WUI division method and device based on house wildfire risk, equipment and storage medium

Abstract

The invention provides a WUI division method and device based on house wildfire risks, equipment and a storage medium. The method comprises the steps that the weighted fuel level of each fuel grid in a to-be-divided area is determined; for each house in the to-be-divided area, calculating to obtain a building wildfire risk index of the house; for each fuel grid, calculating to obtain a vegetation hazard index of the fuel grid; screening out houses of which the building wildfire risk indexes are greater than a preset first threshold value from all houses of the to-be-divided area, and taking the houses as risk houses; for each risk house, screening out fuel grids of which the vegetation danger indexes are greater than a preset second threshold value from all the fuel grids falling in the buffer region corresponding to the risk house, and taking the screened fuel grids as risk fuel grids corresponding to the risk houses; and for each risk house, carrying out region merging on the risk house and the risk fuel grid corresponding to the risk house to obtain a plurality of forest-town junction regions in the to-be-divided region, and the scheme of the invention improves the accuracy of forest-town junction region division.

Description

Technical Field

[0001] The present invention relates to the technical field of wildfire protection, and particularly to a method, device, equipment and storage medium for dividing WUI based on the wildfire risk of houses. Background Art

[0002] In recent years, wildfires have had a significant negative impact on the global ecological environment safety, wildlife protection, population health maintenance, and economic and social development. Whether caused by human or natural factors, when a large fire in nature gets out of control and spreads from the forest to the built-up area, it is very easy to evolve into a major forest fire or a large-scale building fire, becoming a coupling emergency that straddles natural disasters and accident disasters. This risk is concentrated in the area where houses meet or mix with natural vegetation such as forests, which is called the wildland-urban interface (WUI).

[0003] As a zone where human-environment conflicts and risks are concentrated, when a wildfire occurs, the WUI often becomes the main place where casualties, house damage, and fire extinguishing expenses occur. The WUI area exists globally, and with the transformation of the human land use pattern (such as urban-rural expansion, construction of industrial facilities, and increase in residential areas), the area of the WUI region continues to grow, making more human lives, property, and infrastructure face more obvious wildfire risks. Therefore, accurately mapping the WUI area is of great scientific significance and practical value for wildfire prevention and fire risk management.

[0004] In the prior art, the research content of the wildland-urban interface mainly includes WUI definition, mapping method, management strategy, fire risk assessment, and intervention priority evaluation. The common point of the existing methods is to combine two land cover types (buildings and vegetation), and then use geographic information systems for spatial analysis. Although the definition of WUI is relatively consistent in many studies, there are significant differences in the methods and parameter settings for WUI division. For example, how to determine the specific locations of buildings and vegetation, how to determine the danger threshold, how to select a reasonable buffer distance or moving window size, etc.

[0005] In the prior art, most of the WUI division methods are based on the method of giving priority to building density, and are refined and optimized on this basis. However, the existing methods are limited by subjective definitions such as building density and vegetation coverage, and the accuracy of WUI division is poor. Summary of the Invention

[0006] The present invention provides a method, device, equipment and storage medium for dividing WUI based on the wildfire risk of houses, so as to solve the defect of poor accuracy in WUI division in the prior art, realize a more refined and practical WUI division, and improve the accuracy of WUI division.

[0007] The present invention provides a method for dividing the WUI based on the wildfire risk of houses, comprising the following steps: Divide the area to be divided according to a predetermined resolution to obtain a plurality of fuel grids, and determine the weighted fuel level of each fuel grid; For each house in the area to be divided, establish a buffer zone corresponding to the house, and calculate the building wildfire risk index of the house based on the weighted fuel levels of each fuel grid falling within the buffer zone corresponding to the house; For each fuel grid, establish a buffer zone corresponding to the fuel grid, and calculate the vegetation hazard index of the fuel grid based on the building risk values of each house falling within the buffer zone corresponding to the fuel grid; Select houses with a building wildfire risk index greater than a preset first threshold from all the houses in the area to be divided as risk houses; For each risk house, select fuel grids with a vegetation hazard index greater than a preset second threshold from all the fuel grids falling within the buffer zone corresponding to the risk house as the risk fuel grids corresponding to the risk house; For each risk house, merge the area of the risk house and the risk fuel grids corresponding to the risk house to obtain a plurality of forest-town boundary regions within the area to be divided.

[0008] According to the method for dividing the WUI based on the wildfire risk of houses provided by the present invention, the determination of the weighted fuel level of each fuel grid includes: Determine the cost weight values corresponding to different land cover types according to the suppression difficulty of different land cover types; Calculate the aggregation index corresponding to each fuel grid; For each fuel grid, determine the weighted fuel level of the fuel grid according to the cost weight value corresponding to the land cover type of the fuel grid and the aggregation index corresponding to the fuel grid.

[0009] According to the method for dividing the WUI based on the wildfire risk of houses provided by the present invention, the calculation of the building wildfire risk index of the house based on the weighted fuel levels of each fuel grid falling within the buffer zone corresponding to the house includes: Select, from all the fuel grids falling within the buffer zone corresponding to the house, the fuel grids corresponding to the house that affect the house according to the weighted fuel levels of each fuel grid falling within the buffer zone corresponding to the house and the buffer distance of each fuel grid; wherein, the buffer distance of the fuel grid is the distance between the fuel grid and the house; The building wildfire risk index of the house is calculated based on the weighted fuel grade of each fuel grid affected by the house, the buffer distance, and the number of land cover types of the fuel grid affected by the house.

[0010] According to a method for WUI division based on house wildfire risk provided by the present invention, the vegetation hazard index of the fuel grid is calculated based on the building risk values of each house located in the buffer area corresponding to the fuel grid, including: Count all the houses located in the buffer area corresponding to the fuel grid; The vegetation hazard index of the fuel grid is calculated based on the building risk value of each house located in the buffer area corresponding to the fuel grid and the building area of each house.

[0011] According to a method for WUI division based on house wildfire risk provided by the present invention, the buffer area corresponding to the house is an annular buffer area with a preset radius length.

[0012] According to a method for WUI division based on house wildfire risk provided by the present invention, for each risk house, after merging the risk house and the risk fuel grid corresponding to the risk house to obtain multiple forest-town boundary regions in the area to be divided, the method further includes: Apply the single-factor method once to verify the effectiveness of the multiple forest-town boundary regions in the area to be divided.

[0013] According to a method for WUI division based on house wildfire risk provided by the present invention, for each risk house, after merging the risk house and the risk fuel grid corresponding to the risk house to obtain multiple forest-town boundary regions in the area to be divided, the method further includes: Perform a filling process on the multiple forest-town boundary regions in the area to be divided to divide the non-forest-town boundary regions completely surrounded by the forest-town boundary regions into forest-town boundary regions.

