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

Abstract

The invention relates to the technical field of agricultural disaster treatment, and discloses an agricultural disaster early warning and emergency response method fused with meteorological big data. The method comprises the following steps: receiving a heterogeneous meteorological data stream of a target area, performing space-time alignment and feature fusion through a disaster feature decoupling model, and generating a disaster potential energy field distribution diagram and a disaster category probability matrix; and constructing a self-adaptive early warning threshold model, and generating a multi-modal emergency response instruction set. A disaster chain propagation model is utilized to simulate a disaster secondary event cascade effect, collaborative decision parameters are optimized, iterative optimization is carried out through a distributed reinforcement learning framework, and a disaster response action sequence is output to an agricultural emergency command platform. According to the method, meteorological big data is comprehensively utilized, the agricultural disaster early warning accuracy is improved, efficient emergency response is achieved, resources are reasonably allocated, cross-department collaboration is promoted, and agricultural disaster losses are effectively reduced.

Description

Technical Field

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

[0002] Agriculture, as the basic industry of the country, plays a crucial role in the national economy and social stability. However, agricultural production is extremely vulnerable to various natural disasters, such as floods, droughts, typhoons, hailstorms, etc. These disasters not only lead to crop yield reduction, quality decline, and even possible crop failure, bringing huge economic losses to farmers, but also affect the market supply and price stability of agricultural products, and then have a chain reaction on the entire social economy.

[0003] Traditional agricultural disaster early warning mainly relies on single data sources or simple meteorological monitoring means, and the accuracy and comprehensiveness of the data are relatively poor. For example, relying only on the observation data of ground meteorological stations, it is impossible to obtain timely meteorological change information over a large area, and it is difficult to give early warnings for some sudden local disasters. Moreover, these data often lack in-depth analysis of the potential impacts of disasters and cannot accurately judge the specific damage degrees of disasters to different crop varieties and regions with different soil conditions.

[0004] In terms of emergency response, there are many deficiencies in the existing emergency mechanisms. The distribution of materials lacks scientific planning, and there are often unreasonable situations in material allocation. Some severely affected areas are short of materials, while there are overstocking and waste of materials in some areas. The formation of emergency teams is also relatively fixed and cannot be flexibly adjusted according to the actual situation of disasters, resulting in low rescue efficiency. There are obstacles in the cooperation between different departments, and information communication is not smooth, so an effective emergency joint force cannot be formed. For example, during a flood disaster, the information sharing between the water conservancy department and the transportation department is not timely, making it difficult to promote the coordination of flood control, farmland drainage, and transportation of rescue materials.

[0005] With the development of meteorological observation technology, meteorological big data has shown explosive growth, including various types of data such as radar reflectivity maps, atmospheric vertical profile data, and vegetation index remote sensing images. However, how to effectively integrate and utilize this large amount of heterogeneous meteorological big data has become a major challenge in the field of agricultural disaster early warning and emergency response. In addition, the occurrence of disasters is often not isolated, but there are complex chain propagation effects, and the impact of secondary disasters sometimes even exceeds that of primary disasters. The existing early warning and emergency response systems are difficult to accurately simulate and effectively respond to the chain propagation of disasters and cannot formulate comprehensive preventive 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 integrating meteorological big data to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: an agricultural disaster early warning and emergency response method integrating meteorological big data, the method comprising: Receiving heterogeneous meteorological data streams in 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 time-space aligned and feature fused to generate a disaster potential energy field distribution map and a disaster category probability matrix; According to 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, wherein the instruction set 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 time window in the future, and optimize the collaborative decision-making parameters in the emergency response instruction set; 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.

[0009] Preferably, the steps of constructing the disaster characteristic decoupling model include: Collect disaster event case libraries across years and construct a multi-dimensional feature training set that includes meteorological anomaly patterns, surface deformation data, and disaster triggering conditions; Decoupling latent variables of the multi-dimensional feature training set by using 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 nonlinear coupling relationships 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.

[0010] Preferably, the adaptive warning threshold model includes: Dynamically classifying disaster energy density levels according to the gradient change of the disaster potential energy field distribution map; Calculate the disaster impact sensitivity score based on regional population density and infrastructure vulnerability index; Non-linearly map the disaster impact sensitivity score and the energy density level through the hyperbolic tangent function to generate a region-specific early warning threshold; Trigger the activation conditions of the multi-level emergency response protocol according to the threshold.

