Disaster response management method based on vulnerability and exposure of disaster-bearing body

Abstract

The invention relates to the technical field of disaster response management, in particular to a disaster response management method based on vulnerability and exposure of a disaster-bearing body. The technical problems are that a traditional disaster response management method is slow in response speed, low in precision and unreasonable in resource allocation, and most of existing disaster response management systems only pay attention to emergency response after a disaster occurs and neglect prevention and early warning before the disaster occurs and rapid and accurate assessment of disaster loss. According to the technical scheme, the disaster response management method based on the vulnerability and the exposure of the disaster-bearing body comprises the steps that dynamic risk assessment is conducted on the vulnerability and the exposure of the disaster-bearing body through an intelligent analysis model in combination with real-time monitoring data, and potential disaster risks are found in time through continuous monitoring and assessment; according to the invention, through integration of multi-source data and utilization of big data and artificial intelligence technologies, dynamic risk assessment and intelligent early warning of disaster-bearing bodies are realized, and the efficiency and accuracy of disaster response management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster response management, and in particular to a disaster response management method based on vulnerability and exposure of a disaster-bearing body. Background Art

[0002] With the frequent occurrence of natural disasters today, how to effectively respond to disasters and reduce losses has become a global challenge; traditional disaster management methods often rely on manual experience and historical data, but this method has many limitations, such as slow response speed, low accuracy, and unreasonable resource allocation; with the rapid development of technologies such as big data and artificial intelligence, new ideas and means are provided for disaster management; however, most existing disaster management systems only focus on emergency response after a disaster occurs, but ignore prevention and early warning before a disaster occurs, as well as rapid and accurate assessment of disaster losses; therefore, the present invention proposes a disaster management method that can comprehensively consider the vulnerability and exposure of the disaster-bearing body, so as to achieve full-process disaster management, including prevention, early warning, emergency response, and post-disaster recovery and reconstruction. Summary of the invention

[0003] In order to overcome the slow response speed, low accuracy and unreasonable resource allocation of traditional disaster response management methods, most existing disaster response management systems only focus on emergency response after a disaster occurs, but ignore the prevention and early warning before the disaster occurs, as well as the rapid and accurate assessment of disaster losses.

[0004] The technical solution of the present invention is: a disaster response management method based on the vulnerability and exposure of the disaster-bearing body, comprising the following steps:

[0005] S1: Use big data technology to integrate information from multiple data sources, including but not limited to satellite remote sensing data, ground monitoring station data, social media data, and historical disaster records;

[0006] S2: Clean the collected raw data, including deduplication and format unification, and use machine learning algorithms to extract features and reduce the dimension of the data;

[0007] S3: Based on the processed data, a deep learning algorithm is used to construct an intelligent analysis model of vulnerability and exposure of the hazard-bearing body;

[0008] S4: Through intelligent analysis models combined with real-time monitoring data, dynamic risk assessment of the vulnerability and exposure of disaster-prone objects is carried out, and potential disaster risks are discovered in a timely manner through continuous monitoring and assessment;

[0009] S5: Through the intelligent early warning system, when a disaster is detected, an early warning is triggered quickly and early warning information is sent to relevant departments and personnel;

[0010] S6: After a disaster occurs, use artificial intelligence technology to provide decision support for emergency response, including determining the allocation of rescue resources, developing evacuation routes, and assessing disaster losses;

[0011] S7: Use drones and remote sensing technology to quickly assess post-disaster losses, and combine artificial intelligence technology to conduct intelligent analysis of loss data;

[0012] S8: Based on the results of post-disaster loss assessment, use intelligent tools to plan post-disaster recovery and reconstruction, including determining reconstruction priorities, formulating reconstruction plans, and optimizing resource allocation.

[0013] Preferably, big data technology is used to integrate information from multiple data sources, including but not limited to satellite remote sensing data, ground monitoring station data, social media data, and historical disaster records; the specific steps are as follows:

[0014] S201: Acquire remote sensing images and data from different satellites, including information on land cover, topography, and meteorological conditions;

[0015] S202: Collect physical and chemical parameters from ground monitoring stations, including air temperature, humidity, wind speed, rainfall, and soil moisture;

[0016] S203: Collect records of disaster events that have occurred in the past, including disaster type, time of occurrence, location, impact range, and loss;

[0017] S204: converting data from different sources into a unified format and storing it in the big data platform;

[0018] S205: Establishing a mapping relationship between data according to the attributes and characteristics of the data;

[0019] S206: integrating data from different sources and creating an index for the integrated data;

[0020] Among them, in step S205, the specific steps of establishing the mapping relationship between data according to the attributes and characteristics of the data are as follows:

[0021] S251: Identify the source, format, content, unit and relationship between each type of data according to the field meaning, data type and data range of the data;

[0022] S252: Identify fields with the same or similar attributes in different data sets;

[0023] S253: Based on the result of attribute matching, mapping rules between data are formulated. The rules define how to convert field values ​​in one data set into field values ​​in another data set, and how to establish associations between different data sets;

[0024] S254: Compare and analyze the mapped data through visualization tools to verify the correctness and accuracy of the mapping relationship;

[0025] In step S206, the specific steps of fusing data from different sources and creating an index for the integrated data are as follows:

[0026] S261: merging data from different sources according to the mapping relationship, including merging data values ​​of the same attribute and associating data values ​​of different attributes;

[0027] S262: Cleaning the data merged in step S261, including removing duplicate data, processing missing data, and correcting erroneous data;

[0028] S263: Create an index for the data processed in step S262 according to the attributes, values ​​and structure of the data.