[0014] The present invention also provides a WUI division device based on house wildfire risk, including the following modules: A grid division module for dividing the area to be divided into multiple fuel grids according to a predetermined resolution and determining the weighted fuel grade of each fuel grid; A first calculation module for, for each house in the area to be divided, establishing a buffer area corresponding to the house and calculating the building wildfire risk index of the house based on the weighted fuel grade of each fuel grid located in the buffer area corresponding to the house; A second calculation module, configured to, for each fuel grid, establish a buffer zone corresponding to the fuel grid, and calculate a vegetation hazard index of the fuel grid based on the building risk value of each house falling within the buffer zone corresponding to the fuel grid; A first screening module, configured to screen out houses with a building wildfire risk index greater than a preset first threshold from all houses in the area to be divided, as risk houses; A second screening module, configured to, for each risk house, screen out fuel grids with a vegetation hazard index greater than a preset second threshold from all fuel grids falling within the buffer zone corresponding to the risk house, as the risk fuel grids corresponding to the risk house; A region merging module, configured to, for each risk house, merge the risk house and the risk fuel grids corresponding to the risk house to obtain a plurality of forest-town boundary regions within the area to be divided.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and running on the processor, where when the processor executes the computer program, the method for dividing a WUI based on house wildfire risk as described in any one of the above is implemented.

[0016] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for dividing a WUI based on house wildfire risk as described in any one of the above is implemented.

[0017] The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the method for dividing a WUI based on house wildfire risk as described in any one of the above is implemented.

[0018] The WUI division method, device, equipment, and storage medium based on the wildfire risk of houses provided by the present invention divide the area to be divided according to a predetermined resolution to obtain a plurality of fuel grids, and determine the weighted fuel level of each fuel grid, so as to distinguish the risk level of each fuel grid, which is conducive to more refined management of wildfires; further, based on the weighted fuel level of each fuel grid, calculate the building wildfire risk index of each house and the vegetation hazard index of each fuel grid in the area to be divided, so as to realize the quantitative risk assessment for each house and the vegetation around the house separately; further, according to the building wildfire risk index and the vegetation hazard index, screen out the risk houses and the corresponding risk fuel grids of the risk houses, which helps to optimize the allocation of emergency resources and ensure a rapid and effective response in case of a fire. Further, merge each risk house and the corresponding risk fuel grid of each risk house to obtain a plurality of forest-town boundary regions in the area to be divided. Therefore, the solution of the present invention realizes a fine and practical WUI division, improves the accuracy of the WUI division, and provides more scientific and effective decision-making support for wildfire management. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] Figure 1 It is a schematic flowchart of the WUI division method based on the wildfire risk of houses provided by the present invention.

[0021] Figure 2 It is a schematic structural diagram of the WUI division device based on the wildfire risk of houses provided by the present invention.

[0022] Figure 3 It is a schematic structural diagram of the electronic equipment provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0024] The following uses specific embodiments to elaborate in detail on the technical solution of this application and how the technical solution of this application solves the above technical problems. The following several specific embodiments can be combined with each other, and for the same or similar concepts or processes, they may not be repeated in some embodiments. The following combines Figure 1 to describe the WUI division method based on the wildfire risk of houses of the present invention.

[0025] In practical applications, the execution subject of the WUI division method based on the wildfire risk of houses can be a WUI division device based on the wildfire risk of houses. There are various implementation methods for the WUI division device based on the wildfire risk of houses. For example, it can be implemented through a computer program, such as an application software, etc.; or, for example, a chip, etc. It can also be implemented as a medium storing relevant computer programs, such as a USB flash drive, a cloud disk, etc.; or, furthermore, it can be implemented through an entity device integrated or installed with relevant computer programs, such as a server, a smart device, etc.

[0026] Figure 1 is a schematic flowchart of the WUI division method based on the wildfire risk of houses provided by the present invention. As Figure 1 shown, this method includes steps 101 to 109.

[0027] Step 101: Divide the area to be divided according to a predetermined resolution to obtain a plurality of fuel grids.

[0028] Among them, the resolution refers to the spatial resolution of land cover data when performing geographic information system (GIS) analysis. The spatial resolution refers to the size of the smallest unit in an image or dataset, which determines the size of the smallest feature that can be recognized. In a geographic information system, the resolution is usually used to describe the accuracy of raster data, that is, the actual ground area represented by each raster unit (pixel). In practice, the resolution refers to the spatial detail degree of the data, that is, the geographical range covered by each data point.

[0029] Exemplarily, the area to be divided can be divided into a plurality of grids according to a 30 - meter resolution, that is, each grid unit represents a ground area of 30 meters × 30 meters. Such a resolution allows researchers to evaluate and quantify the wildfire risk of each individual house and its surrounding vegetation in units of 30 meters.

[0030] In practical applications, when dividing the area to be divided according to a predetermined resolution, a plurality of grids with equal areas can be obtained. The plurality of grids include fuel grids and non - fuel grids. Further, a weighted fuel grade is determined for each fuel grid. For non - fuel grids, in this embodiment, the weighted fuel grade is assigned a preset value. For example, the weighted fuel grade is assigned a preset value of 10.

[0031] Step 102: Determine the weighted fuel level of each fuel grid.

[0032] Exemplarily, all combustible land cover types were extracted from the GlobeLand30 - 2020 dataset. Combustible land cover types include, but are not limited to: shrublands, grasslands, vegetated wetlands, tundra, herbaceous plants, and all types of forests. Non - fuel land cover types include, but are not limited to: croplands, true wetlands, pastures, sparse vegetation on rocks, bare soil, impervious surfaces, exposed land, rocks, snow or ice, barren, water, and unknown land cover types.

[0033] Specifically, for each fuel grid, considering the land cover type of the vegetation in the fuel grid and the horizontal fuel connectivity of the fuel grid, comprehensively determine the weighted fuel level of the fuel grid. It can be understood that the weighted fuel level of each fuel grid, comprehensively considering the corresponding land cover type and connectivity of the fuel grid, provides important data support for subsequent wildfire risk assessment.

[0034] As an example, in a possible implementation manner, the above - mentioned step 102 includes: Determine the cost weight values corresponding to different land cover types according to the suppression difficulty of different land cover types; Calculate the aggregation index corresponding to each fuel grid; For each fuel grid, determine the weighted fuel level of the fuel grid according to the cost weight value corresponding to the land cover type of the fuel grid and the aggregation index corresponding to the fuel grid.

[0035] Among them, the suppression difficulty of the land cover type refers to the degree of difficulty in suppressing or controlling a fire for a specific land cover type in wildfire management. Specifically, different types of vegetation have different combustion characteristics. Some vegetation is easier to ignite, has a faster burning speed, or is more difficult to extinguish. Therefore, the suppression difficulty of different land cover types is different.

[0036] In practical applications, considering the differences in the burning degree and spreading speed of different vegetation around houses when a wildfire occurs, rank different land cover types according to the suppression difficulty of different land cover types. Specifically, in this embodiment, a variable - width buffer technique is used to determine the cost weight values corresponding to different land cover types. The variable - width buffer technique uses cost distance calculation to associate different cost weights with the land cover types in GlobeLand30 - 2020. The cost weights corresponding to different land cover types are shown in Table 1.

