Methods, apparatus, electronic devices and storage media for hail disaster risk assessment

Abstract

This invention provides a method, apparatus, electronic device, and storage medium for hail disaster risk assessment, belonging to the technical field of risk assessment. When determining the target disaster hazard index for each grid, it utilizes not only hail observation data from various meteorological observation stations but also historical disaster data and geographic grid data. This results in high accuracy of the target disaster hazard index for each grid, and the distribution of disaster hazard levels within the target area matches the actual disaster hazard distribution within the target area. Furthermore, when determining the risk assessment index for different disaster-bearing bodies within each grid, it uses accurate target disaster hazard indices for each grid and also employs vulnerability and disaster-prone environment sensitivity indices for different disaster-bearing bodies. This results in high accuracy of the risk assessment index for different disaster-bearing bodies within each grid, and the risk level distribution of the target disaster-bearing bodies within the target area matches the actual situation.

Description

Technical Field

[0001] This invention relates to the technical field of risk assessment, and in particular to a method, apparatus, electronic device, and storage medium for hail disaster risk assessment. Background Technology

[0002] my country, with its diverse topography and complex climate system, is one of the countries most severely affected by meteorological disasters in the world. Meteorological disasters in my country are characterized by their variety, high frequency, long duration, uneven spatial and temporal distribution, and wide impact. Hailstorms are particularly frequent, especially during spring and summer, often causing serious disruptions to industrial and agricultural production and people's lives. With the continuous advancement of disaster prevention and mitigation efforts, a scientifically sound and reasonable hailstorm risk assessment can reveal the spatial distribution patterns of risks, providing a theoretical basis for developing risk zoning management measures and offering effective risk warning information for industrial and agricultural production and people's daily lives.

[0003] Currently, most hail disaster risk assessment methods are based on meteorological observation data. However, due to the spatial limitations of the distribution of observation stations, research on hail disaster risk assessment is still relatively limited. The hail disaster risk assessment methods based on meteorological observation records mainly include: (1) using statistical methods such as fuzzy evaluation, and using landform, disaster frequency, population and socio-economic factors as evaluation factors to construct a hail disaster risk assessment model; (2) based on the natural disaster risk theory of probability, loss and variability, considering the variation of hail disaster situation under a certain time series, and determining the average risk status of the region based on the frequency of hail under different loss coefficients on the basis of disaster loss assessment, and establishing the overall risk level of the risk area based on the average risk; (3) based on subjective experience judgment, identifying and calculating the disaster-causing factors of hail disaster, and making terrain correction for the frequency of hail to make up for the problem of low spatial resolution of hail observation at national meteorological observation stations, and revealing the degree of difference in the spatial distribution of hail disaster.

[0004] In high-altitude areas such as the Qinghai-Tibet Plateau, due to the unique terrain and sparse meteorological observation stations, existing methods that rely solely on meteorological observation data for assessment cannot accurately reflect the actual spatial risk distribution of hail disasters in the region. In other words, the hail disaster risk assessment results obtained by traditional hail disaster risk assessment methods based on meteorological observation records do not match the actual spatial risk distribution of hail disasters in the region, resulting in poor accuracy of hail disaster management measures formulated for different regions.

[0005] In summary, accurately reflecting the actual spatial risk distribution of hail disasters in areas with sparse meteorological observation stations, and then formulating accurate hail disaster management measures by region, has become an urgent technical problem to be solved. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a method, apparatus, electronic device and storage medium for hail disaster risk assessment, so as to alleviate the technical problem that traditional technologies cannot accurately reflect the actual spatial risk distribution of regional hail disasters.

[0007] In a first aspect, embodiments of the present invention provide a method for assessing hail disaster risk, comprising:

[0008] Acquire geographic raster data, historical disaster data, historical disaster raster data, and hail observation data from various meteorological observation stations for the target area. The geographic raster data includes: slope raster data, water network density raster data, agricultural and pastoral zoning raster data, and disaster-bearing body raster data.

[0009] The disaster risk index of each meteorological observation station is calculated based on the hail observation data and the historical disaster data, and the disaster risk index of each grid in the target area is determined based on the disaster risk index of each meteorological observation station.

[0010] Based on the slope raster data, the water network density raster data, and the agricultural and pastoral zoning raster data, the disaster risk index of each raster is corrected to obtain the corrected disaster risk index of each raster. Then, based on the historical disaster raster data, the corrected disaster risk index of each raster is further corrected to obtain the target disaster risk index of each raster.

[0011] The percentile method is used to classify the disaster risk level of the target disaster risk index of each grid, so as to obtain the disaster risk level distribution in the target area;

[0012] The vulnerability of different disaster-bearing bodies in each grid is calculated based on the disaster-bearing body grid data, and the disaster-prone environment sensitivity index of each grid is determined based on the altitude of each grid.

[0013] Based on the target disaster hazard index of each grid, the vulnerability of different disaster-bearing bodies in each grid, and the disaster-inducing environment sensitivity index of each grid, the risk assessment index of different disaster-bearing bodies in each grid is calculated.

[0014] The risk level of the target disaster-bearing bodies in each grid is classified by the percentile method to obtain the risk level distribution of the target disaster-bearing bodies in the target area, thereby completing the hail disaster risk assessment of the target area. Based on the disaster hazard level distribution and the risk level distribution of the target disaster-bearing bodies in the target area, hail disaster management measures are formulated for the target area by region. The target disaster-bearing bodies traverse any of the different disaster-bearing bodies.

[0015] Furthermore, acquire geographic raster data, historical disaster data, and historical disaster raster data for the target area, including:

[0016] Extract the slope raster data and the water network density raster data from the altitude data of the target area;

[0017] The target area is divided into agricultural and pastoral zones using regional vector data from statistical yearbooks, resulting in raster data of the agricultural and pastoral zones.

[0018] The GDAL function is used to uniformly process the disaster-bearing body data to obtain the disaster-bearing body raster data;

[0019] Historical disaster data on hail disasters are obtained from the original historical disaster data, which includes: data on the affected population, data on direct economic losses, and data on the affected area of ​​crops.

[0020] The historical disaster data is rasterized to obtain the historical disaster raster data.

[0021] Furthermore, based on hail observation data from each meteorological observation station and historical disaster data, a disaster risk index is calculated for each meteorological observation station, including:

[0022] Based on the historical disaster data, a first target meteorological observation station is determined from all the meteorological observation stations, wherein the number of historical disasters corresponding to the first target meteorological observation station is greater than a preset threshold.

[0023] The hail observation data of the first target meteorological observation station and the historical disaster data are spatiotemporally matched to obtain the hail observation data of each first target meteorological observation station for each time and the historical disaster data corresponding to each hail observation data.

[0024] The hail disaster-causing factors of each first target meteorological observation station are determined based on the hail observation data of each first target meteorological observation station and the historical disaster data corresponding to each hail observation data, and the hail intensity of each first target meteorological observation station is calculated based on the hail disaster-causing factors of each first target meteorological observation station.

[0025] The disaster risk index of each first target meteorological observation station is calculated based on the hail intensity of each first target meteorological observation station and the number of hail days at the corresponding first target meteorological observation station.

[0026] Obtain a second target meteorological observation station, wherein the second target meteorological observation station is a meteorological observation station other than the first target meteorological observation station among all the meteorological observation stations;

[0027] The disaster risk index of each second target meteorological observation station is calculated based on the hail observation data of each second target meteorological observation station.

