A method for constructing a large safety emergency model based on artificial intelligence
Through artificial intelligence technology, combined with multimodal data and meta-learning, the decision-making lag problem of natural disaster emergency models in complex and diverse scenarios is solved, rapid adaptation and efficient emergency response are achieved, and the flexibility and accuracy of disaster response are improved. sex.
Patent Information
- Application Number
- CN202411304651.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-19
- Publication Date
- 2024-12-13
- Estimated Expiration
- 2044-09-19
AI Technical Summary
Existing natural disaster emergency models are difficult to adapt quickly and make decisions when facing complex and diverse disaster scenarios, especially when data is incomplete or disasters are large, resulting in decision-making lag and inefficient.
Adopt the security emergency model construction method based on artificial intelligence, including multimodal data acquisition and preprocessing, initial emergency decision training of reinforcement learning models, dynamic optimization of emergency resource of graph neural networks, rapid adaptation training of meta-learning and federated learning. Cross-regional collaboration, combining convolutional neural networks to perform feature extraction of satellite images and social media data, optimize resource scheduling and evacuation routes through reinforcement learning and graph neural networks, and use meta-learning and federated learning to improve the adaptability of models in new disaster scenarios. .
It realizes rapid adjustment of decisions when facing new disasters, reduces model retraining time, improves the flexibility and accuracy of emergency response, can effectively handle the diversity and complexity of disasters, and share emergency experience among different regions to ensure Data privacy.
Smart Images
Figure CN118822814B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of safety management, and in particular to a method for constructing a large safety emergency model based on artificial intelligence. Background Art
[0002] Currently, common natural disaster emergency models rely on static rules or predefined decision trees for emergency response. However, static rules cannot cope with the complex changes between different disasters and the dynamic development of emergencies. The rule set can only cover known situations. For some emergencies or complex environments, traditional rule sets are prone to decision-making errors or delays.
[0003] To address these challenges, some emergency systems have begun to introduce reinforcement learning, allowing models to conduct trial and error learning in disaster simulations and gradually optimize decision-making strategies. Reinforcement learning can learn the optimal strategy through interaction with the environment. Some emergency systems have also introduced meta-learning for rapid adaptation. However, although meta-learning can increase the speed at which the model adapts to new disaster scenarios, its generalization ability depends on the similarity of the model between different disaster types. If the new disaster is significantly different from previous disasters in dynamics and structure, the meta-learning model may still not be able to adapt effectively. Meta-learning relies on part of historical data to complete adaptation to the new environment. When data is incomplete or of poor quality, its effectiveness is limited.
[0004] Disaster emergency scenarios require instant decision-making, but reinforcement learning has a long convergence time. The model needs to learn repeatedly in a simulated environment to find the best strategy. It cannot adapt to new environments quickly and performs poorly when faced with new natural disasters or unprecedented scenarios. Although meta-learning improves the adaptability of the model in new scenarios, it is difficult to solve the problem of large differences between disasters. Therefore, there is an urgent need for a method to build a large safety emergency model based on artificial intelligence to solve such problems. Summary of the invention
[0005] In view of the above existing problems, the present invention is proposed.
[0006] Therefore, the present invention provides a method for constructing a large safety emergency model based on artificial intelligence to solve the problem that reinforcement learning requires a lot of time for model optimization, cannot quickly adjust itself in an emergency, and has delayed decision-making. Although meta-learning improves the adaptability of the model in new scenarios, it is difficult to solve the problem of large differences between disasters.
[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:
[0008] The present invention provides a method for constructing a large safety emergency model based on artificial intelligence, which comprises:
[0009] Step S1, multimodal data acquisition and preprocessing,
[0010] Collect multimodal data, clean, denoise and uniformly format the multimodal data;
[0011] Step S2, initial emergency decision training of the reinforcement learning model,
[0012] Based on the multimodal data output in step S1, a reinforcement learning model RL is constructed for preliminary training of emergency decision-making. This model simulates emergency resource scheduling and evacuation route optimization tasks according to different disaster scenarios;
[0013] Step S3: Dynamic optimization of emergency resources based on graph neural network.
[0014] In order to solve the resource allocation problem in complex disaster scenarios, based on the preliminary emergency strategy in step S2, a graph neural network GNN model is constructed, and disaster emergency resources (such as hospitals, rescue teams, supplies, etc.) are modeled as nodes in the graph, and the routes between the disaster site and the rescue point are used as edges of the graph;
[0015] Step S4: Meta-learning training to quickly adapt to new disaster scenarios.
[0016] Introducing meta-learning MAML. In order to improve the adaptability of the reinforcement learning model in new disaster scenarios, the Model Agnostic Meta Learning (MAML) meta-learning algorithm is used to optimize the initial parameters of the reinforcement learning model. Various natural disaster scenarios (such as earthquakes, fires, floods, etc.) are defined as meta-tasks, and the training model is used to extract commonalities from different disaster scenarios.