[0037] Table 1

[0038] Among them, fc is the fractional cover, which is an indicator representing the vegetation cover degree and is used to describe the proportion of the ground covered by vegetation in a specific area. The value of fc ranges from 0 to 1, where 0 indicates no vegetation cover and 1 indicates complete cover.

[0039] Furthermore, the horizontal fuel connectivity of each fuel grid is evaluated, that is, the aggregation index corresponding to each fuel grid is calculated.

[0040] Among them, the aggregation index (AI) provides a measure of the fuel connectivity or aggregation degree dispersed in the area, and it also provides a reference for the ease of fire spread throughout the landscape. For example, the aggregation index of all fuel grids in City A was calculated in a 5*5 moving window, and the calculation formula of the aggregation index is as follows: Among them, refers to the aggregation degree of the corresponding fuel level That is, the sum of the number of adjacent pairs of all pixels with a fuel level of in the 5×5 pane and pixels of the same type. By dividing by , it represents the ratio of the number of similar adjacent patches of the current landscape type to the maximum value. Then, multiplying this ratio by 100 gives the percentage form of the aggregation index (AI). The value range of the aggregation index is from 0 (no aggregation, that is, each unit is isolated) to 100 (fully aggregated, that is, the fuel is continuous).

[0041] Furthermore, the aggregation index is simplified and classified. Specifically, AI>90 is classified as "high aggregation" and assigned a value of 0; 0<AI ≤ 90 is "lower clustering" and assigned a value of 1; AI = 0 is "no aggregation" and assigned a value of 2.

[0042] It can be understood that according to Table 1 above, for each fuel grid, according to the land cover type of the fuel grid, the cost weight value corresponding to the land cover type of the fuel grid can be determined.

[0043] Furthermore, for each fuel grid, based on the cost weight value corresponding to the land cover type of the fuel grid and the aggregation index corresponding to the fuel grid, the weighted fuel grade of the fuel grid is determined. Specifically, for each fuel grid, the cost weight value corresponding to the land cover type of the fuel grid and the aggregation index corresponding to the fuel grid are combined to calculate the weighted fuel grade of each fuel grid. For example, a fuel grid with a high aggregation index (AI value of 0) and a land cover type of open deciduous coniferous forest (grade 1) has a weighted fuel grade value of 1. The weighted fuel grade value of a non-fuel grid is assigned 10.

[0044] It can be understood that for each fuel grid, based on the cost weight value corresponding to the land cover type of the fuel grid and the aggregation index corresponding to the fuel grid, the weighted fuel grade of the fuel grid is determined. The calculation of the weighted fuel grade comprehensively considers the connectivity of the fuel and the difficulty of suppression, providing a more refined risk indicator for wildfire risk assessment and WUI zoning. This comprehensive assessment helps identify areas with higher fire risks and provides a scientific basis for formulating effective fire prevention and response strategies.

[0045] Step 103: For each house in the area to be zoned, establish a buffer zone corresponding to the house.

[0046] In practical applications, establishing a buffer zone corresponding to each house in the area to be zoned means creating a virtual boundary area around each building in a Geographic Information System (GIS). This area is usually a circular or polygonal area, centered on the building and extending outward a certain distance. This buffer zone is used to evaluate and quantify the wildfire risk of each individual house and the surrounding vegetation.

[0047] It can be understood that the buffer zone can be circular, square, or other polygons, and the specific shape depends on the research needs and geographical features.

[0048] In one example, the buffer zone corresponding to the house is an annular buffer zone with a preset radius length.

[0049] Specifically, the size of the buffer zone can be determined according to the research purpose and actual needs. For example, different levels of buffer zones can be set, such as buffer zones of 2400 meters, 1200 meters, 800 meters, etc.

[0050] Furthermore, based on the building footprint data, a quantitative analysis of the wildfire risk of each house is carried out. By calculating the building risk index of the house, the assessment of the specific wildfire risk of each house in the study area is realized. This not only helps managers more intuitively and precisely understand the distribution of wildfire risks, but also provides a scientific basis for formulating targeted building fire prevention strategies and emergency plans.

[0051] Step 104: Calculate the building wildfire risk index of the house based on the weighted fuel levels of each fuel grid falling within the buffer zone corresponding to the house.

[0052] As an example, in a possible implementation, the above step 104 includes: According to the weighted fuel levels of each fuel grid falling within the buffer zone corresponding to the house and the buffer distances of each fuel grid, screen out the fuel grids affecting the house from all the fuel grids falling within the buffer zone corresponding to the house; wherein, the buffer distance of the fuel grid is the distance between the fuel grid and the house; Calculate the building wildfire risk index of the house based on the weighted fuel levels, buffer distances, and the number of land cover types of the fuel grids affecting the house corresponding to the house.

[0053] It can be understood that the larger the fuel level of the wild vegetation, the larger the range it can affect. The buffer zone levels, buffer distances, and the corresponding effective weighted fuel levels are shown in Table 2.

[0054] Table 2

[0055] Specifically, according to the buffer distance of each fuel grid, the buffer zone level corresponding to each grid can be determined. Further, it is judged whether the weighted fuel level corresponding to each fuel grid is valid. If it is valid, the fuel grid with a valid weighted fuel level is used as the fuel grid affecting the house corresponding to the house.

[0056] Combined with Table 2, for example, the buffer distance of fuel grid A from house 1 is 1200 meters, and the buffer distance of fuel grid B from house 1 is 800 meters. The buffer distance of fuel grid A is 1200 meters, and the weighted fuel level of fuel grid A is 5. According to Table 2, the buffer zone level corresponding to a buffer distance of 1200 meters is 2, and the effective weighted fuel levels corresponding to buffer zone level 2 are 1 and 2. It can be seen that the weighted fuel level of fuel grid A is invalid, and fuel grid A is not the fuel grid affecting house 1. For another example, the buffer distance of fuel grid B is 800 meters, and the weighted fuel level of fuel grid B is 2. According to Table 2, the buffer zone level corresponding to a buffer distance of 800 meters is 3, and the effective weighted fuel levels corresponding to buffer zone level 2 are 1, 2, and 3. It can be seen that the weighted fuel level of fuel grid B is valid, and fuel grid B is the fuel grid affecting house 1.

[0057] Further, based on the influence of fuel levels in different buffer zones around the building, calculate the Building Wildfire Risk Index (BWRI) for each house. The lower the weighted fuel level of the vegetation, the greater the potential risk. At the same time, considering the distance from the hazardous vegetation to the house, the farther the distance, the smaller the risk.

