A method, device and equipment for identifying driving factors of vulnerability of a disaster-affected body under typhoon disaster, and a storage medium

Abstract

The application provides a typhoon disaster vulnerability driving factor identification method, device, equipment and 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 multiple regions; obtaining an optimal function and a target disaster-causing intensity variable according to the comprehensive data set and multiple preset candidate functions; obtaining a disaster body loss rate and a disaster body absolute loss value according to the optimal function and the target disaster-causing intensity variable; performing typhoon disaster vulnerability assessment according to the disaster body loss rate and the disaster body absolute loss value to obtain a typhoon disaster vulnerability assessment result; and determining an identification result of a typhoon disaster vulnerability driving factor according to the typhoon disaster vulnerability assessment result and multiple preset driving factors. The application can improve the accuracy of typhoon disaster vulnerability assessment and driving factor identification, and effectively improve the pertinence and scientificity of typhoon disaster risk management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of disaster risk assessment, and further relates to a method and device for identifying driving factors of vulnerability of a disaster-affected body under a typhoon disaster, an equipment and a storage medium. BACKGROUND

[0002] Typhoon vulnerability refers to the degree to which a disaster-affected body (including population, buildings, infrastructure, ecological system, economic and social system, etc.) is vulnerable to damage or loss under the influence of a typhoon and its secondary disasters (such as strong wind, heavy rain, storm surge, flood, landslide, etc.), as well as the ability to cope with disasters, resist risks and recover after disasters. The higher the typhoon vulnerability, the more vulnerable the disaster-affected body is to typhoon, and the more difficult it is to recover after the disaster; on the contrary, the lower the vulnerability, the stronger the ability of the disaster-affected body to resist typhoon disasters, and the relatively smaller the loss. The existing typhoon vulnerability assessment methods mostly focus on a single hazard factor or use a fixed function form, which is difficult to accurately reflect the differences in disaster bearing capacity of different regions, has poor accuracy, and cannot identify the driving factors that cause different vulnerabilities of the disaster-affected body. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a method and device for identifying driving factors of vulnerability of a disaster-affected body under a typhoon disaster, and an equipment and a storage medium, so as to improve the accuracy of typhoon disaster vulnerability assessment results and driving factor identification.

[0004] To solve the above technical problems, the technical solutions of the present application are as follows:

[0005] In a first aspect, the present application provides a method for identifying driving factors of vulnerability of a disaster-affected body under a typhoon disaster, comprising:

[0006] obtaining historical typhoon disaster intensity data and historical typhoon disaster loss characterization data of a plurality of regions;

[0007] matching the historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data to obtain a comprehensive data set;

[0008] obtaining an optimal function and a target disaster-causing intensity variable according to the comprehensive data set and a plurality of preset candidate functions;

[0009] obtaining a disaster-affected body loss rate and a disaster-affected body absolute loss value according to the optimal function and the target disaster-causing intensity variable;

[0010] performing typhoon disaster vulnerability assessment according to the disaster-affected body loss rate and the disaster-affected body absolute loss value to obtain typhoon disaster vulnerability assessment results;

[0011] According to the typhoon disaster vulnerability assessment result and a plurality of preset driving factors, an identification result of a typhoon disaster vulnerability driving factor of a bearing body is determined.

[0012] Optionally, historical typhoon disaster intensity data and historical typhoon disaster loss characterization data of a plurality of regions are acquired, including:

[0013] Original typhoon disaster data and original typhoon disaster loss data of a plurality of regions in a historical time period are acquired;

[0014] According to the original typhoon disaster data, historical typhoon disaster intensity data are determined; the historical typhoon disaster intensity data include total rainfall, unit area cumulative wind energy and wind-rain coupling intensity;

[0015] According to the original typhoon disaster loss data, historical typhoon disaster loss characterization data are determined; the historical typhoon disaster loss characterization data include 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 data set, 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 are matched in a preset matching mode to obtain a comprehensive data set.

