A method and device for assessing vulnerability of a disaster-bearing body

Abstract

The present invention provides a method and device for assessing the vulnerability of hazard-prone objects. The method comprises: obtaining a rainstorm event dataset; extracting indicators from the rainstorm event dataset to obtain a rainstorm hazard factor; obtaining a return period rainstorm hazard intensity based on the rainstorm hazard factor; obtaining a comprehensive index of the hazard intensity of the rainstorm hazard factor based on the return period rainstorm hazard intensity; and performing a vulnerability assessment on the hazard-prone object based on the comprehensive index of the hazard intensity of the rainstorm hazard factor to obtain a rainstorm vulnerability curve for the hazard-prone object. The present invention's solution can integrate historical precipitation data and historical disaster data to quantitatively, objectively, and comprehensively evaluate the impact of rainstorm disasters on the vulnerability of major hazard-prone objects, thereby enabling a vulnerability assessment of multiple hazard-prone objects by rainstorm.

Description

Technical Field

[0001] The present invention relates to the technical field of vulnerability assessment of rainstorm and flood disasters, and in particular to a vulnerability assessment method and device for a disaster-bearing body. Background Art

[0002] At present, the vulnerability assessment methods for rainstorm and flood disasters can be roughly divided into four categories, namely, historical disaster data method, indicator system method, data envelopment analysis method, and vulnerability curve method.

[0003] The historical disaster data method uses data from past rainstorm disasters and, based on mathematical statistics, conducts a post-disaster vulnerability assessment based on the results. This method is widely used and easily combined with other methods, but it is significantly affected by the completeness and accuracy of historical records. It also fails to consider other factors in the disaster system and lacks a deep understanding and discussion of the mechanisms that form vulnerability.

[0004] The indicator system approach uses a variety of comprehensive indicators to analyze and study the relationship between rainstorm disaster vulnerability and to broadly characterize regional vulnerability. This method offers advantages such as easy indicator acquisition, strong reference value, and mature development. However, this method is more suitable for assessing vulnerability in large-scale regions. Data availability is problematic for smaller scales, and excessive subjectivity in determining indicator weights can affect assessment results.

[0005] Data envelopment analysis (DEA) is primarily used to assess the performance of a group of decision-making units. In recent years, it has been explored in the field of disaster risk, showing great potential for development and continuing to improve with the advancement of vulnerability theory. However, the method lacks sufficient explanation for the inherent structure of vulnerability and is still in its exploratory stage, lacking maturity.

[0006] The vulnerability curve method, developed based on the historical disaster analysis method, is essentially a function whose underlying mechanism is to explore the relationship between hazard-causing factors and losses in vulnerable objects. This method provides a deeper understanding of the mechanisms of rainstorm disasters, but it is generally based on individual vulnerable objects and fails to consider the complex relationships between various elements within the disaster system. Furthermore, it struggles to quantify the intensity and vulnerability of hazard-causing factors, making the selection of indicators challenging.

[0007] In summary, the assessment of rainstorm vulnerability is gradually shifting from the individual to the systemic level, requiring consideration of the interactions between various elements within the disaster system. However, disaster vulnerability assessment involves a variety of complex factors, including natural, social, economic, and cultural factors, making it difficult to quantify. Furthermore, comprehensive vulnerability assessments of multiple hazard-prone entities are rarely explored. Summary of the Invention

[0008] The present invention provides a vulnerability assessment method and device for disaster-prone objects, which can comprehensively consider the disaster-causing intensity of rainstorm disasters and quantitatively and objectively implement the vulnerability assessment of multiple disaster-prone objects caused by rainstorms.

[0009] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0010] A vulnerability assessment method for a hazard-bearing body, comprising:

[0011] Get the rainstorm event dataset;

[0012] Performing index extraction processing on the rainstorm event data set to obtain rainstorm disaster factors;

[0013] According to the rainstorm disaster factor, the rainstorm disaster intensity during the recurrence period is obtained;

[0014] According to the rainstorm disaster intensity during the recurrence period, a comprehensive index of the hazard intensity of rainstorm disaster factors is obtained;

[0015] According to the comprehensive index of the intensity of the hazard of rainstorm disaster factors, the vulnerability of the disaster-prone body is assessed to obtain the rainstorm vulnerability curve of the disaster-prone body.

[0016] Optionally, performing index extraction processing on the rainstorm event dataset to obtain rainstorm disaster factors includes:

[0017] Normalizing the rainstorm event dataset to obtain an intermediate dataset;

[0018] According to the intermediate data set, at least one of the annual maximum hourly precipitation, the annual maximum daily precipitation, and the annual maximum process precipitation is extracted as a rainstorm disaster factor.

[0019] Optionally, normalizing the rainstorm event dataset to obtain an intermediate dataset includes:

[0020] For the positive indicator data in the rainstorm event dataset, we use:

[0021] Performing normalization processing to obtain a first intermediate data set;

[0022] For the negative indicator data in the rainstorm event dataset, we use:

[0023] Performing normalization processing to obtain a second intermediate data set;

[0024] Among them, Y ij Indicates the standardized value of each indicator data, Y ij ∈[0,1],X ijrepresents the jth indicator data in the i-th sample in the rainstorm event data set, i, j∈(1,∞), min(X i ) represents the minimum index data in the i-th sample, max(X i ) represents the maximum index data in the i-th sample.

