A typhoon disaster scenario generation method with improved accuracy and efficiency

Abstract

The application discloses a typhoon disaster scene generation method with high accuracy and high efficiency, and aims at solving the problem of low simulation accuracy of an existing typhoon disaster simulator.The typhoon disaster scene generation method comprises the following steps: 1, selecting all typhoon samples passing through a target area from a database, and extracting typhoon information from the typhoon samples; 2, calculating gradient wind speed, superimposing a moving wind field, and converting the superimposed total wind speed field into ground wind speed; 3, discretizing the target area into a plurality of grid units, and screening out the maximum ground wind speed when each typhoon sample passes, so as to form a maximum wind speed intensity measurement map of the target area; 4, reducing the number of maximum wind speed intensity measurement maps of all typhoon samples by using a disaster quantification method; and 5, outputting N HQ optimized quanta and weights of the optimized quanta.The application can more accurately evaluate the damage caused by typhoon disasters to target infrastructure systems and the like, and meanwhile, the analysis efficiency is improved.

Description

Technical Field

[0001] This invention belongs to the field of typhoon disaster simulation methods for disaster prevention and mitigation, specifically involving a method for generating typhoon disaster scenarios that simultaneously considers accuracy and efficiency, and is suitable for improving the accuracy of typhoon simulation. Background Technology

[0002] Typhoons, as frequent natural disasters, cause severe structural damage to infrastructure systems such as water networks and power grids, resulting in huge economic losses. Therefore, modeling typhoon disasters and analyzing the damage state and functional losses of infrastructure systems under such disasters to conduct resilience assessments and consider resilience enhancement has become a research hotspot and is of great significance for effectively preventing typhoon disasters.

[0003] Existing research on damage analysis of infrastructure systems under typhoon disasters is limited, and many studies are based on single historical typhoon events. This approach cannot effectively reproduce the typhoon disaster situation in the areas where infrastructure systems are located. Accurate disaster analysis requires a large number of typhoon simulation scenarios, but this approach also reduces efficiency. How to balance the accuracy and efficiency of typhoon disaster scenario generation is a problem worthy of further research. Summary of the Invention

[0004] The purpose of this invention is to solve the problem of low simulation accuracy in existing typhoon disaster simulators, and to provide a method for generating typhoon disaster scenarios to improve the efficiency of disaster simulation and achieve a balance between accuracy and efficiency.

[0005] The method for generating typhoon disaster scenarios with improved accuracy and efficiency according to the present invention is implemented according to the following steps:

[0006] Step 1: Select the target area (i.e., the area where the infrastructure system is located), select all typhoon samples that pass through the target area from the simulated typhoon database, and extract typhoon information from the selected typhoon samples. The typhoon information includes longitude, latitude, typhoon center pressure difference, maximum wind speed and translation speed.

[0007] Step 2, Step 2.1: Calculate parameters A and B in the gradient wind field model;

[0008] The formula for calculating parameter A is as follows:

[0009]

[0010] The formula for calculating parameter B is as follows:

[0011]

[0012] Where C and C0 represent correction parameters, f is the Coriolis parameter, r is the distance from the wind speed calculation point to the typhoon center, ΔP is the pressure difference at the typhoon center, e is the natural constant, and V m That is the maximum wind speed. It is air density;

[0013] Step 2.2: Calculate the radius R of the maximum wind speed. max The maximum wind speed radius R was calculated using the XGBoost model. max Maximum wind speed radius R max The model is as follows:

[0014]

[0015] in, Let ε be the latitude of the typhoon, ΔP be the pressure difference at the typhoon center, and ε be the latitude of the typhoon. Rmax This is the error term;

[0016] Step 2.3: Combine parameters A, B, and the maximum wind speed radius R. max Substituting into the gradient wind field model, the gradient wind speed V is calculated. G (r, );

[0017]

[0018] Step 2.4: Superimpose the migrating wind field at the gradient wind speed V. G (r, Based on this, the typhoon's migrating wind field V is superimposed. t The total wind speed field V after superposition is obtained. S :

[0019]

[0020] Step 2.5: Combine the superimposed total wind speed field V S Converted to ground wind speed V H (t), the conversion formula is as follows:

[0021]

[0022] Where H represents the target height, i.e., the height above the ground, and z0 represents the surface roughness length;

[0023] Step 3: Discretize the target area into multiple grid cells, record the latitude and longitude coordinates of the center point of each grid cell, and calculate the superimposed total wind speed field V based on the improved wind field model in Step 2. SThis allows us to calculate the ground wind speed at the center of each grid cell at different times when a typhoon sample passes by. For each grid cell center, we select the maximum ground wind speed when each typhoon sample passes by, thus forming a maximum wind speed intensity measurement map of the target area. One maximum wind speed intensity measurement map is obtained for each typhoon sample.

