Identification method, device and equipment for vulnerability driving factors of disaster-bearing body under typhoon disaster and storage medium

Abstract

The invention provides a method, a device and equipment for identifying vulnerability driving factors of disaster-bearing bodies under typhoon disasters and a storage medium. The method comprises the following steps: obtaining a comprehensive data set according to obtained historical typhoon disaster intensity data and historical typhoon disaster loss characterization data of a plurality of areas; obtaining an optimal function and a target disaster-causing intensity variable according to the comprehensive data set and a plurality of preset candidate functions; according to the optimal function and the target disaster-causing intensity variable, obtaining a disaster-bearing body loss rate and a disaster-bearing body absolute loss value; performing typhoon disaster vulnerability assessment according to the disaster-bearing body loss rate and the disaster-bearing body absolute loss value to obtain a typhoon disaster vulnerability assessment result; and according to the typhoon disaster vulnerability assessment result and a plurality of preset driving factors, determining an identification result of the vulnerability driving factors of the disaster-bearing body under the typhoon disaster. According to the invention, the accuracy of typhoon disaster vulnerability assessment and driving factor identification can be improved, and the pertinence and scientificity of typhoon disaster risk management are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of disaster risk assessment technology, and also to a method, apparatus, equipment and storage medium for identifying the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters. Background Technology

[0002] Typhoon vulnerability refers to the degree to which a disaster-bearing entity (including population, buildings, infrastructure, ecosystems, and socio-economic systems) is easily damaged or suffers losses under the influence of typhoons and their secondary disasters (such as strong winds, torrential rains, storm surges, floods, and landslides), and is a comprehensive reflection of its ability to cope with disasters, withstand risks, and recover after disasters. Higher typhoon vulnerability means that the disaster-bearing entity is more easily damaged when facing a typhoon, and the greater the difficulty of post-disaster recovery; conversely, lower vulnerability means that the disaster-bearing entity has a stronger ability to withstand typhoon disasters, and the losses are relatively smaller. Existing typhoon vulnerability assessment methods often focus on single hazard factors or use fixed function forms, making it difficult to accurately reflect the differences in disaster resilience across different regions, resulting in poor accuracy and an inability to identify the driving factors causing differences in the vulnerability of disaster-bearing entities. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method, apparatus, equipment and storage medium for identifying the vulnerability drivers of disaster-bearing bodies under typhoon disasters, so as to improve the accuracy of typhoon disaster vulnerability assessment results and driver identification.

[0004] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0005] A first aspect of the present invention provides a method for identifying the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters, comprising:

[0006] Acquire historical typhoon disaster intensity data and historical typhoon disaster loss characterization data for multiple regions;

[0007] The historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data are matched to obtain a comprehensive dataset;

[0008] Based on the comprehensive dataset and multiple preset candidate functions, the optimal function and the target disaster-causing intensity variable are obtained;

[0009] Based on the optimal function and the target disaster intensity variable, the loss rate and absolute loss value of the disaster-bearing body are obtained;

[0010] The typhoon vulnerability assessment is conducted based on the loss rate and absolute loss value of the affected body to obtain the typhoon vulnerability assessment results.

[0011] Based on the typhoon disaster vulnerability assessment results and multiple preset driving factors, the identification results of the vulnerability driving factors of the disaster-bearing body under typhoon disaster are determined.

[0012] Optionally, obtain historical typhoon disaster intensity data and historical typhoon disaster loss characterization data for multiple regions, including:

[0013] Obtain raw typhoon disaster data and raw typhoon disaster loss data for multiple regions within a historical time period;

[0014] Based on the original typhoon disaster data, historical typhoon disaster intensity data is determined; the historical typhoon disaster intensity data includes total rainfall, cumulative wind energy per unit area, and wind-rain coupling intensity.

[0015] Based on the original typhoon disaster loss data, historical typhoon disaster loss characterization data are determined; the historical typhoon disaster loss characterization data includes economic loss rate, population disaster rate, and crop disaster rate.

[0016] Optionally, the historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data are matched to obtain a comprehensive dataset, including:

[0017] The historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data are preprocessed to obtain preprocessed data;

[0018] The preprocessed data is matched according to a preset matching method to obtain a comprehensive dataset.

[0019] Optionally, based on the comprehensive dataset and multiple preset candidate functions, the optimal function and target disaster-causing intensity variable are obtained, including:

[0020] Based on the comprehensive dataset and multiple preset candidate functions, multiple parameters are determined;

[0021] The optimal function and target disaster intensity variable are obtained by filtering based on the comprehensive dataset, the multiple preset candidate functions, and the multiple parameters.

[0022] Optionally, the optimal function and target disaster-causing intensity variable are obtained by filtering based on the comprehensive dataset, the multiple preset candidate functions, and the multiple parameters, including:

[0023] The indicator value is obtained based on the preset judgment indicator, the comprehensive dataset, the multiple preset candidate functions, and the multiple parameters;

[0024] The optimal function and target disaster intensity variable are obtained by screening according to the preset screening criteria and the index values.

