Method and system for identifying avalanche potential release area based on watershed division

Abstract

The application relates to the technical field of natural disaster monitoring, early warning and prevention, and discloses a snow avalanche potential release area identification method and system based on basin division, which comprises the following steps: collecting DEM data and forest coverage data of a research area, and performing data preprocessing; extracting terrain parameters and performing pixel-by-pixel preliminary screening on the snow avalanche potential release preliminary screening area through multi-factor screening threshold; performing snow avalanche confluence adaptation and depression filling preprocessing on the DEM data corresponding to the snow avalanche potential release preliminary screening area, and marking the confluence blocking unit; calculating the grid water flow direction, introducing a correction flow accumulation formula of a snow avalanche along-path transmission coefficient to calculate the grid flow accumulation value; taking a ridge line as a confluence starting boundary, and segmenting out a candidate snow avalanche potential release area through a reverse basin division algorithm; performing secondary screening on the candidate snow avalanche potential release area, and outputting a final snow avalanche potential release area vector result. According to the application, an independent snow avalanche potential release area corresponding to a single snow avalanche path can be accurately segmented.

Description

Technical Field

[0001] This invention relates to the field of natural disaster monitoring, early warning and prevention technology, specifically to a method and system for identifying potential avalanche release zones based on watershed division. Background Technology

[0002] Avalanches are a frequent and serious mountain disaster in high-altitude mountainous areas, easily causing casualties, damage to buildings and infrastructure, and severely restricting economic development and the safety of residents' lives and property. Potential avalanche release zones refer to areas where natural conditions such as terrain and snow accumulation meet the criteria for avalanche instability triggering. Accurate identification of these zones is the core prerequisite and foundation for conducting avalanche disaster risk zoning, numerical simulation, and disaster prevention and mitigation engineering design.

[0003] Existing automated methods for identifying potential avalanche release zones all rely on topographic parameter analysis. They identify avalanche-prone areas by filtering topographic factors such as slope, elevation, and distance from the ridgeline. However, these methods have some problems: most existing methods are based on pixel-scale topographic feature filtering, which can only identify contiguous avalanche-prone areas. They cannot segment these areas into potential release zones corresponding to individual avalanche paths or independent avalanche events. This can easily generate contiguous release zones that are too large and do not conform to the physical laws of actual avalanche development, leading to distortion of the boundary conditions in subsequent avalanche numerical simulations. Consequently, they cannot provide reliable technical support for avalanche disaster prevention and control.

[0004] To address these issues, we have developed a method and system for identifying potential avalanche release zones based on watershed division, which solves the aforementioned technical problems. Summary of the Invention

[0005] This invention provides a method and system for identifying potential avalanche release areas based on watershed division, which can accurately segment independent potential avalanche release areas corresponding to a single avalanche path, and can balance identification accuracy and large-scale computational efficiency.

[0006] Therefore, the present invention provides the following technical solution: A method for identifying potential avalanche release zones based on watershed delineation, the method comprising: Step 1: Collect DEM data and forest cover data for the study area, and perform data preprocessing; Step 2: Based on the preprocessed DEM data, extract terrain parameters and use multi-factor screening thresholds to screen pixel by pixel to obtain the potential avalanche release area. Step 3: Perform avalanche confluence adaptation and depression filling preprocessing on the DEM data corresponding to the potential avalanche release screening area, and mark the confluence blocking units. Step 4: Based on the preprocessed DEM data, calculate the grid flow direction and calculate the grid flow accumulation value by introducing the modified flow accumulation formula of the avalanche friction transfer coefficient. Step 5: Using the ridgeline as the starting boundary for confluence, the candidate avalanche potential release area is segmented using the reverse watershed partitioning algorithm; Step 6: Set a minimum area threshold according to the resolution of the DEM data to perform secondary screening of the candidate avalanche potential release areas, and output the final vector results of the avalanche potential release areas.