[0028] Furthermore, based on the hail observation data of each of the first target meteorological observation stations and the historical disaster data corresponding to each hail observation data, the hail disaster-causing factors of each of the first target meteorological observation stations are determined, including:

[0029] The disaster damage index of each historical disaster at each of the first target meteorological observation stations is calculated based on the historical disaster data of each of the first target meteorological observation stations.

[0030] The average value of the disaster loss index of each of the first target meteorological observation stations is calculated based on the disaster loss index of each historical disaster.

[0031] The correlation coefficient between the disaster loss index of each first target meteorological observation station and each candidate hail disaster causative factor is calculated based on the disaster loss index of each first target meteorological observation station for each historical disaster, the average disaster loss index of each first target meteorological observation station, the observation data of each candidate hail disaster causative factor of each first target meteorological observation station, and the average observation data of each candidate hail disaster causative factor of each first target meteorological observation station.

[0032] The significance of each candidate hail disaster-causing factor for each of the first target meteorological observation stations is calculated based on the correlation coefficient between the disaster damage index of each of the first target meteorological observation stations and each candidate hail disaster-causing factor.

[0033] Based on the significance of each candidate hail disaster-causing factor at each of the first target meteorological observation stations, the hail disaster-causing factor for each of the first target meteorological observation stations is determined from among the candidate hail disaster-causing factors.

[0034] Furthermore, based on the slope raster data, the water network density raster data, and the agricultural and pastoral zoning raster data, the disaster risk index of each raster is corrected, including:

[0035] The disaster-prone environment sensitivity index of each grid is determined based on the altitude of each grid, the slope impact index of each grid is determined based on the slope grid data, the river system impact index of each grid is determined based on the water network density grid data, and the agricultural and pastoral impact index of each grid is determined based on the agricultural and pastoral zoning grid data.

[0036] The disaster risk index of each grid is corrected based on the disaster-prone environment sensitivity index, slope influence index, river system influence index, and agricultural and pastoral influence index of each grid, resulting in the corrected disaster risk index of each grid.

[0037] Furthermore, based on the historical disaster raster data, the modified disaster risk index of each raster is further corrected, including:

[0038] Calculate the average historical disaster loss for each grid based on the historical disaster grid data;

[0039] The historical disaster loss index for each grid is determined based on the average historical disaster loss of each grid.

[0040] The disaster risk index of each grid is further corrected based on the historical disaster loss index of each grid to obtain the target disaster risk index of each grid.

[0041] Furthermore, based on the target disaster hazard index of each grid, the vulnerability of different disaster-bearing bodies in each grid, and the disaster-inducing environmental sensitivity index of each grid, the risk assessment index of different disaster-bearing bodies in each grid is calculated, including:

[0042] The risk assessment index of the target disaster-causing hazard index, the vulnerability of the target disaster-bearing body in the corresponding grid, and the disaster-inducing environmental sensitivity index of the corresponding grid are weighted and calculated to obtain the risk assessment index of the target disaster-bearing body in each grid.

[0043] Secondly, embodiments of the present invention also provide an apparatus for hail disaster risk assessment, comprising:

[0044] The acquisition unit is used to acquire geographic raster data, historical disaster data, historical disaster raster data and hail observation data from various meteorological observation stations in the target area. The geographic raster data includes: slope raster data, water network density raster data, agricultural and pastoral zoning raster data and disaster-bearing body raster data.

[0045] The first calculation unit is used to calculate the disaster risk index of each meteorological observation station based on the hail observation data and the historical disaster data, and to determine the disaster risk index of each grid in the target area based on the disaster risk index of each meteorological observation station.

[0046] The correction unit is used to correct the disaster risk index of each grid based on the slope grid data, the water network density grid data, and the agricultural and pastoral zoning grid data to obtain the corrected disaster risk index of each grid, and to perform a second correction on the corrected disaster risk index of each grid based on the historical disaster grid data to obtain the target disaster risk index of each grid.

[0047] The first level division unit is used to classify the disaster risk level of the target disaster risk index of each grid using the percentile method, so as to obtain the disaster risk level distribution in the target area.

[0048] The second calculation unit is used to calculate the vulnerability of different disaster-bearing bodies in each grid based on the disaster-bearing body grid data, and to determine the disaster-prone environment sensitivity index of each grid based on the altitude of each grid.

[0049] The third calculation unit is used to calculate the risk assessment index of different disaster-bearing bodies in each grid based on the target disaster hazard index of each grid, the vulnerability of different disaster-bearing bodies in each grid, and the disaster-prone environment sensitivity index of each grid.

[0050] The second-level classification unit is used to classify the risk level of the target disaster-bearing bodies in each grid by using the percentile method, thereby obtaining the risk level distribution of the target disaster-bearing bodies in the target area, and completing the hail disaster risk assessment of the target area. Based on the disaster hazard level distribution and the risk level distribution of the target disaster-bearing bodies in the target area, hail disaster management measures are formulated for the target area by partitioning the target area. The target disaster-bearing bodies traverse any of the different disaster-bearing bodies.

[0051] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described in any of the first aspects above.

[0052] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing machine-executable instructions, which, when invoked and executed by a processor, cause the processor to perform the method described in any of the first aspects above.

[0053] In this embodiment of the invention, a method for hail disaster risk assessment is provided, comprising: acquiring geographic raster data, historical disaster data, historical disaster raster data, and hail observation data from various meteorological observation stations in a target area, wherein the geographic raster data includes: slope raster data, water network density raster data, agricultural and pastoral zoning raster data, and disaster-bearing body raster data; calculating the disaster hazard index of each meteorological observation station based on the hail observation data and historical disaster data, and determining the disaster hazard index of each raster in the target area based on the disaster hazard index of each meteorological observation station; correcting the disaster hazard index of each raster based on the slope raster data, water network density raster data, and agricultural and pastoral zoning raster data to obtain the corrected disaster hazard index of each raster, and further correcting the corrected disaster hazard index of each raster based on the historical disaster raster data to obtain the target disaster hazard index of each raster; and using the percentile method to analyze the hail disaster risk index of each raster. The target disaster hazard index of each grid is used to classify the disaster hazard level, thus obtaining the disaster hazard level distribution within the target area. The vulnerability of different disaster-bearing bodies in each grid is calculated based on the disaster-bearing body grid data, and the disaster-inducing environmental sensitivity index of each grid is determined based on the altitude of each grid. The risk assessment index of different disaster-bearing bodies in each grid is calculated based on the target disaster hazard index, the vulnerability of different disaster-bearing bodies in each grid, and the disaster-inducing environmental sensitivity index of each grid. The risk level of the target disaster-bearing bodies in each grid is classified using the percentile method, thus obtaining the risk level distribution of the target disaster-bearing bodies within the target area. This completes the hail disaster risk assessment of the target area. Based on the disaster hazard level distribution and the risk level distribution of the target disaster-bearing bodies within the target area, hail disaster management measures are formulated for different zones within the target area. The target disaster-bearing bodies encompass any one of the different disaster-bearing bodies.As described above, the hail disaster risk assessment method of the present invention, in determining the target disaster hazard index of each grid, not only uses hail observation data from various meteorological observation stations, but also uses historical disaster data, slope grid data, water network density grid data, agricultural and pastoral zoning grid data, and historical disaster grid data. The resulting target disaster hazard index for each grid is highly accurate, and the resulting distribution of disaster hazard levels within the target area matches the actual disaster hazard distribution within the target area. Furthermore, in determining the risk assessment index of different disaster-bearing bodies in each grid, accurate target disaster hazard indices for each grid are used, along with the vulnerability of different disaster-bearing bodies in each grid and the disaster-prone environment sensitivity index of each grid. The resulting risk assessment index for different disaster-bearing bodies in each grid is highly accurate, and the risk level distribution of the target disaster-bearing bodies within the divided target area matches the actual situation. Finally, the hail disaster management measures formulated for the target area are highly accurate, alleviating the technical problem that traditional technologies cannot accurately reflect the actual spatial risk distribution of hail disasters in a region. Attached Figure Description

[0054] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0055] Figure 1 A flowchart illustrating a method for hail disaster risk assessment provided in an embodiment of the present invention;

[0056] Figure 2 A schematic diagram of a hail disaster risk assessment device provided in an embodiment of the present invention;

[0057] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0058] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Traditional technologies cannot accurately reflect the actual spatial risk distribution of hail disasters in a region.