[0025] Optionally, based on the optimal function and the target disaster intensity variable, the loss rate and absolute loss value of the disaster-bearing body are obtained, including:

[0026] Based on the optimal function, determine the target scenario;

[0027] Based on the target scenario and the optimal function, the loss rate of the disaster-bearing body is obtained;

[0028] Based on the loss rate of the disaster-bearing body, the absolute loss value of the disaster-bearing body is obtained.

[0029] Optionally, a typhoon vulnerability assessment is performed based on the loss rate and absolute loss value of the affected body to obtain the typhoon vulnerability assessment results, including:

[0030] Obtain the preset evaluation criteria;

[0031] Based on the preset assessment criteria, the loss rate of the disaster-bearing body, and the absolute loss value of the disaster-bearing body, a typhoon disaster vulnerability assessment is conducted on the multiple regions to obtain typhoon disaster vulnerability assessment results; the typhoon disaster vulnerability assessment results include at least one of the multiple regions.

[0032] A second aspect of the present invention provides a device for identifying the driving factors of vulnerability of disaster-bearing structures under typhoon disasters, comprising:

[0033] The acquisition module is used to acquire historical typhoon disaster intensity data and historical typhoon disaster loss characterization data for multiple regions;

[0034] The processing module is used to match the historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data to obtain a comprehensive dataset; based on the comprehensive dataset and multiple preset candidate functions, to obtain the optimal function and the target disaster-causing intensity variable; based on the optimal function and the target disaster-causing intensity variable, to obtain the disaster-bearing body loss rate and the absolute loss value of the disaster-bearing body; to conduct a typhoon disaster vulnerability assessment based on the disaster-bearing body loss rate and the absolute loss value of the disaster-bearing body, to obtain the typhoon disaster vulnerability assessment result; and based on the typhoon disaster vulnerability assessment result and multiple preset driving factors, to determine the identification result of the driving factors of the vulnerability of the disaster-bearing body under the typhoon disaster.

[0035] A third aspect of the present invention provides a computing device, comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described in the first aspect.

[0036] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method as described in the first aspect.

[0037] The above-described solution of the present invention has at least the following beneficial effects:

[0038] The above-mentioned solution of the present invention acquires historical typhoon disaster intensity data and historical typhoon disaster loss characterization data from multiple regions, then matches the historical typhoon disaster intensity data and historical typhoon disaster loss characterization data to obtain a comprehensive dataset. Based on the comprehensive dataset and multiple preset candidate functions, an optimal function and a target disaster-causing intensity variable are obtained. Furthermore, based on the optimal function and the target disaster-causing intensity variable, the loss rate and absolute loss value of the disaster-bearing body are obtained. Typhoon disaster vulnerability assessment is performed based on the loss rate and absolute loss value of the disaster-bearing body to obtain the typhoon disaster vulnerability assessment result. Finally, based on the typhoon disaster vulnerability assessment result and multiple preset driving factors, the identification results of the vulnerability driving factors of the disaster-bearing body under typhoon disasters are determined. This can more accurately characterize the vulnerability features of different disaster-bearing bodies, reveal their spatial heterogeneity and formation mechanism, improve the accuracy of typhoon disaster vulnerability assessment and driving factor identification, and thus effectively enhance the pertinence and scientific nature of typhoon disaster risk management. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating the method for identifying the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters in an embodiment of the present invention.

[0040] Figure 2 This is a schematic diagram of the structure of the identification device for the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters in an embodiment of the present invention. Detailed Implementation

[0041] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0042] like Figure 1 As shown, embodiments of the present invention propose a method for identifying the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters, including the following steps:

[0043] Step 101: Obtain historical typhoon disaster intensity data and historical typhoon disaster loss characterization data for multiple regions;

[0044] Step 102: Match the historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data to obtain a comprehensive dataset;

[0045] Step 103: Based on the comprehensive dataset and multiple preset candidate functions, obtain the optimal function and the target disaster intensity variable;

[0046] Step 104: Based on the optimal function and the target disaster intensity variable, obtain the disaster-bearing body loss rate and the absolute loss value of the disaster-bearing body;

[0047] Step 105: Conduct a typhoon vulnerability assessment based on the loss rate and absolute loss value of the affected body to obtain the typhoon vulnerability assessment result.

[0048] Step 106: Based on the typhoon disaster vulnerability assessment results and multiple preset driving factors, determine the identification results of the vulnerability driving factors of the disaster-bearing body under the typhoon disaster.

[0049] The method for identifying the vulnerability drivers of disaster-bearing bodies under typhoon disasters according to embodiments of the present invention acquires historical typhoon disaster intensity data and historical typhoon disaster loss characterization data from multiple regions. Then, it matches the historical typhoon disaster intensity data and historical typhoon disaster loss characterization data to obtain a comprehensive dataset. Based on the comprehensive dataset and multiple preset candidate functions, it obtains the optimal function and the target disaster-causing intensity variable. Furthermore, based on the optimal function and the target disaster-causing intensity variable, it obtains the disaster-bearing body loss rate and the absolute loss value of the disaster-bearing body. Based on the disaster-bearing body loss rate and the absolute loss value of the disaster-bearing body, it conducts a typhoon disaster vulnerability assessment to obtain the typhoon disaster vulnerability assessment result. Finally, based on the typhoon disaster vulnerability assessment result and multiple preset driving factors, it determines the identification result of the vulnerability drivers of disaster-bearing bodies under typhoon disasters. This method can more accurately characterize the vulnerability features of different disaster-bearing bodies, reveal their spatial heterogeneity and formation mechanism, improve the accuracy of typhoon disaster vulnerability assessment and driving factor identification, and thus effectively enhance the pertinence and scientific nature of typhoon disaster risk management.