[0007] Optionally, in step 1, the data preprocessing of the DEM data and forest cover data of the study area includes: performing coordinate system unification, invalid value removal, data cropping and projection transformation preprocessing on all data to ensure that the data spatial reference is consistent with the study area range, and the resolution of the DEM data is 5m-25m.

[0008] Optionally, in step 2, based on the preprocessed DEM data, the topographic parameters of the study area, including ridgeline, elevation, slope, aspect, comprehensive curvature, and vertical distance of pixels from the ridgeline, are extracted; the multi-factor screening threshold is set in combination with the topographic and climatic characteristics of avalanche development in the study area, and pixels that do not meet the avalanche release conditions are initially screened pixel by pixel to obtain the avalanche potential release preliminary screening area.

[0009] Optionally, in step 2, the ridgeline is extracted using the Geomorphon geomorphic unit classification algorithm; the formula for calculating the overall curvature is: ; In the formula, K The composite curvature of the grid cell; , ; , , ; It is a convex landform. It is a concave landform. It is a flat plane; x , y These are the planar coordinates of the grid cells. z The elevation value corresponding to the grid cell; The multi-factor screening thresholds include an elevation threshold, a slope threshold, a distance from the ridgeline threshold, and a forest cover screening threshold. The rules for setting the multi-factor screening thresholds are as follows: the elevation threshold retains pixels with an elevation ≥ a preset value and removes low-altitude areas with insufficient snow accumulation conditions; the slope threshold retains pixels with a slope between 28° and 60° and removes areas with a slope less than 28° that are not prone to instability and areas with a slope greater than 60° that are difficult to accumulate snow; the distance from the ridgeline threshold retains pixels whose vertical distance from the nearest ridgeline is ≤ 600m; and the forest cover screening threshold is used to remove pixel areas with dense forest cover.

[0010] Optionally, in step 3, the DEM data corresponding to the potential avalanche release screening area is subjected to avalanche confluence adaptation and depression filling preprocessing. An avalanche critical stopping slope threshold is set, and grids with slopes lower than the avalanche critical stopping slope threshold are marked as confluence blocking units and removed from the potential avalanche release screening area. The confluence blocking units do not receive upstream confluence and do not transmit confluence to downstream. The critical avalanche stop slope threshold The value range is 10°-18°. Based on the snow-surface interface limit equilibrium theory and combined with the field calibration of snow type, snow density and terrain roughness in the study area, the core calibration formula is: ; in, , is the comprehensive friction coefficient of the snow-ground interface; , is the snow type-snow density correction factor. Snow density in the study area, kg / m³ 3 ; , is the terrain roughness correction factor. R This refers to the terrain roughness level. coefficient of friction within snow Determined by snow type and snow density; coefficient of external friction of the ground surface Determined by terrain roughness.

[0011] Optionally, in step 4, the D8 single-direction algorithm is used to calculate the water flow direction. Based on a 3×3 pixel window, the slope drop of the center pixel and its 8 neighboring pixels is calculated using the slope drop formula. The water flow direction is the neighborhood direction with the largest slope drop. The modified flow accumulation formula for the avalanche path transfer coefficient is as follows: ; In the formula, For grid The cumulative traffic value; Grid for water flow in all directions The upstream raster set; The avalanche path transmission coefficient is defined as 0 < <1; constant 1 represents the raster. Its own unit catchment area; avalanche path transfer coefficient The following formula is used for calculation: ; In the formula: The Coulomb friction coefficient of the Earth's surface is determined based on the surface roughness classification. The average slope of the grid cells; To improve the Voellmy avalanche turbulence coefficient, snow density was used as a substitute index based on snow viscosity and its values ​​were graded. This refers to the DEM raster resolution. The initial snow depth for avalanches is determined based on historical snow cover observation data for the study area.

[0012] Optionally, in step 5, taking the ridgeline as the starting boundary of the confluence, the watershed watershed is identified based on the grid flow accumulation value using a reverse watershed division algorithm. The upper boundary is the ridgeline, the lower boundary is the confluence blocking unit, and the lateral boundary is the watershed. The downslope confluence control range corresponding to each ridgeline grid is extracted, and the contiguous avalanche potential release screening area is divided into multiple spatially independent watershed units that correspond one-to-one with a single avalanche path. Each watershed unit serves as a candidate avalanche potential release area.

