Agricultural disaster early warning and emergency response method integrated with meteorological big data

Abstract

The present invention relates to the field of agricultural disaster management technology, and discloses a method for agricultural disaster early warning and emergency response that integrates meteorological big data. The method comprises: receiving heterogeneous meteorological data streams in a target area, performing spatiotemporal alignment and feature fusion through a disaster feature decoupling model, and generating a disaster potential energy field distribution map and a disaster category probability matrix. An adaptive early warning threshold model is constructed to generate a multimodal emergency response instruction set. A disaster chain propagation model is used to simulate the cascading effects of disaster secondary events, optimize collaborative decision-making parameters, and then iteratively optimize through a distributed reinforcement learning framework to output a disaster response action sequence to an agricultural emergency command platform. The method comprehensively utilizes meteorological big data to improve the accuracy of agricultural disaster early warnings, achieve efficient emergency responses, rationally allocate resources, promote cross-departmental collaboration, and effectively reduce agricultural disaster losses.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural disaster management, and specifically to an agricultural disaster early warning and emergency response method integrating meteorological big data. Background Art

[0002] Agriculture, as a national foundational industry, plays a vital role in the national economy and social stability. However, agricultural production is highly susceptible to various natural disasters, such as floods, droughts, typhoons, and hail. These disasters not only lead to reduced crop yields and quality, or even complete crop failure, resulting in significant economic losses for farmers, but also affect the market supply and price stability of agricultural products, resulting in ripple effects across the entire socio-economic landscape.

[0003] Traditional agricultural disaster early warning systems rely primarily on single data sources or simple meteorological monitoring methods, resulting in poor data accuracy and comprehensiveness. For example, relying solely on observations from ground-based weather stations cannot provide timely information on large-scale meteorological changes, making it difficult to provide early warnings for sudden localized disasters. Furthermore, these data often lack in-depth analysis of the potential impact of a disaster, making it difficult to accurately determine the specific extent of damage to different crop varieties and soil conditions.

[0004] The existing emergency response mechanism suffers from numerous shortcomings. The distribution of supplies lacks scientific planning, often leading to irrational allocation. Some severely affected areas face shortages, while others experience backlogs and waste. Emergency response teams are also relatively rigidly organized, unable to flexibly adjust to the specific circumstances of the disaster, resulting in inefficient rescue efforts. Collaboration between different departments is hindered, and information communication is poor, hindering the formation of an effective emergency response team. For example, during flood disasters, information sharing between water conservancy, agriculture, and transportation departments is slow, hindering the coordinated advancement of flood control, farmland drainage, and the transportation of relief supplies.

[0005] With the advancement of meteorological observation technology, meteorological big data has seen explosive growth, encompassing a wide range of data types, including radar reflectivity maps, atmospheric vertical profiles, and remote sensing images of vegetation indices. However, effectively integrating and utilizing this massive and heterogeneous meteorological big data has become a major challenge in agricultural disaster early warning and emergency response. Furthermore, disasters often do not occur in isolation but rather involve complex chain reactions, with the impact of secondary disasters sometimes exceeding that of the primary. Existing early warning and emergency response systems struggle to accurately simulate and effectively address these chain reactions, making it difficult to formulate comprehensive preventative measures in advance.

[0006] Faced with increasingly frequent agricultural disasters and growing agricultural production needs, there is an urgent need for an innovative technical method that can integrate meteorological big data to achieve accurate early warning and efficient emergency response to agricultural disasters, so as to reduce disaster losses and ensure the sustainable development of agriculture. Summary of the Invention

[0007] The purpose of the present invention is to provide an agricultural disaster early warning and emergency response method that integrates meteorological big data to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for agricultural disaster early warning and emergency response integrating meteorological big data, the method comprising:

[0009] Receiving heterogeneous meteorological data streams within a target area, the data streams including radar reflectivity maps, atmospheric vertical profile data, and vegetation index remote sensing images;

[0010] Based on a preset disaster feature decoupling model, the data stream is temporally and spatially aligned and feature fused to generate a disaster potential energy field distribution map and a disaster category probability matrix;

[0011] Based on the potential energy field distribution map of the disaster, an adaptive early warning threshold model is constructed to generate a multimodal emergency response instruction set, which includes a material distribution topology network and a dynamic grouping strategy for emergency teams;

[0012] Based on a preset disaster chain propagation model, simulate the cascading effects of disaster secondary events within a preset future time window and optimize the collaborative decision-making parameters in the emergency response instruction set;

[0013] The collaborative decision-making parameters are iteratively optimized through a distributed reinforcement learning framework, and the disaster response action sequence is output to the agricultural emergency command platform.