[0011] Preferably, the steps for constructing the disaster chain propagation model include: Collect the associated data of secondary disasters in historical disaster events to construct a disaster causal graph dataset; Extract the transfer probability and delay time parameters between disaster events through the causal discovery algorithm; Combine complex network theory to construct a directed acyclic graph of disaster propagation and quantify the vulnerability dependence strength between nodes; Input the dependence strength and real-time meteorological disturbance factors into the spatio-temporal transformer network to generate the disaster chain propagation model.

[0012] Preferably, the method further includes: Identify the key cascade interruption nodes according to the simulation results of the disaster chain propagation model; Configure a blocking intervention strategy for the nodes in the emergency response instruction set; Based on the blocking intervention strategy, automatically generate a cross-departmental collaborative operation plan, including water conservancy facility regulation instructions and power network reconstruction plans.

[0013] Preferably, the calculation of the disaster impact sensitivity score includes: Obtain the real-time traffic network connectivity matrix and the medical resource coverage heat map, and construct a regional disaster resistance resilience assessment cube; Calculate the high-order correlation weights between multi-dimensional features through a hypergraph neural network; Perform a tensor contraction operation on the correlation weights and the assessment cube to obtain a comprehensive sensitivity score; Among them, the calculation formula of the comprehensive sensitivity score is: ; In the formula, represents the value of the comprehensive sensitivity score, represents the vulnerability index of the th type of infrastructure, represents the repair priority weight of the th type of facility, represents the regional basic disaster resistance ability constant, represents the tensor Kronecker product, represents the total number of infrastructure classifications.

[0014] Preferably, the embedding of the differential constraint conditions includes: Perform Lyapunov stability analysis on the decoupled results of the latent variables to screen physically realizable coupling modes; Generate characteristic evolution trajectories that conform to dynamic constraints through Hamiltonian Monte Carlo sampling; Regularize and train the edge weights of the graph attention network using the trajectory data to ensure that the model output conforms to the physical laws of disaster propagation.

[0015] Preferably, define a reward function for multi-agent collaborative decision-making, including the dual objectives of disaster control rate and resource utilization efficiency; Train the policy network of each regional emergency agent through a hierarchical curriculum learning strategy; In each round of training, dynamically adjust the credit assignment weights between agents according to the degree of goal conflict; Output the disaster response action sequence that satisfies the Nash equilibrium condition; Among them, the reward function is: ; In the formula, represents the reward value, represents the disaster diffusion suppression score, represents the resource utilization efficiency score, is the dynamic balance factor.

[0016] Preferably, after configuring the blocking intervention strategy, monitor the state transition probability of the cascading interruption nodes in real time; If the transition probability exceeds the preset critical value, trigger the quantum annealing optimization mechanism to re-plan the spatio-temporal coordination plan for cross-departmental operations.

[0017] Preferably, it also includes the quantification of the resource utilization efficiency target, and its quantification steps are: Establish a spatio-temporal utility decay model for multiple types of rescue resources and define a resource idle penalty function; According to the disaster evolution phase diagram, calculate the marginal utility value of resource scheduling in each time period; Use the marginal utility value as the dynamic gain coefficient of the reward function; Among them, the calculation formula of the spatio-temporal utility decay model is: ; In the formula, represents the initial utility value of the th type of resource, represents the time-effect decay coefficient, represents the resource activation status indicator function at time, represents the total number of resource types.

[0018] Compared with the prior art, the beneficial effects of the present invention are: The agricultural disaster early warning and emergency response method integrating meteorological big data proposed in the present invention has many significant beneficial effects, including: In the early warning stage, by receiving heterogeneous meteorological data streams such as radar reflectivity maps, atmospheric vertical profile data, and vegetation index remote sensing images, and combining the 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 judge the possibility of disaster occurrence and potential impact range. Compared with the traditional early warning method that relies on a single data source, this method uses multi-source meteorological data and comprehensively considers multiple factors such as meteorological conditions and surface conditions, greatly improving the accuracy and advance time of early warning. For example, when predicting drought, not only can the possibility of drought be judged based on precipitation data, but also the actual water demand of crops can be understood through vegetation index remote sensing images, and the impact of drought on crops can be more accurately predicted, providing a reliable basis for farmers to take preventive measures such as irrigation in advance.