[0017] The reinforcement learning model optimized based on meta-learning has the ability to quickly adapt to new disaster scenarios.
[0018] Based on the existing disaster response experience, the reinforcement learning model can quickly adapt to unseen disaster scenarios through a small amount of new disaster data. Through MAML, the model can quickly adjust its strategy in different scenarios and reduce the training time in completely new scenarios. When an unseen type of disaster occurs, the model can quickly converge and adapt to the scenario through a small amount of new data combined with existing meta-learning experience, optimize the decision-making process, and more effectively deal with the diversity and complexity of disasters.
[0019] Step S5: Cross-regional emergency coordination under federated learning.
[0020] Deploy reinforcement learning models in different regions. Models in different regions share model parameters (rather than raw data) through federated learning to conduct cross-regional emergency coordination and strategy optimization.
[0021] The reinforcement learning models of each region are updated based on local disaster data, and the updated parameters are uploaded to the federated server. The federated server performs weighted fusion on the model parameters of each region to generate a global model. This global model, combined with the rapid adaptability of meta-learning, will be able to quickly respond to new disaster scenarios in other regions; the models of each region share emergency experience through federated learning to improve the emergency response capabilities to similar disasters. Through federated learning, models in different regions can share each other's disaster response experience without directly exchanging data, ensuring data privacy while improving the overall adaptability of the model.
[0022] Furthermore, multimodal data, including historical disaster data, field sensor data, satellite image data, and social media data;
[0023] Convolutional neural network (CNN) is used to extract features from unstructured data in satellite images and social media.
[0024] Furthermore, the CNN feature extraction process in satellite image processing in step S1 is:
[0025] Assume the input satellite image size is ,in Indicates the height of the image. Indicates the width of the image. Indicates the number of channels of the image, and defines the input image as a matrix : ,in, Represents the input satellite image matrix;
[0026] The spatial features of the image are extracted through the convolution kernel. Let the convolution kernel be , the size is , and the convolution kernel performs a two-dimensional convolution operation on the input image. The convolution calculation formula is:
[0027] ,in, Indicates After the convolution layer, at position The output feature map of Indicates The convolution kernel of the layer is at position The weights for different channels To weight, Indicates the position of the input image in the convolution window ,aisle The pixel value of Represents the height and width of the convolution kernel, and represents the size of the sliding window in the image. Represents the number of input channels, that is, the convolution kernel is multiplied and summed with each channel of the input image, and the information of the local area is weighted summed through the sliding window to extract the spatial features of the image;
[0028] The result after convolution introduces nonlinearity: ,in, Indicates The activation output after the layer convolution, ReLU represents the ReLU activation function, which sets negative values to 0 and keeps positive values unchanged, suppressing negative values in the convolution result and improving the nonlinear expression ability of the model.
[0029] To perform pooling operation: ,in,
[0030] Represents the position after pooling The output feature map of represents the maximum value operation within the pooling region, Indicates the height and width of the pooling window, controls the size of the pooling area, reduces the spatial dimension, and retains the main features;
[0031] After multiple convolutional layers and pooling layers, a high-dimensional feature map is output, and the feature map is flattened into a vector to obtain a feature vector , expressed as: ,in, Indicates The output feature map after layer pooling, the Flatten operation expands the multi-dimensional feature map into a one-dimensional vector.
[0032] Furthermore, the CNN feature extraction process in social media text processing in step S1 is:
[0033] Represent the text in social media as a word vector matrix, assuming that there are words, each word is mapped to Dimension word vector, define the input matrix for: ,in, Represents the input text word vector matrix, Indicates the number of words in the text, Represents the dimension of the word vector;
[0034] Text word vector matrix Perform one-dimensional convolution, the size of the convolution kernel is , and slide on the word dimension, the convolution calculation formula is: ,in, Indicates After the convolution layer, at position The output features of Indicates The weight matrix of the convolution kernel of the layer, Represents the first word in the text word vector matrix The word dimensional word vector value, Represents the height of the convolution kernel, that is, the number of words covered by the window. This process extracts the information features of the local word sequence in the text;
[0035] The result after convolution is processed by the ReLU activation function: , where the ReLU activation function introduces nonlinearity to the result after convolution, suppresses negative values and improves the expressiveness of the model;
[0036] Pooling operation is performed on the convolution output of the text to compress the feature representation: ,in, represents the features after pooling, Indicates the size of the pooling window. The pooling operation helps the model capture the most important features and reduce the feature dimension.
[0037] Finally, the pooled text features are expanded into feature vectors, expressed as: , represents the extracted text feature vector, Indicates Output features after layer pooling;
[0038] Through CNN convolution, pooling, activation, and Flatten operations, the unstructured data in satellite images and social media texts are converted into structured high-dimensional feature vectors as input for subsequent models.