[0060] Based on this, in the hail disaster risk assessment method of the present invention, when determining the target disaster hazard index of each grid, not only hail observation data from various meteorological observation stations are used, but also historical disaster data, slope grid data, water network density grid data, agricultural and pastoral zoning grid data, and historical disaster grid data are used. The accuracy of the target disaster hazard index of each grid is good, and the distribution of disaster hazard levels in the target area is consistent with the actual disaster hazard distribution in the target area. In addition, when determining the risk assessment index of different disaster-bearing bodies in each grid, the accurate target disaster hazard index of each grid is used, as well as the vulnerability of different disaster-bearing bodies in each grid and the disaster-inducing environment sensitivity index of each grid are also adopted. The accuracy of the risk assessment index of different disaster-bearing bodies in each grid is good, and the risk level distribution of the target disaster-bearing bodies in the divided target area is consistent with the actual situation. Finally, the hail disaster management measures formulated for the target area are accurate.

[0061] To facilitate understanding of this embodiment, a method for hail disaster risk assessment disclosed in this embodiment of the invention will first be described in detail.

[0062] Example 1:

[0063] According to an embodiment of the present invention, an embodiment of a method for hail disaster risk assessment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0064] Figure 1 This is a flowchart of a hail disaster risk assessment method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0065] Step S102: Obtain geographic raster data, historical disaster data, historical disaster raster data and hail observation data from various meteorological observation stations for the target area. The geographic raster data includes: slope raster data, water network density raster data, agricultural and pastoral zoning raster data and disaster-bearing body raster data.

[0066] In this embodiment of the invention, the target area can be a region with sparse meteorological observation stations, or a region with sparse meteorological observation stations and high altitude, such as the Qinghai-Tibet Plateau. This embodiment of the invention does not impose specific restrictions on the target area, which can actually be any region that needs to conduct hail disaster risk assessment.

[0067] Step S104: Calculate the disaster risk index of each meteorological observation station based on hail observation data and historical disaster data, and determine the disaster risk index of each grid in the target area based on the disaster risk index of each meteorological observation station.

[0068] Specifically, each of the above grids can be obtained by dividing the target area into grids according to a preset fixed size. The disaster risk index of each grid indicates the degree of danger of hail disaster to each grid. The grid with the larger the disaster risk index is more susceptible to damage from hail disaster.

[0069] After obtaining the disaster risk index of each meteorological observation station, the disaster risk index of each grid in the target area (i.e., the disaster risk index in grid form) is obtained by using the Kriging spatial interpolation algorithm.

[0070] Step S106: Based on slope raster data, water network density raster data, and agricultural and pastoral zoning raster data, the disaster risk index of each raster is corrected to obtain the corrected disaster risk index of each raster. Then, based on historical disaster raster data, the corrected disaster risk index of each raster is corrected a second time to obtain the target disaster risk index of each raster.

[0071] Step S108: The percentile method is used to classify the disaster risk level of the target disaster risk index of each grid, so as to obtain the disaster risk level distribution in the target area.

[0072] Specifically, the percentile method is used to classify the disaster hazard index of each grid into four levels: I-IV, corresponding to high, relatively high, medium, and low hazard levels, respectively. A hail disaster hazard level distribution map (i.e., disaster hazard level distribution map) is then drawn within the assessment target area according to the classification principles in the table below.

[0073] Percentile range R R≤50％ 50％＜R≤75％ 75％＜R≤95％ R＞95％ Risk level Level IV Level III Level II Level I Level meaning Low lower higher high

[0074] Step S110: Calculate the vulnerability of different disaster-bearing bodies in each grid based on the disaster-bearing body grid data, and determine the disaster-prone environment sensitivity index of each grid based on the altitude of each grid.

[0075] Specifically, the disaster-bearing body raster data includes: disaster-affected population raster data, direct economic loss raster data, and crop-affected area raster data.

[0076] Vulnerability of different disaster-bearing bodies V s Including: Exposure V of different disaster-bearing bodies d Vulnerability of different disaster-bearing bodies V f That is: V s =V d×V f In other words, the vulnerability of a certain type of disaster-bearing body in a grid is equal to the product of the exposure of that type of disaster-bearing body in the grid and the vulnerability of that type of disaster-bearing body in the grid.

[0077] When assessing the disaster-bearing body using direct economic losses (i.e., GDP), the GDP per unit area of ​​each grid represents exposure, and the proportion of direct economic losses from hail disasters to total GDP represents vulnerability. When assessing the disaster-bearing body using the affected population, the population density of each grid represents exposure, and the proportion of casualties caused by hail disasters to the population represents vulnerability. When assessing the disaster-bearing body using the affected crop area, the crop planting area of ​​each grid represents exposure, and the proportion of affected crop area to the planted area represents vulnerability.

[0078] If statistical values ​​for direct economic losses, casualties (i.e., affected population), and affected crop area caused by hail disasters are unavailable, vulnerability can be directly characterized by the exposure degree of the affected body.

[0079] When determining the environmental sensitivity index of each grid cell based on its altitude, different altitude levels are divided using certain altitude intervals as thresholds. Each level is assigned a value from 0 to 1, and the resulting value is the environmental sensitivity index. For example, if the maximum altitude of the target area is 5000m, then different altitude levels can be divided in 500m intervals, resulting in 10 levels such as 0-500m, 500-1000m, and 1000-1500m. When assigning values, 0-500m is assigned 0.1, 500-1000m is assigned 0.2, 1000-1500m is assigned 0.3, and so on, obtaining the corresponding values ​​for each altitude interval. The environmental sensitivity index of each grid cell is then determined based on the assigned value corresponding to the altitude interval to which each grid cell belongs.

[0080] Step S112: Calculate the risk assessment index of different disaster-bearing bodies in each grid based on the target disaster hazard index of each grid, the vulnerability of different disaster-bearing bodies in each grid, and the disaster-inducing environment sensitivity index of each grid.

[0081] Specifically, the risk assessment index of different disaster-bearing bodies in each of the above grids indicates the degree of impact or damage of hail disasters on a specific type of disaster-bearing body (affected population, economy, crops, etc.) in each grid.

[0082] Step S114: The percentile method is used to classify the risk level of the target disaster-bearing body in each grid into risk levels, thereby obtaining the risk level distribution of the target disaster-bearing body in the target area, and completing the hail disaster risk assessment of the target area. Based on the disaster hazard level distribution and the risk level distribution of the target disaster-bearing body in the target area, hail disaster management measures are formulated for the target area by region. The target disaster-bearing body traverses any disaster-bearing body among different disaster-bearing bodies.

