Plateau mountainous air transportation air drop area site selection method

Abstract

This invention discloses a method for selecting airdrop areas in high-altitude mountainous air transport. Based on data using raster images, raster pyramids, and vector hybrid data, and after dimensionless data processing, this invention pre-selects all candidate areas through GIS spatial analysis and constructs an evaluation index system that comprehensively considers the necessary specific needs for airdrop area selection in high-altitude air transport logistics. Then, the AHP decision analysis method is used to select candidate areas within the analysis scope and calculate evaluation values. Finally, K-Means clustering is used to group the candidate areas through iterative calculation, realizing a data-driven method for accurate airdrop area selection in high-altitude mountainous areas. The K-Means clustering grouping, calculated through iterative calculation, serves as the final result, providing a reasonable basis for site selection decisions. This invention offers accurate site selection results and good programming support, and can be widely applied in air transport, emergency management, and other departments to conduct airdrop area site selection data integration, information system construction, and new theoretical research.

Description

Technical Field

[0001] This invention belongs to the field of air transport technology, specifically relating to a method for selecting airdrop areas in plateau and mountainous air transport. Background Technology

[0002] High-altitude and mountainous areas, with their complex terrain, difficult transportation, and poor social support, make air transport a crucial means of delivering supplies in response to emergencies. However, selecting airdrop zones in these regions presents significant challenges compared to plains and hilly areas. The complex topographical, geological, hydrological, and meteorological conditions make traditional methods of searching and determining airdrop zones using topographic maps less accurate, leading to higher rates of damaged supplies and potentially impacting operational progress. Therefore, effectively organizing site selection data, designing evaluation index systems and calculation methods, and improving the accuracy of airdrop zone selection have become key concerns in the industry.

[0003] Currently, research on air transport delivery mainly focuses on calculating the airdrop origin point, analyzing and simulating the airdrop process, and calculating and tracking the landing point. There are few technical solutions regarding the selection of airdrop areas, and even fewer address the selection of airdrop areas in high-altitude and mountainous regions. Business departments still rely on the experience and judgment of technical personnel to manually select airdrop areas, marking them on 1:250,000 and 1:50,000 topographic maps, lacking data standards, calculation methods, and information tools. Summary of the Invention

[0004] Based on the above background, the present invention not only provides a method for selecting airdrop areas in plateau mountain air transport.

[0005] The technical solution of this invention is as follows: A method for selecting airdrop zones in high-altitude mountainous areas includes the following steps: S1: Obtain basic geographic information data of the airdrop target area as a base map for display. The basic geographic information data includes orthophotos, surface simulations, and topographic maps. S2: Acquire analysis data of the airdrop target area and convert the analysis data into raster data; The analysis data includes three categories: terrain analysis data, environmental analysis data, and obstacle avoidance data. The terrain analysis data includes digital elevation models, slope, terrain relief, terrain slope index, and landform type; Environmental analysis data includes meteorological data and traffic data; The obstacle avoidance data includes disaster data and water area data; S3: The attributes of the analytical data obtained in step S2 are normalized to dimensionless using the linear transformation method, and index values ​​are assigned to them to obtain index data. S4: Combining the indicator data obtained in step S3, use GIS-related software to calculate all candidate areas within the airdrop target area and their corresponding evaluation indicator values. Specifically, it includes the following steps: S4.1 Use GlobalMapper software to select the area based on the terrain indicators obtained in step S3; S4.2 Exclude areas with obstructions such as isal water bodies and rivers. S4.2.1 Extract vector features of areal water bodies and rivers from the topographic map obtained in step S1, and perform projection transformation by cropping according to the airdrop target area in ArcGIS software; select water bodies with names or areas greater than 1 square kilometer; S4.2.2 Convert the terrain relief index data obtained in step S3 into surface features; S4.2.3 Using the analysis tools module of ArcGIS software, the topographic relief index surface features are obtained with the surface features obtained in S4.2.2 as input features, and the areas excluding water bodies are obtained with the water body data selected in S4.2.1 as update features; S4.3 Generate candidate area Convert multiple components into a single component, recalculate the area and perimeter, select a certain area as a candidate area; eliminate small holes and generate the bounding rectangle of the candidate area; S4.4 Evaluation Indicator Assignment Using the spatial analysis module of ArcGIS software, based on the candidate area data obtained in step S4.3, select the data to be assigned values, assign values ​​to the data after normalization in step S3 one by one, and finally generate an evaluation index table. S4.5 AHP Decision Analysis Evaluation S4.5.1 Establishing a Hierarchical Model The hierarchical model includes a target layer, an indicator layer, and a criterion layer; The target layer includes an evaluation index system, and the criteria layer includes regional indicators, topographic indicators, meteorological indicators, and transportation indicators. The indicator layer corresponding to the regional indicators includes regional area and regional length; the indicator layer corresponding to the topographic indicators includes elevation, slope and topographic relief. The meteorological indicators include the index layer corresponding to average wind speed, average annual precipitation, snow cover and snow depth. The indicator layer corresponding to the traffic indicators includes the number of roads, the total length of roads, and the highest level; S4.5.2 Constructing the judgment matrix Based on the hierarchical model constructed in step S4.5.1, a site selection judgment matrix for airdrop areas in plateau and mountainous regions is established to obtain the weight values ​​of the indicators. S4.5.3 Ranking Evaluation The weight values ​​of each type of indicator obtained in step 4.5.2 and the evaluation index values ​​after data processing are calculated for overall evaluation. Then, the indicators are sorted in descending order according to the final overall weight values ​​to obtain the overall evaluation index table. The specific calculation formula is shown in equation (1): (1) In the formula: P is the overall weight value of the airdrop area for air transport in plateau and mountainous regions; These are the values ​​of each evaluation index; The weights of each evaluation indicator.