[0025] Optionally, obtaining the rainstorm disaster intensity during the return period according to the rainstorm disaster factor includes:

[0026] According to the first distribution density function, the rainstorm disaster factor is processed to obtain a first value;

[0027] According to the second distribution density function, the rainstorm disaster factor is processed to obtain a second value;

[0028] According to the third distribution density function, the rainstorm disaster factor is processed to obtain the third value;

[0029] Perform a chi-square test on the first value, the second value, and the third value to obtain the rainstorm disaster intensity during the return period;

[0030] Among them, the first distribution density function is:

[0031]

[0032] Where μ, σ, and ξ represent the location parameter, scale parameter, and shape parameter, respectively, and must satisfy σ > 0 and 1 + ξ((x - μ) / σ) > 0;

[0033] The second distribution density function is:

[0034]

[0035]

[0036] Where x0 is the minimum value that the random variable x can take, α is called the shape parameter, β is the scale parameter, and Γ(α) is the gamma function of α;

[0037]

[0038]

[0039]

[0040] Among them, m is the mathematical expectation, σ is the mean square error, c s is the skewness coefficient, c v is the coefficient of variation;

[0041] The third distribution density function is:

[0042] P(x i)=P(X≥x i )=1-exp(-exp(-α(x i -β)));

[0043] Among them, α is the scale function and ∈[0,∞], β is the location parameter of the distribution function;

[0044]

[0045]

[0046] in, To estimate the mean value of the sample observation data, σ xi To estimate the standard deviation of the sample observations.

[0047] Optionally, based on the rainstorm disaster intensity during the return period, a comprehensive index of the intensity of the rainstorm disaster factor risk is obtained, including:

[0048] pass Obtain the comprehensive index of the intensity of the risk of rainstorm disaster factors;

[0049] Among them, H i is the comprehensive index of the intensity of the rainstorm disaster factor at the i-th station, W j (j=1-3) represents the weight corresponding to each rainstorm disaster factor; Int ij represents the precipitation intensity of the j-th rainstorm hazard factor at the i-th station.

[0050] Optionally, the weight of each rainstorm hazard factor is determined through the following process:

[0051] pass Get the ratio of each rainstorm disaster factor in the sample;

[0052] Among them, P ij represents the ratio of the i-th sample under the j-th rainstorm disaster factor;

[0053] pass Obtain the information entropy of each rainstorm disaster factor;

[0054] Among them, E j ≥0, if p ij =0, define E j =0;

[0055] pass Obtain the weight of each rainstorm disaster factor;

[0056] Where k is the number of rainstorm disaster factors.

[0057] Optionally, based on the comprehensive index of the intensity of the hazard intensity of the rainstorm hazard factor, a vulnerability assessment is performed on the hazard-prone body to obtain a rainstorm vulnerability curve of the hazard-prone body, including:

[0058] According to the direct economic loss and the comprehensive index of the intensity of the rainstorm disaster factors, the direct economic loss rate Y is obtained by fitting. e or

[0059] According to the number of people affected by the disaster and the comprehensive index of the intensity of the rainstorm disaster factors, the population disaster rate Y is obtained by fitting p or

[0060] According to the number of damaged houses and the comprehensive index of the intensity of the rainstorm disaster factor, the number of damaged houses I is obtained by fitting. b or

[0061] According to the crop disaster area and the comprehensive index of the intensity of the rainstorm disaster factor, the crop disaster area I is obtained by fitting. c or

[0062] By I 非 =A1Y e +A2Y p , I 实 =A3I b +A4I c The rainstorm vulnerability curve of multiple hazard-bearing bodies is obtained:

[0063] Among them, I 非 It is a comprehensive vulnerability index of non-physical disaster-bearing objects, including direct economic loss rate Y e and population disaster rate Y p ;I 实 It is a comprehensive index of vulnerability of physical disaster-bearing objects, including the number of damaged rooms I b and crop damage area I c ; A1, A2, A3, and A4 are the weights of each disaster-prone body.

[0064] The present invention also provides a disaster-bearing body vulnerability assessment device, comprising:

[0065] Acquisition module, used to obtain rainstorm event dataset;

[0066] The processing module is used to perform index extraction processing on the rainstorm event data set to obtain a rainstorm disaster factor; obtain a rainstorm disaster intensity during the return period based on the rainstorm disaster factor; obtain a comprehensive index of the hazard intensity of the rainstorm disaster factor based on the rainstorm disaster intensity during the return period; and perform a vulnerability assessment on a disaster-prone body based on the comprehensive index of the hazard intensity of the rainstorm disaster factor to obtain a rainstorm vulnerability curve of the disaster-prone body.

[0067] The present invention further provides a computing device, comprising: a processor and a memory storing a computer program, wherein the computer program executes the method described above when executed by the processor.

[0068] The present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above.

[0069] The above solution of the present invention includes at least the following beneficial effects:

[0070] The above-mentioned scheme of the present invention obtains a rainstorm event data set; performs index extraction processing on the rainstorm event data set to obtain a rainstorm disaster factor; obtains the rainstorm disaster intensity in the return period based on the rainstorm disaster factor; obtains a comprehensive index of the hazard intensity of the rainstorm disaster factor based on the rainstorm disaster intensity; performs a vulnerability assessment on the disaster-prone body based on the comprehensive index of the hazard intensity of the rainstorm disaster factor to obtain a rainstorm vulnerability curve of the disaster-prone body; and can comprehensively evaluate the impact of rainstorm disasters on the vulnerability of major disaster-prone bodies by integrating historical precipitation data and historical disaster data, and realize the vulnerability assessment of rainstorms on multiple disaster-prone bodies. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 is a flow chart of a method for assessing vulnerability of a disaster-prone body provided by an embodiment of the present invention;

[0072] Figure 2 1 is a schematic diagram of a rainstorm vulnerability curve of direct economic loss rate provided by an embodiment of the present invention;

[0073] Figure 3 Schematic diagram of a rainstorm vulnerability curve of population disaster rate provided by an embodiment of the present invention;

[0074] Figure 4 This is a schematic diagram of a non-physical multi-hazard-bearing body rainstorm vulnerability curve provided by an embodiment of the present invention;

[0075] Figure 5 It is a module diagram of a device for assessing vulnerability of a disaster-prone object provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0076] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0077] like Figure 1As shown, an embodiment of the present invention provides a vulnerability assessment method for a disaster-prone object, comprising:

[0078] Step 11, obtaining a rainstorm event dataset;

[0079] Step 12, performing index extraction processing on the rainstorm event dataset to obtain rainstorm disaster factors;

[0080] Step 13, obtaining the rainstorm disaster intensity during the return period according to the rainstorm disaster factor;

[0081] Step 14, obtaining a comprehensive index of the intensity of the rainstorm disaster factor based on the rainstorm disaster intensity during the return period;

[0082] Step 15: Conduct vulnerability assessment on the disaster-prone body according to the comprehensive index of hazard intensity of the rainstorm disaster-causing factors to obtain a rainstorm vulnerability curve of the disaster-prone body.