[0039] Furthermore, the training mechanism of the reinforcement learning model is:
[0040] By simulating various natural disaster scenarios (such as earthquakes, floods, fires, etc.), the model gradually optimizes the emergency decision-making strategy by continuously interactively learning the states, actions, and reward mechanisms in the disaster scenarios. In this process, the model seeks the optimal resource allocation and action strategies for dealing with different disasters, gradually converges to better decisions, and outputs the preliminary emergency strategy of the reinforcement learning model.
[0041] Furthermore, the reinforcement learning model RL in step S2 is constructed as follows:
[0042] Construct state space based on multimodal data and define state space , set the state For the moment Description of the environmental status, ,in, Represents satellite image at time The characteristic vector of Indicates the field sensor at time Disaster data provided, Represents social media data at the time The eigenvector of
[0043] Defining the action space , that is, the emergency response measures that the model can take, action Including resource dispatch and rescue route selection, ,in, Indicates at time Dispatching resource allocation (such as medical supplies, rescue teams), Indicates at time Optimal selection of evacuation routes;
[0044] Defining the reward function Feedback on the quality of emergency decision-making, assuming that resource scheduling efficiency and evacuation success rate are and , the reward function is defined as:
[0045] ,in, Indicates that according to the status and resource scheduling decisions The efficiency of resource allocation, Indicates that according to the status and evacuation route decisions The success rate of evacuation Represents the weight parameter of the reward function;
[0046] Defining policies Mapping from state space to action space, strategy Indicates in status Take action The probability of using a deep neural network to represent the strategy , whose parameters are :
[0047] ,in, The neural network representing the policy is parameterized by , Indicates in status Take action The probability distribution of .
[0048] Furthermore, the reinforcement learning model RL construction method in step S2 also includes:
[0049] Training reinforcement learning models, using simulated disaster scenarios, and using them to gradually optimize emergency decision-making through interaction;
[0050] Maximize future cumulative rewards, discount cumulative rewards Defined as:
[0051] ,in, Indicates from time Start accumulating total rewards in the future, Represents the discount factor, which takes a value of , express The step size for accumulating the reward for each future time step;
[0052] Define the policy gradient algorithm loss function Negative expected reward:
[0053] ,in, Represents the loss function, and the optimization goal is to minimize the loss function. Representation strategy The expectation of cumulative rewards under
[0054] During the training process, the parameters of the strategy are updated through gradient descent :
[0055] ,in, Represents the learning rate, which controls the step size of parameter updates. , represents the cumulative reward with respect to the parameter gradient.
[0056] Furthermore, in step S2, a reinforcement learning model is used to simulate disaster scenarios, including:
[0057] In earthquake scenarios, the status Contains vibration data from the sensor , satellite imagery of collapsed buildings and social media crowd density , the model selects and dispatches resources to different rescue locations and the best evacuation route ;
[0058] In the fire scene, the status Contains on-site temperature and smoke concentration sensor data and satellite images of the fire spread , the model dispatches firefighting resources according to the progress of the fire and planning evacuation routes ;
[0059] In flood scenarios, sensors provide water level rise data and the extent of waterlogging in satellite images , dispatching relief supplies under time constraints And output evacuation plan ;
[0060] Through multiple iterations, the reinforcement learning model is updated based on feedback from real-time scenarios, and the strategy The optimization goal is to maximize the cumulative reward by continuously adjusting resource scheduling and evacuation route decisions;
[0061] After each emergency task is completed, the cumulative reward is calculated , and update the policy parameters ;
[0062] According to the efficiency of dispatching resources in different disaster scenarios, the model is adjusted based on the reward function feedback strategies to ensure that resources reach disaster-stricken areas quickly.
[0063] Constantly simulate the success rate of different evacuation routes and adjust choice to ensure the safety and speed of evacuation of people.
[0064] Furthermore, in step S3, the neural network GNN model is constructed as follows:
[0065] Emergency resources and key facilities (such as hospitals, rescue teams, and material points) are modeled as nodes in the graph, and roads and communication lines in different areas are modeled as edges, forming a dynamic network structure. Each node represents a target area, and the edge represents the connectivity between areas.
[0066] Assign features to each node and edge through multimodal data. The features of nodes include the availability and demand of resources, and the features of edges include the distance and time of resource transportation.
[0067] GNN exchanges and updates information between each node and its neighbor nodes by propagating information in the graph structure. After multiple rounds of propagation, each node accumulates status information from other nodes. Based on the current disaster dynamics and resource allocation status, GNN gradually optimizes the resource scheduling plan for the entire graph.
[0068] GNN can effectively integrate the resource network of the entire disaster scenario, helping the model to optimize resource scheduling from a global perspective and avoid local optimality. As the disaster progresses, GNN can adjust emergency plans in a timely manner through the information dissemination mechanism to improve the real-time and accuracy of emergency resource scheduling. GNN further improves the effectiveness of decision-making by providing better resource scheduling input for the reinforcement learning model.