[0050] Here, the disaster-bearing body refers to the economy, population, and agriculture. Driving factors are those that cause differences in typhoon disaster vulnerability.

[0051] In an optional embodiment of the present invention, step 101 includes:

[0052] Step 1011: Obtain raw typhoon disaster data and raw typhoon disaster loss data for multiple regions within a historical time period;

[0053] Specifically, the historical time period refers to the past few years or decades, which can be selected as needed. Raw typhoon disaster data can be obtained from the Meteorological Bureau's Tropical Cyclone Data Center, including tropical cyclone optimal path datasets and tropical cyclone wind and rain datasets. These datasets provide meteorological station location information that records meteorological elements during typhoon disasters. Raw typhoon disaster loss data can be obtained from official websites and other sources, including statistical data on direct economic losses, affected population, and crop damage caused by typhoons in various regions.

[0054] Step 1012: Determine historical typhoon disaster intensity data based on the original typhoon disaster data; the historical typhoon disaster intensity data includes total rainfall, cumulative wind energy per unit area, and wind-rain coupling intensity.

[0055] Specifically, historical typhoon disaster intensity data mainly includes regional total rainfall, regional cumulative wind energy per unit area, and regional wind-rain coupling intensity. Regional total rainfall directly characterizes the typhoon's rainfall-induced disaster intensity. It can be obtained by extracting the total rainfall from individual meteorological stations within each region and averaging the results. Based on the optimal tropical cyclone path dataset (containing typhoon center pressure and trajectory coordinates), the cumulative wind energy per unit area at each regional meteorological station is calculated, and the average of the cumulative wind energy per unit area at each station is taken as the regional cumulative wind energy per unit area. The wind-rain coupling disaster intensity is obtained by multiplying the total rainfall by the cumulative wind energy per unit area.

[0056] Here, the cumulative wind energy per unit area at the location of the meteorological station is obtained through the following steps:

[0057] pass The maximum gradient wind speed is obtained;

[0058] according to The average maximum wind speed was obtained;

[0059] pass Obtain the first wind speed;

[0060] pass Obtain the average wind speed;

[0061] pass To obtain the cumulative wind energy per unit area;

[0062] The cumulative wind energy per unit area of ​​the region is obtained by calculating the average cumulative wind energy per unit area at all meteorological stations within the region.

[0063] in, Let K be the maximum gradient wind speed, and K be an empirical constant with a value of 6.72. The central pressure difference (unit: hPa) is obtained through... get, The central pressure of the typhoon. The radius of maximum wind speed (unit: km) is reached through We obtain f, which is the Coriolis coefficient, through... get, This is the Earth's rotational angular velocity. It is the latitude of the air mass. Indicates the latitude of the typhoon's center. The average maximum wind speed (the average maximum wind speed over 10 minutes at a height of 10 meters above sea level). The speed at which the typhoon moves, via get, The time interval between two typhoon records. It is the Earth's radius, and 'a' is a preset parameter, obtained through... We obtain the coordinates ( and( These represent the radian coordinates of the typhoon center at the two recorded times; The first wind speed (along the preset ray OM, at a distance from the typhoon center) (where r is the wind speed at a distance of 10 meters and a height of 10 meters, and r is the distance between a point on the preset ray and the center of the typhoon.) This is an empirical constant, typically ranging from 0.5 to 0.7, with a commonly used value of 0.6. The average wind speed is the 10-minute average wind speed at any point N in the wind field, at a height of 10 meters. From the center of the typhoon to The angle between the ray OM and the line segment ON formed from the typhoon center to the study point N, where E is the cumulative wind energy per unit area. For air density, it is generally taken as .

[0064] Here, the preset ray is a ray OM drawn with the typhoon center as point O, rotated 115° clockwise from the typhoon's direction of movement, to ensure that the area of ​​maximum wind speed relative to the ground is always located in the right rear quadrant of the typhoon. Based on this, the distance along this ray OM from the typhoon center is... At a distance of meters, the wind speed at a height of 10 meters is .

[0065] It should be noted that the data used in the above steps or formulas can all be from the tropical cyclone optimal path dataset.

[0066] Step 1013: Based on the original typhoon disaster loss data, determine the historical typhoon disaster loss characterization data; the historical typhoon disaster loss characterization data includes economic loss rate, population disaster rate and crop disaster rate.

[0067] Specifically, the original typhoon disaster loss data can include data such as direct economic losses, affected population, and crop damage area caused by typhoon disasters in various regions. By dividing the direct economic losses, affected population, and crop damage area by the corresponding year's regional GDP, year-end resident population, and cultivated land area, historical typhoon disaster loss characterization data such as economic loss rate, population disaster rate, and crop disaster rate are calculated, thereby reflecting the loss characteristics of different disaster-bearing bodies in various regions under the influence of typhoon disasters.