[0024] Step 4: Apply disaster quantification methods to reduce the number of maximum wind speed intensity measurement maps for all typhoon samples. The process of reducing the number of maximum wind speed intensity measurement maps is as follows:

[0025] Step 4.1: Define the sample as the maximum wind speed intensity measurement map obtained from all typhoon samples. The maximum wind speed intensity measurement map for each sample is used... This indicates that the total number of samples is N. events The quantum is defined as a reduced maximum wind speed intensity measurement map, and the number of quantum is set to N. HQ First, arbitrarily select N from the sample. HQ Each sample is used as a quantum;

[0026] Step 4.2: Calculate the nth quantum. With all samples wind speed field distance , The wind speed field distance represents the square of the maximum wind speed difference between the i-th sample and the n-th quantum at the center point (lon, lat) of the corresponding grid cell. The calculation formula is as follows:

[0027]

[0028] In the formula, lon represents the longitude of the location, lat represents the latitude of the location, and lat min and lat max These represent the minimum and maximum latitude values ​​of the wind speed field region, respectively. min and lon max These represent the minimum and maximum latitude values ​​of the wind speed field region, respectively;

[0029] Step 4.3: Measure the maximum wind speed intensity in the sample. It is assigned to the j-th quantum Q that is closest to it. j ,Right now ,in This represents the wind speed field distance between the i-th sample and quantum j. This represents the wind speed field distance between the i-th sample and quantum k, thus obtaining the set of maximum wind speed intensity measurement maps assigned to quantum j. The number of samples in the set of maximum wind speed intensity measurements assigned to quantum j is N. j ;

[0030] Step 4.4: Calculate the average value of the samples assigned to each quantum to obtain the updated quantum. ;

[0031]

[0032] Step 4.5: Repeat steps 4.2 to 4.4 to iterate over the quantum mechanics until convergence, obtaining N. HQ An optimized quantum;

[0033] Step 4.6: Calculate the weight P of the optimized quantum. j P j The calculation formula is:

[0034]

[0035] Where, N j N is the number of samples assigned to the j-th optimized quantum. events It is the total number of samples;

[0036] Step 5: Output N from Step 4 HQ The optimized quantum and its weight, N HQ Maximum wind speed intensity measurement map and the weight P of the optimized quantum. j This generates a typhoon disaster scenario.

[0037] This invention uses a disaster quantification method to reduce the number of maximum wind speed intensity measurement maps, thereby improving the efficiency of typhoon disaster simulation. The reduced maximum wind speed intensity measurement maps are then input into the infrastructure system under study for damage analysis, recovery analysis, resilience assessment, and other purposes.

[0038] This invention enables the generation of typhoon disaster scenarios that simultaneously considers accuracy and efficiency. It can more accurately assess the damage caused by typhoon disasters to target infrastructure systems while improving analysis efficiency. It is of great significance for damage analysis, functional analysis, resilience assessment and improvement of infrastructure under a large number of typhoon simulation scenarios.

[0039] This invention integrates machine learning and disaster quantification methods, which effectively improves the accuracy and efficiency of typhoon disaster simulation.

[0040] Compared with existing technologies, the typhoon disaster scene generation method of this invention, which improves accuracy and efficiency, has the following advantages:

[0041] 1. The wind field model has been improved, which has increased the accuracy of typhoon disaster simulation.

[0042] 2. Applying disaster quantification methods to the field of typhoon disaster modeling improves the efficiency of typhoon disaster scenario generation without affecting accuracy.

[0043] 3. A typhoon disaster modeling framework that combines accuracy and efficiency is proposed. Attached Figure Description

[0044] Figure 1 This is a comparison chart of extreme wind speed exceedance probabilities before and after HQ disaster reduction in the example;

[0045] Figure 2 This is a comparison chart of the probability of exceeding the number of transmission tower failures before and after HQ disaster reduction in the example;

[0046] Figure 3 This is a comparison chart of the maximum wind speed simulated by different implementation methods and the actual recorded wind speed in the examples. Detailed Implementation

[0047] Specific Implementation Method 1: This implementation method for improving the accuracy and efficiency of typhoon disaster scene generation is carried out according to the following steps:

[0048] Step 1: Select the target area (i.e., the area where the infrastructure system is located), select all typhoon samples that pass through the target area from the simulated typhoon database, and extract typhoon information from the selected typhoon samples. The typhoon information includes longitude, latitude, typhoon center pressure difference, maximum wind speed and translation speed.

