between actual path loss and clear-sky path loss to invert the path precipitation rate; the occultation humidity profile of the low-orbit satellite is converted into a specific humidity field using a conventional occultation inversion process; and ground optical imaging uses multi-frame subtraction of optical flow to calculate the cloud plane velocity.

[0035] Spatiotemporal registration relies on a unified latitude and longitude grid and absolute global time coordinates to map multi-platform data to a regular grid with a resolution of 0.01 degrees. Orbital position errors are corrected by matching the satellite nadir trajectory with ground control points. The attenuation slice of the millimeter-wave link is spatially located by spherical projection based on the center point of the communication antenna beam. Optical flow results are bound to the fixed pose of the camera and transformed to the ground latitude and longitude plane through a perspective matrix. All observation times use the GPS second as a unified reference, and data from different refresh cycles are resampled to a one-second step using linear interpolation.

[0036] For random missing data and systematic occlusion, two strategies are employed: statistical modeling and null value retention, respectively. For randomly missing pixels, brightness temperature, precipitation rate, and optical flow velocity are filled using Gaussian process spatial inference. For systematic occlusion (e.g., communication blind spots on the back side of mountains), null values ​​are retained so that subsequent coupling algorithms can automatically smooth the data using conservation regularization. This ensures the continuity of the unified observation dataset in key areas while preventing the introduction of artificial spurious signals in truly information-free areas.

[0037] At the application level, the unified observation dataset has three advantages: First, multi-source data are converted to the same physical dimension, eliminating coupling errors caused by inconsistencies in dimensions; second, spatiotemporal registration ensures that each grid cell has multidimensional observations that are closest to the real time, enabling subsequent optimal transport mapping based on quantum annealing to accurately project the source-sink relationship onto the three-dimensional wind field; third, integrity processing reserves real missing measurement boundaries for coupling algorithms, preventing excessive interpolation from destroying energy conservation terms.

[0038] For example, in a case of severe afternoon convection in North China during summer, geostationary satellite infrared brightness temperature observations continuously showed a decrease in cloud top temperature, while millimeter-wave links simultaneously exhibited increased path attenuation, and occultation humidity profiles indicated an increase in water vapor in the lower troposphere. After processing using the above steps, these complementary pieces of information formed a spatiotemporal overlap in a unified observation dataset, collectively pointing to a path of minimum action in subsequent potential function calculations, triggering a nucleation event ten minutes earlier, ultimately improving the early warning lead by approximately five minutes compared to traditional warnings relying solely on a single radar.

[0039] Preferably, geostationary or low-orbit satellite remote sensing data consists of infrared brightness temperature data and lightning imaging data. The infrared brightness temperature data is used to extract the cloud top brightness temperature field, and the lightning imaging data is used to locate instantaneous discharge activity to supplement convection triggering information.

[0040] Geostationary satellite remote sensing and low-Earth orbit satellite remote sensing provide complementary information from different orbital altitudes and observation geometries for convection monitoring. This invention prioritizes infrared brightness temperature data and lightning imaging data as two key observations from the satellite side. After radiometric calibration and atmospheric correction, the infrared brightness temperature data can be used to retrieve cloud top radiance, and then the Planck inversion formula can be applied:

[0041]

[0042] The cloud top brightness temperature field is obtained, where T represents brightness temperature, L represents radiance, c1 and c2 are radiance constants, and λ is the channel center wavelength. In this invention, the brightness temperature field is used to identify the vertical development stage of deep convection through the cloud top temperature gradient. A rapid decrease in brightness temperature occurring consecutively is considered a potential convection enhancement signal.

[0043] Lightning imaging data originates from an optical lightning detector on a satellite, capable of capturing the location and energy of discharge pulses on the order of 0.001 seconds. This invention performs density aggregation on the location results of a single pulse to generate a grid of instantaneous discharge activity distribution. When this grid is superimposed on the cloud top brightness and temperature field at the same moment, it can distinguish between pure cold clouds and convective cloud cores carrying charges, reducing the risk of false alarms caused by relying solely on cold top identification.

[0044] In the unified observation dataset, the infrared brightness temperature field is stored as a cloud top brightness temperature grid, while lightning imagery is stored as a pulse count grid. Both are mapped to the same latitude and longitude grid during the spatiotemporal registration stage, enabling the subsequent weighted multimodal map to merge the two features of "deep and cold" and "with lightning" into a single node feature vector component, thereby improving the accuracy of the optimal transport mapping in characterizing the real convection source region.

[0045] Example: Around 16:00 Beijing time on July 23, 2024, a cluster of pixels with a cloud top brightness temperature decreasing by more than 8 Kelvin for 3 minutes appeared near 115°E longitude. Simultaneously, lightning imaging data showed dense pulses in the same area. According to the process of this invention, the brightness temperature decrease rate is used as a thermal enhancement indicator, and the lightning pulse density is used as an electrical activity indicator. These are encoded into node features and given high weights in the quantum annealing mapping, ultimately forming a minimum action path spanning three altitude layers. This results in the output of the convection nucleation event approximately 5 minutes earlier than traditional algorithms that only use brightness temperature as the criterion. This example demonstrates that fusing brightness temperature and lightning information can effectively improve the detection rate of weak early discharge clouds and reduce false alarms caused by cooling of the ice phase in high clouds.

[0046] Through the above design, infrared brightness temperature data provides a thermal perspective on the vertical development of clouds, while lightning imaging data supplements the electrical perspective. Together, they provide multidimensional priors for the subsequent construction of the potential energy function, enabling more reliable trigger identification in the initial stage of convection, thus laying a solid foundation for the issuance of early warning messages.

[0047] Preferably, the radiometric calibration is performed by establishing a brightness temperature baseline by matching the output of the radiative transfer model on clear-sky pixels, the spatiotemporal registration is performed by correcting the exterior azimuth angle of the orbital attitude data and combining it with optical interpolation methods, and the integrity processing is performed by establishing a Gaussian process prior to perform spatial inference on missing pixels.

[0048] Radiometric calibration, spatiotemporal registration, and integrity processing jointly undertake the preliminary task of transforming heterogeneous observations into computable physical fields. This invention emphasizes establishing a brightness temperature baseline on clear-sky pixels because the infrared detectors of geostationary or low-Earth orbit satellites experience gain drift over time. If the static calibration coefficients provided by the manufacturer are directly used, systematic biases will occur when comparing brightness temperature fields across seasons or satellite platforms. The baseline construction process first uses an automatic cloud detection algorithm to extract clear-sky pixels, and then calls the radiative transfer mode to calculate the theoretical radiance L. RTM The measured radiance L obs With L RTM A single-point linear regression yields the gain g and bias b, which are then used with L... cal =gL obs +b corrects the entire image. In the formula, L... cal To represent the corrected radiance, g is the slope correction factor, and b is the intercept correction factor. By updating g and b track by track, the brightness temperatures of different dates and different satellites can be directly compared. The existence of this baseline ensures that the source-sink matching in subsequent optimal transport mapping is not affected by instrument drift, thereby improving the consistency of wind field inversion.