[0014] Preferably, the steps of constructing the disaster characteristic decoupling model include:

[0015] Collect a multi-year disaster event case database to construct a multi-dimensional feature training set that includes meteorological anomaly patterns, surface deformation data, and disaster triggering conditions;

[0016] Decoupling latent variables from the multi-dimensional feature training set through a variational autoencoder to extract independent representations of the dominant and secondary factors of the disaster;

[0017] Combined with the disaster evolution dynamics equation, differential constraints of the nonlinear coupling relationship between factors are constructed;

[0018] The differential constraints are embedded in a graph attention network to generate the disaster feature decoupling model that supports incremental learning.

[0019] Preferably, the adaptive warning threshold model includes:

[0020] Dynamically classifying disaster energy density levels according to the gradient changes of the disaster potential energy field distribution map;

[0021] Calculate the disaster impact sensitivity score based on regional population density and infrastructure vulnerability index;

[0022] Performing nonlinear mapping between the disaster impact sensitivity score and the energy density level through a hyperbolic tangent function to generate a region-specific warning threshold;

[0023] The activation condition of the multi-level emergency response protocol is triggered according to the threshold.

[0024] Preferably, the steps of constructing the disaster chain propagation model include:

[0025] Collect secondary disaster-related data from historical disaster events and construct a disaster cause-effect graph dataset;

[0026] Extract the transmission probability and delay time parameters between disaster events through causal discovery algorithm;

[0027] Combining complex network theory to construct a directed acyclic graph of disaster propagation and quantify the vulnerability dependence strength between nodes;

[0028] The dependency intensity and the real-time meteorological disturbance factor are input into a spatiotemporal transformer network to generate the disaster chain propagation model.

[0029] Preferably, the method further comprises:

[0030] Identifying key cascading interruption nodes based on simulation results of the disaster chain propagation model;

[0031] Configuring a blocking intervention strategy for the node in the emergency response instruction set;

[0032] Based on the blocking intervention strategy, a cross-departmental collaborative operation plan is automatically generated, including water conservancy facility control instructions and power network reconstruction plans.

[0033] Preferably, the calculation of the disaster impact sensitivity score includes:

[0034] Obtain a real-time transportation network connectivity matrix and medical resource coverage heat map to construct a regional disaster resilience assessment cube;

[0035] Calculate high-order correlation weights between multi-dimensional features through hypergraph neural networks;

[0036] Performing a tensor contraction operation on the association weight and the evaluation cube to obtain a comprehensive sensitivity score;

[0037] The calculation formula for the comprehensive sensitivity score is:

[0038]

[0039] In the formula, Λ represents the comprehensive sensitivity score value, ζ i represents the vulnerability index of the i-th type of infrastructure, ξ i represents the repair priority weight of the i-th type facility, represents the regional basic disaster resistance constant, represents the tensor Kronecker product, and p represents the total number of infrastructure categories.

[0040] Preferably, the embedding of the differential constraint condition includes:

[0041] Performing Lyapunov stability analysis on the latent variable decoupling results to screen physically feasible coupling modes;

[0042] Generate characteristic evolution trajectories that meet dynamic constraints through Hamiltonian Monte Carlo sampling;

[0043] Trajectory data is used to regularize the edge weights of the graph attention network to ensure that the model output conforms to the physical laws of disaster propagation.

[0044] Preferably, the execution of the distributed reinforcement learning framework includes:

[0045] Define the reward function for multi-agent collaborative decision-making, including the dual objectives of disaster control rate and resource utilization efficiency;

[0046] The strategy network of each regional emergency agent is trained through a hierarchical curriculum learning strategy;

[0047] In each round of training, the credit allocation weights between agents are dynamically adjusted according to the degree of goal conflict;

[0048] Outputting the disaster response action sequence that satisfies Nash equilibrium conditions;

[0049] Wherein, the reward function is:

[0050]

[0051] In the formula, R represents the reward value, represents the disaster diffusion suppression score, ε r represents the resource utilization efficiency score, and ω is the dynamic balance factor.

[0052] Preferably, the method further comprises:

[0053] After configuring the blocking intervention strategy, the state transition probability of the cascading interruption node is monitored in real time;

[0054] If the transfer probability exceeds the preset critical value, the quantum annealing optimization mechanism will be triggered to re-plan the spatiotemporal coordination plan for cross-departmental operations.