[0068] In an optional embodiment of the present invention, step 102 includes:

[0069] Step 1021: Preprocess the historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data to obtain preprocessed data;

[0070] Step 1022: Match the preprocessed data according to the preset matching method to obtain a comprehensive dataset.

[0071] Specifically, the preprocessing method in step 1021 can be outlier removal and standardization to improve data accuracy and quality. In step 1022, typhoon events are matched with their corresponding affected areas to construct a regional "disaster-causing factor-disaster loss" association dataset for each typhoon event, i.e., a comprehensive dataset, which provides a foundation for subsequent steps.

[0072] Here, "typhoon event" refers to the historical typhoon disaster intensity data in the corresponding preprocessed data. This data is extracted and calculated based on the location of meteorological stations within the region and their observation information during a specific typhoon. The resulting dataset includes: year – typhoon number – region – intensity of disaster-causing factors (including total regional rainfall, cumulative wind energy per unit area, and regional wind-rain coupling intensity). The historical typhoon disaster loss characterization data in the preprocessed data is compiled based on the original statistical data and includes: year – typhoon number – region – disaster loss indicators (including economic loss rate, population disaster rate, and crop disaster rate). Based on this, the historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data in the preprocessed data are matched and merged by year, typhoon number, and region, ultimately resulting in a comprehensive dataset with the structure: year – typhoon number – region – intensity of disaster-causing factors – disaster loss.

[0073] In an optional embodiment of the present invention, step 103 includes:

[0074] Step 1031: Determine multiple parameters based on the comprehensive dataset and multiple preset candidate functions;

[0075] Specifically, multiple preset candidate functions (also known as vulnerability curves) can be selected from the preset candidate function library shown in Tables 1, 2, and 3, including linear functions, power functions, logarithmic functions, logistic functions, etc. By inputting the historical typhoon disaster intensity data in the comprehensive dataset as x and the historical typhoon disaster loss characterization data in the comprehensive dataset as y into each preset candidate function, the values ​​of parameters a, b, and c can be obtained, that is, multiple parameters can be obtained.

[0076] Table 1 Preset Candidate Function Library 1

[0077]

[0078] Table 2 Preset Candidate Function Library 2

[0079]

[0080] Table 3 Preset Candidate Function Library 3

[0081]

[0082] Step 1032: Based on the comprehensive dataset, the multiple preset candidate functions, and the multiple parameters, the optimal function and the target disaster intensity variable are obtained through screening.

[0083] In an optional embodiment of the present invention, step 1032 includes:

[0084] Step 10321: Obtain the indicator value based on the preset judgment indicator, the comprehensive dataset, the multiple preset candidate functions, and the multiple parameters;

[0085] Specifically, the preset judgment indicators may include root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination. Based on the historical typhoon disaster intensity data x and the historical typhoon disaster loss characterization data y in the comprehensive dataset, multiple preset candidate functions, multiple parameters, and the following formula, the value of the preset judgment index, i.e., the index value, can be calculated:

[0086] ;

[0087] ;

[0088] ;

[0089] in, The root mean square error is given, and n is the total number of data points in the comprehensive dataset, which can be understood as the amount of data in the historical typhoon disaster intensity data. To synthesize the historical typhoon disaster loss characterization data in the dataset, To synthesize the historical typhoon disaster intensity data of the i-th data point in the dataset, The mean absolute error, The coefficient of determination is SSE, which is the sum of squared residuals. SST is the sum of squared deviations between the true value and its own mean. get, get.

[0090] Step 10322: Filter according to the preset screening criteria and the index values ​​to obtain the optimal function and the target disaster intensity variable.

[0091] Specifically, the preset screening criteria can be the coefficient of determination among the indicator values. Maximum and root mean square error and mean absolute error Based on this preset screening criterion and the calculated index values ​​corresponding to each candidate function, the target disaster-causing intensity variable can be selected from total rainfall, cumulative wind energy per unit area, and wind-rain coupling intensity. In this embodiment, the target disaster-causing intensity variable is total rainfall. Based on the target disaster-causing intensity variable being total rainfall, the determination coefficient of the index values ​​for each preset candidate function is... The value is used to determine the selection in all areas. The mean is the highest, and all candidate functions in all regions are pre-set to be monotonic and pass through the origin. The pre-set candidate function with the best comprehensive effect is selected as the optimal function (in this embodiment, the optimal function is function form 9, i.e. ).

[0092] In an optional embodiment of the present invention, step 104 includes:

[0093] Step 1041: Determine the target scene based on the optimal function;

[0094] Specifically, based on a comprehensive dataset and the optimal function, several typical intensity scenarios can be defined. In one specific embodiment, the 25%, 50%, 75%, 95%, and 100% quantiles of the total rainfall recorded by all typhoons in each region within a historical time period are selected as typical rainfall intensity scenarios, corresponding to total rainfall of 34.60 mm, 62.54 mm, 102.57 mm, 199.55 mm, and 552.40 mm, respectively. Here, since the total rainfall was determined to have the best fit (i.e., selection effect) to the candidate function among the three disaster-causing intensities in the above steps, the target scenario is selected as total rainfall. The intensity of total rainfall above 95% can be considered as an extreme typhoon-induced rainfall scenario (the total rainfall in this scenario is 552.40 mm). Therefore, the target scenarios in this embodiment include typical rainfall intensity scenarios and extreme typhoon-induced rainfall scenarios.