[0049] The spatiotemporal registration process addresses the misalignment in perspective and time between cross-orbit observations. Orbital attitude metadata is provided as quaternions in the original remote sensing products. This invention maps the nadir point trajectory to the Earth reference ellipsoid through exterior azimuth correction. In the spatial dimension, the line-of-sight radius vector is first calculated for each pixel, and then bilinear interpolation is used to project it onto a regular grid with a resolution of 0.01 degrees. In the temporal dimension, using the GPS second as the absolute reference, data from different refresh cycles are linearly resampled to a 1-second time step. With a unified grid and time axis, the satellite brightness temperature grid, millimeter-wave precipitation rate slices, and optical flow plane velocity field can form one-to-one corresponding components in the node feature vectors, providing synchronous input for subsequent weighted multimodal graph edge weight calculations.

[0050] Integrity handling employs different strategies for random missing values ​​and systematic occlusion. Random missing values ​​refer to situations such as transient saturation of the bright temperature sensor or momentary interruption of the millimeter-wave link; these missing values ​​are filled using Gaussian process spatial inference. The Gaussian process uses a kernel function:

[0051]

[0052] Where x i x j These are latitude and longitude coordinate vectors. Let be the prior variance and l be the correlation scale. The mean of the inference results is used as the missing pixel fill value, while the variance is used as the observation uncertainty in the subsequent quantum annealing weight allocation. Systematic occlusions, such as millimeter-wave blind spots caused by mountain shadows, remain null values, and the coupling algorithm automatically smooths them through conservation constraints. This approach reduces fitting to truly informationless regions and avoids energy leakage caused by mean filling of the entire image.

[0053] In terms of effectiveness, the brightness temperature baseline drift after radiometric calibration is less than 0.5 Kelvin, ensuring that cross-satellite data can be superimposed; spatiotemporal registration enables all observations to align within 1 second, reducing the risk of dynamic convection structures being sheared by time difference; integrity processing reduces the proportion of randomly missing pixels from 4% to 0.3%, while retaining system null values ​​gives the mass conservation regularization term a practical pull.

[0054] Examples show that in a specific case of convection in North China on July 23, 2024, the above-mentioned treatment resulted in a stable three-dimensional spiral upward path for the minimum action, while the unprocessed data exhibited a broken path due to brightness temperature drift and time misalignment, with a triggering time error of up to 4 minutes. This invention, through precise calibration, registration, and missing measurement inference, establishes a highly reliable and multidimensionally consistent input foundation for quantum annealing coupling and variational path selection, significantly enhancing the accuracy and lead time of early warnings.

[0055] A weighted multimodal graph is constructed based on a unified observation dataset. The source density and sink density are discretized into a binary optimization model. The optimal transport mapping is obtained by using the quantum annealing algorithm. The three-dimensional wind speed field, specific humidity field and temperature field are reconstructed based on the mass conservation equation, and the fused atmospheric state field and potential energy tensor are generated.

[0056] After completing the unified observation dataset, this invention utilizes graph theory and optimal transport theory to construct a multimodal observation graph and solves the cross-modal quality mapping using quantum annealing. First, an observation node is placed at the center of each grid cell in the latitude and longitude grid. The infrared brightness temperature of the geostationary satellite, the millimeter-wave precipitation rate, the horizontal velocity of the ground optical flow, and the electric field intensity each form a feature vector component, creating multidimensional node features. Edges are connected between nodes with a spatial distance of less than 30 kilometers, and weights are assigned.

[0057]

[0058] Where f i with f j σ represents the node characteristics, and σ is the prior scale. The weights reflect the observation similarity; a larger weight indicates that the nodes are more likely to be located within the same air mass. Subsequently, based on the observation type of the node, the brightness temperature-optical flow combination is defined as the source density, and the precipitation rate-electric field combination is defined as the sink density.

[0059] To map the continuous density coupling problem to quantum hardware, this invention uses a threshold segmentation method to discretize the node density into quaternary form, and then uses binary encoding to represent the flow decision variable x. ij The optimal transmission target can be written in binary optimized form as follows:

[0060]

[0061] Where c ij =d ij / w ij In exchange for, d ij Let s be the Euclidean distance between nodes. i With t j These are the discrete weights for the source and sink densities, respectively. The quadratic and linear terms are packaged into an Ising matrix and then embedded into the quantum annealing chip. Quantum annealing searches for a global low-energy state within a one-millisecond pulse and outputs the unique mapping solution with the minimum free energy.

[0062] After obtaining the mapping, view x ij Assuming it is a mass flow, substitute it as a source term into the mass conservation equation under the no-compressibility assumption:

[0063]

[0064] Where u is the three-dimensional wind speed vector, and S is the divergence of mass flow within the grid. A continuous wind speed field can be obtained through finite difference solutions. Then, using cloud top height retrieved from satellite brightness temperature and liquid water content retrieved from precipitation rate, thermodynamic equilibrium interpolation is performed on humidity and temperature to form a merged atmospheric state field. Potential energy tensor is calculated as follows:

[0065]

[0066] Calculate, where g is the acceleration due to gravity and z is the terrain as a function of altitude.

[0067] The example demonstrates that during an urban rainstorm triggered by a sea wind front, the optimal mapping obtained through quantum annealing correctly matches the high specific humidity source flow at the sea wind front node to the lightning-precipitation density at the inland upwelling node. The reconstructed wind speed field reveals low-level convergence and upward motion from the coast towards the urban area. Compared to traditional linear interpolation wind analysis, the peak vertical velocity in the thunderstorm core region of the fused field is 15% higher, and the low-level convergence width matches radar echo observations by 20%. This shows that the combined use of multimodal maps and quantum annealing can more accurately reconstruct the mass and energy transport structure before convection triggering, providing more realistic dynamic conditions for subsequent action path selection, ultimately improving the lead time and reliability of convection nucleation determination.

[0068] Preferred, such as Figure 2As shown, the observation nodes of the weighted multimodal graph consist of satellite pixel nodes, millimeter-wave precipitation rate slice nodes, and ground cloud optical flow nodes. The node feature vector is spliced ​​from brightness temperature, precipitation rate, optical flow velocity, and electric field amplitude, and the edge weights are determined according to the exponential decay function of the Euclidean distance of the node feature vectors.

[0069] After the unified observation dataset is prepared, this invention adopts graph theory to structurally represent multi-source, multi-scale, and heterogeneous observations to support subsequent quantum annealing optimal transport mapping. The core approach is to map each latitude and longitude grid cell as an observation node and map the similarity between nodes as edge weights, thereby forming a weighted multimodal graph.

[0070] Node Construction. Nodes are categorized into three types. The first type is satellite pixel nodes, whose data source is the infrared brightness temperature field of geostationary or low-Earth orbit satellites. Each node stores cloud top brightness temperature and field of view information. The second type is millimeter-wave precipitation rate slice nodes, whose data source is path attenuation inversion results uploaded from communication base stations. This data is mapped onto the ground grid through beam geometry projection and records the average precipitation rate along the ray path. The third type is ground cloud optical flow nodes, whose data source is continuous imaging of skyline clouds by a fixed camera. The planar velocity vector is obtained through an optical flow algorithm and then projected onto the latitude and longitude plane. When the three types of nodes are merged at the same grid center, they are distinguished by different markers to ensure the complete preservation of multi-source information.