[0069] Furthermore, GNN uses its powerful graph structure processing capabilities to optimize the dynamic allocation of resources. As the disaster progresses, GNN generates a dynamic resource allocation plan based on real-time changes at the disaster site and adjusts the allocation of cross-regional resources. The dynamic resource allocation plan generated by GNN serves as the input of the reinforcement learning model, and the RL model re-optimizes the strategy based on the changed resource situation. The output of the GNN model not only optimizes resource allocation, but also serves as feedback for the reinforcement learning model. Combined with the multimodal data at the disaster site, the reinforcement learning model is further trained to enable it to make better emergency decisions in different disaster scenarios. The resource allocation strategy optimized by GNN is used as input to retrain the reinforcement learning model, thereby improving the real-time emergency response of the model.
[0070] The beneficial effects of the present invention are:
[0071] The present invention extracts commonalities from a variety of disaster scenarios and only requires a small amount of data to quickly adjust decisions when faced with a new disaster. It effectively solves the problem that existing models perform poorly when faced with new natural disasters or unprecedented scenarios, reduces the time required for model retraining, and makes emergency response more flexible.
[0072] The present invention introduces multimodal data processing and uses convolutional neural network (CNN) to perform deep feature extraction on satellite images, sensor data, and social media information, so that the emergency model can more comprehensively and accurately obtain and process information from multiple data sources, greatly improving the model's ability to perceive disaster conditions.
[0073] The present invention, the reinforcement learning model RL, by combining with the graph neural network GNN, can better handle the resource scheduling problem in the complex network structure and dynamically optimize the resource allocation and evacuation routes in real time.
[0074] The present invention realizes the collaborative work of emergency response models in various regions through federated learning, and the models in various regions can share emergency response experience and optimization parameters.
[0075] The present invention, through the combination of meta-learning and federated learning, enables the model to extract the commonalities of different disaster scenarios while utilizing data and experience from different regions, further reducing the adaptation difficulties caused by differences between disasters, and enabling the model to cope with disaster scenarios with large differences, such as simultaneously handling different types of disaster tasks such as earthquakes, fires and floods. BRIEF DESCRIPTION OF THE DRAWINGS
[0076] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0077] Figure 1 The figure is a flow chart of the method for constructing a large safety emergency model of the present invention. DETAILED DESCRIPTION
[0078] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.
[0079] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.
[0080] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.
[0081] Example 1, reference Figure 1 , this embodiment provides a method for constructing a large safety emergency model based on artificial intelligence, comprising the following steps:
[0082] Step S1, multimodal data acquisition and preprocessing,
[0083] Collect multimodal data, clean, denoise and uniformly format the multimodal data;
[0084] Multimodal data, including historical disaster data, field sensor data, satellite imagery data, and social media data;
[0085] Use convolutional neural networks (CNNs) to extract features from unstructured data in satellite images and social media;
[0086] The CNN feature extraction process in satellite image processing in step S1 is:
[0087] Assume the input satellite image size is ,in Indicates the height of the image. Indicates the width of the image. Indicates the number of channels of the image, and defines the input image as a matrix : ,in, Represents the input satellite image matrix;
[0088] The spatial features of the image are extracted through the convolution kernel. Let the convolution kernel be , the size is , and the convolution kernel performs a two-dimensional convolution operation on the input image. The convolution calculation formula is:
[0089] ,in, Indicates After the convolution layer, at position The output feature map of Indicates The convolution kernel of the layer is at position The weights for different channels To weight, Indicates the position of the input image in the convolution window ,aisle The pixel value of Represents the height and width of the convolution kernel, and represents the size of the sliding window in the image. Represents the number of input channels, that is, the convolution kernel is multiplied and summed with each channel of the input image, and the information of the local area is weighted summed through the sliding window to extract the spatial features of the image;
[0090] The result after convolution introduces nonlinearity: ,in, Indicates The activation output after the layer convolution, ReLU represents the ReLU activation function, which sets negative values to 0 and keeps positive values unchanged, suppressing negative values in the convolution result and improving the nonlinear expression ability of the model.
[0091] To perform pooling operation: ,in,
[0092] Represents the position after pooling The output feature map of represents the maximum value operation within the pooling region, Indicates the height and width of the pooling window, controls the size of the pooling area, reduces the spatial dimension, and retains the main features;
[0093] After multiple convolutional layers and pooling layers, a high-dimensional feature map is output, and the feature map is flattened into a vector to obtain a feature vector , expressed as: ,in, Indicates The output feature map after layer pooling, Flatten operation expands the multi-dimensional feature map into a one-dimensional vector;
[0094] The CNN feature extraction process in social media text processing in step S1 is:
[0095] Represent the text in social media as a word vector matrix, assuming that there are words, each word is mapped to Dimension word vector, define the input matrix for: ,in, Represents the input text word vector matrix, Indicates the number of words in the text, Represents the dimension of the word vector;
[0096] Text word vector matrix Perform one-dimensional convolution, the size of the convolution kernel is , and slide on the word dimension, the convolution calculation formula is: ,in, Indicates After the convolution layer, at position The output features of Indicates The weight matrix of the convolution kernel of the layer, Represents the first word in the text word vector matrix The word dimensional word vector value, Represents the height of the convolution kernel, that is, the number of words covered by the window. This process extracts the information features of the local word sequence in the text;
[0097] The result after convolution is processed by the ReLU activation function: , where the ReLU activation function introduces nonlinearity to the result after convolution, suppresses negative values and improves the expressiveness of the model;
[0098] Pooling operation is performed on the convolution output of the text to compress the feature representation: ,in, represents the features after pooling, Indicates the size of the pooling window. The pooling operation helps the model capture the most important features and reduce the feature dimension.