[0019] Optionally, according to the comprehensive data set and a plurality of preset candidate functions, an optimal function and a target disaster-causing intensity variable are obtained, including:

[0020] According to the comprehensive data set and a plurality of preset candidate functions, a plurality of parameters are determined;

[0021] According to the comprehensive data set, the plurality of preset candidate functions and the plurality of parameters, screening is performed to obtain an optimal function and a target disaster-causing intensity variable.

[0022] Optionally, according to the comprehensive data set, the plurality of preset candidate functions and the plurality of parameters, screening is performed to obtain an optimal function and a target disaster-causing intensity variable, including:

[0023] According to a preset judgment index, the comprehensive data set, the plurality of preset candidate functions and the plurality of parameters, an index value is obtained;

[0024] According to a preset screening standard and the index value, screening is performed to obtain an optimal function and a target disaster-causing intensity variable.

[0025] Optionally, according to the optimal function and the target disaster-causing intensity variable, a hazard-bearing body loss rate and a hazard-bearing body absolute loss value are obtained, comprising:

[0026] According to the optimal function, a target scenario is determined;

[0027] According to the target scenario and the optimal function, a hazard-bearing body loss rate is obtained;

[0028] According to the hazard-bearing body loss rate, a hazard-bearing body absolute loss value is obtained.

[0029] Optionally, according to the hazard-bearing body loss rate and the hazard-bearing body absolute loss value, a typhoon disaster vulnerability assessment is performed to obtain a typhoon disaster vulnerability assessment result, comprising:

[0030] A preset evaluation standard is obtained;

[0031] According to the preset evaluation standard, the hazard-bearing body loss rate and the hazard-bearing body absolute loss value, a typhoon disaster vulnerability assessment is performed on the multiple regions to obtain a typhoon disaster vulnerability assessment result; the typhoon disaster vulnerability assessment result comprises at least one region in the multiple regions.

[0032] In a second aspect of the present application, a device for identifying a vulnerability driving factor of a hazard-bearing body under a typhoon disaster is provided, comprising:

[0033] An acquisition module is configured to acquire historical typhoon disaster intensity data and historical typhoon disaster loss representation data of multiple regions;

[0034] A processing module is configured to match the historical typhoon disaster intensity data and the historical typhoon disaster loss representation data to obtain a comprehensive data set; according to the comprehensive data set and multiple preset candidate functions, an optimal function and a target disaster-causing intensity variable are obtained; according to the optimal function and the target disaster-causing intensity variable, a hazard-bearing body loss rate and a hazard-bearing body absolute loss value are obtained; according to the hazard-bearing body loss rate and the hazard-bearing body absolute loss value, a typhoon disaster vulnerability assessment is performed to obtain a typhoon disaster vulnerability assessment result; according to the typhoon disaster vulnerability assessment result and multiple preset driving factors, an identification result of a vulnerability driving factor of a hazard-bearing body under a typhoon disaster is determined.

[0035] In a third aspect of the present application, a computing device is provided, comprising a processor and a memory storing a computer program, wherein the computer program is executed by the processor to perform the method of the first aspect.

[0036] In a fourth aspect of the present application, a computer-readable storage medium is provided, storing instructions, when the instructions are executed on a computer, causing the computer to perform the method of the first aspect.

[0037] The above scheme of the present application at least includes the following beneficial effects:

[0038] The above scheme of the present application, by acquiring historical typhoon disaster intensity data and historical typhoon disaster loss representation data of multiple regions, then matching the historical typhoon disaster intensity data and the historical typhoon disaster loss representation data, obtaining a comprehensive data set, then according to the comprehensive data set and multiple preset candidate functions, obtaining an optimal function and a target disaster-causing intensity variable, then 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, according to the disaster-bearing body loss rate and the disaster-bearing body absolute loss value, performing typhoon disaster vulnerability assessment, obtaining a typhoon disaster vulnerability assessment result, and finally according to the typhoon disaster vulnerability assessment result and multiple preset driving factors, determining an identification result of a typhoon disaster vulnerability driving factor of a disaster-bearing body, can more accurately depict the vulnerability characteristics of different disaster-bearing bodies, reveal the spatial heterogeneity and formation mechanism thereof, improve the accuracy of typhoon disaster vulnerability assessment and driving factor identification, and thus effectively improve the pertinence and scientificity of typhoon disaster risk management. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 It is a flowchart of a typhoon disaster vulnerability driving factor identification method of a disaster-bearing body in an embodiment of the present application;

[0040] Figure 2 It is a structural schematic diagram of a typhoon disaster vulnerability driving factor identification device in an embodiment of the present application. DETAILED DESCRIPTION

[0041] Exemplary embodiments of the present application will be described in greater detail below, with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it is understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present application can be more thoroughly understood, and the scope of the present application can be accurately conveyed to those skilled in the art.

[0042] As Figure 1 shown, an embodiment of the present application proposes a typhoon disaster vulnerability driving factor identification method of a disaster-bearing body, including the following steps:

[0043] Step 101, acquiring historical typhoon disaster intensity data and historical typhoon disaster loss representation data of multiple regions;

[0044] Step 102, matching the historical typhoon disaster intensity data and the historical typhoon disaster loss representation data, to obtain a comprehensive data set;

[0045] Step 103, according to the comprehensive data set and multiple preset candidate functions, obtaining an optimal function and a target disaster-causing intensity variable;

[0046] In step 104, a loss rate of the hazard-affected body and an absolute loss value of the hazard-affected body are obtained according to the optimal function and the target disaster-causing intensity variable.

[0047] In step 105, a typhoon disaster vulnerability assessment result is obtained by performing typhoon disaster vulnerability assessment according to the loss rate of the hazard-affected body and the absolute loss value of the hazard-affected body.

[0048] In step 106, an identification result of a typhoon disaster vulnerability driving factor of the hazard-affected body is determined according to the typhoon disaster vulnerability assessment result and a plurality of preset driving factors.

[0049] The method for identifying a typhoon disaster vulnerability driving factor of a hazard-affected body according to the embodiment of the application can more accurately depict the vulnerability characteristics of different disaster carriers, reveal the spatial heterogeneity and formation mechanism thereof, and improve the accuracy of typhoon disaster vulnerability assessment and driving factor identification, thereby effectively improving the pertinence and scientificity of typhoon disaster risk management.

[0050] Here, the hazard-affected body refers to economy, population, and agriculture. The driving factor is a factor causing the difference in typhoon disaster vulnerability.

[0051] In an optional embodiment of the application, step 101 comprises:

[0052] In step 1011, original typhoon disaster data and original typhoon disaster loss data of a plurality of regions in a historical time period are obtained.

[0053] Specifically, the historical time period refers to the past several years or decades, which is selected according to needs. The original typhoon disaster data can be obtained from the Tropical Cyclone Data Center of the Meteorological Bureau, including a tropical cyclone best path data set and a tropical cyclone wind and rain data set, which provide location information of meteorological stations recording meteorological elements in the typhoon disaster process. The original typhoon disaster loss data can be obtained from official websites and the like, and includes statistical data of direct economic losses, population affected by disasters, and crop affected areas caused by typhoon disasters in each region.

[0054] Step 1012, determining historical typhoon disaster intensity data according to the original typhoon disaster data; the historical typhoon disaster intensity data includes total rainfall, unit area cumulative wind energy and wind-rain coupling intensity;

[0055] Specifically, the historical typhoon disaster intensity data mainly includes regional total rainfall, regional unit area cumulative wind energy and regional wind-rain coupling intensity. Among them, the regional total rainfall is directly used to represent the rainfall disaster intensity of the typhoon, and the regional total rainfall can be obtained by extracting the single total rainfall of the meteorological station in each region and taking the average value; based on the tropical cyclone best path data set (including the typhoon center pressure and trajectory coordinates), the single unit area cumulative wind energy at the position of each regional meteorological station is calculated, and the average value of the single unit area cumulative wind energy of each station is taken as the regional unit area cumulative wind energy; the wind-rain coupling disaster intensity is obtained by the product of the total rainfall and the unit area cumulative wind energy.