[0055] Preferably, the quantification of resource utilization efficiency targets includes:

[0056] Establish a spatiotemporal utility decay model for multiple types of rescue resources and define a resource idleness penalty function;

[0057] According to the disaster evolution phase diagram, calculate the marginal utility value of resource scheduling in each period;

[0058] Using the marginal utility value as a dynamic gain coefficient of the reward function;

[0059] The calculation formula of the spatiotemporal utility attenuation model is:

[0060]

[0061] Where K j represents the initial utility value of the j-th type of resource, represents the aging attenuation coefficient, x j (t) represents the resource activation status indicator function at time t, and q represents the total number of resource types.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] The agricultural disaster early warning and emergency response method proposed in this invention, which integrates meteorological big data, has many significant beneficial effects, including:

[0064] In the early warning phase, by receiving heterogeneous meteorological data streams such as radar reflectivity maps, atmospheric vertical profile data, and vegetation index remote sensing images, and combining them with a preset disaster feature decoupling model for spatiotemporal alignment and feature fusion, the generated disaster potential energy field distribution map and disaster category probability matrix can more accurately determine the possibility of disaster occurrence and the potential impact range. Compared with traditional early warning methods that rely on a single data source, this method utilizes multi-source meteorological data and comprehensively considers multiple factors such as meteorological conditions and surface conditions, greatly improving the accuracy and lead time of early warning. For example, when predicting drought, not only can the possibility of drought be determined based on precipitation data, but the actual water requirements of crops can also be understood through vegetation index remote sensing images. This allows for more accurate prediction of the impact of drought on crops, providing a reliable basis for farmers to take preventive measures such as irrigation in advance.

[0065] The adaptive warning threshold model constructed comprehensively considers factors such as the gradient of the disaster potential energy field distribution map, regional population density, and infrastructure vulnerability index to generate region-specific warning thresholds. This model can flexibly adjust warning standards based on the characteristics of different regions, avoiding the drawbacks of a "one-size-fits-all" approach. Even in densely populated areas with fragile infrastructure, timely warnings can be issued, prompting relevant departments to prepare for disasters in advance, effectively reducing casualties and property losses.

[0066] In terms of emergency response, the generated multimodal emergency response instruction set includes a material distribution topology network and a dynamic organization strategy for emergency teams, enabling the scientific deployment of materials and personnel. Relief supplies are rationally distributed based on the actual disaster situation and regional needs, ensuring timely and adequate support for severely affected areas and avoiding waste and backlogs. Furthermore, the dynamic organization strategy for emergency teams can quickly assemble the most appropriate rescue team based on the type, scale, and development of the disaster, improving rescue efficiency. For example, in the event of a fire, a professional fire brigade can be quickly organized and equipped with appropriate firefighting equipment to immediately respond to fires.

[0067] Based on a disaster chain propagation model, we simulate the cascading effects of secondary disaster events within a preset time window and optimize collaborative decision-making parameters to ensure that emergency responses fully consider the chain reactions of disasters. We can identify potential secondary disasters in advance, such as mudslides caused by floods and epidemics following earthquakes, and formulate corresponding preventive measures in advance to effectively reduce the harm caused by secondary disasters.

[0068] By iteratively optimizing collaborative decision-making parameters through a distributed reinforcement learning framework, the resulting disaster response action sequence is more scientific and rational. This framework, with the dual objectives of disaster control and resource efficiency, uses a hierarchical curriculum learning strategy and dynamically adjusts credit allocation weights between agents to enable regional emergency agents to make optimal decisions in complex disaster scenarios. While ensuring effective disaster control, it also improves resource utilization efficiency and reduces unnecessary resource consumption. For example, when allocating relief supplies, it comprehensively considers transportation costs, timeliness, and actual demand to achieve optimal resource allocation.

[0069] Furthermore, the present invention possesses dynamic adjustment and optimization capabilities. After configuring the blocking intervention strategy, it monitors the state transition probabilities of cascading interruption nodes in real time. Once a preset critical value is exceeded, a quantum annealing optimization mechanism is triggered to replan the spatiotemporal coordination plan for cross-departmental operations. This dynamic adjustment mechanism can promptly optimize emergency response measures based on real-time changes in disasters, ensuring that the response plan remains optimal and maximizing the safety of agricultural production and the protection of people's lives and property. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A diagram showing the steps of the agricultural disaster early warning and emergency response method according to the present invention;

[0071] Figure 2 A flowchart constructed for the adaptive warning threshold model;

[0072] Figure 3 This is a flowchart of response measures based on the disaster chain transmission model;

[0073] Figure 4 Flowchart of a graph attention network for embedding differentiable constraints. DETAILED DESCRIPTION

[0074] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0075] See also Figure 1-4 The present invention provides an agricultural disaster early warning and emergency response method integrating meteorological big data, and its specific implementation steps are as follows:

[0076] Through specialized data acquisition equipment and networks, heterogeneous meteorological data streams within the target area are acquired, including radar reflectivity maps, atmospheric vertical profile data, and vegetation index remote sensing imagery. These data provide a rich foundation for subsequent disaster analysis by providing a comprehensive view of the target area's meteorological conditions and surface vegetation from various perspectives. For example, radar reflectivity maps can reveal the structure and intensity of precipitation clouds, atmospheric vertical profile data can show the distribution of meteorological elements at different altitudes, and vegetation index remote sensing imagery can help understand the growth and health of crops.