[0013] Optionally, in step 6, the minimum area threshold is set according to the resolution of the DEM data, and the candidate avalanche potential release areas are screened a second time to remove invalid units with an area smaller than the minimum area threshold, so as to obtain the final vector result of the avalanche potential release area. At the same time, the corresponding terrain attribute information is attached to each avalanche potential release area and stored.

[0014] A system for identifying potential avalanche release zones based on watershed delineation, the system comprising: The data acquisition and preprocessing module collects DEM data and forest cover data of the study area and performs data preprocessing. The initial screening module for avalanche release is based on preprocessed DEM data, extracts terrain parameters and uses multi-factor screening thresholds to screen pixel by pixel to obtain potential avalanche release areas. The confluence blocking unit marking module performs avalanche confluence adaptation and depression filling preprocessing on the DEM data corresponding to the avalanche potential release screening area and marks the confluence blocking units. The grid flow accumulation calculation module calculates the grid flow direction based on the preprocessed DEM data and calculates the grid flow accumulation value by introducing the modified flow accumulation formula with the avalanche friction transfer coefficient. The candidate release area segmentation module uses the ridgeline as the confluence starting boundary and a reverse watershed division algorithm to segment the potential release areas of avalanches. The secondary screening module for the release area sets a minimum area threshold according to the resolution of the DEM data to perform secondary screening of the candidate potential avalanche release areas and outputs the final vector results of the potential avalanche release areas.

[0015] A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to perform the steps of the watershed-based avalanche potential release zone identification method.

[0016] The present invention provides a method and system for identifying potential avalanche release zones based on watershed division, which overcomes the technical shortcomings of existing methods that cannot accurately segment independent release zones corresponding to a single avalanche path and are prone to generating non-contiguous areas. This invention offers high accuracy and robustness, balancing large-scale computational efficiency with the physical plausibility of the identification results. It can be widely applied to avalanche disaster risk prevention and control, land use planning, and disaster prevention and mitigation engineering design in high-altitude mountainous areas. Compared with existing technologies, this invention has the following technical advantages: (1) This invention innovatively introduces a watershed division algorithm. Based on the similarity between the physical processes of avalanche movement and water flow confluence, it divides the potential release screening area of ​​contiguous avalanche into independent watershed units corresponding to a single avalanche path. This solves the defect of existing methods that easily generate non-real contiguous release areas, improves the matching degree between the identification results and real avalanche events, and provides a reliable input boundary for subsequent avalanche numerical simulation and risk assessment.

[0017] (2) The multi-factor screening threshold of the present invention can be adjusted according to the terrain and climate characteristics of different mountainous areas. The method has strong local adaptability and can be applied to various high-altitude avalanche-prone areas around the world.

[0018] (3) The present invention preferably uses a 25m resolution DEM as the core terrain data source, which takes into account both the identification accuracy and calculation efficiency of large-scale areas, and can realize the rapid and automated identification of potential avalanche release areas. The entire method process can be implemented by mainstream geographic information software such as QGIS, ArcGIS, and SAGAGIS. The algorithm is simple to operate, highly portable, and does not require complex programming development, making it convenient for engineering technicians to apply in practice.

[0019] (4) The present invention effectively eliminates areas with no possibility of avalanche release through dual filtering of pixel-scale multi-factor initial screening and minimum area threshold secondary screening, thereby improving the accuracy of the identification results; at the same time, it attaches complete terrain attribute information to each release area unit, providing basic data for subsequent avalanche disaster risk analysis and disaster prevention and mitigation engineering design, thereby reducing the technical cost of subsequent work. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a method for identifying potential avalanche release zones based on watershed division in a specific embodiment of the present invention; Figure 2This is a schematic diagram of the structure of an avalanche potential release zone identification system based on watershed division in a specific embodiment of the present invention. Detailed Implementation

[0022] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0024] like Figure 1 As shown, Figure 1 This is a flowchart of a method for identifying potential avalanche release zones based on watershed division in a specific embodiment of the present invention. The method includes: Step 1: Collect digital elevation model (DEM) data and forest cover data for the study area. Perform coordinate system unification, invalid value removal, data clipping and projection transformation preprocessing on all data to ensure that the spatial reference of the data is consistent with the scope of the study area. The resolution of DEM data is 5m-25m, and this embodiment preferably uses a 25m resolution DEM.