[0095] Step 1042: Based on the target scenario and the optimal function, obtain the disaster-bearing body loss rate;

[0096] Specifically, by inputting the total rainfall in typical rainfall intensity scenarios of the target scene (such as 34.60mm, 62.54mm, 102.57mm, 199.55mm and 552.40mm) into the optimal function, the loss rate of the disaster-bearing body can be obtained.

[0097] Step 1043: Obtain the absolute loss value of the disaster-bearing body based on the loss rate of the disaster-bearing body.

[0098] Specifically, by multiplying the disaster-bearing body loss rate by the region's socio-economic data, such as the economic loss rate being the disaster-bearing body loss rate multiplied by the regional GDP, the population disaster rate being the disaster-bearing body loss rate multiplied by the year-end resident population, and the crop disaster rate being the disaster-bearing body loss rate multiplied by the cultivated land area, the absolute disaster-bearing body loss value of the region under different target scenarios can be calculated (the absolute disaster-bearing body loss value includes total economic loss, the number of affected people, and the area of ​​affected crops).

[0099] In an optional embodiment of the present invention, step 105 includes:

[0100] Step 1051: Obtain the preset evaluation criteria;

[0101] Specifically, the preset evaluation criteria can be the top m regions in terms of disaster loss rate and absolute disaster loss value, where m is at least 1. The preset evaluation criteria can be set according to the actual situation. For example, in a specific embodiment, the preset evaluation criteria are regions with relatively large disaster loss rates and relatively large absolute disaster loss values.

[0102] Step 1052: Based on the preset assessment criteria, the loss rate of the disaster-bearing body, and the absolute loss value of the disaster-bearing body, conduct a typhoon disaster vulnerability assessment on the multiple regions to obtain typhoon disaster vulnerability assessment results; the typhoon disaster vulnerability assessment results include at least one of the multiple regions.

[0103] Specifically, data on the loss rate of disaster-bearing bodies in each region can be input into the ArcGIS platform (a scalable, comprehensive geographic information system platform) to create loss rate zoning maps for each region. This allows for a clear view of which regions experience the highest loss rates under typhoon-induced damage. If the preset assessment criteria are the highest loss rate and the highest absolute loss value of disaster-bearing bodies, then the regions with the highest loss rates and absolute losses are selected as part of the typhoon vulnerability assessment results. The typhoon vulnerability assessment results can also include comprehensive datasets, optimal functions, target damage intensity variables, loss rates, and absolute losses of disaster-bearing bodies, etc., so that users can intuitively view all the data in the assessment process. Regions in the typhoon vulnerability assessment results often exhibit high vulnerability to typhoon disasters and therefore require close attention.

[0104] In an optional embodiment of the present invention, step 106 includes:

[0105] Step 1061, through Obtain the parameter vector;

[0106] Step 1062: Determine the target driving factors based on the parameter vector;

[0107] Step 1063: Based on the target driving factors and the typhoon disaster vulnerability assessment results, determine the identification results of the vulnerability driving factors of the disaster-bearing body under the typhoon disaster.

[0108] in, For parameter vectors ( ), For the first The weight of each driving factor This refers to the number of observed samples (i.e., the number of target regions). To preset the number of driving factors, Indicates the first The first observation sample One driving factor value, Indicates the first The optimal function (optimal vulnerability function) of an observation sample has a certain parameter value (e.g., the optimal function 9 has two parameters. Since the two parameters represent different physical meanings, the driving factors of the two parameters are identified independently). This is the regularization parameter.

[0109] Specifically, step 1061 extracts the optimal function from the typhoon vulnerability assessment results. The optimal function includes the determined function parameter values, which form the function parameter vector (observation vector 𝑣) of the optimal function for subsequent calculations. Taking the derivative and setting it to zero directly yields the parameter vector. Closed-form solution .in, To design a matrix with dimensions of × Each row represents a sample, and each column represents a feature. For regularization parameters; The identity matrix has dimensions and parameter vectors. Consistent; This is an observation vector with dimensions n×1, where each row represents the target vulnerability curve parameter value in a sample. This represents the transpose operation of a vector or matrix.

[0110] In practice, the glmnet package (a tool for regression models) in R can be used to perform regularized regression analysis on the data. In this embodiment, the closed-form solution of the calculated parameter vector is... The regression coefficients of multiple pre-defined driving factors (the regression coefficients can be used to characterize a certain pre-defined driving factor) right The magnitude of the impact is used to screen the preset driving factors that have the greatest (or relatively large) influence on the parameters of the optimal function, which are the target driving factors. The identification results of the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters include the typhoon disaster vulnerability assessment results and target driving factors, and identify the driving role of different socio-economic factors on the spatial differences in vulnerability; providing data support and decision-making basis for improving regional disaster prevention and mitigation capabilities and resilience.