[0083] In this embodiment, a rainstorm event data set is obtained; index extraction processing is performed on the rainstorm event data set to obtain a rainstorm disaster factor; based on the rainstorm disaster factor, a rainstorm disaster intensity in a recurrence period is obtained; based on the rainstorm disaster intensity in a recurrence period, a comprehensive index of the hazard intensity of the rainstorm disaster factor is obtained; based on the comprehensive index of the hazard intensity of the rainstorm disaster factor, a vulnerability assessment is performed on the disaster-prone body to obtain a rainstorm vulnerability curve of the disaster-prone body; historical precipitation data and historical disaster data can be integrated to quantitatively, objectively and comprehensively evaluate the impact of rainstorm disasters on the vulnerability of major disaster-prone bodies, and realize the vulnerability assessment of rainstorms on multiple disaster-prone bodies.

[0084] In an optional embodiment of the present invention, step 11 includes:

[0085] Step 111: Acquire a rainstorm event dataset based on hourly precipitation data from historical ground meteorological observation stations.

[0086] In this embodiment, based on the rainstorm event process standard, Python software is used to extract rainstorm processes that meet the conditions at national sites to form a rainstorm event dataset;

[0087] Among them, the standard for the rainstorm event process is: the number of consecutive precipitation days is divided into one process, and the process is considered to be over once there is no precipitation, and the precipitation in at least one day in the process must reach or exceed 50 mm, and the daily boundary is 20:00-20:00.

[0088] In an optional embodiment of the present invention, step 12 includes:

[0089] Step 121, normalizing the rainstorm event dataset to obtain an intermediate dataset;

[0090] Step 122: extract at least one of the annual maximum hourly precipitation, the annual maximum daily precipitation, and the annual maximum process precipitation as a rainstorm disaster factor based on the intermediate data set.

[0091] Furthermore, step 121 may include:

[0092] Step 1211: for the positive indicator data in the rainstorm event dataset, by:

[0093] Performing normalization processing to obtain a first intermediate data set;

[0094] Step 1212: For the negative indicator data in the rainstorm event dataset, the following steps are performed:

[0095] Performing normalization processing to obtain a second intermediate data set;

[0096] Among them, Y ij Indicates the standardized value of each indicator data, Y ij ∈[0,1],X ij represents the jth indicator data in the i-th sample in the rainstorm event data set, i, j∈(1,∞), min(X i ) represents the minimum index data in the i-th sample, max(X i ) represents the maximum index data in the i-th sample.

[0097] In this embodiment, the rainstorm event dataset is normalized so that the obtained intermediate datasets are all between 0 and 1, thereby reducing the adverse effects caused by singular sample data.

[0098] Among them, the annual maximum hourly precipitation refers to the maximum hourly precipitation at a certain station in a certain year; the annual maximum daily precipitation refers to the maximum sum of precipitation for 24 consecutive hours at a certain station in a certain year; the annual maximum process precipitation refers to the cumulative maximum value of process precipitation at a certain station in a certain year; the annual maximum duration days refers to the cumulative maximum value of the duration of the precipitation process at a certain station in a certain year.

[0099] In an optional embodiment of the present invention, step 13 includes:

[0100] Step 131: Process the rainstorm disaster factor according to the first distribution density function to obtain a first value;

[0101] Step 132: Process the rainstorm disaster factor according to the second distribution density function to obtain a second value;

[0102] Step 133: Process the rainstorm disaster factor according to the third distribution density function to obtain a third value;

[0103] Step 134: Perform a chi-square test on the first value, the second value, and the third value to obtain the rainstorm disaster intensity during the return period;

[0104] Among them, the first distribution density function is:

[0105]

[0106] Where μ, σ, and ξ represent the location parameter, scale parameter, and shape parameter, respectively, and must satisfy σ > 0 and 1 + ξ((x - μ) / σ) > 0;

[0107] The second distribution density function is:

[0108]

[0109]

[0110] Where x0 is the minimum value that the random variable x can take, α is called the shape parameter, β is the scale parameter, and Γ(α) is the gamma function of α;

[0111]

[0112]

[0113]

[0114] Among them, m is the mathematical expectation, σ is the mean square error, c s is the skewness coefficient, c v is the coefficient of variation;

[0115] The third distribution density function is:

[0116] P(x i )=P(X≥x i )=1-exp(-exp(-α(x i -β)));

[0117] Among them, α is the scale function and ∈[0,∞], β is the location parameter of the distribution function;

[0118]

[0119]

[0120] in, To estimate the mean value of the sample observation data, σ xi To estimate the standard deviation of the sample observations.

[0121] In this embodiment, the recurrence period refers to the average time interval greater than or equal to the recurrence of meteorological or hydrological elements over a longer period of time within a certain statistical period of data recording information, and is usually expressed in terms of a certain number of years.