[0025] Step 2: Based on the preprocessed DEM data, extract the topographic parameters of the study area, including ridgeline, elevation, slope, aspect, comprehensive curvature, and vertical distance of pixels from the ridgeline; combine the topographic and climatic characteristics of avalanche development in the study area to set multi-factor screening thresholds, screen pixels one by one and remove pixels that do not meet the avalanche release conditions to obtain the avalanche potential release preliminary screening area. Ridge lines were extracted using the Geomorphon geomorphic unit classification algorithm; the comprehensive curvature calculation formula is as follows: ; In the formula: K The composite curvature of the grid cell; , ; , , ; It is a convex landform. It is a concave landform. It is a flat plane; x , yThese are the planar coordinates of the grid cells. z This represents the elevation value corresponding to the grid cell.

[0026] The rules for setting the multi-factor screening threshold are as follows: Elevation threshold: Retain pixels with elevation ≥ preset value and remove low-altitude areas with insufficient snow accumulation conditions; Slope threshold: Retain pixels with a slope between 28° and 60°, and remove areas with a slope less than 28° that are not prone to instability and areas with a slope greater than 60° that are difficult to accumulate snow. Ridgeline distance threshold: Preserve areas where the vertical distance between pixels and the nearest ridgeline is ≤600m; Forest cover filtering: Remove pixel areas with dense forest cover (coverage ≥ 70%).

[0027] Step 3: Perform avalanche confluence adaptation preprocessing and depression filling preprocessing on the DEM corresponding to the avalanche potential release screening area. Set the critical avalanche stopping slope threshold, mark the grid with the slope below the critical stopping slope threshold as a confluence blocking unit, and remove it from the avalanche potential release screening area. The confluence blocking unit does not receive upstream confluence and does not transmit confluence to downstream. The critical avalanche stopping slope threshold ranges from 10° to 18°. Based on the snow-surface interface limit equilibrium theory and combined with field calibration of snow type, snow density, and terrain roughness in the study area, the core calibration formula is: ; in, , is the comprehensive friction coefficient of the snow-ground interface; , is the snow type-snow density correction factor. Snow density (kg / m³) in the study area 3 ); , is the terrain roughness correction factor. R Terrain roughness level (1-4); coefficient of friction within snow Determined by snow type and snow density; coefficient of external friction of the ground surface Determined by terrain roughness.

[0028] Step 4: Based on the preprocessed DEM, calculate the grid flow direction, and calculate the upstream flow accumulation value of each grid by introducing the corrected flow accumulation formula of the avalanche friction transfer coefficient. The D8 single-direction algorithm is used to calculate the water flow direction. Based on a 3×3 pixel window, the slope drop of the center pixel and its 8 neighboring pixels is calculated using the slope drop formula. The water flow direction is the neighborhood direction with the largest slope drop.

[0029] The corrected flow accumulation formula, incorporating the avalanche friction factor, is as follows: ; In the formula: For grid The cumulative traffic value; Grid for water flow in all directions The upstream raster set; This is the avalanche path transfer coefficient, with a value range of 0 < <1; constant 1 represents the raster. Its own unit catchment area.

[0030] Specifically, the avalanche path transmission coefficient The following formula is used for calculation: ; In the formula: The Coulomb friction coefficient of the Earth's surface is determined based on the surface roughness classification. The average slope of the grid cells; To improve the Voellmy avalanche turbulence coefficient, snow density was used as a substitute index based on snow viscosity and its values ​​were graded. This refers to the DEM raster resolution. The initial snow depth for avalanches is determined based on historical snow cover observation data for the study area.