[0058] Specifically, calculate the Building Wildfire Risk Index of the house based on the weighted fuel level, buffer distance of each impact fuel grid corresponding to the house, and the number of land cover types of the impact fuel grid corresponding to the house. By way of example, the calculation formula for the Building Wildfire Risk Index is as follows: Where b is the buffer zone level, f is the weighted fuel level, and n is the number of land cover types of the impact fuel grid corresponding to the house.

[0059] In this embodiment, calculate the Building Wildfire Risk Index of the house based on the weighted fuel level, buffer distance of each impact fuel grid corresponding to the house, and the number of land cover types of the impact fuel grid corresponding to the house. This risk assessment method provides a specific assessment of the wildfire risk of houses in different regions, which can help local governments and residents take appropriate preventive measures, such as adding fire-fighting facilities, improving vegetation management, or re-planning the regional layout, to reduce the potential fire risk.

[0060] Step 105: For each fuel grid, establish a buffer zone corresponding to the fuel grid.

[0061] Step 106: Based on the building risk value of each house located in the buffer zone corresponding to the fuel grid, calculate the Vegetation Danger Index of the fuel grid.

[0062] When a wildfire occurs, a crucial factor is whether there is flammable vegetation around the building, which directly determines whether the wildfire can spread. Therefore, it is particularly important to deeply explore and understand the spatial distribution of wild vegetation in the area and the fire risk it poses to adjacent buildings. To more accurately assess this risk, this application further calculates the Vegetation Danger Index (VDI) of the fuel grid based on the building risk index. The Vegetation Danger Index is used to measure the wildfire threat degree of wild vegetation to surrounding houses.

[0063] For the convenience of calculation, in this embodiment, first, a raster-to-point operation is performed on the land cover data (GlobeLand30 - 2020 dataset), and then, according to the weighted fuel raster level, a buffer corresponding to each raster point is established. Finally, all houses (buildings that fall within the buffer or overlap with the buffer) falling within the buffer corresponding to the raster point are counted, and the vegetation hazard index of the fuel raster is calculated.

[0064] As an example, in a possible implementation manner, step 106 above includes: Count all houses that fall within the buffer corresponding to the fuel raster; According to the building risk value of each house that falls within the buffer corresponding to the fuel raster and the building area of each house, the vegetation hazard index of the fuel raster is calculated and obtained.

[0065] Exemplarily, the calculation formula of the vegetation hazard index is as follows: Among them, BWRI is the building risk value of each house that falls within the buffer corresponding to the fuel raster, and A represents the area of the house (unit: square meters). Considering the huge differences that may exist in the product of the building hazard index and the building area in different rasters, the present invention adopts a logarithmic processing method. This processing can effectively reduce the gap between data, make the hazard levels between different rasters more comparable, and thus obtain a more balanced and easy-to-understand index.

[0066] Step 107: From all houses in the area to be divided, screen out the houses with a building wildfire risk index greater than a preset first threshold as risk houses.

[0067] Step 108: For each risk house, from all fuel rasters that fall within the buffer corresponding to the risk house, screen out the fuel rasters with a vegetation hazard index greater than a preset second threshold as the risk fuel rasters corresponding to the risk house.

[0068] Step 109: For each risk house, merge the risk house and the risk fuel rasters corresponding to the risk house for regional merging to obtain multiple forest-town boundary regions within the area to be divided.

[0069] In practical applications, in order to reduce the risk of structural losses during wildfires, during the WUI division process, houses are first concerned and then spread outwards from the houses. Specifically, by initially screening houses through the building risk index and then paying attention to the vegetation that affects them, areas that are more likely to pose a wildfire threat to humans can be more scientifically divided.

[0070] It should be noted that the first threshold and the second threshold are preset according to the requirements of the WUI area. In practice, they can be adjusted according to the actual requirements of the WUI area. By way of example, the second threshold can be 0, and the fuel grids with a vegetation hazard index greater than 0 are used as the risk fuel grids corresponding to the risk houses.

[0071] In this embodiment, by dividing the area to be divided according to a predetermined resolution, a plurality of fuel grids are obtained, and the weighted fuel level of each fuel grid is determined, so as to distinguish the risk level of each fuel grid, which is conducive to more refined management of wildfires; further, based on the weighted fuel level of each fuel grid, the building wildfire risk index of each house and the vegetation hazard index of each fuel grid in the area to be divided are calculated, so as to realize the quantitative risk assessment for each house and the vegetation around the house separately; further, according to the building wildfire risk index and the vegetation hazard index, the risk houses and the risk fuel grids corresponding to the risk houses are screened out, which helps to optimize the allocation of emergency resources and ensure a rapid and effective response in case of a fire. Further, each risk house and the risk fuel grid corresponding to each risk house are merged into regions to obtain a plurality of forest-town boundary regions in the area to be divided. Therefore, the solution of the present invention realizes a refined and practical WUI division, improves the accuracy of the WUI division, and provides more scientific and effective decision-making support for wildfire management.

[0072] In addition, in a possible implementation manner, after step 109, the method further includes: Using the one-factor-at-a-time (OFAT) method to verify the effectiveness of the multiple forest-town boundary regions in the area to be divided.

[0073] Among them, the one-factor-at-a-time (OFAT) method is an experimental design and analysis method. It studies the influence of a variable (factor) on the result by changing the level of one variable while keeping all other variables at fixed levels. This method can help researchers understand the contribution of each independent variable to the experimental result. The OFAT method is easy to understand and implement because it only considers one variable at a time. By gradually changing one factor, its direct influence on the result can be observed and recorded. When analyzing a single factor, all other factors are controlled at a constant level to eliminate their interference.

[0074] Specifically, the effectiveness of delineating the WUI area within the experimental area is evaluated by three indicators: the number of buildings within the WUI, the wildfire ignition points within the WUI, and the relationship between the percentage change in the area of burned areas and the change in the building risk index. In practice, when the percentage change of the indicator is less than the percentage change of the WUI components, the threshold can be regarded as stable. For example, for the risk index, when the threshold is below 25, the number of buildings within the WUI and the change in wildfire ignition points within the WUI are relatively stable; for another example, when the threshold is below 30, the change in the area of burned areas is relatively stable.

[0075] Among them, the percentage change of the WUI components refers to the change in the relative proportion or quantity of each part (such as the building area, vegetation area, etc.) that constitutes the WUI during the delineation process of the wildland-urban interface (WUI). In the delineation and management of the WUI, monitoring the percentage change of these components is crucial for understanding the dynamic changes in the area and formulating corresponding risk management measures. By comparing the percentage change of these components with the percentage change in the number of buildings, wildfire ignition points, and the area of burned areas, the stability and effectiveness of the WUI delineation can be evaluated. If the changes in the number of buildings, wildfire ignition points, and the area of burned areas are less than the changes in the WUI components, then the current WUI delineation can be considered relatively stable because these key indicators do not fluctuate significantly due to minor changes within the WUI.