[0099] Finally, the pooled text features are expanded into feature vectors, expressed as: , represents the extracted text feature vector, Indicates Output features after layer pooling;
[0100] Through CNN convolution, pooling, activation, and Flatten operations, the unstructured data in satellite images and social media texts are converted into structured high-dimensional feature vectors as input for subsequent models;
[0101] Specifically, multimodal data processing is used to enhance the ability to integrate information in complex environments. Through convolutional neural networks (CNN), key features are extracted from satellite images, sensor data, and social media texts, and unstructured data is converted into processable structured information to more accurately capture the real-time status and dynamic changes of disasters.
[0102] Step S2, initial emergency decision training of the reinforcement learning model,
[0103] Based on the multimodal data output in step S1, a reinforcement learning model RL is constructed for preliminary training of emergency decision-making. This model simulates emergency resource scheduling and evacuation route optimization tasks according to different disaster scenarios;
[0104] The training mechanism of the reinforcement learning model is:
[0105] Simulate various natural disaster scenarios (such as earthquakes, floods, fires, etc.). The model continuously interactively learns the state, action and reward mechanism in the disaster scenario, and gradually optimizes the emergency decision-making strategy. In this process, the model seeks the optimal resource allocation and action strategy to deal with different disasters, gradually converges to a better decision, and outputs the preliminary emergency strategy of the reinforcement learning model;
[0106] The reinforcement learning model RL in step S2 is constructed as follows:
[0107] Construct state space based on multimodal data and define state space , set the state For the moment Description of the environmental status, ,in, Represents satellite image at time The characteristic vector of Indicates the field sensor at time Disaster data provided, Represents social media data at the time The eigenvector of
[0108] Defining the action space , that is, the emergency response measures that the model can take, action Including resource dispatch and rescue route selection, ,in, Indicates at time Dispatching resource allocation (such as medical supplies, rescue teams), Indicates at time Optimal selection of evacuation routes;
[0109] Defining the reward function Feedback on the quality of emergency decision-making, assuming that resource scheduling efficiency and evacuation success rate are and , the reward function is defined as:
[0110] ,in, Indicates that according to the status and resource scheduling decisions The efficiency of resource allocation, Indicates that according to the status and evacuation route decisions The success rate of evacuation Represents the weight parameter of the reward function;
[0111] Defining policies Mapping from state space to action space, strategy Indicates in status Take action The probability of using a deep neural network to represent the strategy , whose parameters are :
[0112] ,in, The neural network representing the policy is parameterized by , Indicates in status Take action The probability distribution of
[0113] The reinforcement learning model RL construction method in step S2 also includes:
[0114] Training reinforcement learning models, using simulated disaster scenarios, and using them to gradually optimize emergency decision-making through interaction;
[0115] Maximize future cumulative rewards, discount cumulative rewards Defined as:
[0116] ,in, Indicates from time Start accumulating total rewards in the future, Represents the discount factor, which takes a value of , express The step size for accumulating the reward for each future time step;
[0117] Define the policy gradient algorithm loss function Negative expected reward:
[0118] ,in, Represents the loss function, and the optimization goal is to minimize the loss function. Representation strategy The expectation of cumulative rewards under
[0119] During the training process, the parameters of the strategy are updated through gradient descent :
[0120] ,in, Represents the learning rate, which controls the step size of parameter updates. , represents the cumulative reward with respect to the parameter The gradient of
[0121] Specifically, the initial emergency decision-making training based on the reinforcement learning model can autonomously learn the best resource scheduling and evacuation strategies in a variety of natural disaster simulation environments. The reinforcement learning model continuously interacts with the simulated disaster scenarios to optimize the resource allocation method and evacuation route planning. Through the mechanism of state-action rewards, the system can adjust emergency measures according to changes in disasters and achieve gradual optimization of disaster response plans. When faced with new scenarios, the model can find the optimal decision-making plan in a relatively short time, thereby shortening the lag time of emergency response and improving rescue efficiency.