[0029] As a preferred method, the collected raw data is cleaned, including deduplication and format unification, and machine learning algorithms are used to extract features and reduce the dimension of the data. The specific steps are as follows:

[0030] S301: Loading the collected raw data from the big data platform;

[0031] S302: Use the unique identifier to detect and remove duplicate records. For data without a unique identifier, use the cosine similarity algorithm to detect duplicate items.

[0032] S303: Convert data in different formats into a unified format, such as using the mean, median, and mode to fill missing values, and using the Z-score method to detect outliers;

[0033] S304: Standardize the data by subtracting the mean value and dividing the data by the standard deviation;

[0034] S305: Calculate the covariance matrix based on the standardized data to capture the correlation between different variables;

[0035] S306: Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors;

[0036] S307: Select the first k principal components according to the size of the eigenvalues. The principal components are used to explain most of the variance in the original data. The original data is projected onto the selected principal components to obtain the data after dimensionality reduction.

[0037] S308: Save the cleaned and dimension-reduced data to the big data platform.

[0038] As a preferred method, based on the processed data, a deep learning algorithm is used to construct an intelligent analysis model of vulnerability and exposure of the hazard-prone body; the specific steps are as follows:

[0039] S401: Construct the network structure of the CNN model, including convolutional layer, pooling layer, and fully connected layer; the convolutional layer is used to extract image features, the pooling layer is used to reduce feature dimensions and reduce the amount of calculation, and the fully connected layer is used to integrate features and perform classification and regression tasks;

[0040] S402: Setting the size, step size, and filling method parameters of the convolution kernel, as well as the type of the pooling layer and the pooling window size parameters;

[0041] S403: labeling the cleaned and dimensionally reduced data, using the vulnerability and exposure levels of the disaster-prone objects as labels;

[0042] S404: Divide the labeled data into a training set, a validation set, and a test set;

[0043] S405: Use the training set data to train the CNN model, update the model parameters through the back propagation algorithm and the gradient descent optimization algorithm, and minimize the loss function;

[0044] S406: Evaluate the performance of the model on the test set, including accuracy, recall, and F1 score indicators;

[0045] S407: Optimize the model according to the evaluation results, including adjusting hyperparameters and increasing the number of network layers;

[0046] S408: Export the trained model into a deployable format.

[0047] As a preferred method, a dynamic risk assessment of the vulnerability and exposure of the disaster-prone body is conducted through intelligent analysis models combined with real-time monitoring data. Through continuous monitoring and assessment, potential disaster risks can be discovered in a timely manner. The specific steps are as follows:

[0048] S501: Acquire monitoring data from ground monitoring stations, satellite remote sensing systems, etc. in real time, including but not limited to meteorological conditions and surface conditions;

[0049] S502: Integrate the real-time monitoring data with the historical data and the static data to form an input data set;

[0050] S503: Load the CNN model that has been trained and optimized in step S3 from the model library;

[0051] S504: Using the convolution layer of the CNN model, extracting features from the input data. The extracted features include texture, shape, and color in the image, which are used to reflect the vulnerability and exposure of the disaster-prone body.

[0052] S505: Input the extracted features into the fully connected layer of the CNN model to perform classification and regression tasks;

[0053] S506: For classification tasks, the model will output the level or category of vulnerability and exposure of the hazard-prone body; for regression tasks, the model will output a continuous value indicating the vulnerability or exposure degree of the hazard-prone body;

[0054] S507: Analyze the results output by the model to determine whether the vulnerability and exposure of the disaster-prone body exceed the preset threshold; if the threshold is exceeded, trigger the early warning mechanism; if the threshold is not exceeded, continue monitoring;

[0055] S508: Update the monitoring data in real time and repeat the above steps to perform dynamic risk assessment.

[0056] As a preferred method, an intelligent early warning system is used to quickly trigger an early warning when a disaster is detected, and send early warning information to relevant departments and personnel; the specific steps are as follows:

[0057] S601: When the risk assessment results trigger the warning conditions, the intelligent warning system sends warning information to relevant departments and personnel. The warning information includes the disaster type, expected time and location, and possible impact range.

[0058] S602: Generate a risk assessment report, recording the results of each assessment, triggered warning information, and subsequent emergency response;

[0059] S603: Store the report in the big data platform.

[0060] As a preferred option, after a disaster occurs, artificial intelligence technology is used to provide decision support for emergency response, including determining the allocation of rescue resources, developing evacuation routes, and assessing disaster losses. UAVs and remote sensing technology are used to quickly assess post-disaster losses, and artificial intelligence technology is combined to conduct intelligent analysis of loss data. The specific steps are as follows:

[0061] S701: Confirmation information of the disaster, including the type of disaster, time of occurrence, and location;

[0062] S702: Collect real-time data from disaster sites through UAVs, remote sensing technology, and ground monitoring stations, and integrate historical disaster data, geospatial data, and population distribution data to form a disaster data set;

[0063] S703: Use artificial intelligence algorithms to analyze disaster data sets and assess the type and amount of rescue resource requirements;

[0064] S704: Formulate a rescue resource dispatch plan based on the resource demand assessment results;

[0065] S705: Analyze the population distribution and traffic conditions at the disaster site, determine the evacuation needs, and use artificial intelligence algorithms to predict the possible flow and number of evacuees;

[0066] S706: Formulate an evacuation route planning plan based on the evacuation demand analysis results;

[0067] S707: Use the intelligent monitoring system to track the flow of evacuees in real time, and provide evacuation guidance through the intelligent guidance system to ensure that the evacuees evacuate in an orderly manner according to the planned route;

[0068] S708: Use drones and remote sensing technology to collect loss data at disaster sites;

[0069] S709: Use artificial intelligence algorithms to analyze loss data sets to assess the type, extent and scope of disaster losses;

[0070] S710: Prepare disaster loss assessment report and record loss assessment results.