[0076] In this embodiment, the single-factor method for each single time is applied to verify the effectiveness of multiple wildland-urban interfaces in the area to be delineated, systematically evaluate and verify the influence of different factors on the effectiveness of WUI delineation, so as to provide more scientific decision-making support for wildfire risk management and WUI management, and improve the accuracy and reliability of WUI delineation.

[0077] In addition, in a possible implementation manner, after the above step 109, the above method further includes: Performing a filling process on multiple wildland-urban interfaces in the area to be delineated, so as to delineate the non-wildland-urban interface completely surrounded by the wildland-urban interface as a wildland-urban interface.

[0078] In practical applications, the geographical information system (GIS) is used to identify the non-WUI areas completely surrounded by the WUI area. Further, a filling process is applied to these completely surrounded non-WUI areas, reclassifying them as WUI areas, and updating the boundaries of the WUI to ensure that the newly delineated areas are included within the boundaries of the WUI.

[0079] In this embodiment, the filling process is performed on multiple forest-town boundary regions within the area to be divided, so as to divide the non-forest-town boundary regions completely surrounded by the forest-town boundary regions into forest-town boundary regions, ensuring the continuity and integrity of the WUI area and improving the accuracy and reliability of the WUI division.

[0080] In addition, in some embodiments, the WUI area is divided into three risk levels of low, medium, and high based on the building wildfire risk index.

[0081] Furthermore, for the forest-town boundary regions with low, medium, and high risks, population data, road data, and infrastructure data can be further integrated, and in cooperation with the wildfire management department, the land use plan can be adjusted, the building standards can be clarified, and the wildfire management and rescue activities can be optimized for regions with different risk levels, so as to ensure the timeliness of rescue operations and provide more scientific decision-making support. For example, by combining the population density data with high, medium, and low risk regions, it is possible to accurately determine which high-population-density areas need to be prioritized for evacuation preparation; at the same time, optimize the road use and evacuation plan to ensure the smoothness of roads in high-risk regions, facilitating the rapid deployment of emergency evacuation and rescue resources. In addition, for the wildfire protection of forest-town boundary regions with different risks, building owners can also adopt the best combination of adaptation measures according to the vulnerability of the building and the wildfire exposure situation. Through this multi-dimensional integration, it is possible to effectively guide emergency management and resource allocation, ultimately reducing the impact of wildfire disasters, providing a more scientific and systematic wildfire risk management tool for local governments and communities, and enhancing the safety of towns and the disaster response capabilities.

[0082] In the WUI division method based on the wildfire risk of houses provided in this embodiment, by dividing the area to be divided according to a predetermined resolution, a plurality of fuel grids are obtained, and the weighted fuel level of each fuel grid is determined, so as to distinguish the risk levels of each fuel grid, which is conducive to more refined management of wildfires; furthermore, based on the weighted fuel level of each fuel grid, the building wildfire risk index of each house and the vegetation hazard index of each fuel grid within the area to be divided are calculated, realizing the risk quantification assessment for each house and the vegetation around the house separately; furthermore, according to the building wildfire risk index and the vegetation hazard index, the risk houses and the risk fuel grids corresponding to the risk houses are screened out, which helps to optimize the allocation of emergency resources and ensure a rapid and effective response in case of a fire. Furthermore, each risk house and the risk fuel grid corresponding to each risk house are merged into regions to obtain a plurality of forest-town boundary regions within the area to be divided. Therefore, the solution of this embodiment realizes a fine and practical WUI division, improves the accuracy of the WUI division, and provides more scientific and effective decision-making support for wildfire management.

[0083] The following describes the WUI division device based on the wildfire risk of houses provided by the present invention. The WUI division device based on the wildfire risk of houses described below can be correspondingly referred to the WUI division method based on the wildfire risk of houses described above.

[0084] Figure 2 It is a schematic structural diagram of the WUI division device based on the wildfire risk of houses provided by the present invention. As Figure 2 shown, the WUI division device based on the wildfire risk of houses includes: a grid division module 21, a first calculation module 22, a second calculation module 23, a first screening module 24, a second screening module 25, and a region merging module 26.

[0085] The grid division module 21 is used to divide the area to be divided according to a predetermined resolution, obtain a plurality of fuel grids, and determine the weighted fuel level of each fuel grid.

[0086] Among them, the resolution refers to the spatial resolution of land cover data when performing geographic information system (GIS) analysis. The spatial resolution refers to the size of the smallest unit in an image or dataset, which determines the size of the smallest feature that can be recognized. In a geographic information system, the resolution is usually used to describe the accuracy of raster data, that is, the actual ground area represented by each raster unit (pixel). In practice, the resolution refers to the spatial detail level of the data, that is, the geographical range covered by each data point.

[0087] In practical applications, the grid division module 21 divides the area to be divided according to a predetermined resolution, and a plurality of grids with equal areas can be obtained. The plurality of grids include fuel grids and non-fuel grids. Further, the weighted fuel level is determined for each fuel grid. For non-fuel grids, in this embodiment, the grid division module 21 assigns the weighted fuel level to a preset value. For example, the weighted fuel level is assigned to a preset value of 10.

[0088] Specifically, for each fuel grid, the grid division module 21 comprehensively determines the weighted fuel level of the fuel grid by considering the land cover type of the vegetation in the fuel grid and the horizontal fuel connectivity of the fuel grid. It can be understood that the weighted fuel level of each fuel grid comprehensively considers the land cover type and connectivity corresponding to the fuel grid, providing important data support for subsequent wildfire risk assessment.

[0089] As an example, in a possible implementation manner, when the above grid division module 21 is used to determine the weighted fuel level of each fuel grid, it is specifically used for: Determine the cost weight values corresponding to different land cover types according to the suppression difficulty of different land cover types; Calculate the aggregation index corresponding to each fuel grid; For each fuel grid, the weighted fuel level of the fuel grid is determined according to the cost weight value corresponding to the land cover type of the fuel grid and the aggregation index corresponding to the fuel grid.

[0090] Among them, the suppression difficulty of the land cover type refers to the degree of difficulty in suppressing or controlling fires for a specific land cover type in wildfire management. Specifically, different types of vegetation have different combustion characteristics. Some vegetation is easier to ignite, has a faster burning speed, or is more difficult to extinguish. Therefore, the suppression difficulty of different land cover types is different.

[0091] In practical applications, considering the differences in the burning degree and spreading speed of different vegetation around houses during wildfires, different land cover types are ranked according to the suppression difficulty of different land cover types. Specifically, in this embodiment, the variable-width buffer technology is used to determine the cost weight values corresponding to different land cover types. The variable-width buffer technology uses cost distance calculation to associate different cost weights with the land cover types in GlobeLand30 - 2020 to determine the cost weights corresponding to different land cover types.

[0092] Furthermore, the horizontal fuel connectivity of each fuel grid is evaluated, that is, the aggregation index corresponding to each fuel grid is calculated.