[0049] Step 2, Step 2.1: Calculate parameters A and B in the gradient wind field model;

[0050] The formula for calculating parameter A is as follows:

[0051]

[0052] The formula for calculating parameter B is as follows:

[0053]

[0054] Where C and C0 represent correction parameters, f is the Coriolis parameter, r is the distance from the wind speed calculation point to the typhoon center, ΔP is the pressure difference at the typhoon center, e is the natural constant, and V m That is the maximum wind speed. It is air density;

[0055] Step 2.2: Calculate the radius R of the maximum wind speed. max The maximum wind speed radius R was calculated using the XGBoost model. max Maximum wind speed radius R max The model is as follows:

[0056]

[0057] in, Let ε be the latitude of the typhoon, ΔP be the pressure difference at the typhoon center, and ε be the latitude of the typhoon. Rmax This is the error term;

[0058] Step 2.3: Combine parameters A, B, and the maximum wind speed radius R. max Substituting into the gradient wind field model, the gradient wind speed V is calculated. G (r, );

[0059]

[0060] Step 2.4: Superimpose the migrating wind field at the gradient wind speed V. G (r, Based on this, the typhoon's migrating wind field V is superimposed. t The total wind speed field V after superposition is obtained. S :

[0061]

[0062] Step 2.5: Combine the superimposed total wind speed field V S Converted to ground wind speed V H (t), the conversion formula is as follows:

[0063]

[0064] Where H represents the target height, i.e., the height above the ground, and z0 represents the surface roughness length;

[0065] Step 3: Discretize the target area into multiple grid cells, record the latitude and longitude coordinates of the center point of each grid cell, and calculate the superimposed total wind speed field V based on the improved wind field model in Step 2. S This allows us to calculate the ground wind speed at the center of each grid cell at different times when a typhoon sample passes by. For each grid cell center, we select the maximum ground wind speed when each typhoon sample passes by, thus forming a maximum wind speed intensity measurement map of the target area. One maximum wind speed intensity measurement map is obtained for each typhoon sample.

[0066] Step 4: Apply disaster quantification methods to reduce the number of maximum wind speed intensity measurement maps for all typhoon samples. The process of reducing the number of maximum wind speed intensity measurement maps is as follows:

[0067] Step 4.1: Define the sample as the maximum wind speed intensity measurement map obtained from all typhoon samples. The maximum wind speed intensity measurement map for each sample is used... This indicates that the total number of samples is N. events The quantum is defined as a reduced maximum wind speed intensity measurement map, and the number of quantum is set to N. HQ First, arbitrarily select N from the sample. HQ Each sample is used as a quantum;

[0068] Step 4.2: Calculate the nth quantum. With all samples wind speed field distance , The wind speed field distance represents the square of the maximum wind speed difference between the i-th sample and the n-th quantum at the center point (lon, lat) of the corresponding grid cell. The calculation formula is as follows:

[0069]

[0070] In the formula, lon represents the longitude of the location, lat represents the latitude of the location, and lat min and lat max These represent the minimum and maximum latitude values ​​of the wind speed field region, respectively. min and lon max These represent the minimum and maximum latitude values ​​of the wind speed field region, respectively;

[0071] Step 4.3: Measure the maximum wind speed intensity in the sample. It is assigned to the j-th quantum Q that is closest to it. j ,Right now ,in This represents the wind speed field distance between the i-th sample and quantum j. This represents the wind speed field distance between the i-th sample and quantum k, thus obtaining the set of maximum wind speed intensity measurement maps assigned to quantum j. The number of samples in the set of maximum wind speed intensity measurements assigned to quantum j is N. j ;

[0072] Step 4.4: Calculate the average value of the samples assigned to each quantum to obtain the updated quantum. ;

[0073]

[0074] Step 4.5: Repeat steps 4.2 to 4.4 to iterate over the quantum mechanics until convergence, obtaining N. HQ An optimized quantum;

[0075] Step 4.6: Calculate the weight P of the optimized quantum.j P j The calculation formula is:

[0076]

[0077] Where, N j N is the number of samples assigned to the j-th optimized quantum. events It is the total number of samples;

[0078] Step 5: Output N from Step 4 HQ The optimized quantum and its weight, N HQ Maximum wind speed intensity measurement map and the weight P of the optimized quantum. j This generates a typhoon disaster scenario.