[0031] Step 5: Using the ridgeline as the starting boundary for confluence, the watershed is identified by the reverse watershed division algorithm based on the accumulated grid flow value. The upper boundary is the ridgeline, the lower boundary is the confluence blocking unit, and the lateral boundary is the watershed. The downslope confluence control range corresponding to each ridgeline grid is extracted, and the contiguous avalanche potential release screening area is divided into multiple spatially independent watershed units that correspond one-to-one with a single avalanche path. Each watershed unit is a candidate avalanche potential release area. Step 6: Set a minimum area threshold based on the DEM resolution, perform secondary screening on the candidate avalanche potential release areas, remove invalid units with an area smaller than the threshold, and obtain the final vector result of the avalanche potential release area. At the same time, attach the corresponding terrain attribute information to each avalanche potential release area unit and store it.

[0032] The minimum area threshold is positively correlated with the DEM resolution; in this embodiment, a 25µm resolution DEM is used, and the minimum area threshold is 6250µm corresponding to 10 pixels. 2 .

[0033] Accordingly, embodiments of the present invention also provide a system for identifying potential avalanche release zones based on watershed division, such as... Figure 2 The diagram shown is a structural schematic of the system. This avalanche potential release zone identification system based on watershed delineation includes the following modules: The data acquisition and preprocessing module 201 collects DEM data and forest cover data of the study area and performs data preprocessing. The release screening module 202 extracts terrain parameters based on the preprocessed DEM data and obtains the potential avalanche release screening area pixel by pixel through multi-factor screening thresholds. The confluence blocking unit marking module 203 performs avalanche confluence adaptation and depression filling preprocessing on the DEM data corresponding to the avalanche potential release screening area and marks the confluence blocking units. The grid flow accumulation calculation module 204 calculates the grid flow direction based on the preprocessed DEM data and calculates the grid flow accumulation value by introducing the modified flow accumulation formula with the avalanche friction transfer coefficient. The candidate release area segmentation module 205 uses the ridgeline as the confluence starting boundary and the reverse watershed division algorithm to segment the candidate avalanche potential release area. The secondary screening module 206 for the release area sets a minimum area threshold according to the resolution of the DEM data to perform secondary screening of the candidate avalanche potential release areas and outputs the final vector results of the avalanche potential release areas.

[0034] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0035] The present invention also provides a storage medium, which is a computer-readable storage medium storing a computer program thereon, the computer program being executable when it runs. Figure 1 The method shown may include some or all of the steps. The storage medium may include read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc. The storage medium may also include non-volatile memory or non-transitory memory, etc.

[0036] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data provider to another website, computer, server, or data provider via wired or wireless means.