[0019] The constructed adaptive warning threshold model comprehensively considers factors such as the gradient change of the potential energy field distribution map of potential disasters, regional population distribution density and infrastructure vulnerability index, and generates regional specific warning thresholds. This model can flexibly adjust the warning standards according to the characteristics of different regions, avoiding the drawbacks of "one size fits all". For densely populated areas with fragile infrastructure, even if the potential energy of disasters is low, early warnings can be issued in time to remind relevant departments to make precautionary preparations in advance, effectively reducing casualties and property losses.

[0020] In terms of emergency response, the generated multimodal emergency response instruction set includes a material distribution topology network and a dynamic grouping strategy for emergency teams, which realizes the scientific deployment of materials and personnel. According to the actual situation of the disaster and the needs of various regions, rescue materials are reasonably allocated to ensure that severely affected areas can obtain sufficient material support in a timely manner to avoid waste and backlog of materials. At the same time, the dynamic grouping strategy of the emergency team can quickly form the most suitable rescue team according to the type, scale and development trend of the disaster, thereby improving the rescue efficiency. For example, in a fire disaster, a professional fire brigade can be quickly organized and equipped with corresponding fire-fighting equipment to put out the fire as soon as possible.

[0021] Based on the disaster chain propagation model, the cascade effect of secondary disaster events in the future preset time window is simulated, and the collaborative decision-making parameters are optimized so that the emergency response can fully take into account the chain reaction of disasters. Possible secondary disasters can be identified in advance, such as mudslides caused by floods and epidemics after earthquakes, and corresponding preventive measures can be formulated in advance to effectively reduce the harm caused by secondary disasters.

[0022] By iteratively optimizing the collaborative decision-making parameters through a distributed reinforcement learning framework, the output disaster response action sequence is more scientific and reasonable. This framework takes the disaster control rate and resource utilization efficiency as dual objectives. Through a hierarchical curriculum learning strategy and dynamically adjusting the credit assignment weights among agents, each regional emergency agent can make optimal decisions in complex disaster scenarios. While ensuring effective control of the disaster, it improves the utilization efficiency of resources and reduces unnecessary resource consumption. For example, when allocating relief supplies, it can comprehensively consider the transportation cost, usage timeliness, and actual needs of the supplies to achieve the optimal allocation of resources.

[0023] In addition, the present invention also has the ability of dynamic adjustment and optimization. After configuring the blocking intervention strategy, it monitors the state transition probability of cascade interruption nodes in real time. Once it exceeds the preset critical value, it triggers the quantum annealing optimization mechanism to re-plan the spatio-temporal collaboration plan for cross-departmental operations. This dynamic adjustment mechanism can optimize the emergency response measures in a timely manner according to the real-time changes of the disaster, ensuring that the response plan is always in the optimal state and maximizing the protection of agricultural production safety and people's lives and property safety. Brief Description of the Drawings

[0024] Figure 1 It is a step diagram of the agricultural disaster early warning and emergency response method described in the present invention; Figure 2 It is a flowchart for constructing an adaptive early warning threshold model; Figure 3 It is a flowchart of countermeasures based on the disaster chain propagation model; Figure 4 It is a flowchart for embedding differential constraint conditions into a graph attention network. Detailed Embodiment

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0026] Please refer to Figures 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: Through professional data acquisition equipment and networks, heterogeneous meteorological data streams within the target area are obtained, including radar reflectivity maps, atmospheric vertical profile data, and vegetation index remote sensing images. These data reflect the meteorological conditions and surface vegetation information of the target area from different dimensions, providing a rich data basis for subsequent disaster analysis. For example, radar reflectivity maps can display the structure and intensity of precipitation cloud systems, atmospheric vertical profile data can present the distribution of meteorological elements at different heights, and vegetation index remote sensing images help to understand the growth status and health of crops.

[0027] Based on a preset disaster feature decoupling model, the obtained heterogeneous meteorological data is subjected to spatio-temporal alignment and feature fusion. Spatio-temporal alignment ensures the consistency of data from different sources in terms of time and space, facilitating comprehensive analysis. Feature fusion integrates the features of multiple data to generate a disaster potential energy field distribution map and a disaster category probability matrix. This step can uncover the potential connections between data and more accurately assess the likelihood and potential impact of disasters.