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

[0057] The maximum gradient wind speed is obtained by

[0058] The average maximum wind speed is obtained according to

[0059] The first wind speed is obtained by

[0060] The average wind speed is obtained by

[0061] The single unit area cumulative wind energy is obtained by

[0062] The regional unit area cumulative wind energy is obtained according to the single unit area cumulative wind energy. In this step, the average value of the single unit area cumulative wind energy at the position of all meteorological stations in the region is taken to obtain the regional unit area cumulative wind energy.

[0063] Among them, is the maximum gradient wind speed, K is an empirical constant, and the value is 6.72, is the central pressure difference (unit: hPa), which is obtained by , is the typhoon center pressure, is the maximum wind speed radius (unit: km), which is obtained by , f is the Coriolis coefficient, which is obtained by , is the earth rotation angular velocity, is the latitude of the air mass, represents the latitude of the typhoon center position,​​​​​ 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 formed 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 application, step 102 comprises:

[0069] Step 1021, pre-processing the historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data to obtain pre-processed data;

[0070] Step 1022, matching the pre-processed data according to a preset matching mode to obtain a comprehensive data set.

[0071] Specifically, the pre-processing mode in step 1021 can be outlier rejection and standardization processing to improve data accuracy and quality. In step 1022, the typhoon event is matched with its corresponding affected area to construct a regional “disaster-causing factor-disaster loss” correlation data set for each typhoon event, i.e., a comprehensive data set, to provide a basis for subsequent steps.

[0072] Here, the typhoon event refers to the historical typhoon disaster intensity data in the corresponding pre-processed data, which is calculated based on the site location of the meteorological station within the regional range and its observation information during a typhoon process, and the data set formed includes: year-typhoon number-region-disaster-causing factor intensity (disaster-causing factor intensity includes regional total rainfall, regional cumulative wind energy per unit area, and regional wind-rain coupling intensity). The historical typhoon disaster loss characterization data in the pre-processed data is obtained by organizing on the basis of original statistical data, including: year-typhoon number-region-disaster loss index (disaster loss index includes economic loss rate, population disaster rate, and crop disaster rate). On this basis, the historical typhoon disaster intensity data in the pre-processed data and the historical typhoon disaster loss characterization data in the pre-processed data are matched according to year, typhoon number, and region, and finally a comprehensive data set with the structure of: year-typhoon number-region-disaster-causing factor intensity-disaster loss is obtained.

[0073] In an optional embodiment of the present application, step 103 comprises:

[0074] Step 1031, determining a plurality of parameters according to the comprehensive data set and a plurality of preset candidate functions;

[0075] Specifically, the plurality of preset candidate functions (the preset candidate function can also be called a vulnerability curve) can be selected from the preset candidate function library shown in Table 1, Table 2, and Table 3, including linear function, power function, logarithmic function, Logistic function, etc.; the historical typhoon disaster intensity data in the comprehensive data set is input as x, and the historical typhoon disaster loss characterization data in the comprehensive data set is input as y into each preset candidate function, and the values of parameters a, b, and c can be obtained, i.e., a plurality of parameters.

[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, screening according to the comprehensive data set, the plurality of preset candidate functions and the plurality of parameters, obtaining the optimal function and the target disaster-causing intensity variable.

[0083] In an optional embodiment of the present application, step 1032 comprises:

[0084] Step 10321, obtaining an index value according to a preset judgment index, the comprehensive data set, the plurality of preset candidate functions and the plurality of parameters.