[0122] The first distribution density function, the second distribution density function, and the third distribution density function are used to fit the rainstorm rainfall with various return periods (5-year, 10-year, 30-year, 50-year, and 100-year). The chi-square test is used to statistically calculate the degree of deviation between the return period precipitation obtained by fitting the three functions and the actual observed precipitation. The larger the chi-square value, the greater the deviation between the return period precipitation obtained by fitting and the actual observed precipitation, and the smaller the chi-square value, the smaller the deviation. The chi-square test is used to compare the goodness of fit of the return period precipitation obtained by fitting the three functions, and the result calculated by the optimal function is selected as the final return period rainstorm disaster intensity.

[0123] In an optional embodiment of the present invention, step 14 includes:

[0124] Step 141, pass Obtain the comprehensive index of the intensity of the risk of rainstorm disaster factors;

[0125] Among them, H i is the comprehensive index of the intensity of the rainstorm disaster factor at the i-th station, W j (j=1-3) represents the weight corresponding to each rainstorm disaster factor; Int ij represents the precipitation intensity of the j-th rainstorm hazard factor at the i-th station.

[0126] Furthermore, the weights of each rainstorm hazard factor are determined through the following process:

[0127] pass Get the ratio of each rainstorm disaster factor in the sample;

[0128] Among them, P ij represents the ratio of the i-th sample under the j-th rainstorm disaster factor;

[0129] pass Obtain the information entropy of each rainstorm disaster factor;

[0130] Among them, E j ≥0, if p ij =0, define E j =0;

[0131] pass Obtain the weight of each rainstorm disaster factor;

[0132] Where k is the number of rainstorm disaster factors.

[0133] In this embodiment, the information entropy of each rainstorm disaster factor is obtained according to the ratio of each rainstorm disaster factor in the sample, and finally the weight of each rainstorm disaster factor is obtained, which can make the evaluation result more objective, accurate and scientific, and ensure that it is not affected by subjective factors.

[0134] Further, through The comprehensive score of each sample can be obtained, and the comprehensive score of each sample can be used as a reference for the comprehensive index of the intensity of danger of rainstorm disaster factors to ensure the accuracy of the comprehensive index of the intensity of danger of rainstorm disaster factors.

[0135] In an optional embodiment of the present invention, step 15 includes:

[0136] According to the direct economic loss and the comprehensive index of the intensity of the rainstorm disaster factors, the direct economic loss rate Y is obtained by fitting. e or

[0137] According to the number of people affected by the disaster and the comprehensive index of the intensity of the rainstorm disaster factors, the population disaster rate Y is obtained by fitting p or

[0138] According to the number of damaged houses and the comprehensive index of the intensity of the rainstorm disaster factor, the number of damaged houses I is obtained by fitting. b or

[0139] According to the crop disaster area and the comprehensive index of the intensity of the rainstorm disaster factor, the crop disaster area I is obtained by fitting. c or

[0140] By I 非 =A1Y e +A2Y p , I 实 =A3I b +A4I c The rainstorm vulnerability curve of multiple hazard-bearing bodies is obtained:

[0141] Among them, I 非 It is a comprehensive vulnerability index of non-physical disaster-bearing objects, including direct economic loss rate Y e and population disaster rate Y p ;I 实 It is a comprehensive index of vulnerability of physical disaster-bearing objects, including the number of damaged rooms I b and crop damage area I c ; A1, A2, A3, and A4 are the weights of each disaster-prone body.

[0142] In this embodiment, four disaster-prone objects, namely population, economy, infrastructure, and crops, which are susceptible to rainstorms, are selected as research objects for rainstorm disaster vulnerability assessment. They are divided into physical and non-physical categories according to their properties, and vulnerability assessment is carried out in a distributed manner.

[0143] According to the direct economic losses, the number of people affected, the area of crops affected, and the number of houses damaged, the relationships between the direct economic loss rate, the population affected rate, the area of crops affected, the number of houses damaged, and the comprehensive index of the intensity of the hazard of rainstorm disaster factors were fitted, thereby obtaining the corresponding rainstorm vulnerability curves of single hazard-bearing bodies.

[0144] The comprehensive vulnerability indicators of non-physical disaster-bearing objects include direct economic loss rate and population affected rate, and the comprehensive vulnerability indicators of physical disaster-bearing objects include the number of damaged rooms and crop affected area. 非 =A1Y e +A2Y p , I 实 =A3I b +A4I c The rainstorm vulnerability curve of multiple hazard-bearing bodies is obtained. A1, A2, A3, and A4 are the weights of each hazard-bearing body, determined by the average weight method, and all have a value of 0.5.

[0145] Therefore, according to The rainstorm vulnerability curve of multiple hazard-bearing bodies is obtained.

[0146] Specific examples:

[0147] Based on the defined rainstorm event process standards, rainstorm processes that meet the conditions in a certain province at multiple meteorological stations between 1960 and 2020 are obtained to form a rainstorm event dataset.

[0148] Four indicators, namely, the annual maximum hourly precipitation, the annual maximum daily precipitation, and the annual maximum process precipitation, were selected as the measurement indicators of rainstorm disaster factors in a certain province. Based on the rainstorm event process dataset, four rainstorm disaster factors were extracted.

[0149] The first, second and third distribution density functions were used to fit the rainstorm disaster factors of each station, and the maximum hourly precipitation, the annual maximum daily precipitation, the annual maximum process precipitation, and the rainstorm disaster intensity of the three rainstorm disaster factors with different recurrence periods (5-year, 10-year, 30-year, 50-year and 100-year) were obtained.

[0150] Chi-square tests were performed on the rainstorm disaster intensity and actual precipitation extreme value data of some meteorological stations obtained by fitting the three functions. The comparison of the test results is shown in Table 1.