[0083] Specifically, the risk assessment index of the target disaster-bearing bodies in each grid is classified into risk levels using the percentile method, which can be divided into levels I-V, corresponding to high, relatively high, medium, relatively low, and low risk levels, respectively. Based on the classification principles in the table below, a hail disaster risk level distribution map of different disaster-bearing bodies within the assessment target area is drawn (e.g., risk level distribution of the affected population within the target area, risk level distribution of direct economic losses within the target area, and risk level distribution of the affected crop area within the target area).

[0084]

[0085] After completing the hail disaster risk assessment of the target area, hail disaster management measures can be formulated by dividing the target area into zones based on the distribution of disaster-causing hazard levels and the risk levels of target disaster-bearing bodies within the target area. For example, if the disaster-causing hazard level of location A in the target area is Level IV, and the risk level of the affected crop area in location A is Level V, then high-altitude artillery positions can be erected in location A to artificially intervene in the hail path and reduce the disaster. Therefore, the method of this invention can provide a scientific basis for decisions regarding hail disaster prevention, agricultural production layout, and land spatial planning in the target area.

[0086] In this embodiment of the invention, a method for hail disaster risk assessment is provided, comprising: acquiring geographic raster data, historical disaster data, historical disaster raster data, and hail observation data from various meteorological observation stations in a target area, wherein the geographic raster data includes: slope raster data, water network density raster data, agricultural and pastoral zoning raster data, and disaster-bearing body raster data; calculating the disaster hazard index of each meteorological observation station based on the hail observation data and historical disaster data, and determining the disaster hazard index of each raster in the target area based on the disaster hazard index of each meteorological observation station; correcting the disaster hazard index of each raster based on the slope raster data, water network density raster data, and agricultural and pastoral zoning raster data to obtain the corrected disaster hazard index of each raster, and further correcting the corrected disaster hazard index of each raster based on the historical disaster raster data to obtain the target disaster hazard index of each raster; and using the percentile method to analyze the hail disaster risk index of each raster. The target disaster hazard index of each grid is used to classify the disaster hazard level, thus obtaining the disaster hazard level distribution within the target area. The vulnerability of different disaster-bearing bodies in each grid is calculated based on the disaster-bearing body grid data, and the disaster-inducing environmental sensitivity index of each grid is determined based on the altitude of each grid. The risk assessment index of different disaster-bearing bodies in each grid is calculated based on the target disaster hazard index, the vulnerability of different disaster-bearing bodies in each grid, and the disaster-inducing environmental sensitivity index of each grid. The risk level of the target disaster-bearing bodies in each grid is classified using the percentile method, thus obtaining the risk level distribution of the target disaster-bearing bodies within the target area. This completes the hail disaster risk assessment of the target area. Based on the disaster hazard level distribution and the risk level distribution of the target disaster-bearing bodies within the target area, hail disaster management measures are formulated for different zones within the target area. The target disaster-bearing bodies encompass any one of the different disaster-bearing bodies.As described above, the hail disaster risk assessment method of the present invention, in determining the target disaster hazard index of each grid, not only uses hail observation data from various meteorological observation stations, but also uses historical disaster data, slope grid data, water network density grid data, agricultural and pastoral zoning grid data, and historical disaster grid data. The resulting target disaster hazard index for each grid is highly accurate, and the resulting distribution of disaster hazard levels within the target area matches the actual disaster hazard distribution within the target area. Furthermore, in determining the risk assessment index of different disaster-bearing bodies in each grid, accurate target disaster hazard indices for each grid are used, along with the vulnerability of different disaster-bearing bodies in each grid and the disaster-prone environment sensitivity index of each grid. The resulting risk assessment index for different disaster-bearing bodies in each grid is highly accurate, and the risk level distribution of the target disaster-bearing bodies within the divided target area matches the actual situation. Finally, the hail disaster management measures formulated for the target area are highly accurate, alleviating the technical problem that traditional technologies cannot accurately reflect the actual spatial risk distribution of hail disasters in a region.

[0087] In an optional embodiment of the present invention, acquiring geographic raster data, historical disaster data, and historical disaster raster data of the target area specifically includes the following steps:

[0088] (1) Extract slope raster data and water network density raster data from the altitude data of the target area;

[0089] Specifically, the elevation data of the target area can be downloaded, and then the slope raster data and water network density raster data can be extracted from it using the spatial analysis module tools in ArcGIS software.

[0090] (2) Use statistical yearbook data to divide the regional vector data of the target area into agricultural and pastoral zones to obtain agricultural and pastoral zone raster data;

[0091] Specifically, based on statistical yearbook data, the regional vector data of the target area (which can be downloaded) is classified into agricultural, pastoral, and semi-agricultural / semi-pastoral areas, generating agricultural and pastoral zoning raster data.

[0092] (3) Use the GDAL function to process the disaster-bearing body data in a unified manner to obtain the disaster-bearing body raster data;

[0093] Specifically, the GDAL function is used to perform unified processing on disaster-bearing body data (population, GDP, and crops) such as format conversion, projection transformation, and georegistration, thereby obtaining disaster-bearing body raster data.

[0094] (4) Obtain historical disaster data of hail disaster from the original historical disaster data, including: data on the affected population, data on direct economic losses, and data on the affected area of ​​crops;

[0095] Specifically, Python was used to clean the original historical disaster data, select historical disaster data of hail disasters, remove outliers and unify the data format. Among them, historical disaster data such as the affected population, direct economic losses and crop damage area caused by hail disasters were used to calculate the total loss of various disaster-bearing bodies at the county level. In other words, the historical disaster data is the loss of various disaster-bearing bodies at the county level.

[0096] (5) Rasterize the historical disaster data to obtain historical disaster raster data.

[0097] Specifically, historical disaster data represents the loss of various disaster-bearing entities at the county / district level. Each grid in the target area is divided according to a preset fixed size. By mapping the county / district level to the grid, historical disaster grid data can be obtained, which is the historical disaster data of each grid.

[0098] In an optional embodiment of the present invention, the disaster risk index of each meteorological observation station is calculated based on hail observation data and historical disaster data, specifically including the following steps:

[0099] (1) Based on historical disaster data, determine the first target meteorological observation station among all meteorological observation stations, wherein the number of historical disasters corresponding to the first target meteorological observation station is greater than a preset threshold.

[0100] Specifically, the aforementioned preset threshold can be 30. This embodiment of the invention does not impose specific restrictions on it. The number of historical disasters corresponding to the first target meteorological observation station is not only greater than the preset threshold, but also the number of hail observation records corresponding to the first target meteorological observation station is greater than the preset threshold (determined based on the hail observation data of each meteorological observation station).

[0101] (2) Spatiotemporally match the hail observation data and historical disaster data of the first target meteorological observation station to obtain the hail observation data of each first target meteorological observation station and the historical disaster data corresponding to each hail observation data.

[0102] Specifically, based on the observation time and station location, the hail observation data and historical disaster data of the first target meteorological observation station are spatiotemporally matched to obtain the hail observation data for each first target meteorological observation station and the historical disaster data corresponding to each hail observation data.

[0103] (3) Determine the hail disaster-causing factors of each first target meteorological observation station based on the hail observation data of each first target meteorological observation station and the historical disaster data corresponding to each hail observation data, and calculate the hail intensity of each first target meteorological observation station based on the hail disaster-causing factors of each first target meteorological observation station.