[0079] In step four of this implementation method, the maximum wind speed intensity measurement map of each sample is used... It indicates that it is a sequence of maximum wind speeds calculated at each grid point in the target area, arranged sequentially according to the division number of each location (lon,lat) in the target area.

[0080] Specific Implementation Method Two: This implementation method differs from Specific Implementation Method One in that the simulated typhoon database in step one is the CMA database, and the number of typhoon samples is greater than 2000.

[0081] Specific Implementation Method Three: This implementation method differs from Specific Implementation Method One or Two in that the calculation formula for the correction parameter C in step 2.1 is as follows:

[0082] .

[0083] In this embodiment, the value of C0 is 1.0628. This is the air density, taken as 1.15 * 10⁻⁶. -3 kg / m 3 .

[0084] Specific Implementation Method Four: This implementation method differs from Specific Implementation Methods One to Three in that step 2.2 uses the XGBoost model to calculate the maximum wind speed radius R. max The process involves inputting the typhoon latitude and typhoon center pressure difference from all typhoon samples into the XGBoost model for training, and then substituting the typhoon latitude and typhoon center pressure difference into the trained XGBoost model to predict the maximum wind speed radius R. max .

[0085] Specific Implementation Method Five: This implementation method differs from Specific Implementation Method Four in that the maximum wind speed radius R calculated in step 2.2 using the XGBoost model is not used. maxThe method is replaced by using empirical formulas to calculate the maximum wind speed radius R. max The empirical formula is as follows:

[0086] .

[0087] Specific Implementation Method Six: This implementation method differs from Specific Implementation Methods One to Five in that the moving wind field V in step 2.4 is... t The calculation formula is as follows:

[0088] V t = c · e -r / 500

[0089] In the formula, c represents the translational speed of the typhoon point.

[0090] Specific Implementation Method Seven: This implementation method differs from Specific Implementation Methods One to Six in that in step three, the target area is discretized into multiple grid units, with the longitude and latitude of each grid unit being 0.01 degrees apart.

[0091] Specific Implementation Method Eight: This implementation method differs from Specific Implementation Methods One through Seven in that the number of quantum N in step 4.1 is... HQ It represents 4% to 8% of the total sample size.

[0092] Specific Implementation Method Nine: This implementation method differs from Specific Implementation Methods One through Eight in that step 4.5 involves calculating the distortion metric ∆. n+1 The formula for determining whether the iteration has converged is as follows:

[0093] .

[0094] Specific Implementation Method Ten: This implementation method differs from Specific Implementation Method Nine in that the distortion metric satisfies a predefined convergence criterion, namely, the Δ at the next time step. n+1 and the ∆ of the previous moment n The relative deviation between (∆) n+1 -∆ n ) / ∆ n If the value is less than 0.001, the algorithm converges.

[0095] Example: This example demonstrates a method for generating typhoon disaster scenarios that improves accuracy and efficiency. The method is implemented according to the following steps:

[0096] Step 1: Select the target area (i.e., the area where the infrastructure system is located). In this embodiment, the target area is a circular area with a radius of 250km centered at (116.5°N, 23.5°E). Select all typhoon samples that pass through the target area from the simulated typhoon database. Extract typhoon information from the selected typhoon samples, including longitude, latitude, typhoon center pressure difference, and maximum wind speed.

[0097] Step 2, Step 2.1: Calculate parameters A and B in the gradient wind field model;

[0098] The formula for calculating parameter A is as follows:

[0099]

[0100] The formula for calculating parameter B is as follows:

[0101]

[0102] Where C0 represents the correction parameter, with a value of 1.0628, and C is calculated according to the following formula:

[0103]

[0104] f is the Coriolis parameter, r is the distance from the point where the wind speed is to be calculated to the typhoon center, ΔP is the pressure difference at the typhoon center, e is the natural constant, and V m That is the maximum wind speed. This is the air density, taken as 1.15 * 10⁻⁶. -3 kg / m 3 ;