[0151] Table 1 Chi-square test results of three precipitation return period calculation methods

[0152] Chi-square CS test value Site 1 Site 2 Site 3 average First distribution density function 0.246 0.409 0.893 0.516 Second distribution density function 0.209 0.457 0.9 0.522 Third distribution density function 0.235 0.42 0.86 0.505

[0153] As shown in Table 1, the chi-square test results of the three meteorological stations are all greater than the 0.05 confidence level and all pass the test, indicating that these three distribution functions can be used to fit the rainstorm disaster intensity in the study area; among them, the chi-square test result of the third distribution density function is the smallest, indicating that the fitting result is better. Therefore, this example uses the third distribution density function to fit the rainstorm disaster intensity with different return periods for all meteorological observation stations in the study area.

[0154] The annual maximum hourly precipitation, maximum daily precipitation, and annual maximum process precipitation were selected as comprehensive assessment indicators for rainstorm disaster-causing factors. The entropy method was used to calculate the weights of each rainstorm disaster-causing factor. The weight value of the annual maximum hourly precipitation was 0.246, the weight value of the annual maximum daily precipitation was 0.354, and the weight value of the annual maximum process precipitation was 0.401. The weights of each item were relatively even, all around 0.333, as shown in Table 2.

[0155] Table 2 Summary of weight calculation results using entropy method

[0156] Disaster factor intensity index Information entropy value e Information utility value d Weight coefficient w Annual maximum hourly precipitation intensity 0.9826 0.0174 24.56％ Annual maximum daily precipitation intensity 0.9750 0.0250 35.39％ Annual maximum precipitation intensity 0.9717 0.0283 40.05％

[0157] Based on the above results, the calculation formula for the comprehensive index of the intensity of rainstorm disaster factors in a province is constructed as follows:

[0158] H i =0.246Int1+0.354Int2+0.401Int3

[0159] Where Int1 represents the annual maximum hourly precipitation intensity, Int2 represents the annual maximum daily precipitation intensity, and Int3 represents the annual maximum process precipitation intensity. Based on this formula, a comprehensive index of the intensity of rainstorm hazard factors for a province and the intensity of rainstorm hazard factors for five typical return periods were calculated. Some results are shown in Table 3.

[0160] Table 3 Calculation of comprehensive risk index of rainstorm disaster factors in a certain province

[0161] Site Int1 Int2 Int3 MMS_Int1 MMS_Int2 MMS_Int3 H 1 36.6 67.5 113.1 0.0458 0.0199 0.1147 0.1024 2 70.9 98 128.9 0.0625 0.0881 0.2489 0.1520 3 39.5 97.5 113.5 0.0462 0.0869 0.1260 0.1435 4 53.6 72.2 89.7 0.0210 0.0304 0.1812 0.0698 5 51.1 73.4 85 0.0161 0.0331 0.1714 0.1279 6 27.4 61.6 70.9 0.0012 0.0068 0.0787 0.0375 7 44 58.6 82.9 0.0139 0 0.1436 0.1331 8 38.2 88.7 94.5 0.0261 0.0673 0.1209 0.1308 9 98.3 296.2 366.4 0.3137 0.5313 0.3561 0.4536 10 85.2 140 240.8 0.1809 0.1820 0.3049 0.3469

[0162] A total of 772 data sets on rainstorm disasters in various counties and districts in a province from 2000 to 2020 were compiled. Each data set includes information on economic, population, crop, and building damage, as well as corresponding precipitation intensity data. Using the economic and population as examples, we describe the construction of vulnerability assessment models for rainstorm disasters for both single and multiple hazard-bearing entities.

[0163] (1) Rainstorm vulnerability curve for a single hazard-bearing body

[0164] 1) Heavy Rain Disaster-Economic Vulnerability Curve

[0165] Comprehensive index of the intensity of the risk of rainstorm disaster factors: X

[0166] Hourly maximum precipitation X1, daily maximum precipitation X2, process maximum precipitation X3

[0167] X=0.246X1+0.354X2+0.401X3

[0168] Fitting of the comprehensive index of hazard intensity of rainstorm disaster factors and direct economic loss rate;

[0169] A total of 436 data on economic losses from rainstorm disasters in a province were screened (Table 4). The comprehensive index of the intensity of the rainstorm disaster factor risk X and the direct economic loss rate Y under each disaster data were calculated. e Perform curve fitting and refer to the significance test result R 2 The most suitable curve is selected to construct the economic vulnerability curve model. The rainstorm vulnerability curve model with the optimal direct economic loss rate for a province is fitted as follows: 2 If it is greater than 0.7, it means that it is significant and the model combination effect is good. Figure 2 As shown:

[0170] Y e =0.003e 8.027X

[0171] R e 2 =0.734

[0172] Where Y e is the direct economic loss rate, that is, the ratio of regional direct economic loss to regional gross domestic product (GDP); X is the disaster intensity index of rainstorm disaster, Re 2 is the model significance value, Re 2 The higher the value, the more accurate the fitted vulnerability curve model is in predicting the economic losses caused by rainstorm disasters.

[0173] 2) Construction of a rainstorm disaster-population vulnerability curve

[0174] Comprehensive index of the intensity of the risk of rainstorm disaster factors: X p

[0175] Hourly maximum precipitation X1, daily maximum precipitation X2, process maximum precipitation X3

[0176] X p =0.246X1+0.354X2+0.401X3

[0177] Curve fitting of the comprehensive index of hazard intensity of rainstorm disaster factors and the population disaster rate;

[0178] A total of 398 data on the population affected by rainstorm disasters in a province were screened (Table 4). The comprehensive index of the intensity of the rainstorm disaster factor X and the population affected rate Y were calculated for each rainstorm disaster. p Perform curve fitting and refer to the significance test result R 2 The best-fitting curve is selected to construct the population vulnerability curve model. The optimal population disaster rate rainstorm vulnerability curve model for a province is fitted as follows: 2 The value passed the significance test, and the specific fitting results are as follows Figure 3 As shown:

[0179] Y P =0.5617x 2 -0.281x+0.0428

[0180] R P 2 =0.8272

[0181] Where Y p is the population disaster rate, that is, the proportion of the disaster-affected population in the region to the total population in the region; X is the comprehensive index of the intensity of the risk of rainstorm disaster factors, R p 2 is the model significance value, R p 2 The higher the value, the more accurate the fitted population vulnerability curve model.