[0104] Specifically, the process includes the following steps:

[0105] (31) Calculate the disaster damage index of each historical disaster at each first target meteorological observation station based on the historical disaster data of each first target meteorological observation station.

[0106] Specifically, the calculation formula is based on the disaster loss index. Calculate the disaster damage index for each historical disaster at each primary target meteorological observation station, where X i G represents the disaster damage index of the i-th historical disaster at a specific primary target meteorological observation station. i T represents the direct economic loss (or affected crop area) in the area of ​​the first target meteorological observation station caused by the i-th historical disaster. i This represents the total GDP (or total crop planting area) of the area of ​​the first target meteorological observation station during the i-th historical disaster.

[0107] (32) Calculate the average value of the disaster loss index of each first target meteorological observation station based on the disaster loss index of each historical disaster.

[0108] (33) Calculate the correlation coefficient between the disaster loss index of each first target meteorological observation station and each candidate hail disaster causative factor based on the disaster loss index of each first target meteorological observation station for each historical disaster, the average value of the disaster loss index of each first target meteorological observation station, the observation data of each candidate hail disaster causative factor of each first target meteorological observation station, and the average value of the observation data of each candidate hail disaster causative factor of each first target meteorological observation station.

[0109] Specifically, the candidate hail disaster causative factors include: maximum hail diameter, hail duration, maximum wind speed during hail, and number of hail days.

[0110] Calculation formula based on correlation coefficient Calculate the correlation coefficient between the disaster damage index of each primary target meteorological observation station and each candidate hail disaster causative factor, where R represents the correlation coefficient between the disaster damage index and the candidate hail disaster causative factors, and X represents the correlation coefficient between the disaster damage index and the candidate hail disaster causative factors. i The disaster damage index represents the damage caused by a single historical disaster. Y represents the average value of the disaster damage index. iObservational data representing a candidate hail disaster-causing factor. This represents the average value of the observed data for this candidate hail disaster-causing factor, and N represents the number of historical disasters.

[0111] (34) Calculate the significance of each candidate hail disaster-causing factor for each first target meteorological observation station based on the correlation coefficient between the disaster damage index of each first target meteorological observation station and each candidate hail disaster-causing factor.

[0112] Specifically, the formula is based on significance. Calculate the significance of each candidate hail disaster-causing factor for each first target meteorological observation station, where t represents the significance of a certain candidate hail disaster-causing factor for a certain first target meteorological observation station, R represents the correlation coefficient between the disaster damage index of the first target meteorological observation station and the candidate hail disaster-causing factor, and N represents the number of historical disasters.

[0113] (35) Based on the significance of each candidate hail disaster-causing factor of each first target meteorological observation station, determine the hail disaster-causing factor of each first target meteorological observation station among the candidate hail disaster-causing factors.

[0114] Specifically, the hail disaster-causing factors of a certain first target meteorological observation station whose significance is greater than a preset significance threshold are taken as the hail disaster-causing factors of that first target meteorological observation station.

[0115] After obtaining the hail disaster-causing factors of a certain primary target meteorological observation station, the hail intensity of the primary target meteorological observation station is calculated by normalizing each hail disaster-causing factor and then summing them with equal weights.

[0116] (4) Calculate the disaster risk index of each first target meteorological observation station based on the hail intensity and the number of hail days at each first target meteorological observation station.

[0117] Specifically, the formula V is calculated based on the disaster risk index. E =0.5X G +0.5X R Calculate the disaster risk index for each primary target meteorological observation station, where V E X represents the disaster risk index of a specific primary target meteorological observation station. G X represents the intensity of hail at the primary target meteorological observation station. R This indicates the number of days with hail at the primary target meteorological observation station.

[0118] (5) Obtain the second target meteorological observation station, wherein the second target meteorological observation station is the meteorological observation station other than the first target meteorological observation station among all meteorological observation stations;

[0119] (6) Calculate the disaster risk index of each second target meteorological observation station based on the hail observation data of each second target meteorological observation station.

[0120] Specifically, the maximum hail diameter X is directly selected from the hail observation data. D Hailfall duration X T and the number of days with hail X R These three factors, considered as causative agents of hail disasters, are used to calculate the hail disaster causative agents using a weighted average method, V. E =X D W D +X T W T +X R W R Calculate the disaster risk index for each secondary target meteorological observation station, where V E W represents the disaster risk index of a certain secondary target meteorological observation station. D W T W R X represents the maximum hail diameter. D Hailfall duration X T and the number of days with hail X R The weights are determined using the information entropy weighting method.

[0121] When using the information entropy weighting method, the three types of hail disaster-causing factor arrays are first merged and normalized to obtain an m×n matrix. Then, the proportion ρ of the i-th sample value of the j-th factor is... ij The entropy value e of the j-th factor j and the weight values ​​W of each factor j The calculation formula is as follows:

[0122]

[0123]

[0124]

[0125] Where, x ij Let m represent the element in the i-th row and j-th column of an m×n matrix, where m represents m in the m×n matrix and n represents n in the m×n matrix.

[0126] In an optional embodiment of the present invention, the disaster risk index of each grid is corrected based on slope grid data, water network density grid data, and agricultural and pastoral zoning grid data, specifically including the following steps:

[0127] (1) Determine the disaster-prone environment sensitivity index of each grid based on the altitude of each grid, determine the slope impact index of each grid based on the slope grid data, determine the river system impact index of each grid based on the water network density grid data, and determine the agricultural and pastoral impact index of each grid based on the agricultural and pastoral zoning grid data.

[0128] Specifically, the process for determining the disaster-prone environment sensitivity index of each grid is the same as the process mentioned in step S110, and will not be repeated here.

[0129] Slope raster data consists of the slope data of each individual raster. The slope data of each raster is used as the slope influence index for that raster. The slope influence index is considered because terrain undulations can affect hail processes.

[0130] The water network density raster data consists of the water network density data of each individual raster. This water network density data is used as the river system impact index for the corresponding raster. The reason for considering the river system impact index is that most agricultural planting areas are located in major river zones.

[0131] The agricultural and pastoral zoning raster data represents the agricultural and pastoral type (agricultural area, pastoral area, or semi-agricultural / semi-pastoral area) of each raster. The agricultural and pastoral type of each raster is used as the corresponding agricultural and pastoral impact index. This index can be 1 for all agricultural and pastoral types, but the weight of this index varies depending on the type. Agricultural areas are more affected by hail damage and are assigned a higher weight, followed by semi-agricultural / semi-pastoral areas, while pastoral areas are assigned a lower weight. In this way, multi-source geographic information factors are obtained.

[0132] (2) The disaster risk index of each grid is corrected based on the disaster-prone environment sensitivity index, slope influence index, river system influence index and agricultural and pastoral influence index of each grid, so as to obtain the corrected disaster risk index of each grid.

[0133] Specifically, the formula V is calculated based on the revised disaster risk index. Geo =V E W E +k×W Geo Calculate the corrected hazard index for each grid cell, where V Geo V represents the modified hazard index of a certain grid. E W represents the hazard index of the grid. EW represents the weight of the disaster risk index. E =0.65, k represents the geographical influence factor coefficient, k = V a W a +V b W b +V c W c +V d W d V a V represents the environmental sensitivity index of the disaster-prone area for this grid. b V represents the slope influence index of the grid. c V represents the agricultural impact index of this raster. d W represents the river system influence index of this grid. a W represents the weight of the environmental sensitivity index for disaster-prone areas. b W represents the weight of the slope's influence on the index. c W represents the weight of the agricultural and livestock impact index. d W represents the weight of the river system impact index. Geo W represents the weight of the geographical influence factor coefficient. Geo =0.35, W a W b W c W d The information entropy weighting method was used to calculate and determine the value.