[0105] Step 2.2: Calculate the radius R of the maximum wind speed. max The maximum wind speed radius R was calculated using the XGBoost model. max Maximum wind speed radius R max The model is as follows:

[0106]

[0107] in, Let ε be the latitude of the typhoon, ΔP be the pressure difference at the typhoon center, and ε be the latitude of the typhoon. Rmax This is the error term;

[0108] Step 2.3: Combine parameters A, B, and the maximum wind speed radius R. max Substituting into the gradient wind field model, the gradient wind speed V is calculated. G (r, );

[0109]

[0110] Step 2.4: Superimpose the migrating wind field at the gradient wind speed V. G (r, Based on this, the typhoon's migrating wind field V is superimposed. t The total wind speed field V after superposition is obtained. S :

[0111]

[0112] Step 2.5: Combine the superimposed total wind speed field V S Converted to ground wind speed V H (t), the conversion formula is as follows:

[0113]

[0114] Where H represents the height above the ground, and H is taken as 10m; z0 represents the surface roughness length, which is obtained according to the land use type map of the target area, and then the value is taken by referring to the table below; in this embodiment, the reference type is the suburbs with sparse buildings, and the value is taken as 0.3.

[0115] Table 1. z0 values ​​for different surface roughness

[0116]

[0117] Step 3: Discretize the target area into multiple grid cells, record the latitude and longitude coordinates of the center point of each grid cell, and calculate the superimposed total wind speed field V based on the improved wind field model in Step 2. S The ground wind speed at the center of each grid cell was obtained at different times when a typhoon sample passed by.

[0118] For the center point of each grid cell, the maximum ground wind speed when each typhoon sample passes through is selected to form a maximum wind speed intensity measurement map of the target area. Each typhoon sample yields a maximum wind speed intensity measurement map. In this embodiment, the maximum wind speed intensity measurement maps with excessively low wind speeds are removed, resulting in 4383 maximum wind speed intensity measurement maps.

[0119] Step 4: Apply disaster quantification methods to reduce the number of maximum wind speed intensity measurement maps for all typhoon samples. The process of reducing the number of maximum wind speed intensity measurement maps is as follows:

[0120] Step 4.1: Define the sample as the maximum wind speed intensity measurement map obtained from all typhoon samples, with a total sample size of N. events The quantum is defined as a reduced maximum wind speed intensity measurement map, and the number of quantum is set to N. HQ=200, meaning the maximum wind speed intensity measurement maps are reduced to 200; first, N is randomly selected from the sample. HQ Each sample is used as a quantum;

[0121] Step 4.2: Calculate the nth quantum. All samples wind speed field distance Wind speed field distance The calculation formula is as follows:

[0122]

[0123] In the formula, lon represents the longitude of the location, lat represents the latitude of the location, and lat min and lat max These represent the minimum and maximum latitude values ​​of the wind speed field region, respectively. min and lon max Let L represent the minimum and maximum latitude values ​​of the wind speed field region, respectively. Then, based on all grid center points in the region, calculate the integral of the square of the sample and quantum wind speed difference for each location (lon, lat).

[0124] Step 4.3: Measure the maximum wind speed intensity in the sample. It is assigned to the j-th quantum Q that is closest to it. j ,Right now ,in This represents the wind speed field distance between the i-th sample and quantum j. This represents the wind speed field distance between the i-th sample and quantum k, thus obtaining the set of maximum wind speed intensity measurement maps assigned to quantum j. The number of samples in the set of maximum wind speed intensity measurements assigned to quantum j is N. j ;

[0125] Step 4.4: Calculate the average value of the samples assigned to each quantum to obtain the updated quantum. ;

[0126]

[0127] Step 4.5: Repeat steps 4.2 to 4.4 to iterate over the quantum mechanics until convergence, obtaining N. HQ An optimized quantum;

[0128] In this embodiment, the distortion varies with the number of iterations, as shown in Table 2 below:

[0129] Table 2. Distortion as a function of iteration number

[0130]

[0131] Step 4.6: Calculate the weight P of the optimized quantum. j P j The calculation formula is:

[0132]

[0133] Where, N j N is the number of samples assigned to the j-th optimized quantum. events It is the total number of samples;

[0134] Step 5: Output N from Step 4 HQ The optimized quantum and its weight, N HQ Maximum wind speed intensity measurement map and the weight P of the optimized quantum. j This generates a typhoon disaster scenario.