[0093] Among them, the aggregation index (AI) provides a measure of the fuel connectivity or aggregation degree dispersed in the area, and it also provides a reference for the ease of fire spreading throughout the landscape. For example, the aggregation index of all fuel grids in City A is calculated in a 5*5 moving window, and the calculation formula of the aggregation index is as follows: Among them, refers to the aggregation degree of the corresponding fuel level, that is, the sum of the number of adjacent pairs of all pixels with a fuel level of in the 5×5 pane and pixels of the same type.

[0094] Furthermore, the aggregation index is simplified and classified. Specifically, AI > 90 is classified as "high aggregation" and assigned a value of 0; 0 < AI ≤ 90 is "lower clustering" and assigned a value of 1; AI = 0 is "no aggregation" and assigned a value of 2.

[0095] It can be understood that according to Table 1 above, for each fuel grid, according to the land cover type of the fuel grid, the cost weight value corresponding to the land cover type of the fuel grid can be determined.

[0096] ​Further, for each fuel grid, the weighted fuel level of the fuel grid is determined according to the cost weight value corresponding to the land cover type of the fuel grid and the aggregation index corresponding to the fuel grid. Specifically, for each fuel grid, the cost weight value corresponding to the land cover type of the fuel grid and the aggregation index corresponding to the fuel grid are combined to calculate the weighted fuel level of each fuel grid. For example, a fuel grid with a high aggregation index (AI value of 0) and a land cover type of open deciduous coniferous forest (level 1) has a weighted fuel level value of 1. The weighted fuel level value of a non-fuel grid is assigned a value of 10.

[0097] It can be understood that for each fuel grid, the weighted fuel level of the fuel grid is determined according to the cost weight value corresponding to the land cover type of the fuel grid and the aggregation index corresponding to the fuel grid. The calculation of the weighted fuel level comprehensively considers the connectivity and suppression difficulty of the fuel, providing a more refined risk indicator for wildfire risk assessment and WUI delineation. This comprehensive assessment helps identify areas with higher fire risks and provides a scientific basis for formulating effective fire prevention and response strategies.

[0098] Combine Figure 2 , the first calculation module 22 is configured to, for each house in the area to be delineated, establish a buffer zone corresponding to the house, and calculate the building wildfire risk index of the house based on the weighted fuel levels of each fuel grid falling within the buffer zone corresponding to the house.

[0099] In practical applications, establishing a buffer zone corresponding to each house in the area to be delineated means creating a virtual boundary area around each building in a Geographic Information System (GIS). This area is usually a circular or polygonal area, centered on the building and extending outward a certain distance. This buffer zone is used to evaluate and quantify the wildfire risk of each individual house and its surrounding vegetation.

[0100] It can be understood that the buffer zone can be circular, square, or other polygons, and the specific shape depends on the research needs and geographical features.

[0101] In one example, the buffer zone corresponding to the house is an annular buffer zone with a preset radius length.

[0102] Specifically, the size of the buffer zone can be determined according to the research purpose and actual needs. For example, buffer zones of different levels can be set. For example, buffer zones of 2400 meters, 1200 meters, 800 meters, etc. can be set.

[0103] Furthermore, based on the building footprint data, a quantitative analysis of the wildfire risk for each house was conducted. By calculating the building risk index of the house, an assessment of the specific wildfire risk for each house in the study area was achieved. This not only helps managers understand the distribution of wildfire risks more intuitively and in finer detail but also provides a scientific basis for formulating targeted building fire prevention strategies and emergency response plans.

[0104] As an example, in a possible implementation manner, when the above-mentioned first calculation module 22 is used to calculate the building wildfire risk index of a house based on the weighted fuel levels of each fuel grid falling within the buffer zone corresponding to the house, it is specifically used for: According to the weighted fuel levels of each fuel grid falling within the buffer zone corresponding to the house and the buffer distance of each fuel grid, screen out the fuel grids corresponding to the house that affect the house from all the fuel grids falling within the buffer zone corresponding to the house; wherein, the buffer distance of the fuel grid is the distance between the fuel grid and the house. Calculate the building wildfire risk index of the house based on the weighted fuel levels, buffer distances, and the number of land cover types of the fuel grids corresponding to the house that affect the house.

[0105] Specifically, according to the buffer distance of each fuel grid, the buffer zone level corresponding to each grid can be determined. Further, it is judged whether the weighted fuel level corresponding to each fuel grid is valid. If it is valid, the fuel grids with valid weighted fuel levels are used as the fuel grids corresponding to the house that affect the house.

[0106] Furthermore, based on the influence of fuel levels in different buffer zones around the building, calculate the building wildfire risk index (BWRI) of each house. The lower the weighted fuel level of the vegetation, the greater the potential risk. At the same time, considering the distance from the hazardous vegetation to the house, the farther the distance, the smaller the risk.

[0107] Specifically, calculate the building wildfire risk index of the house based on the weighted fuel levels, buffer distances, and the number of land cover types of the fuel grids corresponding to the house that affect the house. By way of example, the calculation formula for the building wildfire risk index is as follows: Where b is the buffer zone level, f is the weighted fuel level, and n is the number of land cover types of the fuel grids corresponding to the house that affect the house.

[0108] In this embodiment, according to the weighted fuel level, buffer distance, and the number of land cover types of the fuel grids affected by each house, the building wildfire risk index of the house is calculated. This risk assessment method provides a specific assessment of the wildfire risk of houses in different regions, which can help local governments and residents take appropriate preventive measures, such as adding fire-fighting facilities, improving vegetation management, or re-planning the regional layout, to reduce the potential fire risk.

[0109] Combined with Figure 2 , the second calculation module 23 is configured to, for each fuel grid, establish a buffer zone corresponding to the fuel grid, and calculate the vegetation hazard index of the fuel grid based on the building risk value of each house located in the buffer zone corresponding to the fuel grid.

[0110] During a wildfire, a crucial factor is the presence of flammable vegetation around the building, which directly determines whether the wildfire can spread. Therefore, it is particularly important to deeply explore and understand the spatial distribution of wild vegetation in the area and the fire risk it poses to adjacent buildings. To more accurately assess this risk, this application further calculates the vegetation hazard index (VDI) of the fuel grid based on the building risk index. The vegetation hazard index is used to measure the wildfire threat degree of wild vegetation to surrounding houses.

[0111] For the convenience of calculation, in this embodiment, first, raster-to-point operation is performed on the land cover data (GlobeLand30 - 2020 dataset), and then a buffer zone corresponding to each grid point is established according to the weighted fuel grid level. Finally, all houses located in the buffer zone corresponding to the grid point (buildings located in the buffer zone or overlapping with the buffer zone) are counted, and the vegetation hazard index of the fuel grid is calculated.

[0112] As an example, in a possible implementation manner, when the second calculation module 23 calculates the vegetation hazard index of the fuel grid based on the building risk value of each house located in the buffer zone corresponding to the fuel grid, it is specifically configured to: Count all houses located in the buffer zone corresponding to the fuel grid; Calculate the vegetation hazard index of the fuel grid according to the building risk value of each house located in the buffer zone corresponding to the fuel grid and the building area of each house.