[0006] S5: K-Means Clustering Grouping Taking the candidate area set from the AHP decision analysis results in step S4 as the object, and the AHP evaluation value as distance, after iterative calculation, the candidate areas are finally divided into four levels based on the AHP decision analysis results and grouped as the final calculation results to assist in site selection decision.

[0007] Specifically, for example, based on the AHP decision analysis results, the candidate areas can be divided into four levels: the highest evaluation score (82-100 points), the second highest evaluation score (65-82 points), the medium evaluation score (42-65 points), and the lowest evaluation score (0-42 points). The candidate areas can then be visually displayed according to their level using different colors. For example, the highest evaluation score is displayed in green, the second highest evaluation score in blue, the medium evaluation score in orange, and the lowest evaluation score in red.

[0008] Furthermore, the step S3, which involves normalizing and dimensionlessly processing the topographic relief in the analyzed data, is as follows: The topographic relief (Range) reflects the undulation of the earth's surface. It is characterized by the difference (relative elevation difference) between the elevation of a point and the average elevation of its surrounding area. It is expressed by equation (2): (2) In the formula: Range represents the topographic relief, and DEM represents the elevation of a point on the Earth's surface. It represents the average elevation of a certain range around this point.

[0009] The topographic relief is normalized using the following formula (3) through a linear transformation method: (3) In the formula: F represents the slope index value in the comprehensive evaluation, which is dimensionless and has a value range of 0-100; R represents the topographic relief, in meters. This represents the maximum topographic relief during linear transformation and is dimensionless. This represents the minimum topographic relief during linear transformation and is dimensionless.

[0010] Furthermore, the normalization and dimensionless processing of snow cover in the analysis data in step S3 is as follows: The source data for the snow cover is the ERA5 reanalysis dataset, which is then normalized using the linear transformation method according to the following equation (4): (4) In the formula: F represents the comprehensive evaluation index value of annual snow cover, which is dimensionless and has a value range of 0-100; Indicates annual snow cover; This represents the maximum annual snow cover under a linear transformation and is dimensionless. This represents the minimum annual snow cover under a linear transformation and is dimensionless.

[0011] Furthermore, the normalization and dimensionless processing of the water areas in the analysis data in step S3 is as follows: First, extract the isal water features from the source data, convert the data into Shape format, and use WGS84 coordinates as the spatial reference. The attribute fields of water area data mainly include code, water area feature name, water area feature type, and river code. Based on the influence of water area on airdrop site selection, the water area part is assigned a value of 0 and the background part is assigned a value of 100 during data normalization.

[0012] Furthermore, since water surfaces can be used for airdrops after freezing in winter, seasonal factors should be considered when using water area data in practice. Values ​​should be assigned to various water area elements according to the actual situation. The attributes of perennial rivers include name and river code, the attributes of seasonal rivers include name, season and river code, the attributes of perennial lakes include name and type, and the attributes of seasonal lakes include name, type and season.