[0134] In an optional embodiment of the present invention, the modified disaster risk index of each grid is further corrected based on historical disaster grid data, specifically including the following steps:

[0135] (1) Calculate the average historical disaster loss of each grid based on historical disaster grid data;

[0136] The revised disaster risk index mentioned above does not take into account the actual extent of damage caused by hail disasters. Therefore, based on the consideration of geographical factors, the average value of historical disaster losses (direct economic losses or affected crop area) within each grid is calculated.

[0137] (2) Determine the historical disaster loss index of each grid based on the average historical disaster loss of each grid;

[0138] Specifically, the average historical disaster loss of each grid is normalized to obtain the historical disaster loss index of each grid.

[0139] (3) The disaster risk index of each grid is modified a second time based on the historical disaster loss index of each grid to obtain the target disaster risk index of each grid.

[0140] Specifically, the formula V is calculated based on the target disaster risk index. Danger =V Geo ×W h∝ard Calculate the target hazard index for each grid cell, where V Danger V represents the hazard index of a target in a certain grid. Geo W represents the modified hazard index of the grid. h∝ard This indicates the historical disaster loss index for this grid.

[0141] Optionally, the risk assessment index of different disaster-bearing bodies in each grid is calculated based on the target disaster hazard index of each grid, the vulnerability of different disaster-bearing bodies in each grid, and the disaster-inducing environmental sensitivity index of each grid. This specifically includes the following steps:

[0142] The risk assessment index of the target disaster-causing hazard index, the vulnerability of the target disaster-bearing body in the corresponding grid, and the disaster-inducing environmental sensitivity index of the corresponding grid are weighted and calculated to obtain the risk assessment index of the target disaster-bearing body in each grid.

[0143] Specifically, based on the calculation formula V of the risk assessment index of the target disaster-bearing body... Risk =V Danger W Danger +V a W a +V S W S Calculate the risk assessment index of the target disaster-bearing body in each grid, where V Risk V represents the risk assessment index of a target disaster-bearing body in a certain grid. Danger W represents the target hazard index of this grid. Danger V represents the weight of the target disaster risk index. a W represents the environmental sensitivity index of the disaster-prone area for this grid. a V represents the weight of the environmental sensitivity index for disaster-prone areas. S W represents the vulnerability of the target disaster-bearing body in this grid. S W represents the weight of the vulnerability of the target disaster-bearing body. Danger W a W S The information entropy weighting method was used to calculate and determine the value.

[0144] Example 2:

[0145] This invention also provides an apparatus for hail disaster risk assessment. This apparatus is mainly used to perform the hail disaster risk assessment method provided in Embodiment 1 of this invention. The apparatus for hail disaster risk assessment provided in this invention will be described in detail below.

[0146] Figure 2 This is a schematic diagram of a hail disaster risk assessment device according to an embodiment of the present invention, as shown below. Figure 2 As shown, the device mainly includes: an acquisition unit 10, a first calculation unit 20, a correction unit 30, a first level division unit 40, a second calculation unit 50, a third calculation unit 60, and a second level division unit 70, wherein:

[0147] The acquisition unit is used to acquire geographic raster data, historical disaster data, historical disaster raster data and hail observation data of various meteorological observation stations in the target area. The geographic raster data includes: slope raster data, water network density raster data, agricultural and pastoral zoning raster data and disaster-bearing body raster data.

[0148] The first calculation unit is used to calculate the disaster risk index of each meteorological observation station based on hail observation data and historical disaster data, and to determine the disaster risk index of each grid in the target area based on the disaster risk index of each meteorological observation station.

[0149] The correction unit is used to correct the disaster risk index of each grid based on slope grid data, water network density grid data, and agricultural and pastoral zoning grid data to obtain the corrected disaster risk index of each grid. Then, it performs a second correction on the corrected disaster risk index of each grid based on historical disaster grid data to obtain the target disaster risk index of each grid.

[0150] The first-level classification unit is used to classify the disaster risk level of the target disaster risk index of each grid using the percentile method, so as to obtain the disaster risk level distribution within the target area.

[0151] The second calculation unit is used to calculate the vulnerability of different disaster-bearing bodies in each grid based on the disaster-bearing body grid data, and to determine the disaster-prone environment sensitivity index of each grid based on the altitude of each grid.

[0152] The third calculation unit is used to calculate the risk assessment index of different disaster-bearing bodies in each grid based on the target disaster hazard index of each grid, the vulnerability of different disaster-bearing bodies in each grid, and the disaster-inducing environment sensitivity index of each grid.

[0153] The second-level classification unit is used to classify the risk level of the target disaster-bearing body in each grid by using the percentile method, thereby obtaining the risk level distribution of the target disaster-bearing body in the target area, and then completing the hail disaster risk assessment of the target area. Based on the disaster hazard level distribution and the risk level distribution of the target disaster-bearing body in the target area, hail disaster management measures are formulated for the target area by zone. The target disaster-bearing body traverses any disaster-bearing body among different disaster-bearing bodies.

[0154] In this embodiment of the invention, an apparatus for hail disaster risk assessment is provided, comprising: acquiring geographic raster data, historical disaster data, historical disaster raster data, and hail observation data from various meteorological observation stations in a target area, wherein the geographic raster data includes: slope raster data, water network density raster data, agricultural and pastoral zoning raster data, and disaster-bearing body raster data; calculating the disaster hazard index of each meteorological observation station based on the hail observation data and historical disaster data, and determining the disaster hazard index of each raster in the target area based on the disaster hazard index of each meteorological observation station; correcting the disaster hazard index of each raster based on the slope raster data, water network density raster data, and agricultural and pastoral zoning raster data to obtain the corrected disaster hazard index of each raster, and performing a second correction on the corrected disaster hazard index of each raster based on the historical disaster raster data to obtain the target disaster hazard index of each raster; and using the percentile method to... The target disaster hazard index of each grid is used to classify the disaster hazard level, thus obtaining the disaster hazard level distribution within the target area. The vulnerability of different disaster-bearing bodies in each grid is calculated based on the disaster-bearing body grid data, and the disaster-inducing environmental sensitivity index of each grid is determined based on the altitude of each grid. The risk assessment index of different disaster-bearing bodies in each grid is calculated based on the target disaster hazard index, the vulnerability of different disaster-bearing bodies in each grid, and the disaster-inducing environmental sensitivity index of each grid. The risk level of the target disaster-bearing bodies in each grid is classified using the percentile method, thus obtaining the risk level distribution of the target disaster-bearing bodies within the target area. This completes the hail disaster risk assessment of the target area. Based on the disaster hazard level distribution and the risk level distribution of the target disaster-bearing bodies within the target area, hail disaster management measures are formulated for different zones within the target area. The target disaster-bearing bodies encompass any one of the different disaster-bearing bodies.As described above, the hail disaster risk assessment device of the present invention, in determining the target disaster hazard index of each grid, not only uses hail observation data from various meteorological observation stations, but also uses historical disaster data, slope grid data, water network density grid data, agricultural and pastoral zoning grid data, and historical disaster grid data. The resulting target disaster hazard index for each grid is highly accurate, and the resulting distribution of disaster hazard levels within the target area matches the actual disaster hazard distribution within the target area. Furthermore, in determining the risk assessment index of different disaster-bearing bodies in each grid, accurate target disaster hazard indices for each grid are used, along with the vulnerability of different disaster-bearing bodies in each grid and the disaster-prone environment sensitivity index of each grid. The resulting risk assessment index for different disaster-bearing bodies in each grid is highly accurate, and the risk level distribution of the target disaster-bearing bodies within the divided target area matches the actual situation. Finally, the hail disaster management measures formulated for the target area are highly accurate, alleviating the technical problem that traditional technologies cannot accurately reflect the actual spatial risk distribution of hail disasters in a region.