[0182] Table 4: Historical rainstorm disaster data for a certain province (partial)

[0183]

[0184] (2) Heavy rain vulnerability curve for multiple hazard-bearing bodies

[0185] A comprehensive rainstorm vulnerability curve model corresponding to the comprehensive index of the hazard intensity of rainstorm disaster factors and multiple hazard-bearing bodies (population, economy, crops, and buildings) is constructed to realize the vulnerability assessment of rainstorms to multiple hazard-bearing bodies. The comprehensive vulnerability of multiple hazard-bearing bodies is the comprehensive fragility of non-physical and physical hazard-bearing bodies. This example takes non-physical population and economy as examples to construct a multi-hazard-bearing rainstorm vulnerability model.

[0186] 1) Heavy rain disaster - Construction of comprehensive heavy rain vulnerability curve for non-physical hazard-bearing objects

[0187] Construction of a comprehensive index model for the intensity of rainstorm disaster risk factors

[0188] Comprehensive index of the intensity of the risk of rainstorm disaster factors: X

[0189] Hourly maximum precipitation X1, daily maximum precipitation X2, process maximum precipitation X3

[0190] X=0.246X1+0.354X2+0.401X3

[0191] The comprehensive vulnerability of non-physical rainstorm disasters includes the comprehensive vulnerability of population (including death, missing persons and disaster-affected persons) and the comprehensive vulnerability of the economy. The formula is as follows:

[0192]

[0193] Y e is the direct economic loss rate, Y p is the population disaster rate;

[0194] This study screened out 362 pieces of non-physical loss and damage data of rainstorm disasters in a province, and calculated the comprehensive index X of the hazard intensity of rainstorm disaster factors and the comprehensive index I of non-physical disaster-bearing bodies for each disaster data. 非 Perform curve fitting to construct the comprehensive vulnerability curve of non-physical rainstorm disasters. 2 The highest is 0.9181; the optimal non-physical vulnerability curve model of a province is fitted as follows. The specific fitting results are as follows Figure 4 As shown:

[0195] Y 非 =1.5906x 3 -1.3892x 2 +0.3313x-0.0096

[0196] R 非 =0.9181

[0197] Where Y 非 is the disaster rate of non-physical disaster-bearing objects; X is the disaster intensity index of rainstorm disaster, R 非 2 is the model significance value, R 非 2 The higher the value, the more accurate the fitted fragility curve model.

[0198] Based on the rainstorm disaster vulnerability assessment model constructed by the above process, as well as the comprehensive risk index of rainstorm disaster factors in a certain province and the five typical recurrence period intensity index values, the economic and population vulnerabilities and comprehensive vulnerability of non-physical disaster-bearing objects to rainstorm disasters in a certain province under the comprehensive precipitation intensity and the precipitation intensity of different annual occurrence types are evaluated and calculated.

[0199] like Figure 5 As shown, an embodiment of the present invention further provides a disaster-prone body vulnerability assessment device 50, comprising:

[0200] An acquisition module 51 is used to acquire a rainstorm event dataset;

[0201] The processing module 52 is used to perform index extraction processing on the rainstorm event data set to obtain a rainstorm disaster factor; obtain the rainstorm disaster intensity during the return period based on the rainstorm disaster factor; obtain a comprehensive index of the hazard intensity of the rainstorm disaster factor based on the rainstorm disaster intensity during the return period; and perform a vulnerability assessment on the disaster-prone body based on the comprehensive index of the hazard intensity of the rainstorm disaster factor to obtain a rainstorm vulnerability curve of the disaster-prone body.

[0202] Optionally, performing index extraction processing on the rainstorm event dataset to obtain rainstorm disaster factors includes:

[0203] Normalizing the rainstorm event dataset to obtain an intermediate dataset;

[0204] According to the intermediate data set, at least one of the annual maximum hourly precipitation, the annual maximum daily precipitation, and the annual maximum process precipitation is extracted as a rainstorm disaster factor.

[0205] Optionally, normalizing the rainstorm event dataset to obtain an intermediate dataset includes:

[0206] For the positive indicator data in the rainstorm event dataset, we use:

[0207] Performing normalization processing to obtain a first intermediate data set;

[0208] For the negative indicator data in the rainstorm event dataset, we use:

[0209] Performing normalization processing to obtain a second intermediate data set;

[0210] Among them, Y ij Indicates the standardized value of each indicator data, Y ij ∈[0,1],X ij represents the jth indicator data in the i-th sample in the rainstorm event data set, i, j∈(1,∞), min(X i ) represents the minimum index data in the i-th sample, max(X i ) represents the maximum index data in the i-th sample.

[0211] Optionally, obtaining the rainstorm disaster intensity during the return period according to the rainstorm disaster factor includes:

[0212] According to the first distribution density function, the rainstorm disaster factor is processed to obtain a first value;

[0213] According to the second distribution density function, the rainstorm disaster factor is processed to obtain a second value;

[0214] According to the third distribution density function, the rainstorm disaster factor is processed to obtain the third value;

[0215] Perform a chi-square test on the first value, the second value, and the third value to obtain the rainstorm disaster intensity during the return period;

[0216] Among them, the first distribution density function is:

[0217]

[0218] Where μ, σ, and ξ represent the location parameter, scale parameter, and shape parameter, respectively, and must satisfy σ > 0 and 1 + ξ((x - μ) / σ) > 0;

[0219] The second distribution density function is:

[0220]

[0221]

[0222] Where x0 is the minimum value that the random variable x can take, α is called the shape parameter, β is the scale parameter, and Γ(α) is the gamma function of α;

[0223]

[0224]

[0225]

[0226] Among them, m is the mathematical expectation, σ is the mean square error, c s is the skewness coefficient, c v is the coefficient of variation;

[0227] The third distribution density function is:

[0228] P(x i )=P(X≥x i )=1-exp(-exp(-α(x i -β)));

[0229] Among them, α is the scale function and ∈[0,∞], β is the location parameter of the distribution function;

[0230]

[0231]

[0232] in, To estimate the mean value of the sample observation data, σ xi To estimate the standard deviation of the sample observations.