[0028] According to 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 distribution density, infrastructure vulnerability index, etc., to generate a multi-modal emergency response instruction set. The instruction set includes a material distribution topological network and an emergency team dynamic grouping strategy to achieve a rapid and effective response to agricultural disasters. For example, according to the likelihood and degree of disaster in different regions, relief materials are reasonably allocated and emergency teams are arranged to improve the rescue efficiency.

[0029] Based on a preset disaster chain propagation model, the cascading effect of disaster secondary events within a preset future time window is simulated. Through this simulation, a series of chain reactions that may be triggered by disasters can be predicted, and preparations can be made in advance. At the same time, the collaborative decision-making parameters in the emergency response instruction set are optimized according to the simulation results to make the decision-making more scientific and reasonable.

[0030] The collaborative decision-making parameters are iteratively optimized through a distributed reinforcement learning framework, and finally a disaster response action sequence is output to the agricultural emergency command platform. This enables the entire early warning and emergency response system to continuously learn and improve, adapt to different disaster scenarios, and provide strong support for the response to agricultural disasters.

[0031] The following further illustrates the implementation of the present invention in conjunction with Examples 1 to 6.

[0032] Example 1, this example elaborates in detail the construction process of the disaster feature decoupling model. Specifically, it includes: Collect a cross-year disaster event case library. This requires collecting information on various agricultural disaster events that have occurred in the target area and other relevant areas over the years, including meteorological anomaly patterns, such as precipitation intensity and duration during heavy rain, and the amplitude and time of abnormal temperature changes; surface deformation data, such as changes in the land inundation range caused by floods and the degree of soil cracking caused by droughts; disaster triggering conditions, such as under what meteorological conditions, soil moisture, etc. are likely to trigger specific agricultural disasters. After organizing this information, construct a multi-dimensional feature training set containing the above-mentioned various information.

[0033] Use a variational autoencoder to decouple the latent variables of the multi-dimensional feature training set. A variational autoencoder is a deep learning model that can learn the latent representation of data. In this process, the model separates the dominant and secondary factors of disasters in the multi-dimensional features and extracts their respective independent representations. For example, for drought disasters, insufficient precipitation may be the dominant factor, while wind speed, evaporation, etc. may be secondary factors, and the variational autoencoder can represent the impacts of these factors separately.

[0034] Combine the disaster evolution dynamics equation to construct differential constraint conditions for the non-linear coupling relationship between factors. The disaster evolution dynamics equation describes the development law of disasters over time and space, and through this equation, the interaction methods between different factors can be determined. The differential constraint conditions constructed based on this can more accurately depict the relationship between disaster factors.

[0035] Embed the differential constraint conditions into the graph attention network. The graph attention network can automatically learn the importance weights between nodes in the graph. After embedding the differential constraint conditions, a disaster feature decoupling model that supports incremental learning is generated. This means that the model can continuously update and optimize as new disaster data is added, improving the decoupling ability of disaster features and prediction accuracy.

[0036] In the above process, through in-depth data mining and ingenious model construction, the disaster feature decoupling model can analyze disaster data more accurately, providing strong support for subsequent early warning and emergency response. For example, when facing an upcoming heavy rain disaster, the model can accurately analyze which areas are more likely to be threatened by floods due to factors such as terrain and soil conditions, providing a basis for taking preventive measures in advance.

[0037] Example 2. In terms of the adaptive early warning threshold model, the disaster energy density levels are dynamically divided according to the gradient changes in the disaster potential energy field distribution map. The disaster potential energy field distribution map reflects the potential possibility of disaster occurrence and the energy distribution. By analyzing its gradient changes, the region can be divided into different energy density levels, such as high, medium, and low levels. For example, in areas with large gradient changes in the potential energy field, it may mean that the possibility and intensity of disaster occurrence change significantly, and it can be divided into high energy density levels.