[0155] Optionally, the acquisition unit is also used to: extract slope raster data and water network density raster data from the altitude data of the target area; divide the regional vector data of the target area into agricultural and pastoral zones using statistical yearbook data to obtain agricultural and pastoral zone raster data; perform unified processing on the disaster-bearing body data using the GDAL function to obtain disaster-bearing body raster data; acquire historical disaster data of hail disasters from the original historical disaster data, wherein the historical disaster data includes: disaster-affected population data, direct economic loss data, and crop disaster area data; and perform rasterization processing on the historical disaster data to obtain historical disaster raster data.

[0156] Optionally, the first calculation unit is further configured to: determine a first target meteorological observation station from all meteorological observation stations based on historical disaster data, wherein the number of historical disasters corresponding to the first target meteorological observation station is greater than a preset threshold; perform spatiotemporal matching of hail observation data and historical disaster data of the first target meteorological observation station to obtain hail observation data for each instance of hail observation at each first target meteorological observation station and historical disaster data corresponding to each instance of hail observation data; and determine the hail level of each first target meteorological observation station based on the hail observation data for each instance of hail observation at each first target meteorological observation station and the historical disaster data corresponding to each instance of hail observation data. The hail disaster-causing factors are determined, and the hail intensity of each first-target meteorological observation station is calculated based on the hail disaster-causing factors of each first-target meteorological observation station. The disaster risk index of each first-target meteorological observation station is calculated based on the hail intensity and the corresponding number of hail days at each first-target meteorological observation station. Second-target meteorological observation stations are obtained, wherein the second-target meteorological observation stations are all meteorological observation stations excluding the first-target meteorological observation stations. The disaster risk index of each second-target meteorological observation station is calculated based on the hail observation data of each second-target meteorological observation station.

[0157] Optionally, the first calculation unit is further configured to: calculate the disaster damage index of each historical disaster at each first target meteorological observation station based on the historical disaster data of each first target meteorological observation station; calculate the average disaster damage index of each first target meteorological observation station based on the disaster damage index of each historical disaster at each first target meteorological observation station; and calculate the average disaster damage index of each first target meteorological observation station based on the disaster damage index of each historical disaster at each first target meteorological observation station, the average disaster damage index of each first target meteorological observation station, the observation data of each candidate hail disaster causative factor of each first target meteorological observation station, and the observation data of each candidate hail disaster causative factor of each first target meteorological observation station. The average value of the observation data is used to calculate the correlation coefficient between the disaster damage index of each first target meteorological observation station and each candidate hail disaster-causing factor. The observation data of each candidate hail disaster-causing factor is the data from each hail observation. The significance of each candidate hail disaster-causing factor of each first target meteorological observation station is calculated based on the correlation coefficient between the disaster damage index of each first target meteorological observation station and each candidate hail disaster-causing factor. Based on the significance of each candidate hail disaster-causing factor of each first target meteorological observation station, the hail disaster-causing factor of each first target meteorological observation station is determined from the candidate hail disaster-causing factors.

[0158] Optionally, the correction unit is also used to: determine the disaster-prone environmental sensitivity index of each grid based on the altitude of each grid, determine the slope impact index of each grid based on the slope grid data, determine the river system impact index of each grid based on the water network density grid data, and determine the agricultural and pastoral impact index of each grid based on the agricultural and pastoral zoning grid data; and correct the disaster-causing hazard index of each grid based on the disaster-prone environmental sensitivity index, the slope impact index, the river system impact index, and the agricultural and pastoral impact index of each grid, to obtain the corrected disaster-causing hazard index of each grid.

[0159] Optionally, the correction unit is also used to: calculate the average historical disaster loss of each grid based on the historical disaster grid data; determine the historical disaster loss index of each grid based on the average historical disaster loss of each grid; and perform a second correction on the corrected disaster risk index of each grid based on the historical disaster loss index of each grid to obtain the target disaster risk index of each grid.

[0160] Optionally, the third calculation unit is also used to: perform weighted calculations on the target disaster hazard index of each grid, the vulnerability of the target disaster-bearing body in the corresponding grid, and the disaster-inducing environmental sensitivity index of the corresponding grid, to obtain the risk assessment index of the target disaster-bearing body in each grid.

[0161] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0162] like Figure 3 As shown in the embodiment of this application, an electronic device 600 includes a processor 601, a memory 602, and a bus. The memory 602 stores machine-readable instructions executable by the processor 601. When the electronic device is running, the processor 601 communicates with the memory 602 via the bus, and the processor 601 executes the machine-readable instructions to perform the steps of the hail disaster risk assessment method described above.

[0163] Specifically, the memory 602 and processor 601 mentioned above can be general-purpose memory and processor, without any specific limitations. When the processor 601 runs the computer program stored in the memory 602, it can execute the above-mentioned method for hail disaster risk assessment.

[0164] The processor 601 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 601 or by instructions in software form. The processor 601 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 602, and processor 601 reads the information from memory 602 and, in conjunction with its hardware, completes the steps of the above method.

[0165] Corresponding to the above-described method for hail disaster risk assessment, this application also provides a computer-readable storage medium storing machine-executable instructions. When these machine-executable instructions are invoked and executed by a processor, they cause the processor to perform the steps of the above-described method for hail disaster risk assessment.

[0166] The hail disaster risk assessment device provided in this application embodiment can be specific hardware on a device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in this application embodiment are the same as those in the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0167] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0168] For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0169] The units described as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0170] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0171] If the aforementioned functions are implemented as 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 this application, in essence, or the part that contributes to the prior art, or a portion 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 to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the hail disaster risk assessment method described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0172] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0173] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for assessing hail disaster risk, characterized in that, include: Acquire geographic raster data, historical disaster data, historical disaster raster data, and hail observation data from various meteorological observation stations for the target area. The geographic raster data includes: slope raster data, water network density raster data, agricultural and pastoral zoning raster data, and disaster-bearing body raster data. The disaster risk index of each meteorological observation station is calculated based on the hail observation data and the historical disaster data, and the disaster risk index of each grid in the target area is determined based on the disaster risk index of each meteorological observation station. Based on the slope raster data, the water network density raster data, and the agricultural and pastoral zoning raster data, the disaster risk index of each raster is corrected to obtain the corrected disaster risk index of each raster. Then, based on the historical disaster raster data, the corrected disaster risk index of each raster is further corrected to obtain the target disaster risk index of each raster. The percentile method is used to classify the disaster risk level of the target disaster risk index of each grid, so as to obtain the disaster risk level distribution in the target area; The vulnerability of different disaster-bearing bodies in each grid is calculated based on the disaster-bearing body grid data, and the disaster-prone environment sensitivity index of each grid is determined based on the altitude of each grid. Based on the target disaster hazard index of each grid, the vulnerability of different disaster-bearing bodies in each grid, and the disaster-inducing environment sensitivity index of each grid, the risk assessment index of different disaster-bearing bodies in each grid is calculated. The risk level of the target disaster-bearing bodies in each grid is classified by the percentile method to obtain the risk level distribution of the target disaster-bearing bodies in the target area, thereby completing the hail disaster risk assessment of the target area. Based on the disaster hazard level distribution and the risk level distribution of the target disaster-bearing bodies in the target area, hail disaster management measures are formulated for the target area by region. The target disaster-bearing bodies traverse any of the different disaster-bearing bodies.