[0071] As a preferred method, artificial intelligence algorithms are used to analyze disaster data sets to assess the type and quantity of rescue resource requirements; the specific steps are as follows:

[0072] S801: Collect and integrate disaster data sets, including disaster type, occurrence time, location, impact range, population distribution, and historical rescue resource usage, and pre-process the data, including missing value filling, outlier detection and processing, and data standardization;

[0073] S802: Use the Gini coefficient feature selection method to select key features that affect the prediction of rescue resource demand from the disaster data set, and construct a feature vector as the input of the random forest algorithm;

[0074] S803: Divide the data set into a training set and a test set, and use the training set data to build a random forest model. During the construction process, random sampling and random feature selection are used to increase the diversity of the model;

[0075] S804: Set the number of decision trees, maximum depth, and minimum sample number parameters, and perform model tuning;

[0076] S805: Input the new disaster data set into the trained random forest model;

[0077] S806: The model predicts the type and quantity of rescue resource requirements based on the input features;

[0078] S807: Output prediction results, including the quantity and priority of various rescue resources;

[0079] Analyze the population distribution and traffic conditions at the disaster site, determine the evacuation needs, and use artificial intelligence algorithms to predict the possible flow and number of evacuees; the specific steps are as follows:

[0080] S901: Collect population distribution data, traffic conditions data and historical disaster evacuation data at the disaster site;

[0081] S902: According to the requirements of evacuation demand analysis, key features are extracted from the original data, feature vectors are constructed, and the features are encoded and converted as inputs of the deep learning model;

[0082] S903: Select a recurrent neural network model, and construct a neural network structure according to the dimension of the feature vector and the requirements of the model;

[0083] S904: Setting parameters of the recurrent neural network model, including learning rate, batch size, and number of iterations;

[0084] S905: Use historical disaster evacuation data as a training set to train the recurrent neural network model;

[0085] S906: Input new disaster site data into the trained recurrent neural network model;

[0086] S907: The model predicts the possible flow direction and number of evacuees based on the input features;

[0087] S908: Interpret the prediction results, analyze the rationality of the flow direction and number of evacuees, apply the prediction results to evacuation route planning, provide a basis for formulating evacuation plans, and adjust the allocation of rescue resources according to the prediction results.

[0088] Preferably, artificial intelligence algorithms are used to analyze the loss data set to assess the type, extent and scope of disaster losses; the specific steps are as follows:

[0089] S1001: Collect disaster loss data sets, including data on building damage, casualties, and economic losses;

[0090] S1002: Extract key features based on the characteristics of disaster losses, including disaster type, impact area, population distribution, and building type, and encode and transform the features;

[0091] S1003: Select the random forest algorithm for classification and regression tasks;

[0092] S1004: construct a machine learning model structure according to the dimension of the feature vector and the requirements of the model, and use the historical disaster loss data as a training set to train the machine learning model;

[0093] S1005: Input new disaster loss data into the trained machine learning model;

[0094] S1006: The model classifies and regresses disaster losses based on input features to obtain information on the type, extent and scope of the losses;

[0095] S1007: Explain and analyze the forecast results and make a quantitative assessment of the losses based on the actual situation.

[0096] As a preferred method, based on the post-disaster loss assessment results, intelligent tools are used to plan post-disaster recovery and reconstruction, including determining reconstruction priorities, formulating reconstruction plans, and optimizing resource allocation; the specific steps are as follows:

[0097] S1101: Collect post-disaster loss data, integrate the data, and form a complete post-disaster loss data set;

[0098] S1102: Use the random forest algorithm to analyze the post-disaster loss data set and determine the priority of reconstruction based on the degree of loss and impact range;

[0099] S1103: Develop a post-disaster reconstruction plan based on reconstruction priorities, including reconstruction goals, timetable, and key information on resource requirements;

[0100] S1104: Determine the type and quantity of resource requirements in each region based on post-disaster loss assessment data;

[0101] S1105: Use the spatial analysis function of the GIS system to visualize resource requirements;

[0102] S1106: Use the buffer analysis function of the GIS system to determine the coverage of resource supply;

[0103] S1107: Using the network analysis function of the GIS system, calculate the shortest path and optimal path from each resource point to the demand point, and determine the transportation path and transportation capacity of the resources;

[0104] S1108: Output resource allocation plan.