[0135] Example 2: This example differs from Example 1 in that the maximum wind speed radius R calculated using the XGBoost model in step 2.2 is used instead of the previous one. max The method is replaced by using empirical formulas to calculate the maximum wind speed radius R. max The empirical formula is as follows:

[0136] .

[0137] The output of step four in Example 1 is a sequence containing N HQ Maximum wind speed intensity measurement map (quantum) and its corresponding weight p j The set of values. This quantizer provides an optimal representation of regional wind speed hazards based on minimum mean square error, which can be used for subsequent wind disaster risk analysis or loss prediction and assessment. In this embodiment, the wind speed exceedance probability calculated for the maximum wind speed intensity measurement map of each grid point in the study area before and after disaster reduction is first calculated, such as... Figure 1 As shown.

[0138] It can be seen that even after reducing a large number of wind speed samples, the accuracy remains at a high level, and the efficiency is greatly improved. Then, by analyzing a real power grid, the number of transmission towers damaged before the HQ typhoon disaster scenario map reduction and the number of transmission towers damaged after the HQ method of this invention are obtained. Exceedance probability curves are plotted and compared, as shown below. Figure 2 As shown.

[0139] The accuracy of the power grid damage analysis remained essentially unchanged before and after disaster reduction. This embodiment simulated power grid damage and recovery using Monte Carlo simulations 50,000 times. If the simulation were based on the previous 4,383 typhoon events, the computational load would be 4,383 * 50,000. After applying the HQ method for disaster reduction, the efficiency increased by approximately 20 times. This is of significant importance for damage analysis, resilience assessment, and resilience enhancement of infrastructure systems (such as power grids) under numerous artificially simulated disaster scenarios.

[0140] Furthermore, this embodiment also includes a comparison. That is, compared with R in Embodiment 2. max The estimations were compared using empirical formulas, calculating and comparing the maximum wind speeds recorded and simulated by several real-world weather stations along the path of a typhoon. Figure 3 As shown in the figure, the method proposed in this invention has a significant improvement effect, and the typhoon model is more accurate.

Claims

1. A method for generating typhoon disaster scenarios with improved accuracy and efficiency, characterized in that... The method for generating typhoon disaster scenarios is implemented according to the following steps: Step 1: Select the target area. Select all typhoon samples that pass through the target area from the simulated typhoon database. Extract typhoon information from the selected typhoon samples. Typhoon information includes longitude, latitude, typhoon center pressure difference, maximum wind speed and translation speed. Step 2, Step 2.1: Calculate parameters A and B in the gradient wind field model; The formula for calculating parameter A is as follows: The formula for calculating parameter B is as follows: Where C and C0 represent correction parameters, f is the Coriolis parameter, r is the distance from the wind speed calculation point to the typhoon center, ΔP is the pressure difference at the typhoon center, e is the natural constant, and V m That is the maximum wind speed. It is air density; Step 2.2: Calculate the radius R of the maximum wind speed. max The maximum wind speed radius R was calculated using the XGBoost model. max Maximum wind speed radius R max The model is as follows: in, Let ε be the latitude of the typhoon, ΔP be the pressure difference at the typhoon center, and ε be the latitude of the typhoon. Rmax This is the error term; Step 2.3: Combine parameters A, B, and the maximum wind speed radius R. max Substituting into the gradient wind field model, the gradient wind speed V is calculated. G (r, ); Step 2.4: Superimpose the migrating wind field at the gradient wind speed V. G (r, Based on this, the typhoon's migrating wind field V is superimposed. t The total wind speed field V after superposition is obtained. S : Step 2.5: Combine the superimposed total wind speed field V S Converted to ground wind speed V H (t), the conversion formula is as follows: Where H represents the target height, i.e., the height above the ground, and z0 represents the surface roughness length; Step 3: Discretize the target area into multiple grid cells, record the latitude and longitude coordinates of the center point of each grid cell, and calculate the superimposed total wind speed field V based on the improved wind field model in Step 2. S This allows for the calculation of the ground wind speed at the center of each grid cell at different times when the typhoon sample passes through. For each grid cell center, the maximum ground wind speed at each typhoon sample is selected to form a maximum wind speed intensity measurement map of the target area. One maximum wind speed intensity measurement map is obtained for each typhoon sample. Step 4: Apply disaster quantification methods to reduce the number of maximum wind speed intensity measurement maps for all typhoon samples. The process of reducing the number of maximum wind speed intensity measurement maps is as follows: Step 4.1: Define the sample as the maximum wind speed intensity measurement map obtained from all typhoon samples. The maximum wind speed intensity measurement map for each sample is used... This indicates that the total number of samples is N. events The quantum is defined as a reduced maximum wind speed intensity measurement map, and the number of quantum is set to N. HQ First, arbitrarily select N from the sample. HQ Each sample is used as a quantum; Step 4.2: Calculate the nth quantum. With all samples wind speed field distance , The wind speed field distance represents the square of the maximum wind speed difference between the i-th sample and the n-th quantum at the center point (lon, lat) of the corresponding grid cell. The calculation formula is as follows: In the formula, lon represents the longitude of the location, lat represents the latitude of the location, and lat min and lat max These represent the minimum and maximum latitude values ​​of the wind speed field region, respectively. min and lon max These represent the minimum and maximum latitude values ​​of the wind speed field region, respectively; Step 4.3: Measure the maximum wind speed intensity in the sample. It is assigned to the j-th quantum Q that is closest to it. j ,Right now ,in This represents the wind speed field distance between the i-th sample and quantum j. This represents the wind speed field distance between the i-th sample and quantum k, thus obtaining the set of maximum wind speed intensity measurement maps assigned to quantum j. The number of samples in the set of maximum wind speed intensity measurements assigned to quantum j is N. j ; Step 4.4: Calculate the average value of the samples assigned to each quantum to obtain the updated quantum. ; Step 4.5: Repeat steps 4.2 to 4.4 to iterate over the quantum mechanics until convergence, obtaining N. HQ An optimized quantum; Step 4.6: Calculate the weight P of the optimized quantum. j P j The calculation formula is: Where, N j N is the number of samples assigned to the j-th optimized quantum. events It is the total number of samples; Step 5: Output N from Step 4 HQ The optimized quantum and its weight, N HQ Maximum wind speed intensity measurement map and the weight P of the optimized quantum. j This generates a typhoon disaster scenario.