[0085] Specifically, the preset judgment index can include root mean square error RMSE, mean absolute error MAE and coefficient of determination According to the historical typhoon disaster intensity data x in the comprehensive data set, the historical typhoon disaster loss representation data y in the comprehensive data set, the plurality of preset candidate functions, the plurality of parameters and the following formula, the value of the preset judgment index, i.e. the index value, can be calculated:

[0086]

[0087]

[0088]

[0089] Wherein, is the root mean square error, n is the total number of data in the comprehensive data set, which can be understood as the data amount of the historical typhoon disaster intensity data, is the i-th historical typhoon disaster loss representation data in the comprehensive data set, is the i-th historical typhoon disaster intensity data in the comprehensive data set, is the mean absolute error, is the coefficient of determination, SSE is the sum of squared residuals, which is obtained by , SST is the sum of squared deviations of true values from their mean, which is obtained by , .

[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, according to the hazard-bearing body loss rate, the hazard-bearing body absolute loss value is obtained.

[0098] Specifically, on the basis of the hazard-bearing body loss rate, the social and economic data of the region are multiplied, for example, the economic loss rate is the hazard-bearing body loss rate multiplied by the gross regional product, the population disaster rate is the hazard-bearing body loss rate multiplied by the annual end resident population, and the crop disaster rate is the hazard-bearing body loss rate multiplied by the cultivated land area. The hazard-bearing body absolute loss value (including total economic loss, the number of disaster population and the disaster crop area) of the region under different target scenarios can be calculated.

[0099] In an optional embodiment of the present application, step 105 comprises:

[0100] Step 1051, a preset evaluation standard is obtained;

[0101] Specifically, the preset evaluation standard can be the top m regions in terms of the hazard-bearing body loss rate and the hazard-bearing body absolute loss value, and m is at least 1. According to the actual situation, the preset evaluation standard can be set, for example, in a specific embodiment, the preset evaluation standard is the region with relatively large hazard-bearing body loss rate and relatively large hazard-bearing body absolute loss value.

[0102] Step 1052, according to the preset evaluation standard, the hazard-bearing body loss rate and the hazard-bearing body absolute loss value, the typhoon disaster vulnerability assessment of the multiple regions is carried out, and the typhoon disaster vulnerability assessment result is obtained; the typhoon disaster vulnerability assessment result includes at least one region in the multiple regions.

[0103] Specifically, the hazard-bearing body loss rate data of each region can be input into the Arcgis platform (scalable and comprehensive geographic information system platform), and the loss rate zoning map of each region is drawn, so that it can be directly seen which region has the maximum hazard-bearing body loss rate under the typhoon disaster intensity. If the preset evaluation standard is the region with the maximum hazard-bearing body loss rate and the maximum hazard-bearing body absolute loss value, the region with the maximum hazard-bearing body loss rate and the maximum hazard-bearing body absolute loss value is selected as part of the typhoon disaster vulnerability assessment result. The typhoon disaster vulnerability assessment result can also include comprehensive data set, optimal function, target disaster intensity variable, hazard-bearing body loss rate and hazard-bearing body absolute loss value, etc. so as to enable the user to directly see all the data in the evaluation process. The region in the typhoon disaster vulnerability assessment result often shows high vulnerability when suffering from typhoon disaster, and therefore needs to be focused on.

[0104] In an optional embodiment of the present application, step 106 comprises:

[0105] Step 1061, by the parameter vector is obtained;

[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 influence size), for screening the preset driving factors with the largest (or relatively large) parameter influence on the optimal function, i.e. the target driving factors. The identification result of the typhoon disaster-bearing body vulnerability driving factor under typhoon disaster includes a typhoon disaster vulnerability assessment result and the target driving factors, and the driving action of different social and economic factors on the spatial difference of vulnerability is identified; and data support and decision basis are provided for regional disaster prevention and mitigation ability and resilience promotion.