[0077] Based on a pre-defined disaster feature decoupling model, the acquired heterogeneous meteorological data undergoes spatiotemporal alignment and feature fusion. Spatiotemporal alignment ensures temporal and spatial consistency of data from different sources, facilitating comprehensive analysis. Feature fusion integrates the characteristics of multiple data sets to generate a potential disaster energy field distribution map and a disaster category probability matrix. This step uncovers potential connections between data and more accurately assesses the likelihood and potential impact of disasters.

[0078] Based on the generated disaster potential energy field distribution map, an adaptive early warning threshold model is constructed. This model comprehensively considers various factors, such as regional population density and infrastructure vulnerability index, to generate a multimodal emergency response instruction set. This instruction set includes a resource distribution topology network and a dynamic emergency team organization strategy to achieve a rapid and effective response to agricultural disasters. For example, it can rationally allocate relief supplies and deploy emergency teams based on the likelihood and severity of disaster impact in different regions, thereby improving rescue efficiency.

[0079] Based on a pre-set disaster chain propagation model, the cascading effects of secondary disaster events within a pre-set time window are simulated. This simulation allows for the prediction of the chain reactions a disaster may trigger, allowing for proactive response preparation. Simulation results are also used to optimize the collaborative decision-making parameters within the emergency response command set, ensuring more scientific and rational decision-making.

[0080] A distributed reinforcement learning framework is used to iteratively optimize collaborative decision-making parameters, ultimately outputting a disaster response action sequence to the agricultural emergency command platform. This enables the entire early warning and emergency response system to continuously learn and improve, adapting to different disaster scenarios and providing strong support for agricultural disaster response.

[0081] The implementation of the present invention will be further described below with reference to Examples 1 to 6.

[0082] Example 1

[0083] This embodiment describes in detail the process of constructing a disaster characteristic decoupling model. Specifically, it includes:

[0084] Compile a database of multi-year disaster event cases. This requires collecting information on various agricultural disaster events that have occurred in the target area and other related areas over the years. This includes meteorological anomaly patterns, such as the intensity and duration of rainstorms, and the magnitude and timing of abnormal temperature fluctuations; surface deformation data, such as changes in land inundation caused by floods and the degree of soil cracking caused by droughts; and disaster triggering conditions, such as the meteorological conditions and soil moisture levels that are most likely to trigger specific agricultural disasters. After organizing this information, a multidimensional feature training set is constructed that incorporates this diverse information.

[0085] A variational autoencoder is used to decouple latent variables from a multidimensional feature training set. A variational autoencoder is a deep learning model that learns latent representations of data. During this process, the model separates the dominant and secondary factors of a disaster within the multidimensional features, extracting independent representations of each. For example, in the case of a drought, insufficient precipitation may be the dominant factor, while wind speed, evaporation, and other factors may be secondary. The variational autoencoder can then represent the impact of these factors separately.

[0086] By combining the disaster evolution dynamics equation with the nonlinear coupling relationships between factors, differential constraints are constructed. This equation describes the temporal and spatial development of disasters. This equation can be used to determine the interaction between different factors. The differential constraints constructed based on this equation can more accurately characterize the relationships between disaster factors.

[0087] Differentiation constraints are embedded in a graph attention network. The graph attention network automatically learns the importance weights between nodes in the graph. After embedding the differentiation constraints, a disaster feature decoupling model supporting incremental learning is generated. This means the model can be continuously updated and optimized as new disaster data is added, improving its ability to decouple disaster features and predictive accuracy.

[0088] Through deep data mining and ingenious model construction, the Disaster Characteristics Decoupling Model can more accurately analyze disaster data and provide strong support for subsequent early warning and emergency response. For example, when faced with an impending rainstorm, the model can accurately analyze which areas are more vulnerable to flooding due to factors such as topography and soil conditions, providing a basis for taking preventive measures in advance.

[0089] Example 2

[0090] In the adaptive warning threshold model, disaster energy density levels are dynamically classified based on the gradient changes of the disaster potential energy field distribution map. The disaster potential energy field distribution map reflects the potential possibility of disasters and the energy distribution. By analyzing its gradient changes, regions can be divided into different energy density levels, such as high, medium, and low. For example, areas with large changes in the potential energy field gradient may indicate a significant change in the probability and intensity of a disaster, and thus may be classified as high energy density.