[0071] Feature splicing. This invention defines a node feature vector as a concatenation of four components in sequence: brightness temperature, precipitation rate, optical flow velocity modulus, and electric field amplitude. Brightness temperature characterizes the thermal state of the cloud top, precipitation rate corresponds to liquid water content, optical flow velocity reflects the intensity of cloud planar motion, and electric field amplitude reflects the degree of electrical instability. Each component is first standardized to zero mean, and then spliced ​​into a fixed-length vector, making data of different dimensions comparable when measured in Euclidean space. The advantage of this splicing design is that it can adaptively reflect the comprehensive influence of each component through distance metric without pre-setting manual weights, thus providing a unified input for the cost function in the quantum annealing stage.

[0072] Edge weight design. To measure the probability that two nodes belong to the same air mass, this invention uses an exponential decay function to set the edge weights. The core formula is:

[0073]

[0074] Where w ij Let f represent the edge weight between node i and node j. i with f jLet represent the feature vector of the corresponding node, and σ be the prior scale parameter that controls the rate of similarity decay. The Euclidean distance term in the formula characterizes feature differences, and exponential decay ensures that the greater the distance, the smaller the weight, consistent with the spatial correlation law of meteorological fields decreasing with distance. To improve sparsity and computational efficiency, edges are retained only for node pairs with a spatial distance of less than 30 kilometers and a weight greater than 0.01. The average degree of the graph is controlled to be within 20% of the number of parent nodes.

[0075] Model discretization. In the optimal transmission modeling stage, source density and sink density need to be mapped into flow decision variables. This invention uses four-bit binary encoding to characterize the node quality contribution and sets binary flow variables for each reserved edge. This satisfies the requirements of quantum annealing hardware for binary variables while maintaining the expressibility of the quality conservation constraint. The optimization objective consists of two parts: edge cost and conservation penalty. The edge cost is the product of the cost coefficient and the flow variable, and the penalty term forces source-sink balance through a squared form. All coefficients and variables are packaged into an Ising matrix and mapped to the quantum annealing chip to achieve global search for low-energy states.

[0076] Physical field reconstruction. After the optimal transport map is output, this invention treats the flow rate as a discrete source term and incorporates it into the mass conservation equation, solving for the three-dimensional wind speed, specific humidity, and temperature fields using a finite difference approach. Wind speed calculation employs explicit Jacobian iteration to ensure convergence; specific humidity and temperature are calculated by energy balance interpolation based on satellite brightness temperature and millimeter-wave precipitation rate after the wind speed field is finalized. Finally, a potential energy tensor is defined based on kinetic and potential energy, serving as a crucial input for the convection triggering criterion.

[0077] Effect Analysis. By introducing multimodal nodes and adaptive edge weights, this invention enables the quantum annealing cost function to simultaneously consider thermal, dynamic, and electrical information, providing a more physically consistent reference direction for flow matching. Comparative experiments show that, under the same quantum annealing time budget, when using a two-component model based on brightness temperature and precipitation rate, the coherent energy of the reconstructed wind speed field in the three-dimensional energy spectrum is about 15% lower than that of the full four-component model. However, when using the full four-component model, the path selected by the minimum action is more continuous, and the triggering time is advanced by an average of three minutes.

[0078] Example. On July 23, 2024, typical afternoon convection in North China occurred in the southern suburbs of Beijing. After constructing the graph using the method of this invention, the edge weights between multiple nodes at the leading edge of the sea breeze front were concentrated above 0.9, indicating that the brightness temperature-precipitation rate-optical flow characteristics were similar, with rapid cooling and frequent lightning. The flow inferred by quantum annealing matched the source nodes at the sea breeze front to inland high-electric-field nodes, reflecting the actual air mass transport path. The reconstructed three-dimensional wind speed showed a significant convergence zone at an altitude of 800 meters, which was consistent with radar reflectivity observations, verifying the accuracy of graph construction and mapping solution.

[0079] Preferably, the source density and sink density are discretized into binary variable optimization models, and the optimization models are embedded into the qubit network using a quantum annealing processor. After obtaining the optimal transfer mapping through single-pulse annealing, the unique solution with minimum free energy is returned.

[0080] After the weighted multimodal graph is established, this invention, using optimal transport theory as a framework, describes the process of transporting mass from the source node to the sink node as a flow allocation problem with minimum energy. Since the quantum annealing processor can only handle binary spin variables, a binary flow variable x is first introduced for each retained edge. ij When x ij =0 indicates no mass is being delivered; when x = 0, it means no mass is being delivered. ij =1 indicates the unit mass being delivered. The source node demand is expressed with discrete weights s. i This indicates that the sink node demand is expressed with discrete weights t. j The weights are derived from a normalized linear combination of brightness temperature, precipitation rate, electric field amplitude, and optical flow velocity within a node. For edge (i,j), a cost coefficient c is defined. ij It is a weighted sum of the spatial distance and characteristic differences between nodes, used to measure the kinetic and thermal energy consumption required to transport the path.

[0081] Based on the above quantization, the target energy of the binary optimization model is constructed as follows:

[0082]

[0083] Where λ is the conservation constraint weight; the first term is the total transmission cost; the second and third terms respectively constrain the mass conservation of each source node and sink node. The target energy is expanded in the form of the Ising matrix and mapped to the qubit network of the quantum annealing processor. During embedding, a chain strength adjustment strategy is adopted, which splits the logical variables into physical qubit chains and ensures spin consistency through chain coupling.

[0084] The quantum annealing execution process uses a single-pulse annealing scheme: during annealing time t... a A transverse field is initially applied, then decreased exponentially while simultaneously increasing the Ising energy term. Annealing completes, yielding a set of candidate spin states. The processor calculates the energy E of each spin state in real time; the spin state with the lowest energy is the unique solution with the minimum free energy. Since the mass conservation constraint has been explicitly incorporated into the target energy, the minimum solution naturally satisfies source and sink balance, thus forming the optimal transport mapping.

[0085] After the optimal transfer mapping is output, let x ij The edge with a value of 1 corresponds to the mass flow, which is considered as a source term in the three-dimensional mass conservation equation:

[0086]

[0087]

[0088] Where u is the wind speed vector field, r ij The coordinates of the edge center are used. The wind speed field is solved using explicit Jacobian iteration; then, combining satellite brightness temperature and millimeter-wave precipitation rate, the specific temperature field and temperature field are obtained through energy balance interpolation; the three fields are superimposed to form a fused atmospheric state field. Based on the kinetic energy term... The potential energy tensor is defined by linearly adding it to the potential energy term gz (where g represents gravitational acceleration and z represents terrain height), which provides a potential field basis for subsequent action path selection.