[0105] Beneficial effects of the present invention:

[0106] 1. Improve the efficiency and accuracy of disaster response management: By integrating multi-source data and using big data and artificial intelligence technologies, dynamic risk assessment and intelligent early warning of disaster-prone bodies can be achieved, thus improving the efficiency and accuracy of disaster response management;

[0107] 2. Optimize resource allocation: During the emergency response phase, use artificial intelligence technology to provide a scientific basis for the allocation of rescue resources, ensuring that resources can reach where they are most needed quickly and accurately;

[0108] 3. Improve the efficiency of post-disaster recovery and reconstruction: Use drones, remote sensing technology and other means to quickly collect post-disaster loss data, and combine artificial intelligence technology for intelligent analysis to provide scientific basis and planning solutions for post-disaster recovery and reconstruction;

[0109] 4. Enhance the scientificity and intelligence level of disaster response management: The disaster response management method of the present invention comprehensively uses advanced technologies such as big data and artificial intelligence to achieve scientific, intelligent and refined disaster response management throughout the process. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] Figure 1 What is shown is a schematic flow chart of the steps of the disaster response management method based on the vulnerability and exposure of the disaster-bearing body of the present invention;

[0111] Figure 2 What is presented is a schematic diagram of a dynamic risk assessment process of a disaster response management method based on vulnerability and exposure of a disaster-bearing body according to the present invention. DETAILED DESCRIPTION

[0112] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0113] See also Figure 1-2 The present invention provides an embodiment: a disaster response management method based on vulnerability and exposure of a disaster-bearing body, comprising the following steps:

[0114] S1: Use big data technology to integrate information from multiple data sources, including but not limited to satellite remote sensing data, ground monitoring station data, social media data, and historical disaster records;

[0115] S2: Clean the collected raw data, including deduplication and format unification, and use machine learning algorithms to extract features and reduce the dimension of the data;

[0116] S3: Based on the processed data, a deep learning algorithm is used to construct an intelligent analysis model of vulnerability and exposure of the hazard-bearing body;

[0117] S4: Through intelligent analysis models combined with real-time monitoring data, dynamic risk assessment of the vulnerability and exposure of disaster-prone objects is carried out, and potential disaster risks are discovered in a timely manner through continuous monitoring and assessment;

[0118] S5: Through the intelligent early warning system, when a disaster is detected, an early warning is triggered quickly and early warning information is sent to relevant departments and personnel;

[0119] S6: After a disaster occurs, use artificial intelligence technology to provide decision support for emergency response, including determining the allocation of rescue resources, developing evacuation routes, and assessing disaster losses;

[0120] S7: Use drones and remote sensing technology to quickly assess post-disaster losses, and combine artificial intelligence technology to conduct intelligent analysis of loss data;

[0121] S8: Based on the results of post-disaster loss assessment, use intelligent tools to plan post-disaster recovery and reconstruction, including determining reconstruction priorities, formulating reconstruction plans, and optimizing resource allocation.

[0122] Preferably, big data technology is used to integrate information from multiple data sources, including but not limited to satellite remote sensing data, ground monitoring station data, social media data, and historical disaster records; the specific steps are as follows:

[0123] S201: Acquire remote sensing images and data from different satellites, including information on land cover, topography, and meteorological conditions;

[0124] S202: Collect physical and chemical parameters from ground monitoring stations, including air temperature, humidity, wind speed, rainfall, and soil moisture;

[0125] S203: Collect records of disaster events that have occurred in the past, including disaster type, time of occurrence, location, impact range, and loss;

[0126] S204: converting data from different sources into a unified format and storing it in the big data platform;

[0127] S205: Establishing a mapping relationship between data according to the attributes and characteristics of the data;

[0128] S206: integrating data from different sources and creating an index for the integrated data;

[0129] Among them, in step S205, the specific steps of establishing the mapping relationship between data according to the attributes and characteristics of the data are as follows:

[0130] S251: Identify the source, format, content, unit and relationship between each type of data according to the field meaning, data type and data range of the data;

[0131] S252: Identify fields with the same or similar attributes in different data sets;

[0132] S253: Based on the result of attribute matching, mapping rules between data are formulated. The rules define how to convert field values ​​in one data set into field values ​​in another data set, and how to establish associations between different data sets;

[0133] S254: Compare and analyze the mapped data through visualization tools to verify the correctness and accuracy of the mapping relationship;

[0134] In step S206, the specific steps of fusing data from different sources and creating an index for the integrated data are as follows:

[0135] S261: merging data from different sources according to the mapping relationship, including merging data values ​​of the same attribute and associating data values ​​of different attributes;

[0136] S262: Cleaning the data merged in step S261, including removing duplicate data, processing missing data, and correcting erroneous data;

[0137] S263: Create an index for the data processed in step S262 according to the attributes, values ​​and structure of the data.

[0138] As a preferred method, the collected raw data is cleaned, including deduplication and format unification, and machine learning algorithms are used to extract features and reduce the dimension of the data. The specific steps are as follows:

[0139] S301: Loading the collected raw data from the big data platform;

[0140] S302: Use the unique identifier to detect and remove duplicate records. For data without a unique identifier, use the cosine similarity algorithm to detect duplicate items.

[0141] S303: Convert data in different formats into a unified format, such as using the mean, median, and mode to fill missing values, and using the Z-score method to detect outliers;

[0142] S304: Standardize the data by subtracting the mean value and dividing the data by the standard deviation;

[0143] S305: Calculate the covariance matrix based on the standardized data to capture the correlation between different variables;

[0144] S306: Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors;

[0145] S307: Select the first k principal components according to the size of the eigenvalues. The principal components are used to explain most of the variance in the original data. The original data is projected onto the selected principal components to obtain the data after dimensionality reduction.

[0146] S308: Save the cleaned and dimension-reduced data to the big data platform.