[0113] Exemplarily, the calculation formula of the vegetation hazard index is as follows: Among them, BWRI is the building risk value of each house located in the buffer zone corresponding to the fuel grid, and A represents the area of the house (unit: square meters). Considering the huge differences that may exist in the product of the building hazard index and the building area in different grids, the present invention adopts a logarithmic processing method. This processing can effectively reduce the gap between data, make the hazard levels between different grids more comparable, and thus obtain a more balanced and understandable index.

[0114] Combined with Figure 2 , the first screening module 24 is used to screen out the houses with a building wildfire risk index greater than a preset first threshold from all the houses in the area to be divided as risk houses.

[0115] The second screening module 25 is used to screen out the fuel grids with a vegetation hazard index greater than a preset second threshold from all the fuel grids located in the buffer zone corresponding to each risk house as the risk fuel grids corresponding to the risk houses.

[0116] The area merging module 26 is used to merge the risk house and the risk fuel grid corresponding to the risk house for each risk house to obtain multiple forest-town boundary regions in the area to be divided.

[0117] In practical applications, in order to reduce the risk of structural losses during wildfires, during the WUI division process, houses are first concerned, and then it spreads outwards from the houses. Specifically, by initially screening the houses through the building risk index and then paying attention to the vegetation that affects them, the areas that are more likely to pose a wildfire threat to humans can be divided more scientifically.

[0118] It should be noted that the first threshold and the second threshold are preset according to the WUI area requirements. In practice, they can be adjusted according to the actual WUI area requirements. For example, the second threshold can be 0, and the fuel grids with a vegetation hazard index greater than 0 are used as the risk fuel grids corresponding to the risk houses.

[0119] In addition, in a possible implementation manner, the above device further includes: A verification module for verifying the effectiveness of multiple forest-town boundary regions in the area to be divided by applying the single-shot single-factor method.

[0120] Among them, the one-factor-at-a-time (OFAT) method is an experimental design and analysis method. It studies the impact of a variable (factor) on the result by changing the level of one variable while keeping all other variables at fixed levels. This method can help researchers understand the contribution of each independent variable to the experimental result. The OFAT method is easy to understand and implement because it only considers one variable at a time. By gradually changing one factor, its direct impact on the result can be observed and recorded. When analyzing a single factor, all other factors are controlled at a constant level to eliminate their interference.

[0121] Specifically, the effectiveness of dividing the WUI area within the experimental area is evaluated by three indicators: the number of buildings within the WUI, the wildfire ignition points within the WUI, and the relationship between the percentage change of the burned area and the change of the building risk index. In practice, when the percentage change of the indicator is less than the percentage change of the WUI component, the threshold can be regarded as stable. For example, for the risk index, when the threshold is below 25, the number of buildings within the WUI and the change of wildfire ignition points within the WUI are relatively stable; for another example, when the threshold is below 30, the change of the burned area is relatively stable.

[0122] In this embodiment, the one-factor-at-a-time method is applied to verify the effectiveness of multiple forest-town boundary regions within the area to be divided, systematically evaluate and verify the influence of different factors on the effectiveness of WUI division, so as to provide more scientific decision-making support for wildfire risk management and WUI management, and improve the accuracy and reliability of WUI division.

[0123] In addition, in a possible implementation manner, the above device further includes: A filling module, configured to perform filling processing on multiple forest-town boundary regions within the area to be divided, so as to divide the non-forest-town boundary regions completely surrounded by the forest-town boundary regions into forest-town boundary regions.

[0124] In practical applications, a geographic information system (GIS) is used to identify non-WUI regions completely surrounded by the WUI area. Further, filling processing is applied to these completely surrounded non-WUI regions, reclassifying them as WUI regions, and updating the boundary of the WUI to ensure that the newly divided regions are included within the boundary of the WUI.

[0125] In this embodiment, filling processing is performed on multiple forest-town boundary regions within the area to be divided, so as to divide the non-forest-town boundary regions completely surrounded by the forest-town boundary regions into forest-town boundary regions, ensuring the continuity and integrity of the WUI area, and improving the accuracy and reliability of WUI division.

[0126] In the WUI division device based on the wildfire risk of houses provided in this embodiment, the grid division module divides the area to be divided according to a predetermined resolution to obtain a plurality of fuel grids, and determines the weighted fuel level of each fuel grid, so as to distinguish the risk level of each fuel grid, which is conducive to more refined management of wildfires; further, the first calculation module and the second calculation module calculate the building wildfire risk index of each house and the vegetation hazard index of each fuel grid in the area to be divided based on the weighted fuel level of each fuel grid, so as to realize the quantitative risk assessment for each house and the vegetation around the house separately; further, the first screening module and the second screening module screen out the risk houses and the risk fuel grids corresponding to the risk houses according to the building wildfire risk index and the vegetation hazard index, which helps to optimize the allocation of emergency resources and ensure a rapid and effective response in case of a fire. Further, the area merging module merges each risk house and the risk fuel grid corresponding to each risk house to obtain a plurality of forest-town boundary regions in the area to be divided. Therefore, the solution of this embodiment realizes a fine and practical WUI division, improves the accuracy of the WUI division, and provides more scientific and effective decision-making support for wildfire management.

[0127] Figure 3 is a schematic structural diagram of the electronic device provided by the present invention, as Figure 3As shown, the electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communication bus 340. Among them, the processor 310, the communications interface 320, and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call logic instructions in the memory 330 to execute a WUI division method based on the wildfire risk of houses. The method includes: dividing the area to be divided according to a predetermined resolution to obtain a plurality of fuel grids, and determining the weighted fuel level of each fuel grid; for each house in the area to be divided, establishing a buffer zone corresponding to the house, and calculating the building wildfire risk index of the house based on the weighted fuel levels of each fuel grid falling within the buffer zone corresponding to the house; for each fuel grid, establishing a buffer zone corresponding to the fuel grid, and calculating the vegetation hazard index of the fuel grid based on the building risk values of each house falling within the buffer zone corresponding to the fuel grid; screening out houses with a building wildfire risk index greater than a preset first threshold from all the houses in the area to be divided as risk houses; for each risk house, screening out fuel grids with a vegetation hazard index greater than a preset second threshold from all the fuel grids falling within the buffer zone corresponding to the risk house as the risk fuel grids corresponding to the risk house; for each risk house, merging the risk house and the risk fuel grids corresponding to the risk house in the area to obtain a plurality of forest-town boundary regions in the area to be divided.