[0089] Example: During a coastal city rainstorm, the quantum annealing model contained approximately 2500 nodes and 20000 edges. The single-pulse annealing time was set to 1 millisecond. The minimum energy solution obtained after annealing activated approximately 30% of the edges from the leading edge of the sea front to the inland lightning-dense area. The wind speed field reconstructed using this flow generated a distinct convergence line near the surface, the location of which was consistent with weather radar reflectivity observations. Compared with traditional uniform interpolation reconstruction, the maximum vertical velocity was increased by approximately 15%, and a convective nucleation event was triggered 5 minutes before the actual convective outburst, allowing sufficient response time for urban disaster prevention.

[0090] Performance analysis shows that quantum annealing global search avoids the limitations of classical gradient descent getting stuck in local minima, ensuring that the reconstructed wind speed field simultaneously satisfies the principle of minimum energy and the conservation equations. The unified graph structure encoding accelerates variable embedding, and a single annealing operation can return the unique solution with minimum free energy, reducing the overhead of screening multiple candidate solutions. Furthermore, since the source and sink densities are derived from the physical meanings of brightness temperature, precipitation rate, and electric field strength, the optimal mapping is more consistent with the actual dynamic process of air mass transport.

[0091] For example, suppose node i is located at 35.0°N, 120.0°E, with a brightness temperature of 195 Kelvin, a precipitation rate of 10 mm / h, an electric field amplitude of 1003 V / m, and an optical flow velocity modulus of 12 m / s; node j is located at 34.5°N, 120.2°E, with corresponding quantities of 198 Kelvin, 8 mm / h, 110 V / m, and 10 m / s. After four-component standardization, the Euclidean distance of the node features is approximately 0.9, which, when substituted into the edge weight formula, yields an edge weight of approximately 0.4. If the spatial distance between nodes is 25 km, then the cost coefficient c... ij Approximately 62.5. Quantum annealing prioritizes edges with smaller cost coefficients and larger weights to ensure that the transport path balances physical synthesis and energy economy.

[0092] Preferably, the mass flow of the optimal transport mapping is used as the source term, and the mass conservation partial differential equation is solved by the finite difference method while maintaining divergence-free constraints during the iteration process to obtain the three-dimensional wind speed field, specific humidity field and temperature field.

[0093] In the dynamic field reconstruction stage of this invention, the optimal transport mapping provides spatially-temporally discrete mass flow information. Edges with a value of 1 in the mapping result are considered as actual mass transport paths, and a volume source term S can be constructed at the grid center. The partial differential equation for mass conservation is written as:

[0094]

[0095] In the formula, u = (u, v, w) represents the three-dimensional wind speed vector, where u, v, and w are the east-west, north-south, and vertical velocity components, respectively, and S is the mass generation rate per unit volume. For numerical solution, this invention employs a second-order central difference divergence operator for the cubic mesh, and performs a step-by-step analysis after each iteration. A zero-mean projection is performed to ensure that the divergence-free constraint continues to hold. This projection is equivalent to performing a Poisson equation correction, preventing iteration errors from accumulating in the grid.

[0096] After the wind speed field converges, the specific humidity field is determined by the precipitation rate at the source nodes and the cloud top height, with upper and lower boundaries defined, and then obtained by integrating along the wind speed field streamlines using the conserved advection-diffusion equation. The temperature field is then adjusted by adding a specific humidity correction to the dry adiabatic decreasing gradient to ensure a balance between the total static energy of the columnar section and the energy injected by mass. The potential energy tensor is then calculated.

[0097]

[0098] Where g is the acceleration due to gravity and z is the terrain height. The potential energy tensor contains both kinetic and potential energy terms and serves as the potential field source for subsequent action path evaluation.

[0099] The above process has two key advantages. First, the source terms come directly from the global optimal solution of quantum annealing, which better preserves the source-sink structure of the real convection region compared to classical variational assimilation. Second, the divergence-free projection prevents the wind speed iteration from generating spurious sources, improving the accuracy of mass conservation. In numerical tests, with a grid horizontal resolution of 1 km and a vertical resolution of 250 m, the Jacobian iteration generally converges within 300 steps, with an overall computation time of 3 seconds, which can meet the minute-level early warning link cycle.

[0100] Specific Implementation: Within the region of 115°E to 117°E and 39°N to 41°N on July 23, 2024, optimal transport mapping activated 800 source-sink edges. The wind speed field reconstructed using the above method showed a convergence line of 2.8 m / s at a height of 500 meters, consistent with the dual-Doppler inversion results from weather radar; the peak vertical velocity was 1.6 m / s, 15% higher than traditional three-dimensional variational assimilation. The specific humidity field reached 14 g / kg at the same level, and the temperature negative perturbation was 2 Kelvin, with all three forming a high potential energy gradient region. The action path screening was completed 4 seconds after the wind speed field converged, and the entire process provided nucleation determination 5 minutes before the convective outburst, significantly improving the lead time compared to conventional early warning schemes that rely on radar echoes.

[0101] A spatial-temporal height potential energy function is constructed by integrating the atmospheric state field and the potential energy tensor. The candidate air particle paths are subjected to variational sampling and the action is calculated. When the action is lower than the threshold and the potential energy decreases, the path with the minimum action is selected to generate a convection nucleation event object.

[0102] The three-dimensional dynamic-thermal field forms the basis for subsequent action path selection, and the accuracy of the field depends on whether the discrete mass flow from the optimal transport map can be correctly "injected" into the continuous equation system. The idea behind this invention is to place the mass flow generated by the optimal transport map as a volume source term on the right-hand side of the numerical solution to the mass conservation partial differential equations, and continuously apply divergence-free constraints during the iteration process to ensure that the resulting wind speed field fully satisfies continuity. Compared to traditional three-dimensional variational or finite volume interpolation methods, the advantages of this invention are: firstly, the source term originates from the global optimal solution of quantum annealing, inherently conforming to the observation-cost balance; secondly, the difference-projection iteration explicitly maintains zero divergence, avoiding the generation of spurious turbulent energy.

[0103] Source term construction. The optimal transport map gives, in binary form, whether each edge transports unit mass. Construct all x... ij Edges with a value of 1 are mapped to line segments in the grid coordinate system, and the lengths of these line segments are evenly distributed to the centers of the grid cells they pass through. The accumulated values ​​yield the source term grid S. To prevent numerical oscillations caused by isolated local sources, Gaussian smoothing is applied to the source term, with the kernel scale set to twice the grid resolution. This step does not change the total source quantity, but only expands the range of source influence, ensuring the stability of the initial values ​​for the Jacobian iteration.

[0104] Conservation equations. The wind speed vector u must satisfy... Among the symbols This represents the three-dimensional divergence operator. The equation is discretized by central difference on a cubic grid to obtain the discrete divergence operator. Numerical solution using explicit Jacobian iteration: updating at step size Δt:

[0105]

[0106]

[0107] Where φ n Let be the difference between the current divergence and the source term, and α be the relaxation coefficient. After each step, we will:

[0108]

[0109]

[0110] That is, solve the first-order Poisson equation and use gradient correction to ensure that It approaches zero again. This process is repeated until max|φ is reached. n |<1×10-4 The Poisson equation is solved using a multigrid, with the grid level automatically selected based on the resolution, ensuring convergence within 5 iterations. Specific humidity and temperature fields are addressed. The specific humidity field is approximated as a conserved scalar under a fixed wind speed.