[0147] As a preferred method, based on the processed data, a deep learning algorithm is used to construct an intelligent analysis model of vulnerability and exposure of the hazard-prone body; the specific steps are as follows:

[0148] S401: Construct the network structure of the CNN model, including convolutional layer, pooling layer, and fully connected layer; the convolutional layer is used to extract image features, the pooling layer is used to reduce feature dimensions and reduce the amount of calculation, and the fully connected layer is used to integrate features and perform classification and regression tasks;

[0149] S402: Setting the size, step size, and filling method parameters of the convolution kernel, as well as the type of the pooling layer and the pooling window size parameters;

[0150] S403: labeling the cleaned and dimensionally reduced data, using the vulnerability and exposure levels of the disaster-prone objects as labels;

[0151] S404: Divide the labeled data into a training set, a validation set, and a test set;

[0152] S405: Use the training set data to train the CNN model, update the model parameters through the back propagation algorithm and the gradient descent optimization algorithm, and minimize the loss function;

[0153] S406: Evaluate the performance of the model on the test set, including accuracy, recall, and F1 score indicators;

[0154] S407: Optimize the model according to the evaluation results, including adjusting hyperparameters and increasing the number of network layers;

[0155] S408: Export the trained model into a deployable format.

[0156] As a preferred method, a dynamic risk assessment of the vulnerability and exposure of the disaster-prone body is conducted through intelligent analysis models combined with real-time monitoring data. Through continuous monitoring and assessment, potential disaster risks can be discovered in a timely manner. The specific steps are as follows:

[0157] S501: Acquire monitoring data from ground monitoring stations, satellite remote sensing systems, etc. in real time, including but not limited to meteorological conditions and surface conditions;

[0158] S502: Integrate the real-time monitoring data with the historical data and the static data to form an input data set;

[0159] S503: Load the CNN model that has been trained and optimized in step S3 from the model library;

[0160] S504: Using the convolution layer of the CNN model, extracting features from the input data. The extracted features include texture, shape, and color in the image, which are used to reflect the vulnerability and exposure of the disaster-prone body.

[0161] S505: Input the extracted features into the fully connected layer of the CNN model to perform classification and regression tasks;

[0162] S506: For classification tasks, the model will output the level or category of vulnerability and exposure of the hazard-prone body; for regression tasks, the model will output a continuous value indicating the vulnerability or exposure degree of the hazard-prone body;

[0163] S507: Analyze the results output by the model to determine whether the vulnerability and exposure of the disaster-prone body exceed the preset threshold; if the threshold is exceeded, trigger the early warning mechanism; if the threshold is not exceeded, continue monitoring;

[0164] S508: Update the monitoring data in real time and repeat the above steps to perform dynamic risk assessment.

[0165] As a preferred method, an intelligent early warning system is used to quickly trigger an early warning when a disaster is detected, and send early warning information to relevant departments and personnel; the specific steps are as follows:

[0166] S601: When the risk assessment results trigger the warning conditions, the intelligent warning system sends warning information to relevant departments and personnel. The warning information includes the disaster type, expected time and location, and possible impact range.

[0167] S602: Generate a risk assessment report, recording the results of each assessment, triggered warning information, and subsequent emergency response;

[0168] S603: Store the report in the big data platform.

[0169] As a preferred option, after a disaster occurs, artificial intelligence technology is used to provide decision support for emergency response, including determining the allocation of rescue resources, developing evacuation routes, and assessing disaster losses. UAVs and remote sensing technology are used to quickly assess post-disaster losses, and artificial intelligence technology is combined to conduct intelligent analysis of loss data. The specific steps are as follows:

[0170] S701: Confirmation information of the disaster, including the type of disaster, time of occurrence, and location;

[0171] S702: Collect real-time data from disaster sites through UAVs, remote sensing technology, and ground monitoring stations, and integrate historical disaster data, geospatial data, and population distribution data to form a disaster data set;

[0172] S703: Use artificial intelligence algorithms to analyze disaster data sets and assess the type and amount of rescue resource requirements;

[0173] S704: Formulate a rescue resource dispatch plan based on the resource demand assessment results;

[0174] S705: Analyze the population distribution and traffic conditions at the disaster site, determine the evacuation needs, and use artificial intelligence algorithms to predict the possible flow and number of evacuees;

[0175] S706: Formulate an evacuation route planning plan based on the evacuation demand analysis results;

[0176] S707: Use the intelligent monitoring system to track the flow of evacuees in real time, and provide evacuation guidance through the intelligent guidance system to ensure that the evacuees evacuate in an orderly manner according to the planned route;

[0177] S708: Use drones and remote sensing technology to collect loss data at disaster sites;

[0178] S709: Use artificial intelligence algorithms to analyze loss data sets to assess the type, extent and scope of disaster losses;

[0179] S710: Prepare disaster loss assessment report and record loss assessment results.

[0180] As a preferred method, artificial intelligence algorithms are used to analyze disaster data sets to assess the type and quantity of rescue resource requirements; the specific steps are as follows:

[0181] S801: Collect and integrate disaster data sets, including disaster type, occurrence time, location, impact range, population distribution, and historical rescue resource usage, and pre-process the data, including missing value filling, outlier detection and processing, and data standardization;

[0182] S802: Use the Gini coefficient feature selection method to select key features that affect the prediction of rescue resource demand from the disaster data set, and construct a feature vector as the input of the random forest algorithm;

[0183] S803: Divide the data set into a training set and a test set, and use the training set data to build a random forest model. During the construction process, random sampling and random feature selection are used to increase the diversity of the model;

[0184] S804: Set the number of decision trees, maximum depth, and minimum sample number parameters, and perform model tuning;

[0185] S805: Input the new disaster data set into the trained random forest model;

[0186] S806: The model predicts the type and quantity of rescue resource requirements based on the input features;