[0038] Based on the regional population distribution density and infrastructure vulnerability index, calculate the disaster impact sensitivity score. Obtain the real-time traffic network connectivity matrix and the medical resource coverage heat map, and construct a regional disaster resistance resilience assessment cube. The real-time traffic network connectivity matrix reflects the smoothness of the traffic network within the region, and the medical resource coverage heat map shows the distribution of medical resources within the region. Combining this information with the regional population distribution density, infrastructure vulnerability index, etc., a multi-dimensional assessment cube is constructed.

[0039] Calculate the high-order correlation weights between multi-dimensional features through a hypergraph neural network. The hypergraph neural network can handle complex relationship structures. It can analyze the complex correlations between multiple features such as population distribution, infrastructure vulnerability, traffic network connectivity, and medical resource coverage, and obtain the importance degree of each feature for disaster impact sensitivity, that is, the high-order correlation weight.

[0040] Perform a tensor contraction operation on the correlation weights and the assessment cube to obtain the comprehensive sensitivity score. The calculation formula for the comprehensive sensitivity score is: ; where, represents the value of the comprehensive sensitivity score, which reflects the sensitivity of the region to disasters. The larger the value, the greater the possible impact on the region when suffering from disasters; represents the vulnerability index of the -th type of infrastructure, which is used to measure the vulnerability of different types of infrastructure (such as roads, bridges, water conservancy facilities, etc.) in disasters; represents the repair priority weight of the -th type of facility, which reflects the importance order of various facilities in post-disaster repair; represents the regional basic disaster resistance ability constant, which reflects the basic ability of the region to resist disasters itself; represents the tensor Kronecker product, which is used to calculate the combination relationship between different tensors; represents the total number of infrastructure classifications, that is, the number of infrastructure types participating in the assessment.

[0041] The sensitivity score of disaster impact and the energy density level are non-linearly mapped through the hyperbolic tangent function to generate region-specific warning thresholds. According to this threshold, the activation conditions of the multi-level emergency response protocol are triggered. For example, when the warning threshold of a certain region reaches a certain level, the corresponding level of emergency response is automatically activated, such as deploying more rescue supplies and organizing more emergency teams to go to that region.

[0042] In this way, the adaptive warning threshold model can comprehensively consider various factors, generate warning thresholds that are more in line with the actual situation, and improve the accuracy of agricultural disaster warnings and the pertinence of emergency responses. For example, in a region with a dense population and high vulnerability of infrastructure, even if the disaster energy density level is not particularly high, due to its relatively high sensitivity score of disaster impact, a relatively low warning threshold will be set to activate the emergency response in a timely manner and reduce disaster losses.

[0043] Example 3. This example mainly elaborates on the construction steps of the disaster chain propagation model. Specifically, it includes: Collect the associated data of secondary disasters in historical disaster events to construct a disaster causal graph dataset. This requires extensive collection of various agricultural disaster events that have occurred in history, and detailed recording of the relationship between each disaster and the secondary disasters it triggers, including the time, location, type of the disaster, and their causal connections. For example, a heavy rain may trigger a flood, and the flood may in turn cause a debris flow. All these information are recorded to form a disaster causal graph dataset.

[0044] Extract the transfer probability and delay time parameters between disaster events through the causal discovery algorithm. The causal discovery algorithm can mine the causal relationships between different disasters from a large amount of disaster data, and determine the transfer probability and delay time of disaster occurrence. The transfer probability represents the likelihood of one disaster triggering another disaster, and the delay time represents the time interval from the occurrence of one disaster to the triggering of a secondary disaster. For example, through the analysis of historical data, it is found that in a certain area, the transfer probability of a flood triggering a debris flow is 0.6, and the average delay time is 2 - 3 days.

[0045] Combine the complex network theory to construct a directed acyclic graph of disaster propagation, and quantify the vulnerability dependence strength between nodes. The complex network theory can regard disaster events as nodes in a network, and the causal relationships between them as edges to construct a directed acyclic graph. In this graph, each node represents a disaster, and the direction of the edge represents the propagation direction of the disaster. By calculating the connection relationship and mutual influence degree between nodes, the vulnerability dependence strength between nodes is quantified. For example, if a certain node (disaster) has a close connection with multiple other nodes and plays a key role in the propagation process, then its vulnerability dependence strength is relatively high.