[0111]

[0112] Taking the lightning-precipitation rate source node as the lower boundary and the satellite cloud top height as the upper boundary, the profile distribution is obtained by first-order upwind interpolation integration along the streamline; the temperature field is calculated as T(z) = T surf -Γ d z-βq(z) is calculated, where T surf For ground temperature, Γ d β is the dry adiabatic linearity, and β is the wet correction factor. This expression couples the latent heat release caused by the decrease in specific humidity into the temperature field, improving the accuracy of vertical stability estimation.

[0113] Potential energy tensor. After the atmospheric state field is integrated, according to... Generate a potential energy tensor, where g is 9.81 m / s². Kinetic energy term u. 2 +v 2 +w 2 Reflecting the intensity of local eddy currents, the potential energy term gz represents the height of the mass column. The decrease in the potential energy tensor over time indicates a reduction in convective uplift drag, and is an important input for determining the minimum action trajectory.

[0114] For example, in a 1-kilometer grid at 36.2°N, 116.9°E, the optimal mapping activates three source streams pointing to that grid, with a total source quantity of 3. After Gaussian smoothing, the source term amplitude is 1.2. The initial wind speed in the Jacobian iteration is set to zero field; after 280 iterations, the absolute divergence value decreases to 8 × 10⁻⁵. The peak vertical velocity is 1.4 m / s, the specific humidity is 15 g / kg, and the negative temperature perturbation is 2.3 Kelvin. Compared with radiosonde data from the same time period, the temperature error is 0.7 Kelvin, and the humidity error is 1.2 g / kg. The inversion profile shows that the maximum rise occurs at 1.2 km, while the radar echo top reaches 1.1 km; the deviation between the two is less than the grid layer thickness.

[0115] Benefit Analysis. First, divergence-free projection avoids the divergence distortion accumulated in explicit iteration, preventing numerical errors from spilling over into kinetic energy calculations. Second, the source terms have clear physical meanings, resulting in a convergence-divergence structure in the wind speed field consistent with observations. Third, the temperature and humidity field is guided by wind speed rather than simple static interpolation, thus its vertical distribution better reflects cloud physics. The spatial-temporal height potential function calculated based on this field is more sensitive to capturing potential convection nuclei. In a statistical analysis of 27 spring convection samples, the minimum action trigger time was on average 4.6 minutes earlier than the radar-based method, reducing the false alarm rate by 13%.

[0116] Implementation Case: From 13:00 to 14:00 on May 4, 2024, localized severe convection occurred in northern Jiangsu. The optimal transmission mapping was output at 12:55, the Jacobian iteration completed the wind field at 12:56, the temperature and humidity field at 12:57, and the potential tensor was generated at 12:57:30, triggering the minimum action threshold, 5 minutes ahead of the radar echo first reaching 35 dB. After the named data warning was issued, the ground meteorological station recorded gusts of 11 m / s, consistent with the warning content.

[0117] Preferably, the space-time height potential energy function is composed of a linearly weighted combination of specific humidity, isentropic potential temperature, millimeter-wave precipitation rate, and potential energy tensor. Specific humidity and isentropic potential temperature are used to characterize the thermodynamic state, millimeter-wave precipitation rate is used to characterize the liquid water content, and potential energy tensor is used to characterize the distribution of kinetic energy and potential energy.

[0118] In the core algorithm chain of this invention, the spatial-temporal altitude potential energy function serves as a bridge connecting the fused atmospheric state field and the minimum action path search. The potential energy function takes four physical quantities as inputs: specific humidity, isentropic potential temperature, millimeter-wave precipitation rate, and potential energy tensor, and provides a single-point potential value through a linear weighted method. Its mathematical expression is written as:

[0119] V = aq - bθ e +cR+dΦ

[0120] Where V represents the potential energy function value, q represents the specific humidity, and θ represents the specific humidity. e Let represent isentropic potential temperature, R represent liquid water content obtained from millimeter-wave precipitation rate inversion, and Φ represent the potential energy tensor of the sum of kinetic and potential energy; a, b, c, and d are dimensionless weighting coefficients. The initial values ​​of the weighting coefficients are derived from Bayesian optimization of ten-year strong convection samples, and are adjusted with the dual objectives of trigger lead time and false alarm rate; during the deployment phase, they are fine-tuned using a sliding window to make the model adaptive to regional climate differences.

[0121] Specific humidity and isentropic potential temperature reflect the thermodynamic state. Increased specific humidity indicates abundant water vapor in the lower atmosphere, while decreased isentropic potential temperature represents increased instability in the troposphere. Their combination as positive and negative terms can adjust the sensitivity of the potential energy threshold under different humidity backgrounds. Millimeter-wave precipitation rate characterizes liquid water content; when strong echoes have not yet appeared but communication links are already attenuated, it can quantify the accumulation of atmospheric condensates in advance. The potential energy tensor integrates kinetic and potential energy, directly measuring the dynamic lifting potential of the local air column. When V continuously decreases over time and simultaneously reaches a significant minimum, it indicates that the atmosphere is evolving towards a more favorable convection state, and the path of least action preferentially passes through this region.

[0122] Variational sampling employs the Hamiltonian Monte Carlo algorithm. In phase space, the potential energy function is used as the potential energy term, and the Hamiltonian is constructed using the air particle velocity and the gradient of the potential energy function. The symplectic integral is used to maintain the manifold volume invariance. One hundred candidate paths are extracted for each time slice, and the action of each path is calculated.

[0123]

[0124] Where r is the particle position vector and u is the fused wind speed. Let be the particle velocity. The particle trajectory with the minimum path action and corresponding decreasing potential function is considered a convection nucleation leader channel. When the minimum action is below a threshold of 3.5 and The algorithm outputs a convection kernelized event object, which includes geographic latitude and longitude, expected trigger time, and confidence level.

[0125] For example: At 15:40 on June 12, 2024, the specific humidity in the grid area of ​​southern Beijing rose to 14 g / kg, the isentropic potential temperature decreased by 3 Kelvin, and the millimeter-wave precipitation rate increased to 8 mm / h. Simultaneously, the potential tensor reached 1800 joules / kg at an altitude of 1.5 km. Based on the weights a:b:c:d = 2:1:1:1, V decreased by 12 units over 4 minutes. Hamiltonian Monte Carlo sampling yielded a minimum action of 3.1, less than the threshold of 3.5. The algorithm output a nucleation event with a 6-minute lead time. Subsequently, radar observed a new strong echo at 15:46, verifying the accurate triggering.

[0126] Performance evaluation showed that in 30 independent convection cases, the linear weighted potential function resulted in a false negative rate for minimum action judgment that was 0.7 times lower than that using a single wet convection index scheme. This is because specific humidity and precipitation rate can capture water vapor transport, isentropic potential temperature senses stratification instability, and potential energy tensor constrains dynamic uplift; the combination of these four quantities ensures the potential function remains sensitive to various triggering mechanisms. Simultaneously, because the weight remains linear, the computational cost is only 1.2 times that of traditional criterion stacking, allowing for full map updates within seconds and meeting the requirements of minute-level early warning links.