[0122] In step S2, a reinforcement learning model is used to simulate disaster scenarios, including:
[0123] In earthquake scenarios, the status Contains vibration data from the sensor , satellite imagery of collapsed buildings and social media crowd density , the model selects and dispatches resources to different rescue locations and the best evacuation route ;
[0124] In the fire scene, the status Contains on-site temperature and smoke concentration sensor data and satellite images of the fire spread , the model dispatches firefighting resources according to the progress of the fire and planning evacuation routes ;
[0125] In flood scenarios, sensors provide water level rise data and the extent of waterlogging in satellite images , dispatching relief supplies under time constraints And output evacuation plan ;
[0126] Through multiple iterations, the reinforcement learning model is updated based on feedback from real-time scenarios, and the strategy The optimization goal is to maximize the cumulative reward by continuously adjusting resource scheduling and evacuation route decisions;
[0127] After each emergency task is completed, the cumulative reward is calculated , and update the policy parameters ;
[0128] According to the efficiency of dispatching resources in different disaster scenarios, the model is adjusted based on the reward function feedback strategies to ensure that resources reach disaster-stricken areas quickly.
[0129] Constantly simulate the success rate of different evacuation routes and adjust The choice of evacuation methods can ensure the safety and rapidity of evacuation.
[0130] Step S3: Dynamic optimization of emergency resources based on graph neural network.
[0131] In order to solve the resource allocation problem in complex disaster scenarios, based on the preliminary emergency strategy in step S2, a graph neural network GNN model is constructed, and disaster emergency resources (such as hospitals, rescue teams, supplies, etc.) are modeled as nodes in the graph, and the routes between the disaster site and the rescue point are used as edges of the graph;
[0132] In step S3, the neural network GNN model is constructed as follows:
[0133] Emergency resources and key facilities (such as hospitals, rescue teams, and material points) are modeled as nodes in the graph, and roads and communication lines in different areas are modeled as edges, forming a dynamic network structure. Each node represents a target area, and the edge represents the connectivity between areas.
[0134] Assign features to each node and edge through multimodal data. The features of nodes include the availability and demand of resources, and the features of edges include the distance and time of resource transportation.
[0135] GNN exchanges and updates information between each node and its neighbor nodes by propagating information in the graph structure. After multiple rounds of propagation, each node accumulates status information from other nodes. Based on the current disaster dynamics and resource allocation status, GNN gradually optimizes the resource scheduling plan for the entire graph.
[0136] GNN can effectively integrate the resource network of the entire disaster scenario, helping the model to optimize resource scheduling from a global perspective and avoid local optimality. As the disaster progresses, GNN can adjust the emergency plan in a timely manner through the information dissemination mechanism to improve the real-time and accuracy of emergency resource scheduling. GNN further improves the effectiveness of decision-making by providing better resource scheduling input for the reinforcement learning model.
[0137] GNN uses its powerful graph structure processing capabilities to optimize the dynamic allocation of resources. As the disaster progresses, GNN generates a dynamic resource allocation plan based on the real-time changes at the disaster site and adjusts the allocation of cross-regional resources. The dynamic resource allocation plan generated by GNN is used as the input of the reinforcement learning model, and the RL model re-optimizes the strategy based on the changed resource situation. The output of the GNN model not only optimizes resource allocation, but also serves as feedback for the reinforcement learning model. Combined with the multimodal data at the disaster site, the reinforcement learning model is further trained to enable it to make better emergency decisions in different disaster scenarios. The resource allocation strategy optimized by GNN is used as input to retrain the reinforcement learning model, which improves the real-time emergency response of the model.
[0138] Specifically, the graph neural network GNN further optimizes the dynamic scheduling of resources. Through information dissemination and multiple rounds of updates, it can capture the dependencies between nodes (such as hospitals, rescue teams, and material points) and edges (such as transportation routes and communication networks) in disaster scenarios in real time. It can dynamically adjust resource allocation as the disaster progresses, and can dispatch rescue forces and materials in real time according to the needs of different disaster stages, thereby improving the efficiency and accuracy of emergency response.
[0139] Step S4: Meta-learning training to quickly adapt to new disaster scenarios.
[0140] Introducing meta-learning MAML. In order to improve the adaptability of the reinforcement learning model in new disaster scenarios, the Model Agnostic Meta Learning (MAML) meta-learning algorithm is used to optimize the initial parameters of the reinforcement learning model. Various natural disaster scenarios (such as earthquakes, fires, floods, etc.) are defined as meta-tasks, and the training model is used to extract commonalities from different disaster scenarios.
[0141] The reinforcement learning model optimized based on meta-learning has the ability to quickly adapt to new disaster scenarios.
[0142] Based on the existing disaster response experience, the reinforcement learning model can quickly adapt to unseen disaster scenarios through a small amount of new disaster data. Through MAML, the model can quickly adjust its strategy in different scenarios and reduce the training time in completely new scenarios. When an unseen type of disaster occurs, the model can quickly converge and adapt to the scenario through a small amount of new data combined with existing meta-learning experience, optimize the decision-making process, and more effectively deal with the diversity and complexity of disasters.