[0091] A disaster sensitivity score is calculated based on regional population density and infrastructure vulnerability indices. A real-time transportation network connectivity matrix and a heat map of medical resource coverage are obtained to construct a regional disaster resilience assessment cube. The real-time transportation network connectivity matrix reflects the smoothness of the regional transportation network, while the heat map of medical resource coverage shows the distribution of medical resources within the region. This information is combined with regional population density, infrastructure vulnerability indices, and other factors to construct a multidimensional assessment cube.

[0092] Hypergraph neural networks are used to calculate high-order correlation weights between multidimensional features. Hypergraph neural networks are capable of handling complex relational structures. They can analyze the complex relationships between multiple features, such as population distribution, infrastructure vulnerability, transportation network connectivity, and medical resource coverage, and determine the importance of each feature to the sensitivity of disaster impacts, i.e., the high-order correlation weight.

[0093] The association weight and the evaluation cube are combined by tensor contraction to obtain the comprehensive sensitivity score. The calculation formula of the comprehensive sensitivity score is:

[0094]

[0095] Among them, Λ represents the comprehensive sensitivity score value, which reflects the sensitivity of the region to disasters. The larger the value, the greater the impact the region may suffer when it suffers a disaster; ζ i represents the vulnerability index of the i-th type of infrastructure, which is used to measure the degree of damage to different types of infrastructure (such as roads, bridges, water conservancy facilities, etc.) in disasters; ξ i It represents the repair priority weight of the i-th type of facility, reflecting the order of importance of various types of facilities in post-disaster repair; It represents the regional basic disaster resistance constant, reflecting the basic ability of the region to resist disasters; represents the tensor Kronecker product, which is used to calculate the combination relationship between different tensors; p represents the total number of infrastructure categories, that is, the number of infrastructure types involved in the evaluation.

[0096] The disaster impact sensitivity score and energy density level are nonlinearly mapped using a hyperbolic tangent function to generate a region-specific warning threshold. This threshold triggers the activation conditions of the multi-level emergency response protocol. For example, when the warning threshold in a region reaches a certain level, the corresponding level of emergency response is automatically initiated, such as deploying more relief supplies and organizing more emergency teams to the area.

[0097] In this way, the adaptive warning threshold model can comprehensively consider multiple factors to generate warning thresholds that are more realistic, improving the accuracy of agricultural disaster warnings and the relevance of emergency responses. For example, in a densely populated area with highly vulnerable infrastructure, even if the disaster energy density level is not particularly high, due to the high disaster impact sensitivity score, a relatively low warning threshold will be set to enable timely emergency response and reduce disaster losses.

[0098] Example 3

[0099] This example mainly describes the steps for constructing a disaster chain transmission model, including:

[0100] Data related to secondary disasters from historical disasters are collected to construct a disaster causal graph dataset. This requires extensive collection of historical agricultural disasters and detailed documentation of the relationship between each disaster and the secondary disasters it triggers, including the time, location, and type of disaster, as well as the causal relationship between them. For example, a rainstorm may trigger a flood, which in turn may lead to a mudslide. This information is recorded to form a disaster causal graph dataset.

[0101] Causal discovery algorithms are used to extract the transmission probability and delay time parameters between disaster events. Causal discovery algorithms can mine causal relationships between different disasters from large amounts of disaster data and determine the transmission probability and delay time of disaster occurrence. The transmission probability indicates the likelihood that one disaster will trigger another, while the delay time indicates the time between the occurrence of one disaster and the subsequent disaster. For example, analysis of historical data revealed that in a certain area, the transmission probability of floods triggering mudslides is 0.6, with an average delay time of 2-3 days.

[0102] Integrating complex network theory, a directed acyclic graph (DAG) of disaster propagation is constructed to quantify the vulnerability dependency between nodes. Complex network theory considers disaster events as nodes in a network, and the causal relationships between them as edges, constructing a DAG. In this graph, each node represents a disaster, and the direction of the edge indicates the direction of the disaster's propagation. By calculating the connectivity and mutual influence between nodes, the vulnerability dependency between nodes is quantified. For example, if a node (disaster) has close connections with multiple other nodes and plays a key role in the propagation process, its vulnerability dependency is high.

[0103] The dependency strengths and real-time meteorological disturbance factors are fed into a spatiotemporal transformer network to generate a disaster chain propagation model. The spatiotemporal transformer network, capable of processing spatiotemporal sequence data, combines the vulnerability dependency strengths between nodes with real-time meteorological disturbance factors (such as current precipitation intensity and wind speed variations) to more accurately simulate the disaster propagation process. The resulting disaster chain propagation model can predict the cascading effects of secondary disaster events within a pre-set time window in the future, providing a basis for preemptive response strategy formulation.