[0111] In an optional embodiment of the present invention, the preset driving factors are shown in Tables 4 to 6:

[0112] Table 4 Preset Driving Factors 1

[0113]

[0114] Table 5 Preset Driving Factors 2

[0115]

[0116] Table 6 Preset Driving Factors 3

[0117]

[0118] A specific embodiment of the method for identifying the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters according to the present invention includes:

[0119] Step 111: Obtain historical typhoon disaster intensity data and historical typhoon disaster loss characterization data for multiple regions;

[0120] First, obtain raw typhoon disaster data and raw typhoon disaster loss data for multiple regions within a historical time period (such as the past ten, twenty, or fifty years). Then, based on the raw typhoon disaster data and raw typhoon disaster loss data, determine the historical typhoon disaster intensity data and historical typhoon disaster loss characterization data.

[0121] Step 112: Match the historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data to obtain a comprehensive dataset;

[0122] Historical typhoon disaster intensity data and historical typhoon disaster loss characterization data are matched and merged according to year, typhoon number, and region, resulting in a comprehensive dataset with the structure: year – typhoon number – region – historical typhoon disaster intensity data – historical typhoon disaster loss characterization data.

[0123] Step 113: Based on the comprehensive dataset and multiple preset candidate functions, obtain the optimal function and the target disaster intensity variable;

[0124] Using historical typhoon disaster intensity data from the comprehensive dataset as x and historical typhoon disaster loss characterization data from the comprehensive dataset as y, and inputting multiple preset candidate functions, we can obtain the parameter values ​​of each preset candidate function. Based on the parameter values, multiple candidate functions can be determined. Based on multiple candidate functions and preset judgment indicators, the optimal function can be determined. By substituting the comprehensive dataset into multiple preset candidate functions for curve fitting, we can obtain data on region-disaster-causing factor type-disaster loss type-function form sequence number-fitting parameter results-fitting effect evaluation. For example, Region 1 (with 9 sub-regions) - total rainfall / cumulative wind energy per unit area / wind-rain coupling intensity - economic loss rate / population disaster rate / crop disaster rate - Function 1 (with 34 parameters) - parameters a, b (some may also have c, d depending on the function form) - RMSE, MAE, Based on these results, the results are categorized according to the type of disaster intensity, and all successfully fitted samples are judged by RMSE, MAE, and other metrics. The results show the distribution of data across the three types of disaster intensity. This is based on the average data from the cloud and rain maps. The criteria for determining the maximum and minimum RMSE and MAE are used to identify the target disaster intensity variable (in this example, the total rainfall) that best fits the three types of load cells (total rainfall, cumulative wind energy per unit area, and wind-rain coupling intensity).

[0125] Based on total rainfall as the target disaster-causing intensity variable, the fitting results of each candidate function are analyzed. The value is used to determine which area is selected. Given that the mean of the candidate function is the highest, and that all candidate functions fitted in all regions are monotonic and pass through the origin, the candidate function with the best overall fitting effect is selected as the optimal function.

[0126] Step 114: Based on the optimal function and the target disaster intensity variable, obtain the disaster-bearing body loss rate and the absolute loss value of the disaster-bearing body;

[0127] Based on the selected optimal function, the 25%, 50%, 75%, 95%, and 100% quantiles of the total rainfall recorded by all typhoons within a preset time period for each region are chosen as typical rainfall intensity scenarios, corresponding to total rainfall of 34.60 mm, 62.54 mm, 102.57 mm, 199.55 mm, and 552.40 mm, respectively. (Here, since it was previously determined that the total rainfall has the best fit to the candidate function among the three disaster intensities, the target scenario is chosen as total rainfall). Here, total rainfall intensity above the 95th quantile can be considered as an extreme typhoon-induced rainfall scenario. Since the candidate function is defined as the functional relationship between the disaster-bearing body loss rate and the disaster intensity, given the disaster intensity (here, the total rainfall), the disaster-bearing body loss rate can be obtained by substituting the data. Taking Region 2 as an example, under the extreme typhoon-induced rainfall scenario (total rainfall of 522.4 mm), the economic loss rate can be calculated by substituting the total rainfall of Region 2 and the specific form of the optimal function for the disaster-bearing body. By inputting loss rate data from various regions into the ArcGIS platform, regional loss rate zoning maps can be generated, indicating which regions experienced the greatest loss rates to their affected structures under typhoon-induced damage. Multiplying the loss rate by the region's socioeconomic data—for example, the economic loss rate is the loss rate of affected structures multiplied by the regional GDP, the population disaster rate is the loss rate of affected structures multiplied by the year-end resident population, and the crop damage rate is the loss rate of affected structures multiplied by the cultivated land area—gives the absolute loss value of the affected structures for that region.

[0128] Step 115: Conduct a typhoon vulnerability assessment based on the loss rate and absolute loss value of the affected body to obtain the typhoon vulnerability assessment result.

[0129] The loss rate and absolute loss value of the affected areas are sorted in descending order. The top m areas (where m is an integer greater than 0) in terms of loss rate and / or absolute loss value are selected as the typhoon vulnerability assessment results. These areas tend to be more vulnerable to typhoon disasters and therefore require special attention.