[0233] Optionally, based on the rainstorm disaster intensity during the return period, a comprehensive index of the intensity of the rainstorm disaster factor risk is obtained, including:

[0234] pass Obtain the comprehensive index of the intensity of the risk of rainstorm disaster factors;

[0235] Among them, H i is the comprehensive index of the intensity of the rainstorm disaster factor at the i-th station, W j (j=1-3) represents the weight corresponding to each rainstorm disaster factor; Int ij represents the precipitation intensity of the j-th rainstorm hazard factor at the i-th station.

[0236] Optionally, the weight of each rainstorm hazard factor is determined through the following process:

[0237] pass Get the ratio of each rainstorm disaster factor in the sample;

[0238] Among them, P ij represents the ratio of the i-th sample under the j-th rainstorm disaster factor;

[0239] pass Obtain the information entropy of each rainstorm disaster factor;

[0240] Among them, E j ≥0, if p ij =0, define E j =0;

[0241] pass Obtain the weight of each rainstorm disaster factor;

[0242] Where k is the number of rainstorm disaster factors.

[0243] Optionally, based on the comprehensive index of the intensity of the hazard intensity of the rainstorm hazard factor, a vulnerability assessment is performed on the hazard-prone body to obtain a rainstorm vulnerability curve of the hazard-prone body, including:

[0244] According to the direct economic loss and the comprehensive index of the intensity of the rainstorm disaster factors, the direct economic loss rate Y is obtained by fitting. e or

[0245] According to the number of people affected by the disaster and the comprehensive index of the intensity of the rainstorm disaster factors, the population disaster rate Y is obtained by fitting p or

[0246] According to the number of damaged houses and the comprehensive index of the intensity of the rainstorm disaster factor, the number of damaged houses I is obtained by fitting. b or

[0247] According to the crop disaster area and the comprehensive index of the intensity of the rainstorm disaster factor, the crop disaster area I is obtained by fitting. c or

[0248] By I 非 =A1Y e +A2Y p , I 实 =A3I b +A4I c The rainstorm vulnerability curve of multiple hazard-bearing bodies is obtained:

[0249] Among them, I 非 It is a comprehensive vulnerability index of non-physical disaster-bearing objects, including direct economic loss rate Y e and population disaster rate Y p ;I 实 It is a comprehensive index of vulnerability of physical disaster-bearing objects, including the number of damaged rooms I b and crop damage area I c ; A1, A2, A3, and A4 are the weights of each disaster-prone body.

[0250] It should be noted that the device is a device corresponding to the above method, and all implementation methods in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects.

[0251] An embodiment of the present invention further 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 above embodiment. All implementations of the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0252] The present invention also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to execute the method described in the above embodiment. All implementations of the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0253] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0254] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0255] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0256] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0257] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0258] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0259] In addition, it should be noted that, in the apparatus and method of the present invention, 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 schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it will be understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0260] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved simply by providing a program product containing program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, 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 schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but do not necessarily need to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

[0261] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for assessing vulnerability of a disaster-prone body, characterized in that: include: Get the rainstorm event dataset; Performing index extraction processing on the rainstorm event data set to obtain rainstorm disaster factors; According to the rainstorm disaster factor, the rainstorm disaster intensity during the recurrence period is obtained; According to the rainstorm disaster intensity during the recurrence period, a comprehensive index of the hazard intensity of rainstorm disaster factors is obtained; According to the comprehensive index of the intensity of the rainstorm hazard factor, the vulnerability of the disaster-bearing body is assessed to obtain the rainstorm vulnerability curve of the disaster-bearing body; The rainstorm disaster intensity during the return period is obtained according to the rainstorm disaster factor, including: According to the first distribution density function, the rainstorm disaster factor is processed to obtain a first value; According to the second distribution density function, the rainstorm disaster factor is processed to obtain a second value; According to the third distribution density function, the rainstorm disaster factor is processed to obtain the third value; Perform a chi-square test on the first value, the second value, and the third value to obtain the rainstorm disaster intensity during the return period; Among them, the first distribution density function is: Where μ, σ, and ξ represent the location parameter, scale parameter, and shape parameter, respectively, and must satisfy σ > 0 and 1 + ξ((x - μ) / σ) > 0; The second distribution density function is: Where x0 is the minimum value that the random variable x can take, α is called the shape parameter, β is the scale parameter, and Γ(α) is the gamma function of α; Among them, m is the mathematical expectation, σ is the mean square error, and c is the mean square error. s is the skewness coefficient, c v is the coefficient of variation; The third distribution density function is: P(x i )=P(X≥x i )=1-exp(-exp(-α(x i -β))); Among them, α is the scale function and ∈[0,∞), β is the location parameter of the distribution function; in, To estimate the mean value of the sample observation data, σ xi To estimate the standard deviation of the sample observation data; According to the rainstorm disaster intensity in the recurrence period, a comprehensive index of the intensity of the rainstorm disaster factor risk is obtained, including: pass Obtain the comprehensive index of the intensity of the risk of rainstorm disaster factors; Among them, H i is the comprehensive index of the intensity of the rainstorm disaster factor at the i-th station, W j Indicates the weight corresponding to each rainstorm disaster factor, j∈[1,3]; Int ij represents the precipitation intensity of the j-th rainstorm hazard factor at the i-th station.