2. The method for generating typhoon disaster scenarios with improved accuracy and efficiency according to claim 1, characterized in that... In step one, the simulated typhoon database is the CMA database, with more than 2,000 typhoon samples.

3. The method for generating typhoon disaster scenarios with improved accuracy and efficiency according to claim 1, characterized in that... The formula for calculating the correction parameter C in step 2.1 is as follows: 。 4. The method for generating typhoon disaster scenarios with improved accuracy and efficiency according to claim 1, characterized in that... In step 2.2, the maximum wind speed radius R is calculated using the XGBoost model. max The process involves inputting the typhoon latitude and typhoon center pressure difference from all typhoon samples into the XGBoost model for training, and then substituting the typhoon latitude and typhoon center pressure difference into the trained XGBoost model to predict the maximum wind speed radius R. max .

5. The method for generating typhoon disaster scenarios with improved accuracy and efficiency according to claim 4, characterized in that... The maximum wind speed radius R calculated in step 2.2 using the XGBoost model will be used. max The method is replaced by using empirical formulas to calculate the maximum wind speed radius R. max The empirical formula is as follows: 。 6. The method for generating typhoon disaster scenarios with improved accuracy and efficiency according to claim 1, characterized in that... The moving wind field V in step 2.4 t The calculation formula is as follows: V t = c · e -r / 500 In the formula, c represents the translational speed of the typhoon point.

7. The method for generating typhoon disaster scenarios with improved accuracy and efficiency according to claim 1, characterized in that... In step three, the target area is discretized into multiple grid cells, with the longitude and latitude of each grid cell separated by 0.01 degrees.

8. The method for generating typhoon disaster scenarios with improved accuracy and efficiency according to claim 1, characterized in that... In step 4.1, the number of quantum N HQ It represents 4% to 8% of the total sample size.

9. The method for generating typhoon disaster scenarios with improved accuracy and efficiency according to claim 1, characterized in that... In step 4.5, the distortion metric ∆ is calculated. n+1 The formula for determining whether the iteration has converged is as follows: 。 10. The method for generating typhoon disaster scenarios with improved accuracy and efficiency according to claim 9, characterized in that... The distortion metric satisfies a predefined convergence criterion, namely the Δ at the next time step. n+1 and the ∆ of the previous moment n The relative deviation between (∆) n+1 -∆ n ) / ∆ n If the value is less than 0.001, the algorithm converges.

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