[0130] The method for identifying the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters in this invention can spatially assess the vulnerability of typhoon disasters and identify areas that exhibit high vulnerability when subjected to typhoon disasters. It can be widely applied in fields such as meteorological disaster vulnerability modeling, regional disaster reduction strategy optimization, and disaster prevention and mitigation decision support.

[0131] like Figure 2 As shown, an embodiment of the present invention proposes a device 200 for identifying the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters, comprising:

[0132] Module 201 is used to acquire historical typhoon disaster intensity data and historical typhoon disaster loss characterization data for multiple regions;

[0133] The processing module 202 is used to match the historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data to obtain a comprehensive dataset; based on the comprehensive dataset and multiple preset candidate functions, to obtain the optimal function and the target disaster-causing intensity variable; based on the optimal function and the target disaster-causing intensity variable, to obtain the disaster-bearing body loss rate and the absolute loss value of the disaster-bearing body; to conduct a typhoon disaster vulnerability assessment based on the disaster-bearing body loss rate and the absolute loss value of the disaster-bearing body, to obtain the typhoon disaster vulnerability assessment result; and based on the typhoon disaster vulnerability assessment result and multiple preset driving factors, to determine the identification result of the driving factors of the vulnerability of the disaster-bearing body under the typhoon disaster.

[0134] Optionally, obtain historical typhoon disaster intensity data and historical typhoon disaster loss characterization data for multiple regions, including:

[0135] Obtain raw typhoon disaster data and raw typhoon disaster loss data for multiple regions within a historical time period;

[0136] Based on the original typhoon disaster data, historical typhoon disaster intensity data is determined; the historical typhoon disaster intensity data includes total rainfall, cumulative wind energy per unit area, and wind-rain coupling intensity.

[0137] Based on the original typhoon disaster loss data, historical typhoon disaster loss characterization data are determined; the historical typhoon disaster loss characterization data includes economic loss rate, population disaster rate, and crop disaster rate.

[0138] Optionally, the historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data are matched to obtain a comprehensive dataset, including:

[0139] The historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data are preprocessed to obtain preprocessed data;

[0140] The preprocessed data is matched according to a preset matching method to obtain a comprehensive dataset.

[0141] Optionally, based on the comprehensive dataset and multiple preset candidate functions, the optimal function and target disaster-causing intensity variable are obtained, including:

[0142] Based on the comprehensive dataset and multiple preset candidate functions, multiple parameters are determined;

[0143] The optimal function and target disaster intensity variable are obtained by filtering based on the comprehensive dataset, the multiple preset candidate functions, and the multiple parameters.

[0144] Optionally, the optimal function and target disaster-causing intensity variable are obtained by filtering based on the comprehensive dataset, the multiple preset candidate functions, and the multiple parameters, including:

[0145] The indicator value is obtained based on the preset judgment indicator, the comprehensive dataset, the multiple preset candidate functions, and the multiple parameters;

[0146] The optimal function and target disaster intensity variable are obtained by screening according to the preset screening criteria and the index values.

[0147] Optionally, based on the optimal function and the target disaster intensity variable, the loss rate and absolute loss value of the disaster-bearing body are obtained, including:

[0148] Based on the optimal function, determine the target scenario;

[0149] Based on the target scenario and the optimal function, the loss rate of the disaster-bearing body is obtained;

[0150] Based on the loss rate of the disaster-bearing body, the absolute loss value of the disaster-bearing body is obtained.

[0151] Optionally, a typhoon vulnerability assessment is performed based on the loss rate and absolute loss value of the affected body to obtain the typhoon vulnerability assessment results, including:

[0152] Obtain the preset evaluation criteria;

[0153] Based on the preset assessment criteria, the loss rate of the disaster-bearing body, and the absolute loss value of the disaster-bearing body, a typhoon disaster vulnerability assessment is conducted on the multiple regions to obtain typhoon disaster vulnerability assessment results; the typhoon disaster vulnerability assessment results include at least one of the multiple regions.

[0154] The device for identifying the vulnerability drivers of disaster-bearing bodies under typhoon disasters in this invention acquires historical typhoon disaster intensity data and historical typhoon disaster loss characterization data from multiple regions. It then matches the historical typhoon disaster intensity data and historical typhoon disaster loss characterization data to obtain a comprehensive dataset. Based on the comprehensive dataset and multiple preset candidate functions, it obtains the optimal function and the target disaster-causing intensity variable. Furthermore, based on the optimal function and the target disaster-causing intensity variable, it obtains the disaster-bearing body loss rate and the absolute loss value of the disaster-bearing body. Based on the disaster-bearing body loss rate and the absolute loss value of the disaster-bearing body, it performs a typhoon disaster vulnerability assessment to obtain the typhoon disaster vulnerability assessment result. Finally, based on the typhoon disaster vulnerability assessment result and multiple preset driving factors, it determines the identification result of the vulnerability drivers of disaster-bearing bodies under typhoon disasters. This device can more accurately characterize the vulnerability features of different disaster-bearing bodies, reveal their spatial heterogeneity and formation mechanism, improve the accuracy of typhoon disaster vulnerability assessment and driving factor identification, and thus effectively enhance the pertinence and scientific nature of typhoon disaster risk management.

[0155] It should be noted that this device corresponds to the method described above, and all implementations in the method embodiments described above are applicable to the embodiments of this device and can achieve the same technical effect. Further details will not be provided in this embodiment.