[0187] S807: Output prediction results, including the quantity and priority of various rescue resources;

[0188] Analyze the population distribution and traffic conditions at the disaster site, determine the evacuation needs, and use artificial intelligence algorithms to predict the possible flow and number of evacuees; the specific steps are as follows:

[0189] S901: Collect population distribution data, traffic conditions data and historical disaster evacuation data at the disaster site;

[0190] S902: According to the requirements of evacuation demand analysis, key features are extracted from the original data, feature vectors are constructed, and the features are encoded and converted as inputs of the deep learning model;

[0191] S903: Select a recurrent neural network model, and construct a neural network structure according to the dimension of the feature vector and the requirements of the model;

[0192] S904: Setting parameters of the recurrent neural network model, including learning rate, batch size, and number of iterations;

[0193] S905: Use historical disaster evacuation data as a training set to train the recurrent neural network model;

[0194] S906: Input new disaster site data into the trained recurrent neural network model;

[0195] S907: The model predicts the possible flow direction and number of evacuees based on the input features;

[0196] S908: Interpret the prediction results, analyze the rationality of the flow direction and number of evacuees, apply the prediction results to evacuation route planning, provide a basis for formulating evacuation plans, and adjust the allocation of rescue resources according to the prediction results.

[0197] Preferably, artificial intelligence algorithms are used to analyze the loss data set to assess the type, extent and scope of disaster losses; the specific steps are as follows:

[0198] S1001: Collect disaster loss data sets, including data on building damage, casualties, and economic losses;

[0199] S1002: Extract key features based on the characteristics of disaster losses, including disaster type, impact area, population distribution, and building type, and encode and transform the features;

[0200] S1003: Select the random forest algorithm for classification and regression tasks;

[0201] S1004: construct a machine learning model structure according to the dimension of the feature vector and the requirements of the model, and use the historical disaster loss data as a training set to train the machine learning model;

[0202] S1005: Input new disaster loss data into the trained machine learning model;

[0203] S1006: The model classifies and regresses disaster losses based on input features to obtain information on the type, extent and scope of the losses;

[0204] S1007: Explain and analyze the forecast results and make a quantitative assessment of the losses based on the actual situation.

[0205] As a preferred method, based on the post-disaster loss assessment results, intelligent tools are used to plan post-disaster recovery and reconstruction, including determining reconstruction priorities, formulating reconstruction plans, and optimizing resource allocation; the specific steps are as follows:

[0206] S1101: Collect post-disaster loss data, integrate the data, and form a complete post-disaster loss data set;

[0207] S1102: Use the random forest algorithm to analyze the post-disaster loss data set and determine the priority of reconstruction based on the degree of loss and impact range;

[0208] S1103: Develop a post-disaster reconstruction plan based on reconstruction priorities, including reconstruction goals, timetable, and key information on resource requirements;

[0209] S1104: Determine the type and quantity of resource requirements in each region based on post-disaster loss assessment data;

[0210] S1105: Use the spatial analysis function of the GIS system to visualize resource requirements;

[0211] S1106: Use the buffer analysis function of the GIS system to determine the coverage of resource supply;

[0212] S1107: Using the network analysis function of the GIS system, calculate the shortest path and optimal path from each resource point to the demand point, and determine the transportation path and transportation capacity of the resources;

[0213] S1108: Output resource allocation plan.

[0214] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of those skilled in the art without departing from the spirit of the present invention.

Claims

1. A disaster management method based on vulnerability and exposure of disaster-bearing bodies; characterized by: The following steps are included: S1: Use big data technology to integrate information from multiple data sources, including but not limited to satellite remote sensing data, ground monitoring station data, social media data, and historical disaster records; S2: Clean the collected raw data, including deduplication and format unification, and use machine learning algorithms to extract features and reduce the dimension of the data; S3: Based on the processed data, a deep learning algorithm is used to construct an intelligent analysis model of vulnerability and exposure of the hazard-bearing body; S4: Through intelligent analysis models combined with real-time monitoring data, dynamic risk assessment of the vulnerability and exposure of disaster-prone objects is carried out, and potential disaster risks are discovered in a timely manner through continuous monitoring and assessment; S5: Through the intelligent early warning system, when a disaster is detected, an early warning is triggered quickly and early warning information is sent to relevant departments and personnel; S6: After a disaster occurs, use artificial intelligence technology to provide decision support for emergency response, including determining the allocation of rescue resources, developing evacuation routes, and assessing disaster losses; S7: Use drones and remote sensing technology to quickly assess post-disaster losses, and combine artificial intelligence technology to conduct intelligent analysis of loss data; S8: Based on the results of post-disaster loss assessment, use intelligent tools to plan post-disaster recovery and reconstruction, including determining reconstruction priorities, formulating reconstruction plans, and optimizing resource allocation.