[0104] For example, when predicting typhoon disasters, the disaster chain propagation model can predict the probability and time sequence of a series of secondary disasters such as heavy rain, floods, landslides, etc. that may be caused by typhoons based on historical data and real-time meteorological information, helping relevant departments to make prevention and rescue preparations in advance and minimize disaster losses.

[0105] Example 4

[0106] This implementation is used to describe in detail the application of the disaster chain transmission model simulation results and the implementation of the blocking intervention strategy.

[0107] Based on the simulation results of the disaster chain propagation model, critical cascade interruption nodes are identified. These are nodes in the disaster chain propagation process where intervention can effectively prevent or slow the further development of the disaster chain. For example, in a flood-debris flow disaster chain, a hillside area prone to landslides and subsequent debris flows may be a critical cascade interruption node.

[0108] Configure a blocking intervention strategy for this node in the emergency response command set. Develop corresponding intervention measures for different critical cascade interruption nodes. For example, for slopes prone to landslides, measures such as mountain reinforcement and drainage facilities can be implemented to reduce the likelihood of landslides and thus prevent debris flows.

[0109] Based on the blocking intervention strategy, a cross-departmental collaborative operation plan is automatically generated, including water conservancy facility control instructions and power network reconstruction plans. Water conservancy facility control instructions can adjust reservoir water storage and river flood discharge according to the disaster situation to reduce the pressure of flooding on downstream areas. The power network reconstruction plan replans power lines to ensure power supply to critical areas when a disaster threatens to affect power supply. For example, if flooding threatens to submerge some power facilities, power lines can be promptly adjusted to transmit power to less affected areas, prioritizing the power needs of rescue efforts and critical public facilities.

[0110] After configuring the blocking intervention strategy, the state transition probability of the cascading interruption nodes is monitored in real time. Through sensors, satellite remote sensing and other technical means, relevant information of key nodes is continuously obtained to calculate their state transition probability, that is, the possibility of changing from the current stable state to a disaster-inducing state. If the transition probability exceeds the preset critical value, the quantum annealing optimization mechanism is triggered to re-plan the spatiotemporal coordination plan for cross-departmental operations. The quantum annealing optimization mechanism is an optimization algorithm that can find better solutions in complex search spaces. For example, when the probability of a landslide in a key hillside area is detected to exceed the preset value, the quantum annealing optimization mechanism is used to readjust the control plan of water conservancy facilities and the reconstruction plan of the power network to better respond to possible disasters.

[0111] In this way, we can make full use of the simulation results of the disaster chain transmission model, take effective blocking intervention strategies in a timely manner, and improve the ability to respond to agricultural disasters and reduce losses caused by disasters through cross-departmental collaborative operations and dynamic optimization plans.

[0112] Example 5

[0113] This example describes in detail the specific process of embedding differential constraints into a graph attention network. Specifically, it includes:

[0114] Lyapunov stability analysis is performed on the results of latent variable decoupling to screen for physically feasible coupling patterns. Lyapunov stability analysis is a method used to determine the stability of dynamical systems. In the disaster characteristic decoupling model, Lyapunov stability analysis of the results obtained from latent variable decoupling can determine whether the coupling patterns between different disaster factors are physically feasible. For example, certain coupling patterns may lead to disaster development trends that are inconsistent with actual conditions. Stability analysis can filter out these unreasonable patterns and retain only physically feasible coupling patterns.

[0115] Hamiltonian Monte Carlo sampling is used to generate characteristic evolution trajectories that conform to dynamic constraints. Hamiltonian Monte Carlo sampling is a method for sampling in high-dimensional space. In this embodiment, this method is used to generate evolution trajectories of disaster characteristics while satisfying the disaster evolution dynamics equation and differential constraints. These trajectories reflect the possible development paths of disasters under different time periods and conditions, providing richer data for subsequent model training.

[0116] Trajectory data is used to regularize the edge weights of the graph attention network, ensuring that the model output conforms to the physical laws of disaster propagation. The graph attention network uses edge weights to represent the relationships between nodes. The generated trajectory data is fed into the graph attention network to perform regularized training on the edge weights. Regularized training prevents model overfitting and ensures that the edge weights are more consistent with the physical laws of disaster propagation. For example, during training, edge weights are adjusted based on actual disaster propagation conditions, allowing the model to more accurately reflect the interactions and impacts between different disaster factors.

[0117] Through this series of steps, differential constraints are effectively embedded in the graph attention network, enabling the disaster feature decoupling model to better simulate the development of disasters and improve disaster prediction and analysis capabilities. For example, when analyzing the development of a drought, the model can more accurately predict the spread of the drought and the extent of its impact on crops, providing a basis for formulating appropriate drought relief measures.