[0156] This invention also provides a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method as described in any of the above embodiments. All implementations in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effects. Further details are omitted in this embodiment.

[0157] This invention also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method as described in any of the above embodiments. All implementations in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effects. Further details are omitted in this embodiment.

[0158] It should be noted that in the apparatus and method of the present invention, the components or steps can obviously be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent solutions of the present invention. Furthermore, the steps for performing the above series of processes can naturally be performed in the order described and in chronological order, but are not necessarily required to be performed in chronological order. Some steps can be performed in parallel, overlapping, or independently of each other.

[0159] It should be noted that in the above embodiments, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments described above is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0160] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for identifying the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters, characterized in that, include: Acquire historical typhoon disaster intensity data and historical typhoon disaster loss characterization data for multiple regions; The historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data are matched to obtain a comprehensive dataset; Based on the comprehensive dataset and multiple preset candidate functions, the optimal function and the target disaster-causing intensity variable are obtained; Based on the optimal function and the target disaster intensity variable, the loss rate and absolute loss value of the disaster-bearing body are obtained; The typhoon vulnerability assessment is conducted based on the loss rate and absolute loss value of the affected body to obtain the typhoon vulnerability assessment results. Based on the typhoon disaster vulnerability assessment results and multiple preset driving factors, the identification results of the vulnerability driving factors of the disaster-bearing body under typhoon disaster are determined.

2. The method for identifying the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters according to claim 1, characterized in that, Acquire historical typhoon disaster intensity data and historical typhoon disaster loss characterization data for multiple regions, including: Obtain raw typhoon disaster data and raw typhoon disaster loss data for multiple regions within a historical time period; Based on the original typhoon disaster data, historical typhoon disaster intensity data is determined; the historical typhoon disaster intensity data includes total rainfall, cumulative wind energy per unit area, and wind-rain coupling intensity. Based on the original typhoon disaster loss data, historical typhoon disaster loss characterization data are determined; the historical typhoon disaster loss characterization data includes economic loss rate, population disaster rate, and crop disaster rate.

3. The method for identifying the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters according to claim 1, characterized in that, The historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data are matched to obtain a comprehensive dataset, including: The historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data are preprocessed to obtain preprocessed data; The preprocessed data is matched according to a preset matching method to obtain a comprehensive dataset.

4. The method for identifying the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters according to claim 1, characterized in that, Based on the comprehensive dataset and multiple preset candidate functions, the optimal function and target disaster-causing intensity variables are obtained, including: Based on the comprehensive dataset and multiple preset candidate functions, multiple parameters are determined; The optimal function and target disaster intensity variable are obtained by filtering based on the comprehensive dataset, the multiple preset candidate functions, and the multiple parameters.

5. The method for identifying the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters according to claim 4, characterized in that, Based on the comprehensive dataset, the multiple preset candidate functions, and the multiple parameters, the optimal function and the target disaster-causing intensity variable are obtained through screening, including: The indicator value is obtained based on the preset judgment indicator, the comprehensive dataset, the multiple preset candidate functions, and the multiple parameters; The optimal function and target disaster intensity variable are obtained by screening according to the preset screening criteria and the index values.

6. The method for identifying the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters according to claim 1, characterized in that, Based on the optimal function and the target disaster intensity variable, the loss rate and absolute loss value of the disaster-bearing body are obtained, including: Based on the optimal function, determine the target scenario; Based on the target scenario and the optimal function, the loss rate of the disaster-bearing body is obtained; Based on the loss rate of the disaster-bearing body, the absolute loss value of the disaster-bearing body is obtained.

7. The method for identifying the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters according to claim 1, characterized in that, Typhoon vulnerability assessment is conducted based on the loss rate and absolute loss value of the affected bodies to obtain typhoon vulnerability assessment results, including: Obtain the preset evaluation criteria; Based on the preset assessment criteria, the loss rate of the disaster-bearing body, and the absolute loss value of the disaster-bearing body, a typhoon disaster vulnerability assessment is conducted on the multiple regions to obtain typhoon disaster vulnerability assessment results; the typhoon disaster vulnerability assessment results include at least one of the multiple regions.

8. A device for identifying the driving factors of vulnerability of disaster-bearing bodies under typhoon disasters, characterized in that, include: The acquisition module is used to acquire historical typhoon disaster intensity data and historical typhoon disaster loss characterization data for multiple regions; The processing module is used to match the historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data to obtain a comprehensive dataset; based on the comprehensive dataset and multiple preset candidate functions, to obtain the optimal function and the target disaster-causing intensity variable; based on the optimal function and the target disaster-causing intensity variable, to obtain the loss rate and absolute loss value of the disaster-bearing body; to perform a typhoon disaster vulnerability assessment based on the loss rate and the absolute loss value of the disaster-bearing body, to obtain the typhoon disaster vulnerability assessment result; and based on the typhoon disaster vulnerability assessment result and multiple preset driving factors, to determine the identification result of the driving factors of the vulnerability of the disaster-bearing body under the typhoon disaster.

9. A computing device, characterized in that, include: A processor, a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.

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