[0128] In addition, when the logic instructions in the above-mentioned memory 330 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0129] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the WUI division method based on the wildfire risk of houses provided by the above-mentioned various methods. The method includes: dividing the area to be divided according to a predetermined resolution to obtain a plurality of fuel grids, and determining the weighted fuel level of each fuel grid; for each house in the area to be divided, establishing a buffer zone corresponding to the house, and calculating the building wildfire risk index of the house based on the weighted fuel level of each fuel grid falling within the buffer zone corresponding to the house; for each fuel grid, establishing a buffer zone corresponding to the fuel grid, and calculating the vegetation hazard index of the fuel grid based on the building risk value of each house falling within the buffer zone corresponding to the fuel grid; screening out houses with a building wildfire risk index greater than a preset first threshold from all the houses in the area to be divided as risk houses; for each risk house, screening out fuel grids with a vegetation hazard index greater than a preset second threshold from all the fuel grids falling within the buffer zone corresponding to the risk house as the risk fuel grids corresponding to the risk house; for each risk house, merging the risk house and the risk fuel grids corresponding to the risk house in terms of area to obtain a plurality of forest-town boundary regions within the area to be divided.

[0130] On another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the WUI division method based on the wildfire risk of houses provided by the above-mentioned various methods. The method includes: dividing the area to be divided according to a predetermined resolution to obtain a plurality of fuel grids, and determining the weighted fuel level of each fuel grid; for each house in the area to be divided, establishing a buffer zone corresponding to the house, and calculating the building wildfire risk index of the house based on the weighted fuel level of each fuel grid falling within the buffer zone corresponding to the house; for each fuel grid, establishing a buffer zone corresponding to the fuel grid, and calculating the vegetation hazard index of the fuel grid based on the building risk value of each house falling within the buffer zone corresponding to the fuel grid; screening out houses with a building wildfire risk index greater than a preset first threshold from all the houses in the area to be divided as risk houses; for each risk house, screening out fuel grids with a vegetation hazard index greater than a preset second threshold from all the fuel grids falling within the buffer zone corresponding to the risk house as the risk fuel grids corresponding to the risk house; for each risk house, merging the risk house and the risk fuel grids corresponding to the risk house in terms of area to obtain a plurality of forest-town boundary regions within the area to be divided.

[0131] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative effort.

[0132] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A WUI classification method based on house wildfire risk, characterized in that: include: Dividing the area to be divided according to a predetermined resolution to obtain a plurality of fuel grids, and determining a weighted fuel grade of each fuel grid; For each house in the area to be divided, a buffer zone corresponding to the house is established, and a building wildfire risk index of the house is calculated based on a weighted fuel grade of each fuel grid falling within the buffer zone corresponding to the house; For each fuel grid, a buffer zone corresponding to the fuel grid is established, and based on the building risk value of each house falling within the buffer zone corresponding to the fuel grid, a vegetation hazard index of the fuel grid is calculated; Screening out houses whose building wildfire risk index is greater than a preset first threshold from all houses in the area to be divided as risk houses; For each risk house, a fuel grid having a vegetation hazard index greater than a preset second threshold value is selected from all fuel grids within the buffer zone corresponding to the risk house as the risk fuel grid corresponding to the risk house; For each risk house, the risk house and the risk fuel grid corresponding to the risk house are regionally merged to obtain a plurality of forest-town boundary areas within the area to be divided.

2. The WUI classification method for house wildfire risk according to claim 1, characterized in that: The determining of the weighted fuel grade of each fuel grid comprises: According to the difficulty of suppressing different land cover types, the cost weight values ​​corresponding to different land cover types are determined; Calculate the aggregation index corresponding to each fuel grid; For each fuel grid, a weighted fuel grade of the fuel grid is determined according to a cost weight value corresponding to a land cover type of the fuel grid and an aggregation index corresponding to the fuel grid.

3. The WUI classification method for house wildfire risk according to claim 1, characterized in that: The method of calculating a building wildfire risk index of the house based on a weighted fuel grade of each fuel grid within a buffer zone corresponding to the house comprises: According to the weighted fuel grade of each fuel grid within the buffer zone corresponding to the house and the buffer distance of each fuel grid, the influencing fuel grid corresponding to the house is obtained from all the fuel grids within the buffer zone corresponding to the house; wherein the buffer distance of the fuel grid is the distance between the fuel grid and the house; A building wildfire risk index of the house is calculated according to the weighted fuel grade of each influencing fuel grid corresponding to the house, the buffer distance, and the number of land cover types of the influencing fuel grid corresponding to the house.

4. The WUI classification method for house wildfire risk according to claim 1, characterized in that: The step of calculating the vegetation hazard index of the fuel grid based on the building risk value of each house within the buffer zone corresponding to the fuel grid comprises: Counting all houses that fall within the buffer zone corresponding to the fuel grid; The vegetation hazard index of the fuel grid is calculated based on the building risk value of each house within the buffer zone corresponding to the fuel grid and the building area of ​​each house.

5. The WUI classification method for house wildfire risk according to claim 1, characterized in that: The buffer zone corresponding to the house is a circular buffer zone with a preset radius length.

6. The WUI classification method for house wildfire risk according to any one of claims 1 to 5, characterized in that: For each risk house, after regionally merging the risk house and the risk fuel grid corresponding to the risk house to obtain a plurality of forest-town boundary regions within the area to be divided, the method further comprises: The single factor method was applied to verify the validity of multiple forest-town boundary areas in the area to be divided.

7. The WUI classification method for house wildfire risk according to any one of claims 1 to 5, characterized in that: For each risk house, after regionally merging the risk house and the risk fuel grid corresponding to the risk house to obtain a plurality of forest-town boundary regions within the area to be divided, the method further comprises: Filling processing is performed on a plurality of forest-town boundary areas in the area to be divided, so as to divide the non-forest-town boundary areas completely surrounded by the forest-town boundary areas into forest-town boundary areas.

8. A WUI division device based on house wildfire risk, characterized in that: include: A grid division module, used to divide the area to be divided according to a predetermined resolution, obtain multiple fuel grids, and determine the weighted fuel grade of each fuel grid; A first calculation module is used to establish a buffer zone corresponding to each house in the area to be divided, and calculate the building wildfire risk index of the house based on the weighted fuel level of each fuel grid falling within the buffer zone corresponding to the house; A second calculation module is used to establish a buffer zone corresponding to each fuel grid, and calculate the vegetation hazard index of the fuel grid based on the building risk value of each house falling within the buffer zone corresponding to the fuel grid; A first screening module is used to screen out houses whose building wildfire risk index is greater than a preset first threshold from all houses in the area to be divided as risk houses; A second screening module is used to screen out, for each risk house, a fuel grid whose vegetation hazard index is greater than a preset second threshold value from all fuel grids falling within the buffer zone corresponding to the risk house, as the risk fuel grid corresponding to the risk house; The area merging module is used to regionally merge the risk house and the risk fuel grid corresponding to the risk house for each risk house, so as to obtain a plurality of forest-town boundary areas in the area to be divided.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the WUI classification method based on house wildfire risk is implemented as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the WUI classification method based on house wildfire risk is implemented as described in any one of claims 1 to 7.

Images