[0037] The embodiments of the present invention have been described in detail above. Specific implementation methods have been used to illustrate the present invention. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and systems of the present invention, and are merely some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention, and the content of this specification should not be construed as a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying potential avalanche release zones based on watershed delineation, characterized in that, The method includes: Step 1: Collect DEM data and forest cover data for the study area, and perform data preprocessing; Step 2: Based on the preprocessed DEM data, extract terrain parameters and use multi-factor screening thresholds to screen pixel by pixel to obtain the potential avalanche release area. Step 3: Perform avalanche confluence adaptation and depression filling preprocessing on the DEM data corresponding to the potential avalanche release screening area, and mark the confluence blocking units. Step 4: Based on the preprocessed DEM data, calculate the grid flow direction and calculate the grid flow accumulation value by introducing the modified flow accumulation formula of the avalanche friction transfer coefficient. Step 5: Using the ridgeline as the starting boundary for confluence, the candidate avalanche potential release area is segmented using the reverse watershed partitioning algorithm; Step 6: Set a minimum area threshold according to the resolution of the DEM data to perform secondary screening of the candidate avalanche potential release areas, and output the final vector results of the avalanche potential release areas. In step 3, the DEM data corresponding to the potential avalanche release screening area is subjected to avalanche confluence adaptation and depression filling preprocessing. An avalanche critical stopping slope threshold is set, and grids with slopes lower than the avalanche critical stopping slope threshold are marked as confluence blocking units and removed from the potential avalanche release screening area. The confluence blocking unit does not receive upstream confluence and does not transmit confluence to downstream. The critical avalanche stop slope threshold The value range is 10°-18°. Based on the snow-surface interface limit equilibrium theory and combined with the field calibration of snow type, snow density and terrain roughness in the study area, the core calibration formula is: ； in, , is the comprehensive friction coefficient of the snow-ground interface; , is the snow type-snow density correction factor. Snow density in the study area, kg / m³ 3 ; , is the terrain roughness correction factor. R This refers to the terrain roughness level. coefficient of friction within snow Determined by snow type and snow density; coefficient of external friction of the ground surface Determined by terrain roughness; In step 4, the D8 single-direction algorithm is used to calculate the water flow direction. Based on a 3×3 pixel window, the slope drop of the center pixel and its 8 neighboring pixels is calculated using the slope drop formula. The water flow direction is the neighborhood direction with the largest slope drop. The modified flow accumulation formula for the avalanche path transfer coefficient is as follows: ； In the formula, For grid The cumulative traffic value; Grid for water flow in all directions The upstream raster set; The avalanche path transmission coefficient is defined as 0 < <1; constant 1 represents the raster. Its own unit catchment area; avalanche path transfer coefficient The following formula is used for calculation: ； In the formula: The Coulomb friction coefficient of the Earth's surface is determined based on the surface roughness classification. The average slope of the grid cells; To improve the Voellmy avalanche turbulence coefficient, snow density was used as a substitute index based on snow viscosity and its values ​​were graded. This refers to the DEM raster resolution. The initial snow depth for avalanches is determined based on historical snow cover observation data for the study area.

2. The avalanche potential release zone identification method based on watershed division according to claim 1, characterized in that, In step 1, the data preprocessing of the DEM data and forest cover data of the study area includes: unifying the coordinate system of all data, removing invalid values, cropping data and performing projection transformation preprocessing to ensure that the spatial reference of the data is consistent with the scope of the study area. The resolution of the DEM data is 5m-25m.

3. The avalanche potential release zone identification method based on watershed division according to claim 1, characterized in that, In step 2, based on the preprocessed DEM data, the topographic parameters of the study area, including ridgeline, elevation, slope, aspect, comprehensive curvature, and vertical distance of pixels from the ridgeline, are extracted. The multi-factor screening threshold is set in combination with the topographic and climatic characteristics of avalanche development in the study area. Pixels that do not meet the avalanche release conditions are initially screened and removed to obtain the avalanche potential release preliminary screening area.

4. The avalanche potential release zone identification method based on watershed division according to claim 3, characterized in that, In step 2, the ridgeline is extracted using the Geomorphon geomorphic unit classification algorithm; the formula for calculating the overall curvature is: ； In the formula, K The composite curvature of the grid cell; , ; , , ; It is a convex landform. It is a concave landform. It is a flat plane; x , y These are the planar coordinates of the grid cells. z The elevation value corresponding to the grid cell; The multi-factor screening thresholds include an elevation threshold, a slope threshold, a distance from the ridgeline threshold, and a forest cover screening threshold. The setting rules for the multi-factor screening thresholds are as follows: the elevation threshold retains pixels with an elevation ≥ a preset value and removes low-altitude areas with insufficient snow accumulation conditions; the slope threshold retains pixels with a slope between 28° and 60° and removes areas with a slope less than 28° that are not prone to instability and areas with a slope greater than 60° that are difficult to accumulate snow; the distance from the ridgeline threshold retains pixels whose vertical distance from the nearest ridgeline is ≤ 600m; and the forest cover screening threshold is used to remove pixel areas with dense forest cover.