2. A disaster response management method based on vulnerability and exposure of disaster-affected bodies according to claim 1, characterized in that: Use big data technology to integrate information from multiple data sources, including but not limited to satellite remote sensing data, ground monitoring station data, social media data, and historical disaster records; the specific steps are as follows: S201: Acquire remote sensing images and data from different satellites, including information on land cover, topography, and meteorological conditions; S202: Collect physical and chemical parameters from ground monitoring stations, including air temperature, humidity, wind speed, rainfall, and soil moisture; S203: Collect records of disaster events that have occurred in the past, including disaster type, time of occurrence, location, impact range, and loss; S204: converting data from different sources into a unified format and storing it in the big data platform; S205: Establishing a mapping relationship between data according to the attributes and characteristics of the data; S206: integrating data from different sources and creating an index for the integrated data; Among them, in step S205, the specific steps of establishing the mapping relationship between data according to the attributes and characteristics of the data are as follows: S251: Identify the source, format, content, unit and relationship between each type of data according to the field meaning, data type and data range of the data; S252: Identify fields with the same or similar attributes in different data sets; S253: Based on the result of attribute matching, mapping rules between data are formulated. The rules define how to convert field values ​​in one data set into field values ​​in another data set, and how to establish associations between different data sets; S254: Compare and analyze the mapped data through visualization tools to verify the correctness and accuracy of the mapping relationship; In step S206, the specific steps of fusing data from different sources and creating an index for the integrated data are as follows: S261: merging data from different sources according to the mapping relationship, including merging data values ​​of the same attribute and associating data values ​​of different attributes; S262: Cleaning the data merged in step S261, including removing duplicate data, processing missing data, and correcting erroneous data; S263: Create an index for the data processed in step S262 according to the attributes, values ​​and structure of the data.

3. A disaster response management method based on vulnerability and exposure of disaster-affected bodies according to claim 2, characterized in that: Clean the collected raw data, including deduplication and format unification, and use machine learning algorithms to extract features and reduce the dimension of the data; the specific steps are as follows: S301: Loading the collected raw data from the big data platform; S302: Use the unique identifier to detect and remove duplicate records. For data without a unique identifier, use the cosine similarity algorithm to detect duplicate items. S303: Convert data in different formats into a unified format, such as using the mean, median, and mode to fill missing values, and using the Z-score method to detect outliers; S304: Standardize the data by subtracting the mean value and dividing the data by the standard deviation; S305: Calculate the covariance matrix based on the standardized data to capture the correlation between different variables; S306: Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors; S307: Select the first k principal components according to the size of the eigenvalues. The principal components are used to explain most of the variance in the original data. The original data is projected onto the selected principal components to obtain the data after dimensionality reduction. S308: Save the cleaned and dimension-reduced data to the big data platform.

4. A disaster response management method based on vulnerability and exposure of disaster-affected bodies according to claim 3, characterized in that: Based on the processed data, a deep learning algorithm is used to build an intelligent analysis model of vulnerability and exposure of disaster-prone bodies. The specific steps are as follows: S401: Construct the network structure of the CNN model, including convolutional layer, pooling layer, and fully connected layer; the convolutional layer is used to extract image features, the pooling layer is used to reduce feature dimensions and reduce the amount of calculation, and the fully connected layer is used to integrate features and perform classification and regression tasks; S402: Setting the size, step size, and filling method parameters of the convolution kernel, as well as the type of the pooling layer and the pooling window size parameters; S403: labeling the cleaned and dimensionally reduced data, using the vulnerability and exposure levels of the disaster-prone objects as labels; S404: Divide the labeled data into a training set, a validation set, and a test set; S405: Use the training set data to train the CNN model, update the model parameters through the back propagation algorithm and the gradient descent optimization algorithm, and minimize the loss function; S406: Evaluate the performance of the model on the test set, including accuracy, recall, and F1 score indicators; S407: Optimize the model according to the evaluation results, including adjusting hyperparameters and increasing the number of network layers; S408: Export the trained model into a deployable format.

5. A disaster response management method based on vulnerability and exposure of disaster-affected bodies according to claim 4, characterized in that: Through intelligent analysis models combined with real-time monitoring data, dynamic risk assessment of the vulnerability and exposure of disaster-prone bodies is carried out. Through continuous monitoring and assessment, potential disaster risks can be discovered in a timely manner. The specific steps are as follows: S501: Acquire monitoring data from ground monitoring stations, satellite remote sensing systems, etc. in real time, including but not limited to meteorological conditions and surface conditions; S502: Integrate the real-time monitoring data with the historical data and the static data to form an input data set; S503: Load the CNN model that has been trained and optimized in step S3 from the model library; S504: Using the convolution layer of the CNN model, extracting features from the input data. The extracted features include texture, shape, and color in the image, which are used to reflect the vulnerability and exposure of the disaster-prone body. S505: Input the extracted features into the fully connected layer of the CNN model to perform classification and regression tasks; S506: For classification tasks, the model will output the level or category of vulnerability and exposure of the hazard-prone body; for regression tasks, the model will output a continuous value indicating the vulnerability or exposure degree of the hazard-prone body; S507: Analyze the results output by the model to determine whether the vulnerability and exposure of the disaster-prone body exceed a preset threshold; If the threshold is exceeded, the early warning mechanism is triggered; if the threshold is not exceeded, monitoring continues; S508: Update the monitoring data in real time and repeat the above steps to perform dynamic risk assessment.

6. A disaster response management method based on vulnerability and exposure of disaster-affected bodies according to claim 5, characterized in that: Through the intelligent early warning system, when a disaster is detected, an early warning is triggered quickly and early warning information is sent to relevant departments and personnel; the specific steps are as follows: S601: When the risk assessment results trigger the warning conditions, the intelligent warning system sends warning information to relevant departments and personnel. The warning information includes the disaster type, expected time and location, and possible impact range. S602: Generate a risk assessment report, recording the results of each assessment, triggered warning information, and subsequent emergency response; S603: Store the report in the big data platform.