[0046] Input the dependence intensity and real-time meteorological disturbance factors into the spatio-temporal transformer network to generate a disaster chain propagation model. The spatio-temporal transformer network can process spatio-temporal sequence data. It can combine the vulnerability dependence intensity between nodes and real-time meteorological disturbance factors (such as current precipitation intensity, wind speed change, etc.) to more accurately simulate the disaster propagation process. The generated disaster chain propagation model can predict the cascade effect of secondary disaster events within a preset future time window, providing a basis for formulating response strategies in advance.

[0047] For example, when predicting typhoon disasters, the disaster chain propagation model can, based on historical data and real-time meteorological information, predict the occurrence probability and time sequence of a series of secondary disasters such as heavy rain, floods, and landslides that may be triggered by the typhoon, helping relevant departments make early preparations for prevention and rescue and minimizing disaster losses to the greatest extent.

[0048] Example 4 is used to describe in detail the application of the simulation results of the disaster chain propagation model and the implementation of the blocking intervention strategy.

[0049] Based on the simulation results of the disaster chain propagation model, identify the key cascade interruption nodes. The key cascade interruption nodes refer to the nodes that, if intervened in during the disaster propagation process, can effectively prevent or slow down the further development of the disaster chain. For example, in the flood-debris flow disaster chain, a hillside area prone to landslides that trigger debris flows may be a key cascade interruption node.

[0050] Configure a blocking intervention strategy for this node in the emergency response instruction set. Develop corresponding intervention measures for different key cascade interruption nodes. For example, for a hillside area prone to landslides, measures such as strengthening the mountain body and setting up drainage facilities can be taken to reduce the possibility of landslides and thus block the occurrence of debris flows.

[0051] Based on the blocking intervention strategy, automatically generate a cross-departmental collaborative operation plan, including water conservancy facility regulation instructions and power network reconstruction plans. The water conservancy facility regulation instructions can adjust the water storage capacity of reservoirs, the flood discharge volume of rivers, etc. according to the disaster situation to reduce the pressure of floods on downstream areas. The power network reconstruction plan is to re-plan the power lines in the case where the disaster may affect power supply to ensure power supply to important areas. For example, when floods may submerge some power facilities, adjust the power lines in a timely manner to deliver power to less affected areas and give priority to ensuring the power needs of rescue work and important livelihood facilities.

[0052] After configuring the blocking intervention strategy, the state transition probability of cascade interruption nodes is monitored in real time. Through technical means such as sensors and satellite remote sensing, relevant information of key nodes is continuously obtained, and its state transition probability is calculated, that is, the possibility of changing from the current stable state to the disaster-triggering state. If the transition probability exceeds the preset critical value, the quantum annealing optimization mechanism is triggered to re-plan the spatio-temporal coordination scheme 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 occurring in a certain key hillside area is monitored to exceed the preset value, the quantum annealing optimization mechanism is used to re-adjust the control scheme of water conservancy facilities and the reconstruction plan of the power grid to better cope with possible disasters.

[0053] In this way, the simulation results of the disaster chain propagation model can be fully utilized, effective blocking intervention strategies can be taken in a timely manner, and the ability to respond to agricultural disasters can be improved and the losses caused by disasters can be reduced through cross-departmental collaborative operations and dynamic optimization schemes.

[0054] Example 5, this example details the specific process of embedding differential constraint conditions into the graph attention network. Specifically, it includes: Perform Lyapunov stability analysis on the decoupled results of latent variables to screen physically realizable coupling modes. Lyapunov stability analysis is a method for judging the stability of dynamic systems. In the disaster feature decoupling model, by performing Lyapunov stability analysis on the results obtained from the decoupling of latent variables, it can be judged whether the coupling modes between different disaster factors are physically feasible. For example, some coupling modes may lead to disaster development trends that do not conform to the actual situation, and these unreasonable modes can be screened out through stability analysis, and only physically realizable coupling modes are retained.

[0055] Generate characteristic evolution trajectories that conform to dynamic constraints through Hamiltonian Monte Carlo sampling. Hamiltonian Monte Carlo sampling is a method for sampling in high-dimensional spaces. In this example, this method is used to generate the evolution trajectories of disaster characteristics under the conditions of satisfying the disaster evolution dynamic equation and differential constraint conditions. These trajectories reflect the possible development paths of disasters at different times and conditions, providing richer data for subsequent model training.