[0118] Example 6

[0119] In the distributed reinforcement learning framework execution phase, we first need to define the reward function for multi-agent collaborative decision-making. This function integrates the dual objectives of disaster control rate and resource utilization efficiency. The specific formula is:

[0120]

[0121] Among them, the reward value R comprehensively reflects the quality of multi-agent decision-making. Disaster spread suppression score It is a key indicator to measure the effectiveness of decision-making in controlling the spread of disasters. The higher the value, the stronger the inhibitory effect on the spread of disasters. For example, when responding to a drought, if the agent's decision effectively curbs the expansion of the drought-affected area, the score will increase accordingly. Resource Utilization Efficiency Score ε r , which reflects the efficiency of resource utilization; a higher score indicates more efficient resource utilization. The dynamic balance factor ω can be flexibly adjusted based on different agricultural disaster scenarios and actual needs, balancing the weights of disaster control and resource utilization in the reward function. For example, when facing sudden and severe disasters, ω can be appropriately increased to prioritize disaster control. If the disaster develops relatively slowly, ω can be adjusted to prioritize resource efficiency.

[0122] A hierarchical curriculum learning strategy is used to train the policy networks of each regional emergency response agent. This strategy advances the learning process in layers, starting with simple tasks and gradually increasing the difficulty. For example, in flood response, the agent initially learns basic flood monitoring and early warning strategies, familiarizing itself with obtaining flood-related data and identifying initial flood trends. As training progresses, more complex scenarios are introduced, such as considering the interaction between floods and the surrounding geological environment and the impact of floods on different types of agricultural facilities. This allows the agent to continuously optimize its policy network and enhance its decision-making capabilities when responding to complex flood disaster scenarios.

[0123] During each training round, the credit allocation weights between agents are dynamically adjusted based on the degree of conflict between these objectives. In real-world agricultural disaster response, disaster control and resource efficiency often conflict. For example, in pest control, large quantities of pesticides may be required to quickly control their spread. This, however, leads to excessive consumption of pesticide resources and reduces resource efficiency. In such cases, the credit allocation weights between agents are dynamically adjusted based on the actual degree of conflict. Agents that strike a good balance between the two objectives are given higher credit weights, incentivizing all agents to make more informed decisions.

[0124] The final output is a disaster response action sequence that satisfies Nash equilibrium conditions. In a Nash equilibrium, each agent's strategy is the optimal choice given the strategies of the other agents. Through continuous training within a distributed reinforcement learning framework, each agent continuously adjusts its decision-making until the output disaster response action sequence reaches a Nash equilibrium. This means that the decisions of each agent are coordinated, achieving the optimal overall response to agricultural disasters.

[0125] Regarding the quantification of resource utilization efficiency goals, we first establish a spatiotemporal utility decay model for multiple types of rescue resources and define a resource idleness penalty function. The calculation formula for the resource utility decay model is:

[0126]

[0127] Among them, the resource utilization efficiency score ε r It reflects the actual efficiency of resource utilization. The initial utility value K of the jth type of resource j , represents the role played by the resource in disaster response in the initial stage of use. For example, in response to drought, the initial utility value of irrigation water is higher because it can directly alleviate the water shortage of crops. Used to measure the rate at which the utility of a resource decays over time. Different types of resources have different time-attenuation coefficients. For example, some perishable disaster relief supplies have a larger time-attenuation coefficient. j (t) is a function indicating the resource activation status at time t. If the jth resource type is activated at time t, the function value is 1; otherwise, it is 0. The total number of resource types is represented by q. This model accurately quantifies the actual utility of different resource types at different times. Combined with the resource idleness penalty function, it encourages the agent to rationally plan resource use, avoid idle resources, and improve overall resource utilization efficiency.

[0128] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0129] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for agricultural disaster early warning and emergency response integrating meteorological big data, characterized in that: include: Receiving heterogeneous meteorological data streams within a target area, the data streams including radar reflectivity maps, atmospheric vertical profile data, and vegetation index remote sensing images; Based on a preset disaster feature decoupling model, the data stream is temporally and spatially aligned and feature fused to generate a disaster potential energy field distribution map and a disaster category probability matrix; Based on the potential energy field distribution map of the disaster, an adaptive early warning threshold model is constructed to generate a multimodal emergency response instruction set, which includes a material distribution topology network and a dynamic grouping strategy for emergency teams; Based on a preset disaster chain propagation model, simulate the cascading effects of disaster secondary events within a preset future time window and optimize the collaborative decision-making parameters in the emergency response instruction set; Iteratively optimize the collaborative decision-making parameters through a distributed reinforcement learning framework and output the disaster response action sequence to the agricultural emergency command platform; The steps of constructing the disaster characteristic decoupling model include: Collect a multi-year disaster event case database to construct a multi-dimensional feature training set that includes meteorological anomaly patterns, surface deformation data, and disaster triggering conditions; Decoupling latent variables from the multi-dimensional feature training set through a variational autoencoder to extract independent representations of the dominant and secondary factors of the disaster; Combined with the disaster evolution dynamics equation, differential constraints of the nonlinear coupling relationship between factors are constructed; The differential constraints are embedded in a graph attention network to generate the disaster feature decoupling model that supports incremental learning.