7. A disaster response management method based on vulnerability and exposure of disaster-affected objects according to claim 6, characterized in that: After a disaster occurs, AI technology is used to provide decision support for emergency response, including determining the allocation of rescue resources, developing evacuation routes, and assessing disaster losses. UAVs and remote sensing technology are used to quickly assess post-disaster losses, and AI technology is used to conduct intelligent analysis of loss data. The specific steps are as follows: S701: Confirmation information of the disaster, including the type of disaster, time of occurrence, and location; S702: Collect real-time data from disaster sites through UAVs, remote sensing technology, and ground monitoring stations, and integrate historical disaster data, geospatial data, and population distribution data to form a disaster data set; S703: Use artificial intelligence algorithms to analyze disaster data sets and assess the type and amount of rescue resource requirements; S704: Formulate a rescue resource dispatch plan based on the resource demand assessment results; S705: Analyze the population distribution and traffic conditions at the disaster site, determine the evacuation needs, and use artificial intelligence algorithms to predict the possible flow and number of evacuees; S706: Formulate an evacuation route planning plan based on the evacuation demand analysis results; S707: Use the intelligent monitoring system to track the flow of evacuees in real time, and provide evacuation guidance through the intelligent guidance system to ensure that the evacuees evacuate in an orderly manner according to the planned route; S708: Use drones and remote sensing technology to collect loss data at disaster sites; S709: Use artificial intelligence algorithms to analyze loss data sets to assess the type, extent and scope of disaster losses; S710: Prepare disaster loss assessment report and record loss assessment results.

8. A disaster response management method based on vulnerability and exposure of disaster-affected objects according to claim 7, characterized in that: Use artificial intelligence algorithms to analyze disaster data sets and assess the type and amount of rescue resource requirements; the specific steps are as follows: S801: Collect and integrate disaster data sets, including disaster type, occurrence time, location, impact range, population distribution, and historical rescue resource usage, and pre-process the data, including missing value filling, outlier detection and processing, and data standardization; S802: Use the Gini coefficient feature selection method to select key features that affect the prediction of rescue resource demand from the disaster data set, and construct a feature vector as the input of the random forest algorithm; S803: Divide the data set into a training set and a test set, and use the training set data to build a random forest model. During the construction process, random sampling and random feature selection are used to increase the diversity of the model; S804: Set the number of decision trees, maximum depth, and minimum sample number parameters, and perform model tuning; S805: Input the new disaster data set into the trained random forest model; S806: The model predicts the type and quantity of rescue resource requirements based on the input features; S807: Output prediction results, including the quantity and priority of various rescue resources; Analyze the population distribution and traffic conditions at the disaster site, determine the evacuation needs, and use artificial intelligence algorithms to predict the possible flow and number of evacuees; the specific steps are as follows: S901: Collect population distribution data, traffic conditions data and historical disaster evacuation data at the disaster site; S902: According to the requirements of evacuation demand analysis, key features are extracted from the original data, feature vectors are constructed, and the features are encoded and converted as inputs of the deep learning model; S903: Select a recurrent neural network model, and construct a neural network structure according to the dimension of the feature vector and the requirements of the model; S904: Setting parameters of the recurrent neural network model, including learning rate, batch size, and number of iterations; S905: Use historical disaster evacuation data as a training set to train the recurrent neural network model; S906: Input new disaster site data into the trained recurrent neural network model; S907: The model predicts the possible flow direction and number of evacuees based on the input features; S908: Interpret the prediction results, analyze the rationality of the flow direction and number of evacuees, apply the prediction results to evacuation route planning, provide a basis for formulating evacuation plans, and adjust the allocation of rescue resources according to the prediction results.

9. A disaster response management method based on vulnerability and exposure of disaster-affected objects according to claim 8, characterized in that: Use artificial intelligence algorithms to analyze loss data sets to assess the type, extent, and scope of disaster losses; the specific steps are as follows: S1001: Collect disaster loss data sets, including data on building damage, casualties, and economic losses; S1002: Extract key features based on the characteristics of disaster losses, including disaster type, impact area, population distribution, and building type, and encode and transform the features; S1003: Select the random forest algorithm for classification and regression tasks; S1004: construct a machine learning model structure according to the dimension of the feature vector and the requirements of the model, and use the historical disaster loss data as a training set to train the machine learning model; S1005: Input new disaster loss data into the trained machine learning model; S1006: The model classifies and regresses disaster losses based on input features to obtain information on the type, extent and scope of the losses; S1007: Explain and analyze the forecast results and make a quantitative assessment of the losses based on the actual situation.

10. A disaster response management method based on vulnerability and exposure of disaster-bearing bodies according to claim 9, characterized in that: Based on the post-disaster loss assessment results, intelligent tools are used to plan post-disaster recovery and reconstruction, including determining reconstruction priorities, formulating reconstruction plans, and optimizing resource allocation. The specific steps are as follows: S1101: Collect post-disaster loss data, integrate the data, and form a complete post-disaster loss data set; S1102: Use the random forest algorithm to analyze the post-disaster loss data set and determine the priority of reconstruction based on the degree of loss and impact range factors; S1103: Develop a post-disaster reconstruction plan based on reconstruction priorities, including reconstruction goals, timetable, and key information on resource requirements; S1104: Determine the type and quantity of resource requirements in each region based on post-disaster loss assessment data; S1105: Use the spatial analysis function of the GIS system to visualize resource requirements; S1106: Use the buffer analysis function of the GIS system to determine the coverage of resource supply; S1107: Using the network analysis function of the GIS system, calculate the shortest path and optimal path from each resource point to the demand point, and determine the transportation path and transportation capacity of the resources; S1108: Output resource allocation plan.

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