5. The avalanche potential release zone identification method based on watershed division according to claim 1, characterized in that, In step 5, using the ridgeline as the starting boundary for confluence, the watershed is identified based on the grid flow accumulation value using a reverse watershed division algorithm. The upper boundary is the ridgeline, the lower boundary is the confluence blocking unit, and the lateral boundary is the watershed. The downslope confluence control range corresponding to each ridgeline grid is extracted, and the contiguous avalanche potential release screening area is divided into multiple spatially independent watershed units that correspond one-to-one with a single avalanche path. Each watershed unit serves as a candidate avalanche potential release area.

6. The method for identifying potential avalanche release zones based on watershed division according to claim 1, characterized in that, In step 6, the minimum area threshold is set according to the resolution of the DEM data, and the candidate avalanche potential release areas are screened a second time to remove invalid units with an area smaller than the minimum area threshold, so as to obtain the final vector result of the avalanche potential release area. At the same time, the corresponding terrain attribute information is attached to each avalanche potential release area and stored.

7. A system for identifying potential avalanche release zones based on watershed delineation, characterized in that, The system includes: The data acquisition and preprocessing module collects DEM data and forest cover data of the study area and performs data preprocessing. The initial screening module for avalanche release is based on preprocessed DEM data, extracts terrain parameters and uses multi-factor screening thresholds to screen pixel by pixel to obtain potential avalanche release areas. The confluence blocking unit marking module performs avalanche confluence adaptation and depression filling preprocessing on the DEM data corresponding to the avalanche potential release screening area and marks the confluence blocking units. The grid flow accumulation calculation module calculates the grid flow direction based on the preprocessed DEM data and calculates the grid flow accumulation value by introducing the modified flow accumulation formula with the avalanche friction transfer coefficient. The candidate release area segmentation module uses the ridgeline as the confluence starting boundary and a reverse watershed division algorithm to segment the potential release areas of avalanches. The secondary screening module for the release area sets a minimum area threshold according to the resolution of the DEM data to perform secondary screening of the candidate potential avalanche release areas and outputs the final vector results of the potential avalanche release areas. The confluence blocking unit marking module performs avalanche confluence adaptation and depression filling preprocessing on the DEM data corresponding to the avalanche potential release screening area, sets an avalanche critical stopping slope threshold, marks grids with slopes lower than the avalanche critical stopping slope threshold as confluence blocking units, and removes them from the avalanche potential release screening area. The confluence blocking unit does not receive upstream confluence and does not transmit confluence to downstream. The critical avalanche stop slope threshold The value range is 10°-18°. Based on the snow-surface interface limit equilibrium theory and combined with the field calibration of snow type, snow density and terrain roughness in the study area, the core calibration formula is: ； in, , is the comprehensive friction coefficient of the snow-ground interface; , is the snow type-snow density correction factor. Snow density in the study area, kg / m³ 3 ; , is the terrain roughness correction factor. R This refers to the terrain roughness level. coefficient of friction within snow Determined by snow type and snow density; coefficient of external friction of the ground surface Determined by terrain roughness; The grid flow accumulation value calculation module uses the D8 single flow direction algorithm to calculate the water flow direction. Based on a 3×3 pixel window, it calculates the slope drop of the center pixel and 8 neighboring pixels using the slope drop formula. The water flow direction is the neighboring direction with the largest slope drop. The modified flow accumulation formula for the avalanche path transfer coefficient is as follows: ； In the formula, For grid The cumulative traffic value; Grid for water flow in all directions The upstream raster set; The avalanche path transmission coefficient is defined as 0 < <1; constant 1 represents the raster. Its own unit catchment area; avalanche path transfer coefficient The following formula is used for calculation: ； In the formula: The Coulomb friction coefficient of the Earth's surface is determined based on the surface roughness classification. The average slope of the grid cells; To improve the Voellmy avalanche turbulence coefficient, snow density was used as a substitute index based on snow viscosity and its values ​​were graded. This refers to the DEM raster resolution. The initial snow depth for avalanches is determined based on historical snow cover observation data for the study area.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it performs the steps of the avalanche potential release zone identification method based on watershed division as described in any one of claims 1 to 6.

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