[0056] Regularize the edge weights of the graph attention network using trajectory data to ensure that the model output conforms to the physical laws of disaster propagation. The graph attention network represents the relationship between nodes through edge weights. Input the generated trajectory data into the graph attention network and perform regularization training on the edge weights. Regularization training can prevent the model from overfitting and make the setting of edge weights more in line with the physical laws of disaster propagation. For example, during the training process, adjust the edge weights according to the actual disaster propagation situation so that the model can more accurately reflect the interactions and influences between different disaster factors.

[0057] Through this series of steps, effectively embed the differential constraint conditions into the graph attention network, enabling the disaster feature decoupling model to better simulate the development process of disasters and improve the ability to predict and analyze disasters. For example, when analyzing the development of a drought, the model processed as above can more accurately predict the spread range of the drought and the degree of impact on crops, providing a basis for formulating reasonable drought resistance measures.

[0058] Example 6, in the execution link of the distributed reinforcement learning framework, first define the reward function for multi-agent collaborative decision-making. This function combines the dual objectives of disaster control rate and resource utilization efficiency, and the specific formula is: ; where the reward value comprehensively reflects the quality of multi-agent decision-making. The disaster diffusion suppression score , is a key indicator to measure the effect of decision-making in controlling disaster diffusion. The higher its value, the stronger the inhibitory effect on disaster diffusion. For example, when dealing with a drought, if the agent's decision effectively curbs the expansion of the drought-affected area, this score will increase accordingly. The resource utilization efficiency score reflects the high efficiency of resource use. The higher the score, the more reasonable the resource utilization. And the dynamic balance factor can be flexibly adjusted according to different agricultural disaster scenarios and actual needs to balance the weights of the two goals of disaster control and resource utilization in the reward function. For example, in the face of sudden and highly harmful disasters, the value can be appropriately increased to focus on the disaster control effect; if the disaster develops relatively slowly, then can be adjusted to pay more attention to resource use efficiency.

[0059] The strategy network of each regional emergency agent is trained using a hierarchical curriculum learning strategy. This strategy advances the learning process in layers, starting from simple tasks and gradually increasing the difficulty. Taking flood disaster response as an example, initially, the agent is made to learn basic flood monitoring and warning strategies, getting familiar with how to obtain flood-related data and judge the initial development trend of floods. As the training progresses, more complex scenarios are introduced, such as considering the interaction between floods and the surrounding geological environment, the impact of floods on different types of agricultural facilities, etc., enabling the agent to continuously optimize its strategy network and improve its decision-making ability when dealing with complex flood disaster scenarios.

[0060] In each round of training, the credit assignment weights among agents are dynamically adjusted according to the degree of goal conflict. In actual agricultural disaster response, there are often conflicts between the goals of disaster control and resource utilization efficiency. For example, when controlling pest and disease disasters, in order to quickly control the spread of pests and diseases, a large amount of pesticides may need to be sprayed, but this will lead to excessive consumption of pesticide resources and reduce resource utilization efficiency. At this time, the credit assignment weights among agents need to be dynamically adjusted according to the actual degree of this conflict. Higher credit weights are given to those agents that can find a better balance between the two, motivating all agents to make more reasonable decisions.

[0061] Finally, a disaster response action sequence that satisfies the Nash equilibrium condition is output. In the state of Nash equilibrium, the strategy of each agent is the optimal choice given the strategies of other agents. Through continuous training of the distributed reinforcement learning framework, each agent continuously adjusts its decision-making until the output disaster response action sequence reaches the Nash equilibrium. This means that the decisions of each agent cooperate with each other to achieve the best response effect to agricultural disasters as a whole.

[0062] Regarding the quantification of the resource utilization efficiency goal, first, a spatio-temporal utility decay model for multiple types of rescue resources is established, and a resource idle penalty function is defined. The calculation formula of the resource utility decay model is: ; where the resource utilization efficiency score reflects the actual utilization efficiency of the resources. The initial utility value of the th type of resource , represents the magnitude of the role played by this resource in disaster response at the initial stage of its use. For example, in the face of drought disasters, the initial utility value of irrigation water is relatively high because it can directly relieve the water shortage of crops. The time decay coefficient is used to measure the decay rate of resource utility over time. Different types of resources have different time decay coefficients. For some perishable relief supplies, the time decay coefficient is relatively large. is the resource activation status indicator function at time At the moment If the class of resources is enabled, the function value is 1; otherwise, it is 0. The total number of resource types is represented by Through this model, the actual utility of different types of resources at different times can be accurately quantified. Combining with the resource idle penalty function, it can prompt the intelligent agent to reasonably plan the use of resources, avoid idle waste of resources, and improve the overall resource utilization efficiency.