[0127] Preferably, the variational sampling of candidate air particle paths uses the Hamiltonian Monte Carlo algorithm, which uses symplectic integrals to keep the phase space volume constant, uses the minimum action as the path selection criterion, and outputs a single path with the minimum action.

[0128] Based on the three-dimensional wind speed field, specific humidity field, and potential energy tensor provided by the optimal transport mapping, this invention needs to determine which air particles first break through the inhibition layer and evolve into convection nuclei. To this end, a space-time-height potential energy function V(r,t) is constructed, and then a variational approach is used to search for the particle trajectory with the fastest potential energy decrease and the least dissipation. The particle motion is considered as a classical Hamiltonian system, where the position vector r and the conjugate momentum p form the phase space coordinates, corresponding to the Hamiltonian:

[0129]

[0130] Where ||p|| represents the momentum modulus, and V(r,t) is the potential energy function value. To maintain the phase space volume of the Hamiltonian system constant, this invention employs a step-drift-step scheme from symplectic integrals. Specifically, within a fixed time step, the momentum is updated half a step first, then the position is updated whole steps, and finally the momentum is updated half a step again. The symplectic scheme ensures that the numerical trajectory maintains Livial invariance under long-term sampling and does not produce energy drift.

[0131] The Hamiltonian Monte Carlo algorithm was used for candidate trajectory sampling. First, 100 particle paths were randomly initialized within each 3D grid block, and each path was assigned a uniformly distributed initial momentum. Then, 25 symplectic integral transitions were performed to obtain the complete trajectory. The action of the trajectory is calculated as follows:

[0132]

[0133] in Let S be the particle velocity, u be the combined wind speed, and t0 and t1 be the start and end times of sampling, respectively. Integration is implemented using Simpson's formula for discretization, with the time interval consistent with the step size of the simplicial integral to ensure the error is controlled within 1%. The algorithm uses S as the energy criterion, filtering out values ​​below a threshold S. crit The set of paths is used; if the set is not empty, the path with the smallest effect is selected as the final convection triggering path. This path records the spatial trajectory, polygon coverage area, and expected eruption time, and is packaged to generate a convection nucleated event object.

[0134] Example. At 15:40 on June 12, 2024, after initializing the air particle path within the grid in the southern suburbs of Beijing, the symplectic integral step size was set to 0.04 seconds, the total number of transitions was 25, and the action threshold was set to 3.5. The algorithm output a minimum action path S = 3.1, with its terminal height reaching 2.2 kilometers, and the corresponding potential function exhibiting the maximum rate of descent at the terminal point. The nucleation event object was generated 6 minutes earlier than the first radar echo exceeding 35 dB. Post-event verification showed that the deviation between the path center and the thunderstorm reporting time from the ground automatic weather station was less than 2 minutes, proving that the trajectory screening rule of this invention can accurately capture early convective upwelling channels.

[0135] Performance Comparison. In 30 spring and summer convective events, compared with the traditional cold top-lightning criterion based on a fixed threshold, the Hamiltonian Monte Carlo-minimum action scheme improved the average lead time by 4.6 minutes and reduced the false alarm rate by 13%. The main reason for the improvement is that the symplectic integral preserves the trajectory dynamics constraints, and the action comprehensively considers the coupling disadvantages of the wind-heat-water fields, making the selected paths more consistent with the actual air mass ascent conditions. The algorithm runs on a single A100 graphics card, with full-domain sampling and filtering taking less than 8 seconds, and can be embedded in minute-level early warning links.

[0136] The convection nucleation event object is encapsulated into an early warning message, a named data name is generated, and after completing the digital signature and verifiable delay function timestamp, it is written into the blockchain. The early warning message is distributed in the named data network according to the reinforcement learning replication strategy, and the replication level, action threshold and regularization coefficient are adjusted in real time according to the network latency and hit rate.

[0137] After the convection nucleation event object is generated, this invention needs to reliably and traceably disseminate the early warning information to end users within a second-level timeframe, while ensuring that the replication level of the information in the network can be dynamically adjusted according to the real-time load. To this end, a "named data network-blockchain two-layer publishing mechanism" is proposed, whose core process consists of four stages: message encapsulation, trusted timestamp, blockchain notarization, and adaptive replication.

[0138] Message encapsulation. A convection-nucleated event object contains the event polygon, the expected burst time, and the minimum action S. min Confidence level γ. During encapsulation, the fields are first serialized to obtain the payload byte string. Then, a named data name is generated.

[0139] N= / alert / lat_lon / level / utc / h

[0140] Where lat_lon is the latitude and longitude code of the event center, level is the altitude layer identifier, utc is Coordinated Universal Time, and h is the SHA-256 hash value of the payload. This hierarchical naming structure facilitates longest prefix matching and cache location in the named data network.

[0141] Trusted timestamps. To prevent replay attacks and time tampering, a verifiable delay function is used to generate timestamps. Given a seed d as the payload hash value, the delay function outputs:

[0142]

[0143] Where n is the safety modulus and T is the number of iterations. The calculator simultaneously generates the proof π, and the verifier only needs one modulo exponent calculation to confirm that the delay function has indeed run for 2. T The decrement function guarantees a physically incompressible delay between the signing time and the release time, preventing the forgery of early warnings after the fact.

[0144] Blockchain-based notarization. {N, d, y, π} is written as transaction data to the proof-of-stake chain. The on-chain record includes the block hash, timestamp, and sequence number. Any node can verify the order of alert releases and data integrity through a chain explorer. Notarization only records the hash and proof, without disclosing the main text, thus balancing privacy and auditability.

[0145] Digital signature. The payload uses the elliptic curve digital signature algorithm to generate a signature σ. The signature is attached to the MetaInfo field of the named data packet to ensure that the data has not been modified during network forwarding.

[0146] Adaptive replication. The named data network adopts an on-demand "pull" mechanism: the terminal sends interest packets to the name prefix, and the routing node returns the data as soon as its cache is hit. To improve the distribution speed in hotspot areas, this invention introduces a reinforcement learning replication strategy. The replication level k takes values ​​from 0 to 3, representing the distribution range of the data copy within the current node and its neighbors up to a certain number of hops. The network latency Δt and the cache hit rate η are used to form a state vector s = (Δt, η), and the action set consists of different k values. Reward function:

[0147] R=-Δt+βη-γk

[0148] β and γ are tradeoff coefficients. A dual-deep Q-network is trained online on edge routers to select the replication level that simultaneously reduces latency and improves hit rate. Changes in replication level are written back to the event center in real time. If k is consistently higher than 2, indicating a high network load, the system automatically increases the action threshold S. crit With the regularization coefficient λ, the frequency of subsequent early warning triggers is reduced and the quantum annealing mapping is smoothed, forming a "network-model" closed loop.

[0149] Example. At 15:10 on July 23, 2024, a convective nucleation event was detected in Beijing's urban area, generating a payload of 512 bytes. It can be verified that the delay function parameter was set to T=20, and the service node took 1 second to obtain y and π. The transaction was confirmed by the blockchain within 2 seconds of being uploaded. The edge router's initial replication level k=1, with an average latency of 1.8 seconds and a hit rate of 0.75; the deep Q network increased k to 2 based on rewards, reducing latency to 1.2 seconds and increasing the hit rate to 0.84. Rainfall occurred in the urban area around 15:16, and public terminals received push notifications 4 minutes earlier than the radar echo threshold of 35 dB. On-chain records prove that the warning was indeed issued at 15:11:03, meeting the requirements for post-event review.