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

[0112] Table 4: Preset driving factor 1

[0113]

[0114] Table 5: Preset driving factor 2

[0115]

[0116] Table 6: Preset driving factor 3

[0117]

[0118] One specific embodiment of the typhoon disaster-bearing body vulnerability driving factor identification method of the embodiment of the present application includes:

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

[0120] First, obtain original typhoon disaster data and original typhoon disaster loss data of multiple regions in a historical time period (such as the past ten years or twenty years or fifty years), and then determine the historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data according to the original typhoon disaster data and the original typhoon disaster loss data.

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

[0122] The historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data are matched according to the year, the typhoon number and the region, and finally a comprehensive data set with the structure of year-typhoon number-region-historical typhoon disaster intensity data-historical typhoon disaster loss characterization data is obtained.

[0123] Step 113: Obtain an optimal function and a target disaster-causing intensity variable according to the comprehensive data set and multiple preset candidate functions;

[0124] The historical typhoon disaster intensity data in the comprehensive data set is taken as x, and the historical typhoon disaster loss representation data in the comprehensive data set is taken as y, and the multiple preset candidate functions are input, so that the values of the parameters of the multiple preset candidate functions are obtained. According to the values of the parameters, the multiple candidate functions are determined, and according to the multiple candidate functions and the preset judgment index, the optimal function is determined. Through the curve fitting by substituting the comprehensive data set into the multiple preset candidate functions, the data of the region-disaster-causing factor type-disaster loss type-function form serial number-fitting parameter result-fitting effect evaluation can be obtained, for example, region 1 (a total of 9 sub-regions)-total rainfall / unit area cumulative wind energy / wind-rain coupling intensity-economic loss rate / population disaster rate / crop disaster rate-function 1 (a total of 34)-parameters a, b (according to the function form, some also have c, d)-RMSE, MAE, On the basis of the results, according to the disaster-causing intensity type, all the fitting success is judged, and the RMSE, MAE, The distribution of the results data on the three types of disaster-causing intensity. According to the average The maximum, average RMSE, MAE minimum standard, judge the three types of bearing body (total rainfall, unit area cumulative wind energy and wind-rain coupling intensity) in the fitting effect of the best target disaster intensity variable (in this embodiment, it is judged that the total rainfall is the target disaster intensity variable).

[0125] On the basis of the total rainfall as the target disaster intensity variable, the fitting results of the values of the candidate functions are judged, and the candidate function with the highest mean value in all regions is selected. The candidate function that meets the conditions of being monotonous and passing through the origin in all regions is selected as the optimal function with the best comprehensive fitting effect.

[0126] Step 114, according to the optimal function and the target disaster intensity variable, the disaster-bearing body loss rate and the disaster-bearing body absolute loss value are obtained.

[0127] Based on the selected optimal function, the 25%, 50%, 75%, 95% and 100% quantiles of the total rainfall of each region in a preset time period are selected as typical rainfall intensity scenarios, which correspond to the total rainfall of 34.60mm, 62.54mm, 102.57mm, 199.55mm and 552.40mm, respectively (here, since it is judged in the foregoing that the total rainfall has the best fitting effect on the candidate function among the three disaster intensities, the target scenario is selected as the total rainfall). Here, the total rainfall intensity higher than the 95% quantile can be regarded as an extreme typhoon rainfall scenario. Since the definition of the candidate function is a function relationship between the disaster intensity (here, the total rainfall) and the loss rate of the bearing body, the loss rate of the bearing body can be obtained by bringing the data into the function on the basis of the known disaster intensity. Taking region 2 as an example, the economic loss rate can be obtained by bringing the total rainfall of region 2 and the optimal function specific form of the bearing body economy into the function in the extreme typhoon rainfall scenario (the total rainfall is 522.4mm). The loss rate data of each region are input into the Arcgis platform, and a loss rate zoning map of the region can be drawn, so as to indicate which region has the maximum loss rate of the bearing body under the typhoon disaster intensity. The absolute loss value of the bearing body in the region can be obtained by multiplying the loss rate of the bearing body by the social and economic basic data of the region, for example, the economic loss rate is the loss rate of the bearing body multiplied by the gross regional product, the population disaster rate is the loss rate of the bearing body multiplied by the annual end resident population, and the crop disaster rate is the loss rate of the bearing body multiplied by the cultivated land area.