[0063] It should be noted that in this article, relational terms such as first and second are only used 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 term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0064] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present 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 in 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 time-space aligned and feature fused to generate a disaster potential energy field distribution map and a disaster category probability matrix; According to 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, wherein the instruction set includes a material distribution topology network and a dynamic grouping strategy of an emergency team; Based on a preset disaster chain propagation model, simulate the cascading effects of disaster secondary events within a preset time window in the future, and optimize the collaborative decision-making parameters in the emergency response instruction set; 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.

2. The agricultural disaster early warning and emergency response method according to claim 1, characterized in that: The steps of constructing the disaster characteristic decoupling model include: Collect disaster event case libraries across years and construct a multi-dimensional feature training set that includes meteorological anomaly patterns, surface deformation data, and disaster triggering conditions; Decoupling latent variables of the multi-dimensional feature training set by using 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 nonlinear coupling relationships 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.

3. The agricultural disaster early warning and emergency response method according to claim 1, characterized in that: The adaptive early warning threshold model includes: Dynamically classifying disaster energy density levels according to the gradient change of the disaster potential energy field distribution map; Calculate the disaster impact sensitivity score based on regional population density and infrastructure vulnerability index; The disaster impact sensitivity score and the energy density level are nonlinearly mapped 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.

4. 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; Combined with complex network theory, a directed acyclic graph of disaster propagation is constructed to quantify the vulnerability dependence intensity 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.

5. The agricultural disaster early warning and emergency response method according to claim 4, characterized in that: Also includes: According to the simulation results of the disaster chain propagation model, identifying key cascading interruption nodes; 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.

6. The agricultural disaster early warning and emergency response method according to claim 3, characterized in that: The calculation of the disaster impact sensitivity score includes: Obtain real-time traffic network connectivity matrix and medical resource coverage heat map, and build a regional disaster resilience assessment cube; Calculate the high-order correlation weights between multi-dimensional features through hypergraph neural network; Performing a tensor contraction operation on the association weight and the evaluation cube to obtain a comprehensive sensitivity score; Among them, the calculation formula of the comprehensive sensitivity score is: ； In the formula, Represents the comprehensive sensitivity score value, Indicates Vulnerability index of infrastructure, Indicates The repair priority weight of the type of facility, represents the regional basic disaster resistance constant, represents the tensor Kronecker product, Represents the total number of infrastructure categories.

7. The agricultural disaster early warning and emergency response method according to claim 2, characterized in that: The embedding of the differential constraint condition includes: Performing Lyapunov stability analysis on the hidden variable decoupling results to screen physically achievable coupling modes; Generate characteristic evolution trajectories that meet dynamic constraints through Hamiltonian Monte Carlo sampling; The 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.

8. 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 the Nash equilibrium condition; Wherein, the reward function is: ； In the formula, Represents the reward value, represents the disaster spread suppression score, represents the resource utilization efficiency score, It is a dynamic balance factor.

9. The agricultural disaster early warning and emergency response method according to claim 5, characterized in that: Also includes: After configuring the blocking intervention strategy, monitoring the state transition probability of the cascading interruption node in real time; If the transfer probability exceeds the preset critical value, the quantum annealing optimization mechanism will be triggered to re-plan the space-time coordination plan for cross-departmental operations.

10. The agricultural disaster early warning and emergency response method according to claim 8, characterized in that: It also includes the quantification of resource utilization efficiency targets, and the quantification steps include: Establish a spatiotemporal utility decay model for multiple types of rescue resources and define a resource idle 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 a reward function; The calculation formula of the spatiotemporal utility attenuation model is: ； In the formula, represents the initial utility value of the resource type, represents the time attenuation coefficient, express Resource activation status indication function at the moment, Indicates the total number of resource types.

Images