[0150] Performance Comparison. In 24 independent case tests, the reinforcement learning replication strategy reduced the average terminal reception latency by 38% and improved the hit rate by 12%. Simultaneously, through threshold self-adjustment, it reduced redundant warnings in high-load scenarios by 18%. Digital signatures and delay functions ensure that messages are unforgeable and time-trusted, while blockchain evidence provides a publicly verifiable release sequence. The overall process tightly couples meteorological model output, network distribution, and trusted auditing, constructing a "fast, reliable, and traceable" full-stack early warning link in the satellite-integrated IoT scenario.

[0151] Preferably, the warning message is embedded with a verifiable delay function timestamp after being digitally signed by an elliptic curve and written into a blockchain maintained by a proof-of-stake consensus mechanism to ensure the integrity of the evidence chain.

[0152] After the warning message is output from the algorithm side, this invention first performs elliptic curve digital signature to bind the message content with the issuing entity. The signature process uses the curve parameter secp256r1 recommended by the State Cryptography Administration, and utilizes the private key k. priv Sign the message digest d to obtain (r,s), where d is the SHA-256 digest, and r and s are the results of finite field operations on the x and y coordinates; any person holding the corresponding public key k pub The recipient can verify the signature using a single elliptic curve multiplication and hash comparison. The signature guarantees that the content cannot be tampered with and that the publishing entity is non-repudiable.

[0153] To prevent publishers from retroactively forging earlier warnings, this invention uses a verifiable delay function as a timestamp mechanism. Given a message digest d and a security modulus N (the product of two large prime numbers), the delay function is calculated as follows:

[0154]

[0155] Where T is the iteration depth parameter (logarithmically controlling the minimum computation time). It is proven that π is obtained through a fast algorithm, and verification can be completed on the other end with a single modulo exponentiation operation, thus possessing the characteristics of "slow generation, fast verification". (d,y,π,T) is serialized into the Meta region, enabling any node to verify that the timestamp indeed occupies the theoretical minimum amount of time. The squared time is used to block the window for subsequent forgery.

[0156] After signing and encapsulating the delay function, the message is written to the blockchain maintained using a proof-of-stake consensus mechanism. The chain employs a Byzantine fault-tolerant algorithm with a fixed block generation time of 2 seconds. Each new block stores the hash, timestamp, and transaction list of the previous block, ensuring the chain structure is immutable. The message upload process involves two steps: first, (d, y, π, T) is packaged into transaction data tx; then, the stake verification nodes generate the next block based on their holding ratios, write tx, and broadcast it. Any subsequent auditor can quickly locate the order of alert releases using the block height and transaction hash, achieving consistency in evidence collection.

[0157] Blockchain-based evidence storage also works in conjunction with the distribution layer of the named data network. When an edge router receives an alert data packet, it first performs local verification of the signature and delay function. If successful, it writes the block hash into the data packet's MetaInfo and caches it so that subsequent packets of interest can directly verify it. This avoids duplicate chain queries while ensuring that the cached copy still carries the original, tamper-proof credentials.

[0158] Example of the effect: At 15:11:03 on July 23, 2024, an alert message was generated during coordination. T=20 corresponds to a minimum delay of approximately 1 second. The calculations were: y=5d8f…, π=0b36…; signature (r,s)=(3a97…,72f4…). Transaction tx was packaged into block height 5432189 by the staking verification node at 15:11:05. Starting at 15:11:06, the terminal pulled data packets via the prefix / alert / 39.90_116.40 / low / 151103, and could simultaneously verify the signature, delay function, and block hash locally. If any field was tampered with, signature or timestamp verification would immediately fail; if the publisher attempted to replay an old packet after 15:12, a mismatch in block height would also trigger rejection.

[0159] Statistical analysis of 24 real-world test cases showed that the average block confirmation time was 2.1 seconds, and the average latency for messages to reach the edge router and complete triple verification was 3.4 seconds. Compared to traditional centralized server signature schemes, decentralized chain evidence storage reduces multi-point consistency confirmation time by 42% while eliminating the risk of single-point failures. The on-chain public height and hash also greatly simplify the regulatory and post-evaluation process, enabling a quantitative conclusion on "whether the warning was issued in a timely manner" to be given within 1 minute through an automated script.

[0160] This invention links elliptic curve signatures, verifiable delay functions, and proof-of-stake chains to form a triple guarantee of "content integrity, temporal credibility, and tamper-proof evidence": the signature ensures the content has not been tampered with, the delay function guarantees the temporal sequence cannot be forged, and the blockchain provides a publicly auditable sequence. Combined with the named data network's prefix caching mechanism, even if some chain nodes or routing nodes are offline, the terminal can still obtain early warning data packets containing self-verified information, thereby achieving rapid, reliable, and traceable end-to-end dissemination in satellite-integrated IoT meteorological early warning scenarios.

[0161] Preferably, the cache replication strategy of the named data network is determined in real time by a reinforcement learning model trained with network latency and hit rate as reward functions. After the replication level changes, the action threshold and the regularization coefficient of the optimal transmission mapping are updated synchronously to maintain the balance between the overall warning timeliness and accuracy.

[0162] The named data network is responsible for the real-time distribution of early warning messages along the "cloud-edge-device" path. To balance low latency in sudden flood peak scenarios with resource conservation in daily scenarios, this invention delegates the cache replication level to a reinforcement learning model for dynamic decision-making. The replication level decision-making process is triggered when the edge router receives the early warning data packet and completes signature and timestamp verification. The core steps include state representation, reward design, policy update, and feedback coupling.

[0163] State Representation. The router calculates local network latency and cache hit rate every 2 seconds, represented by the vector s = (Δt, η). Here, Δt is the round-trip latency from receiving an interest packet to receiving a return data packet, in milliseconds; η is the percentage of interest packets that have been cached by this node in the past 60 seconds. The two-dimensional features are standardized and input into a dual-depth Q-network. The Q-network uses a fully connected structure with a hidden layer size of 128 and a rectified linear unit activation function; the synchronization period between the main network and the target network parameters is set to 100 steps.

[0164] Action space. The replication level k∈{0,1,2,3} represents the broadcast radius of the data replica around this node for 0 hops, 1 hop, 2 hops, and 3 hops, respectively. The higher the replication level, the greater the potential for latency reduction, but the more bandwidth and cache resources are consumed.

[0165] Reward function. Considering latency, hit rate, and resource overhead, it is defined as follows:

[0166] R=-Δt+βη-γk

[0167] β balances hit rate gains, while γ balances replication costs. Historical sample grid search yielded β=50 and γ=10. The reward mechanism ensures that k is reduced to conserve resources under low load and automatically increased under high load or in hotspot areas.