[0128] In step 115, the typhoon disaster vulnerability assessment is performed according to the loss rate of the bearing body and the absolute loss value of the bearing body, and a typhoon disaster vulnerability assessment result is obtained.

[0129] The loss rate of the bearing body and the absolute loss value of the bearing body are sorted in descending order, and the top m (m is an integer greater than 0) regions in the loss rate of the bearing body and / or the absolute loss value of the bearing body are selected as the typhoon disaster vulnerability assessment result. These regions often have high vulnerability when suffering from typhoon disasters, and therefore need to be focused on.

[0130] The method for identifying the vulnerability driving factor of the bearing body under a typhoon disaster can spatially evaluate the typhoon disaster vulnerability, can identify the regions that have high vulnerability when suffering from typhoon disasters, and can be widely applied to the fields of meteorological disaster vulnerability modeling, optimization of regional disaster mitigation strategies and decision support for disaster prevention and mitigation.

[0131] As shown in Figure 2 The embodiment of the present application proposes a device 200 for identifying the vulnerability driving factor of the bearing body under a typhoon disaster, which comprises:

[0132] The acquisition module 201 is configured to acquire historical typhoon disaster intensity data and historical typhoon disaster loss characterization data of a plurality of regions.

[0133] The processing module 202 is configured to match the historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data to obtain a comprehensive data set, obtain an optimal function and a target disaster-causing intensity variable according to the comprehensive data set and a plurality of preset candidate functions, obtain a hazard-bearing body loss rate and a hazard-bearing body absolute loss value according to the optimal function and the target disaster-causing intensity variable, perform typhoon disaster vulnerability assessment according to the hazard-bearing body loss rate and the hazard-bearing body absolute loss value to obtain a typhoon disaster vulnerability assessment result, and determine an identification result of a hazard-bearing body vulnerability driving factor under a typhoon disaster according to the typhoon disaster vulnerability assessment result and a plurality of preset driving factors.

[0134] Optionally, the historical typhoon disaster intensity data and the historical typhoon disaster loss characterization data of a plurality of regions are acquired, including:

[0135] The original typhoon disaster data and the original typhoon disaster loss data of a plurality of regions in a historical time period are acquired.

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

[0137] The historical typhoon disaster loss characterization data is determined according to the original typhoon disaster loss data; the historical typhoon disaster loss characterization data includes an economic loss rate, a population disaster rate and a 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 data set, 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 in a preset matching manner to obtain a comprehensive data set.

[0141] Optionally, the optimal function and the target disaster-causing intensity variable are obtained according to the comprehensive data set and a plurality of preset candidate functions, including:

[0142] A plurality of parameters are determined according to the comprehensive data set and the plurality of preset candidate functions.

[0143] The optimal function and the target disaster-causing intensity variable are obtained by screening according to the comprehensive data set, the plurality of preset candidate functions and the plurality of parameters.

[0144] Optionally, the optimal function and the target disaster-causing intensity variable are obtained by screening according to the comprehensive data set, the plurality of preset candidate functions and the plurality of parameters, comprising:

[0145] An index value is obtained according to a preset judgment index, the comprehensive data set, the plurality of preset candidate functions and the plurality of parameters.

[0146] The optimal function and the target disaster-causing intensity variable are obtained by screening according to a preset screening standard and the index value.