2. The agricultural disaster early warning and emergency response method according to claim 1, characterized in that: The adaptive warning threshold model includes: Dynamically classifying disaster energy density levels according to the gradient changes of the disaster potential energy field distribution map; Calculate the disaster impact sensitivity score based on regional population density and infrastructure vulnerability index; Performing nonlinear mapping between the disaster impact sensitivity score and the energy density level through a hyperbolic tangent function to generate a region-specific warning threshold; The activation condition of the multi-level emergency response protocol is triggered according to the threshold.

3. The agricultural disaster early warning and emergency response method according to claim 1, characterized in that: The steps of constructing the disaster chain propagation model include: Collect secondary disaster-related data from historical disaster events and construct a disaster cause-effect graph dataset; Extract the transmission probability and delay time parameters between disaster events through causal discovery algorithm; Combining complex network theory to construct a directed acyclic graph of disaster propagation and quantify the vulnerability dependence strength between nodes; The dependency intensity and the real-time meteorological disturbance factor are input into a spatiotemporal transformer network to generate the disaster chain propagation model.

4. The agricultural disaster early warning and emergency response method according to claim 3, characterized in that: Also includes: Identifying key cascading interruption nodes based on simulation results of the disaster chain propagation model; Configuring a blocking intervention strategy for the node in the emergency response instruction set; Based on the blocking intervention strategy, a cross-departmental collaborative operation plan is automatically generated, including water conservancy facility control instructions and power network reconstruction plans.

5. The agricultural disaster early warning and emergency response method according to claim 2, characterized in that: The calculation of the disaster impact sensitivity score includes: Obtain a real-time transportation network connectivity matrix and medical resource coverage heat map to construct a regional disaster resilience assessment cube; Calculate high-order correlation weights between multi-dimensional features through hypergraph neural networks; Performing a tensor contraction operation on the association weight and the evaluation cube to obtain a comprehensive sensitivity score; The calculation formula for the comprehensive sensitivity score is: In the formula, Λ represents the comprehensive sensitivity score value, ζ i represents the vulnerability index of the i-th type of infrastructure, ξ i represents the repair priority weight of the i-th type facility, represents the regional basic disaster resistance constant, represents the tensor Kronecker product, and p represents the total number of infrastructure categories.

6. The agricultural disaster early warning and emergency response method according to claim 1, characterized in that: The embedding of the differential constraints includes: Performing Lyapunov stability analysis on the latent variable decoupling results to screen physically feasible coupling modes; Generate characteristic evolution trajectories that meet dynamic constraints through Hamiltonian Monte Carlo sampling; Trajectory data is used to regularize the edge weights of the graph attention network to ensure that the model output conforms to the physical laws of disaster propagation.

7. The agricultural disaster early warning and emergency response method according to claim 1, characterized in that: The execution of the distributed reinforcement learning framework includes: Define the reward function for multi-agent collaborative decision-making, including the dual objectives of disaster control rate and resource utilization efficiency; The strategy network of each regional emergency agent is trained through a hierarchical curriculum learning strategy; In each round of training, the credit allocation weights between agents are dynamically adjusted according to the degree of goal conflict; Outputting the disaster response action sequence that satisfies Nash equilibrium conditions; Wherein, the reward function is: In the formula, R represents the reward value, represents the disaster diffusion suppression score, ε r represents the resource utilization efficiency score, and ω is the dynamic balance factor.

8. The agricultural disaster early warning and emergency response method according to claim 4, characterized in that: Also includes: After configuring the blocking intervention strategy, the state transition probability of the cascading interruption node is monitored in real time; If the transfer probability exceeds the preset critical value, the quantum annealing optimization mechanism will be triggered to re-plan the spatiotemporal coordination plan for cross-departmental operations.

9. The agricultural disaster early warning and emergency response method according to claim 7, characterized in that: The quantification of resource efficiency targets includes: Establish a spatiotemporal utility decay model for multiple types of rescue resources and define a resource idleness penalty function; According to the disaster evolution phase diagram, calculate the marginal utility value of resource scheduling in each period; Using the marginal utility value as a dynamic gain coefficient of the reward function; The calculation formula of the spatiotemporal utility attenuation model is: Where K j represents the initial utility value of the j-th type of resource, represents the aging attenuation coefficient, x j (t) represents the resource activation status indicator function at time t, and q represents the total number of resource types.

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