2. The method according to claim 1, characterized in that, Acquire geographic raster data, historical disaster data, and historical disaster raster data for the target area, including: Extract the slope raster data and the water network density raster data from the altitude data of the target area; The target area is divided into agricultural and pastoral zones using regional vector data from statistical yearbooks, resulting in raster data of the agricultural and pastoral zones. The GDAL function is used to uniformly process the disaster-bearing body data to obtain the disaster-bearing body raster data; Historical disaster data on hail disasters are obtained from the original historical disaster data, which includes: data on the affected population, data on direct economic losses, and data on the affected area of ​​crops. The historical disaster data is rasterized to obtain the historical disaster raster data.

3. The method according to claim 1, characterized in that, The disaster risk index of each meteorological observation station is calculated based on hail observation data and historical disaster data, including: Based on the historical disaster data, a first target meteorological observation station is determined from all the meteorological observation stations, wherein the number of historical disasters corresponding to the first target meteorological observation station is greater than a preset threshold. The hail observation data of the first target meteorological observation station and the historical disaster data are spatiotemporally matched to obtain the hail observation data of each first target meteorological observation station for each time and the historical disaster data corresponding to each hail observation data. The hail disaster-causing factors of each first target meteorological observation station are determined based on the hail observation data of each first target meteorological observation station and the historical disaster data corresponding to each hail observation data, and the hail intensity of each first target meteorological observation station is calculated based on the hail disaster-causing factors of each first target meteorological observation station. The disaster risk index of each first target meteorological observation station is calculated based on the hail intensity of each first target meteorological observation station and the number of hail days at the corresponding first target meteorological observation station. Obtain a second target meteorological observation station, wherein the second target meteorological observation station is a meteorological observation station other than the first target meteorological observation station among all the meteorological observation stations; The disaster risk index of each second target meteorological observation station is calculated based on the hail observation data of each second target meteorological observation station.

4. The method according to claim 3, characterized in that, The hail disaster-causing factors for each of the first target meteorological observation stations are determined based on hail observation data for each instance and historical disaster data corresponding to each instance of hail observation data, including: The disaster damage index of each historical disaster at each of the first target meteorological observation stations is calculated based on the historical disaster data of each of the first target meteorological observation stations. The average value of the disaster loss index of each of the first target meteorological observation stations is calculated based on the disaster loss index of each historical disaster. The correlation coefficient between the disaster loss index of each first target meteorological observation station and each candidate hail disaster causative factor is calculated based on the disaster loss index of each first target meteorological observation station for each historical disaster, the average disaster loss index of each first target meteorological observation station, the observation data of each candidate hail disaster causative factor of each first target meteorological observation station, and the average observation data of each candidate hail disaster causative factor of each first target meteorological observation station. The significance of each candidate hail disaster-causing factor for each of the first target meteorological observation stations is calculated based on the correlation coefficient between the disaster damage index of each of the first target meteorological observation stations and each candidate hail disaster-causing factor. Based on the significance of each candidate hail disaster-causing factor at each of the first target meteorological observation stations, the hail disaster-causing factor for each of the first target meteorological observation stations is determined from among the candidate hail disaster-causing factors.

5. The method according to claim 1, characterized in that, Based on the slope raster data, the water network density raster data, and the agricultural and pastoral zoning raster data, the disaster risk index of each raster is corrected, including: The disaster-prone environment sensitivity index of each grid is determined based on the altitude of each grid, the slope impact index of each grid is determined based on the slope grid data, the river system impact index of each grid is determined based on the water network density grid data, and the agricultural and pastoral impact index of each grid is determined based on the agricultural and pastoral zoning grid data. The disaster risk index of each grid is corrected based on the disaster-prone environment sensitivity index, slope influence index, river system influence index, and agricultural and pastoral influence index of each grid, resulting in the corrected disaster risk index of each grid.

6. The method according to claim 1, characterized in that, Based on the historical disaster raster data, the disaster risk index of each raster is further corrected, including: Calculate the average historical disaster loss for each grid based on the historical disaster grid data; The historical disaster loss index for each grid is determined based on the average historical disaster loss of each grid. The disaster risk index of each grid is further corrected based on the historical disaster loss index of each grid to obtain the target disaster risk index of each grid.

7. The method according to claim 1, characterized in that, Based on the target disaster hazard index of each grid, the vulnerability of different disaster-bearing bodies in each grid, and the disaster-inducing environmental sensitivity index of each grid, the risk assessment index of different disaster-bearing bodies in each grid is calculated, including: The risk assessment index of the target disaster-causing hazard index, the vulnerability of the target disaster-bearing body in the corresponding grid, and the disaster-inducing environmental sensitivity index of the corresponding grid are weighted and calculated to obtain the risk assessment index of the target disaster-bearing body in each grid.

8. A device for hail disaster risk assessment, characterized in that, include: The acquisition unit is used to acquire geographic raster data, historical disaster data, historical disaster raster data and hail observation data from various meteorological observation stations in the target area. The geographic raster data includes: slope raster data, water network density raster data, agricultural and pastoral zoning raster data and disaster-bearing body raster data. The first calculation unit is used to calculate the disaster risk index of each meteorological observation station based on the hail observation data and the historical disaster data, and to determine the disaster risk index of each grid in the target area based on the disaster risk index of each meteorological observation station. The correction unit is used to correct the disaster risk index of each grid based on the slope grid data, the water network density grid data, and the agricultural and pastoral zoning grid data to obtain the corrected disaster risk index of each grid, and to perform a second correction on the corrected disaster risk index of each grid based on the historical disaster grid data to obtain the target disaster risk index of each grid. The first level division unit is used to classify the disaster risk level of the target disaster risk index of each grid using the percentile method, so as to obtain the disaster risk level distribution in the target area. The second calculation unit is used to calculate the vulnerability of different disaster-bearing bodies in each grid based on the disaster-bearing body grid data, and to determine the disaster-prone environment sensitivity index of each grid based on the altitude of each grid. The third calculation unit is used to calculate the risk assessment index of different disaster-bearing bodies in each grid based on the target disaster hazard index of each grid, the vulnerability of different disaster-bearing bodies in each grid, and the disaster-prone environment sensitivity index of each grid. The second-level classification unit is used to classify the risk level of the target disaster-bearing bodies in each grid by using the percentile method, thereby obtaining the risk level distribution of the target disaster-bearing bodies in the target area, and completing the hail disaster risk assessment of the target area. Based on the disaster hazard level distribution and the risk level distribution of the target disaster-bearing bodies in the target area, hail disaster management measures are formulated for the target area by partitioning the target area. The target disaster-bearing bodies traverse any of the different disaster-bearing bodies.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores machine-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 7.

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