[0147] Optionally, the loss rate of the hazard-affected body and the absolute loss value of the hazard-affected body are obtained according to the optimal function and the target disaster-causing intensity variable, comprising:

[0148] A target scenario is determined according to the optimal function.

[0149] The loss rate of the hazard-affected body is obtained according to the target scenario and the optimal function.

[0150] The absolute loss value of the hazard-affected body is obtained according to the loss rate of the hazard-affected body.

[0151] Optionally, the typhoon disaster vulnerability assessment result is obtained by performing typhoon disaster vulnerability assessment according to the loss rate of the hazard-affected body and the absolute loss value of the hazard-affected body, comprising:

[0152] A preset assessment standard is obtained.

[0153] The typhoon disaster vulnerability assessment result is obtained by performing typhoon disaster vulnerability assessment on the plurality of regions according to the preset assessment standard, the loss rate of the hazard-affected body and the absolute loss value of the hazard-affected body; the typhoon disaster vulnerability assessment result comprises at least one region in the plurality of regions.

[0154] The device for identifying the driving factor of the vulnerability of the disaster-bearing body under the typhoon disaster in the embodiment of the application can more accurately depict the vulnerability characteristics of different disaster-bearing bodies, reveal the spatial heterogeneity and formation mechanism thereof, improve the accuracy of the typhoon disaster vulnerability assessment and the driving factor identification, and thus effectively improve the pertinence and scientificity of the typhoon disaster risk management.

[0155] It should be noted that the device corresponds to the above method, and all the implementation manners in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects. The embodiments will not be described again.

[0156] The embodiment of the application further provides a computing device, comprising a processor and a memory storing a computer program, wherein the computer program is executed by the processor to perform the method according to any one of the above embodiments. All the implementation manners in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects. The embodiments will not be described again.

[0157] The embodiment of the application further provides a computer readable storage medium, which stores instructions, and the instructions make the computer execute the method according to any one of the above embodiments when the instructions are executed on the computer. All the implementation manners in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects. The embodiments will not be described again.

[0158] It should be noted that in the device and method of the application, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the application. Moreover, the steps of performing the above series of processes can be executed in time sequence according to the order of description, but it is not necessary to be executed in time sequence. Some steps can be executed in parallel, cross or independently of each other.

[0159] It is to be understood that the terminology "including", "comprising", or any other variation thereof, is intended to cover a non-exclusive inclusion such that process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Exclusion of such elements is only present if it is expressly stated that these elements are excluded. Further, it is to be understood that the description of the embodiments of the present application is not limited to the order of the steps of the methods described herein and that unless otherwise specified, the steps of the methods described herein can be performed in any order. Additionally, features described with respect to certain examples can be combined in other examples.

[0160] The above description is considered that the preferred embodiments of the present application, it should be noted that for those skilled in the art, without departing from the principles of the present application described, can also make a number of improvements and refinements, these improvements and refinements should also be considered the scope of protection of the present application.

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; This includes acquiring 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; The optimal function and target disaster-causing intensity variable are obtained based on the comprehensive dataset and multiple preset candidate functions, including: Based on the comprehensive dataset and multiple preset candidate functions, multiple parameters are determined; The optimal function and the target disaster intensity variable are obtained by filtering based on the comprehensive dataset, the multiple preset candidate functions, and the multiple parameters. 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: 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-causing intensity variable are obtained by filtering according to the preset screening criteria and the index values. Specifically, 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.

2. 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.

3. 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.

4. 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. This includes acquiring 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; The optimal function and target disaster-causing intensity variable are obtained based on the comprehensive dataset and multiple preset candidate functions, including: Based on the comprehensive dataset and multiple preset candidate functions, multiple parameters are determined; The optimal function and the target disaster intensity variable are obtained by filtering based on the comprehensive dataset, the multiple preset candidate functions, and the multiple parameters. 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: 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-causing intensity variable are obtained by filtering according to the preset screening criteria and the index values. Specifically, 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.

5. 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 3.

6. 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 3.

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