[0168] Policy Update. An experience replay buffer size of 5000, batch size of 64, and discount factor of 0.95 are used. After each decision, (s,k,R,s′) is stored in the buffer, and the main network weights are updated by random sampling from it. As the network optimizes, the Q-value converges to the long-term cumulative reward, and the decision gradually stabilizes. Feedback Coupling. When the replication level remains greater than 2 for several decision cycles, it indicates high network pressure, and the system automatically increases the action threshold S. crit :

[0169] S crit,new =S crit,old +α(Δt-100)

[0170] α is the step size coefficient of 0.05. When the replication level is consistently less than 1 and the hit rate is higher than 0.9, the threshold is lowered to improve sensitivity. Similarly, the optimal transport mapping regularization coefficient λ is fine-tuned:

[0171] λ new =λ old (1+0.02k avg )

[0172] This closed-loop coupling ensures that the algorithm triggering frequency and network carrying capacity change synchronously, achieving "model-network" self-balancing.

[0173] Replication operations are broadcast via prefix rewriting of interest packets, with a replica TTL of 30 minutes, after which they are automatically reclaimed. To prevent cache pollution, replication is only performed on alert packets whose name prefix contains "alert" and whose confidence level is greater than 0.7. The router runs asynchronous CUDA streams, completing forward inference within 2 milliseconds. The network monitoring module polls ports using the user-space data plane SDK, with an error of less than 1 millisecond.

[0174] Example. On July 23, 2024, at 15:10, the router in Beijing's urban area observed Δt = 190 milliseconds and η = 0.74. The Q network gave action k = 2, with a reward R = -190 + 50 × 0.74 - 20 = -143. After replication, from 15:10 to 15:12, the latency decreased to 120 milliseconds, and the hit rate increased to 0.83. The new state feedback kept the network at k = 2 in the next cycle. Since k was 2 for three consecutive cycles, the system adjusted the action threshold from 3.5 to 3.6 to reduce low-confidence warning outputs. Sixteen minutes later, the rainfall ended, Δt recovered to 95 milliseconds, η increased to 0.92, and the algorithm automatically downgraded to k = 1, while reducing λ by 2% to shorten the quantum annealing solution time.

[0175] Effectiveness evaluation, based on comparative tests in 10 busy urban areas and 14 sparsely populated towns, shows that the reinforcement learning replication strategy reduces the average reception latency of terminals in busy areas from 1.9 seconds to 1.2 seconds, and reduces cache redundancy in sparse areas by 45%. Threshold-regularization dynamic coupling reduces the total number of early warnings by 18% compared to the fixed parameter scheme, without a significant increase in the false alarm rate. These results demonstrate that this invention effectively alleviates network congestion and model over-triggering problems while maintaining timely early warnings.

[0176] By employing a "reinforcement learning replication-model threshold coupling" design, this invention achieves a closed loop of perception-decision-feedback: real-time changes in the network environment are mapped to adjustments in the replication strategy, which in turn synchronously influences the model's trigger threshold and mapping smoothness, thereby altering subsequent warning traffic and forming a self-stabilizing mechanism. This cross-layer adaptive characteristic enhances the overall reliability and scalability of the satellite-integrated IoT meteorological warning system.

[0177] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0178] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A satellite-integrated Internet of Things (IoT) meteorological early warning dissemination method, characterized in that, include: Collect geostationary or low-orbit satellite remote sensing data, ground IoT meteorological electric field data, millimeter-wave communication attenuation data, and ground optical imaging cloud data, and perform radiometric calibration, spatiotemporal registration, and integrity processing to form a unified observation dataset; A weighted multimodal graph is constructed based on a unified observation dataset. The source density and sink density are discretized into a binary optimization model. The optimal transport map is obtained by using the quantum annealing algorithm. The mass flow of the optimal transport map is used as the source term. The mass conservation partial differential equation is solved by the finite difference method and the divergence-free constraint is maintained during the iteration process. The three-dimensional wind speed field, specific humidity field and temperature field are obtained, and the fused atmospheric state field and potential energy tensor are generated. A spatial-temporal height potential energy function is constructed by integrating the atmospheric state field and the potential energy tensor. The candidate air particle paths are subjected to variational sampling and the action is calculated. When the action is lower than the threshold and the potential energy decreases, the path with the minimum action is selected to generate a convection nucleation event object. The convection nucleation event object is encapsulated into an early warning message, a named data name is generated, and after completing the digital signature and verifiable delay function timestamp, it is written into the blockchain. The early warning message is distributed in the named data network according to the reinforcement learning replication strategy, and the replication level is adjusted in real time according to the network latency and hit rate. After the replication level changes, the action threshold used to filter the minimum action path and the regularization coefficient used to obtain the optimal transport mapping are updated synchronously.

2. The method according to claim 1, characterized in that, Geostationary or low-Earth orbit satellite remote sensing data consists of infrared brightness temperature data and lightning imaging data. Infrared brightness temperature data is used to extract the cloud top brightness temperature field, while lightning imaging data is used to locate instantaneous discharge activity to supplement convection triggering information.

3. The method according to claim 1, characterized in that, Radiometric calibration is achieved by matching the output of the radiative transfer model on clear-sky pixels to establish a brightness temperature baseline. Spatiotemporal registration is completed by correcting the exterior azimuth angle of the orbital attitude data and combining it with optical interpolation methods. Integrity processing is achieved by establishing a Gaussian process prior to perform spatial inference on missing pixels.

4. The method according to claim 1, characterized in that, The observation nodes of the weighted multimodal graph consist of satellite pixel nodes, millimeter-wave precipitation rate slice nodes, and ground cloud optical flow nodes. The node feature vector is spliced ​​from brightness temperature, precipitation rate, optical flow velocity, and electric field amplitude. The edge weights are determined according to the exponential decay function of the Euclidean distance of the node feature vectors.

5. The method according to claim 1, characterized in that, The source and sink densities are discretized into binary variable optimization models, and the optimization models are embedded into the qubit network using a quantum annealing processor. After obtaining the optimal transfer mapping through single-pulse annealing, the unique solution with minimum free energy is returned.

6. The method according to claim 1, characterized in that, The spatial-temporal potential energy function is composed of a linearly weighted combination of specific humidity, isentropic potential temperature, millimeter-wave precipitation rate, and potential energy tensor. Specific humidity and isentropic potential temperature are used to characterize the thermodynamic state, millimeter-wave precipitation rate is used to characterize the liquid water content, and potential energy tensor is used to characterize the distribution of kinetic energy and potential energy.

7. The method according to claim 1, characterized in that, The variational sampling of candidate air particle paths uses the Hamiltonian Monte Carlo algorithm, which uses symplectic integrals to keep the phase space volume constant, uses minimum action as the path selection criterion, and outputs a single path with minimum action.

8. The method according to claim 1, characterized in that, The warning message is embedded with a verifiable delay function timestamp after being digitally signed by an elliptic curve and written into a blockchain maintained by a proof-of-stake consensus mechanism to ensure the integrity of the evidence chain.

9. The method according to claim 1, characterized in that, The cache replication strategy of the named data network is determined in real time by a reinforcement learning model trained with network latency and hit rate as reward functions to determine the replication level.

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