[0143] In step S4, the initial parameters of the reinforcement learning model are optimized using the meta-learning algorithm as follows:
[0144] Each natural disaster (such as earthquake, fire, flood, etc.) is defined as a meta-task, and each meta-task includes a specific emergency decision-making problem:
[0145] Earthquake meta-tasks: resource allocation, evacuation route optimization,
[0146] Fire meta-tasks: dispatching rescue teams, configuring shelters,
[0147] Flood mission: material distribution, evacuation route design,
[0148] By training a reinforcement learning model on different meta-tasks, the model can extract common features or decision patterns from each meta-task:
[0149] The model is first trained on multiple different meta-tasks;
[0150] Through multiple rounds of training, we can find out the commonalities of emergency decision-making in different scenarios;
[0151] Specifically, meta-learning MAML greatly improves the adaptability of the model when facing new disaster scenarios. By extracting commonalities from multiple disaster scenarios, the meta-learning algorithm can help the reinforcement learning model converge quickly and reduce the training time for new scenarios. When an unseen natural disaster occurs, the model only needs a small amount of new data to adjust emergency decisions and respond quickly.
[0152] Step S5: Cross-regional emergency coordination under federated learning.
[0153] Deploy reinforcement learning models in different regions. Models in different regions share model parameters (rather than raw data) through federated learning to conduct cross-regional emergency coordination and strategy optimization.
[0154] The reinforcement learning models of each region are updated based on local disaster data, and the updated parameters are uploaded to the federated server. The federated server performs weighted fusion on the model parameters of each region to generate a global model. This global model, combined with the rapid adaptability of meta-learning, will be able to quickly respond to new disaster scenarios in other regions; the models of each region share emergency experience through federated learning to improve the emergency response capabilities to similar disasters. Through federated learning, models in different regions can share each other's disaster response experience without directly exchanging data, ensuring data privacy while improving the overall adaptability of the model.
[0155] Specifically, the introduction of federated learning enables cross-regional emergency coordination. Through federated learning, models in different regions can exchange parameters and experiences without sharing original data.
[0156] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.
Claims
1. A method for constructing a large safety emergency model based on artificial intelligence, characterized in that: include: Step S1, multimodal data collection and preprocessing, collecting multimodal data, cleaning, denoising and uniformly formatting the multimodal data; Step S2, initial emergency decision-making training of the reinforcement learning model. Based on the multimodal data output in step S1, a reinforcement learning model RL is constructed for preliminary training of emergency decision-making. This model simulates emergency resource scheduling and evacuation route optimization tasks according to different disaster scenarios. Step S3, dynamic optimization of emergency resources based on graph neural network, based on the preliminary emergency strategy of step S2, construct a graph neural network GNN model, model the disaster emergency resources as nodes in the graph, and the routes between the disaster site and the rescue point as the edges of the graph; Step S4: Meta-learning training that quickly adapts to new disaster scenarios, using a meta-learning algorithm to optimize the initial parameters of the reinforcement learning model, defining a variety of natural disaster scenarios as meta-tasks, and training the model to extract commonalities from different disaster scenarios; Step S5, cross-regional emergency coordination under federated learning, deploying reinforcement learning models in different regions, and the models in each region share model parameters through federated learning to perform cross-regional emergency coordination and strategy optimization; Multimodal data, including historical disaster data, field sensor data, satellite imagery data, and social media data; Use convolutional neural networks (CNNs) to extract features from unstructured data in satellite images and social media; The CNN feature extraction process in satellite image processing in step S1 is: Assume the input satellite image size is ,in Indicates the height of the image. Indicates the width of the image. Indicates the number of channels of the image, and defines the input image as a matrix : ,in, Represents the input satellite image matrix; The spatial features of the image are extracted through the convolution kernel. Let the convolution kernel be , the size is , and the convolution kernel performs a two-dimensional convolution operation on the input image. The convolution calculation formula is: ,in, Indicates After the convolution layer, at position The output feature map of Indicates The convolution kernel of the layer is at position The weights for different channels To weight, Indicates the position of the input image in the convolution window ,aisle The pixel value of Represents the height and width of the convolution kernel, and represents the size of the sliding window in the image. Represents the number of input channels, that is, the convolution kernel is multiplied by each channel of the input image and the sum is calculated; The result after convolution introduces nonlinearity: ,in, Indicates The activation output after the layer convolution, ReLU activation function, which sets negative values to 0 and keeps positive values unchanged; Perform pooling operation: ,in, Represents the position after pooling The output feature map of represents the maximum value operation within the pooling region, Indicates the height and width of the pooling window, controlling the size of the pooling area; After multiple convolutional layers and pooling layers, a high-dimensional feature map is output, and the feature map is flattened into a vector to obtain a feature vector , expressed as: ,in, Indicates The output feature map after layer pooling, Flatten operation expands the multi-dimensional feature map into a one-dimensional vector; The CNN feature extraction process in social media text