2. The vulnerability assessment method for disaster-prone objects according to claim 1, characterized in that: The rainstorm event dataset is processed by performing index extraction to obtain rainstorm disaster factors, including: Normalizing the rainstorm event dataset to obtain an intermediate dataset; According to the intermediate data set, at least one of the annual maximum hourly precipitation, the annual maximum daily precipitation, and the annual maximum process precipitation is extracted as a rainstorm disaster factor.

3. The vulnerability assessment method for disaster-prone objects according to claim 2, characterized in that: The rainstorm event dataset is normalized to obtain an intermediate dataset, including: For the positive indicator data in the rainstorm event dataset, we use: Performing normalization processing to obtain a first intermediate data set; For the negative indicator data in the rainstorm event dataset, we use: Performing normalization processing to obtain a second intermediate data set; Among them, Y ij Indicates the standardized value of each indicator data, Y ij ∈[0,1],X ij Represents the jth indicator data in the i-th sample in the rainstorm event data set, i, j∈[1,∞), min(X i ) represents the minimum index data in the i-th sample, max(X i ) represents the maximum index data in the i-th sample.

4. The vulnerability assessment method for disaster-prone objects according to claim 1, characterized in that: The weights of each rainstorm hazard factor are determined through the following process: pass Get the ratio of each rainstorm disaster factor in the sample; Among them, p ij represents the ratio of the i-th sample under the j-th rainstorm disaster factor; pass Obtain the information entropy of each rainstorm disaster factor; Among them, E j ≥0, if p ij =0, define E j =0; pass Obtain the weight of each rainstorm disaster factor; Where k is the number of rainstorm disaster factors.

5. The vulnerability assessment method for disaster-prone objects according to claim 4, characterized in that: According to the comprehensive index of the intensity of the rainstorm hazard factor, the vulnerability of the disaster-prone body is assessed to obtain the rainstorm vulnerability curve of the disaster-prone body, including: According to the direct economic loss and the comprehensive index of the intensity of the rainstorm disaster factors, the direct economic loss rate Y is obtained by fitting. e or According to the number of people affected by the disaster and the comprehensive index of the intensity of the rainstorm disaster factors, the population disaster rate Y is obtained by fitting p or According to the number of damaged houses and the comprehensive index of the intensity of the rainstorm disaster factor, the number of damaged houses I is obtained by fitting. b or According to the crop disaster area and the comprehensive index of the intensity of the rainstorm disaster factor, the crop disaster area I is obtained by fitting. c or By I 非 =A1Y e +A2Y p , I 实 =A3I b +A4I c The rainstorm vulnerability curve of multiple hazard-bearing bodies is obtained: Among them, I 非 It is a comprehensive vulnerability index of non-physical disaster-bearing objects, including direct economic loss rate Y e and population disaster rate Y p ;I 实 It is a comprehensive index of vulnerability of physical disaster-bearing objects, including the number of damaged rooms I b and crop damage area I c ; A1, A2, A3, and A4 are the weights of each disaster-prone body.

6. A device for assessing vulnerability of a disaster-prone body, characterized in that: include: Acquisition module, used to obtain rainstorm event dataset; A processing module, configured to extract indicators from the rainstorm event dataset to obtain rainstorm disaster factors; According to the rainstorm disaster factor, the rainstorm disaster intensity during the recurrence period is obtained; According to the rainstorm disaster intensity in the return period, a comprehensive index of the intensity of the hazard of rainstorm disaster factors is obtained; according to the comprehensive index of the intensity of the hazard of rainstorm disaster factors, a vulnerability assessment is performed on the disaster-prone body to obtain a rainstorm vulnerability curve of the disaster-prone body; The rainstorm disaster intensity during the return period is obtained according to the rainstorm disaster factor, including: According to the first distribution density function, the rainstorm disaster factor is processed to obtain a first value; According to the second distribution density function, the rainstorm disaster factor is processed to obtain a second value; According to the third distribution density function, the rainstorm disaster factor is processed to obtain the third value; Perform a chi-square test on the first value, the second value, and the third value to obtain the rainstorm disaster intensity during the return period; Among them, the first distribution density function is: Where μ, σ, and ξ represent the location parameter, scale parameter, and shape parameter, respectively, and must satisfy σ > 0 and 1 + ξ((x - μ) / σ) > 0; The second distribution density function is: Where x0 is the minimum value that the random variable x can take, α is called the shape parameter, β is the scale parameter, and Γ(α) is the gamma function of α; Among them, m is the mathematical expectation, σ is the mean square error, and c is the mean square error. s is the skewness coefficient, c v is the coefficient of variation; The third distribution density function is: P(x i )=P(X≥x i )=1-exp(-exp(-α(x i -β))); Among them, α is the scale function and ∈[0,∞), β is the location parameter of the distribution function; in, To estimate the mean value of the sample observation data, σ xi To estimate the standard deviation of the sample observation data; According to the rainstorm disaster intensity in the recurrence period, a comprehensive index of the intensity of the rainstorm disaster factor risk is obtained, including: pass Obtain the comprehensive index of the intensity of the risk of rainstorm disaster factors; Among them, H i is the comprehensive index of the intensity of the rainstorm disaster factor at the i-th station, W j Indicates the weight corresponding to each rainstorm disaster factor, j∈[1,3]; Int ij represents the precipitation intensity of the j-th rainstorm hazard factor at the i-th station.

7. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 5 is performed.

8. A computer-readable storage medium, characterized in that: The device stores instructions, which, when executed on a computer, enable the computer to execute the method according to any one of claims 1 to 5.

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