[0013] Based on the same inventive concept, the present invention also provides an electronic device, including a memory, a processor, and a storage device stored in the memory. A computer program that can run on a processor, wherein the processor executes the computer program to implement the above-described method for selecting airdrop areas for air transport in high-altitude mountainous regions.

[0014] Based on the same inventive concept, the present invention also provides a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute the above-described method for selecting airdrop areas for air transport in high-altitude mountainous regions.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention is based on data employing raster images, raster pyramids, and vector hybrids, and involves dimensionless data processing. Building upon this, it pre-selects all candidate areas through GIS spatial analysis and constructs a site selection evaluation index system for high-altitude mountainous airdrop areas, including regional, topographical, meteorological, and transportation indicators. Then, it uses AHP decision analysis to select candidate areas within the analysis scope and calculates their evaluation values. Finally, it employs K-Means clustering and iterative calculation to group the candidate areas, realizing a data-driven method for precise site selection of high-altitude mountainous airdrop areas. The K-Means clustering grouping, calculated through iterative calculation, serves as the final result, providing a reasonable basis for site selection decisions. The site selection results obtained by this invention are accurate and have good programming support. It can be widely used in departments such as air transport and emergency management to carry out data integration, information system construction and new theoretical research on airdrop area site selection. Attached Figure Description

[0016] Figure 1 This is the normalized result of the terrain relief data in Embodiment 1 of the present invention; Figure 2 This is the normalized result of snow cover in Embodiment 1 of the present invention; Figure 3 This is the water area normalization result of Example 1 of the present invention; Figure 4 The calculation process is as described in Embodiment 1 of the present invention; Figure 5 This refers to the terrain index selection area in Embodiment 1 of the present invention; Figure 6 This refers to the reclassification topographic relief index of Embodiment 1 of the present invention; Figure 7 The intersection negation operation of Embodiment 1 of the present invention excludes water areas; Figure 8 This refers to the candidate area and circumscribed rectangle of Embodiment 1 of the present invention; Figure 9 This is a partitioned statistical calculation for Embodiment 1 of the present invention; Figure 10 This is the evaluation index table for Embodiment 1 of the present invention; Figure 11 This is the evaluation index system of Embodiment 1 of the present invention; Figure 12 This refers to the clustering grouping in Embodiment 1 of the present invention; Figure 13 This is the result of the address selection module operation in Embodiment 3 of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to its embodiments; it should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0018] Example 1: A method for selecting airdrop areas in high-altitude mountainous air transport, comprising the following steps: S1: First, determine the target area for airdrop. In this embodiment, the area from Qipuchapu River to Qiangchenmo River in Ngari Prefecture, Tibet Autonomous Region, my country, is selected as the target area for airdrop. The longitude is 77°15' E - 80°15' E, the latitude is 33°40' N - 36°00' N, and the area is approximately 71,116 square kilometers. It is a typical plateau and mountainous terrain. The target area for airdrop is characterized by snow-capped mountains, swamps, rivers, and scattered settlements, which meets the requirements for conducting plateau and mountainous site selection research. Then, the basic geographic information data of the target area to be airdropped is obtained as a base map for display. The basic geographic information data includes orthophotos, surface simulations, and topographic maps. The basic geographic information data obtained in this step serves as the base electronic map for the prototype software's base map display. High-resolution orthophotos with a resolution better than 1 meter are selected to facilitate operators' understanding of regional details. Surface simulation data retains only topography and water systems to facilitate mapping and analysis. Topographic maps at scales of 1:250,000 and 1:50,000 are selected to meet the needs of air transport planning and command.

[0019] Basic geographic information data is displayed only as a base map. The data format is raster data, and the storage format is raster tile pyramid format. The mathematical basis uses the CGCS2000 coordinate system. Specific basic geographic information data can be found in Table 1.

[0020] Table 1: Basic Geographic Information Data S2: Obtain analysis data of the area to be airdropped. The analysis data includes three types of data: terrain analysis data, environmental analysis data, and obstacle avoidance data. All of the analysis data are raster data. The terrain analysis data includes digital elevation models, slope, terrain relief, terrain slope index, and landform type, with a 30-meter grid digital elevation model serving as the basic data for terrain analysis. Environmental analysis data includes meteorological data and traffic data; specifically, the meteorological data includes wind speed, precipitation, temperature, snow cover, snow depth, etc. The obstacle avoidance data includes disaster data and water area data; the disaster data includes mudslides, flash floods, and floods.