processing in step S1 is: Represent the text in social media as a word vector matrix, assuming that there are words, each word is mapped to Dimension word vector, define the input matrix for: ,in, Represents the input text word vector matrix, Indicates the number of words in the text, Represents the dimension of the word vector; Text word vector matrix Perform one-dimensional convolution, the size of the convolution kernel is , and slide on the word dimension, the convolution calculation formula is: ,in, Indicates After the convolution layer, at position The output features of Indicates The weight matrix of the convolution kernel of the layer, Represents the first word in the text word vector matrix The word dimensional word vector value, Represents the height of the convolution kernel, that is, the number of words covered by the window; The result after convolution is processed by the ReLU activation function: ; Pooling operation is performed on the convolution output of the text to compress the feature representation: ,in, represents the features after pooling, Indicates the size of the pooling window; Finally, the pooled text features are expanded into feature vectors, expressed as: , represents the extracted text feature vector, Indicates Output features after layer pooling; The training mechanism of the reinforcement learning model is: Simulate various natural disaster scenarios. The model gradually optimizes the emergency decision-making strategy by continuously interactively learning the state, action and reward mechanism in the disaster scenario. In this process, the model seeks the optimal resource allocation and action strategy to deal with different disasters, gradually converges to a better decision, and outputs the preliminary emergency strategy of the reinforcement learning model. The reinforcement learning model RL in step S2 is constructed as follows: Construct state space based on multimodal data and define state space , set the state For the moment Description of the environmental status, ,in, Indicates the satellite image at time The characteristic vector of Indicates the field sensor at time Disaster data provided, Represents social media data at the time The eigenvector of Defining the action space , that is, the emergency response measures that the model can take, action Including resource dispatch and rescue route selection, ,in, Indicates at time Scheduling resource allocation, Indicates at time Optimal selection of evacuation routes; Defining the reward function Feedback on the quality of emergency decision-making, assuming that resource scheduling efficiency and evacuation success rate are and , the reward function is defined as: ,in, Indicates that according to the status and resource scheduling decisions The efficiency of resource allocation, Indicates that according to the status and evacuation route decisions The success rate of evacuation Represents the weight parameter of the reward function; Defining policies Mapping from state space to action space, strategy Indicates in status Take action The probability of using a deep neural network to represent the strategy , whose parameters are : ,in, The neural network representing the policy is parameterized by , Indicates in status Take action The probability distribution of The reinforcement learning model RL construction method in step S2 also includes: Training reinforcement learning models, using simulated disaster scenarios, and using them to gradually optimize emergency decision-making through interaction; Maximize future cumulative rewards, discount cumulative rewards Defined as: ,in, Indicates from time Start accumulating total rewards in the future, Represents the discount factor, which takes a value of , express The step size for accumulating the reward for each future time step; Define the policy gradient algorithm loss function Negative expected reward: ,in, Represents the loss function, and the optimization goal is to minimize the loss function. Representation strategy The expectation of cumulative rewards under During the training process, the parameters of the strategy are updated through gradient descent : ,in, Represents the learning rate, which controls the step size of parameter updates. , represents the cumulative reward with respect to the parameter The gradient of In step S2, a reinforcement learning model is used to simulate disaster scenarios, including: In earthquake scenarios, the status Contains vibration data from the sensor , satellite imagery of collapsed buildings and social media crowd density , the model selects and dispatches resources to different rescue locations and the best evacuation route ; In the fire scene, the status Contains on-site temperature and smoke concentration sensor data and satellite images of the fire spread , the model dispatches firefighting resources according to the progress of the fire and planning evacuation routes ; In flood scenarios, sensors provide water level rise data and the extent of waterlogging in satellite images , dispatching relief supplies under time constraints And output evacuation plan ; Through multiple iterations, the reinforcement learning model is updated based on feedback from real-time scenarios, and the strategy The optimization goal is to maximize the cumulative reward by continuously adjusting resource scheduling and evacuation route decisions; In step S3, the neural network GNN model is constructed as follows: Emergency resources and key facilities are modeled as nodes in the graph, and roads and communication lines in different regions are modeled as edges, forming a dynamic network structure, where each node represents a target area and the edges represent the connectivity between regions; Assign features to each node and edge through multimodal data. The features of nodes include the availability and demand of resources, and the features of edges include the distance and time of resource transportation. GNN exchanges and updates information between each node and its neighbor nodes by propagating information in the graph structure. After multiple rounds of propagation, each node accumulates status information from other nodes. Based on the current disaster dynamics and resource allocation status, GNN gradually optimizes the resource scheduling plan for the entire graph. GNN generates a dynamic resource allocation plan based on the real-time changes at the disaster site and adjusts the allocation of cross-regional resources. The dynamic resource allocation plan generated by GNN serves as the input of the reinforcement learning model.