[0021] The analytical data obtained in this step can be used as input data for the following calculations, including site selection calculations.

[0022] The main conditions for selecting an airdrop site are: ① The drop zone should be wide, flat, and free of obstacles that could affect airdrop safety (forests, cliffs, high-voltage lines, buildings, and deep, fast-flowing rivers, etc.); it should be easily maneuverable and able to accommodate large vehicles; it should have clear markers for easy aerial identification; and it should have good airspace clearance to facilitate the establishment of airdrop routes, lowering the drop altitude, and multiple airdrops. ② Typically, the area of ​​an airdrop zone is: airdrop length = flight speed × drop time + safety range (500 meters × 2); airdrop width = number of aircraft × aircraft wingspan + (number of aircraft - 1) × safety distance (2 times wingspan) + safety range (500 meters × 2). ③ The ground wind speed should not exceed 10 meters per second, and the combined wind speed below the airdrop altitude should not exceed 14 meters per second. Based on the above requirements, the main indicators for selecting an airdrop site are: flat terrain with good transportation access; length and width area meeting aircraft requirements; environmental indicators such as wind speed meeting requirements; and avoiding obstacles such as deep valleys, large bodies of water, cliffs, and disaster areas.

[0023] Therefore, the analysis data in this step includes three types of data: terrain analysis data, environmental analysis data, and obstacle avoidance data. All of these data are in raster format with a grid resolution of 30 meters and are stored in Erdas Image format. The mathematical basis is the CGCS2000 coordinate system, projected using Mercator (WIKI ID 3857), as detailed in Table 2.

[0024] Table 2: Analytical Data S3: The attributes of the analytical data obtained in step S2 are normalized to dimensionless using the linear transformation method, and index values ​​are assigned to them to obtain index data. The data processing method is described below using terrain relief, snow cover, and water area as examples.

[0025] The specific steps for normalizing the terrain relief are as follows: Topographic relief (Range) reflects the undulation of the earth's surface. It is characterized by the difference between the elevation of a point and the average elevation of its surrounding area (relative elevation difference), and is expressed by formula (2): (2) In the formula: Range represents the topographic relief, and DEM represents the elevation of a point on the Earth's surface. It represents the average elevation of a certain range around this point. The search radius is 2km, the spatial reference of the final data is WGS84 coordinates, the spatial resolution is 0.0002777°, and the normalization is performed by linear transformation using the following formula (3).

[0026] (3) In the formula: F represents the slope index value in the comprehensive evaluation, which is dimensionless and has a value range of 0-100; R represents the topographic relief, in meters. This represents the maximum topographic relief during linear transformation, dimensionless, and is taken as 2500m. This represents the minimum topographic relief during the linear transformation, dimensionless, and taken as 30m. If R > 2500, the final transformation value F is 0; if R < 30, the final transformation value F is 100. The processing results are shown in Table 3 and... Figure 1 .

[0027] Table 3: Assignment Table for Topographic Relief Index Snow cover data is categorized into annual and monthly snow cover, both representing multi-year averages (39 years, 1981-2019), expressed as a percentage. The data source is the ERA5 reanalysis dataset, with a spatial resolution of 0.1°, approximately 11 km in equatorial regions. The following formula is used... (4) Normalization is performed using the linear transformation method. (4) In the formula: F represents the comprehensive evaluation index value of annual snow cover, which is dimensionless and has a value range of 0-100; Indicates annual snow cover; This represents the maximum annual snow cover under linear transformation; it is dimensionless and taken as 80%. This represents the minimum annual snow cover during a linear transformation, dimensionless, and taken as 5%. In specific transformations, if... If >80, then the final transformation value F takes the value 0; if If the value is less than 5, the final transformation value F is set to 100. The processing results are shown in Table 4 and... Figure 1 .

[0028] Table 4: Annual Snow Cover Grading Index Assignment Table Waters data, sourced from 2012 1:50,000 scale topographic maps in MappingStar format, Gaussian projection, and the 2000 China Geodetic Coordinate System, were used. First, areal water features, such as perennial rivers and lakes, were extracted from the source data. Then, the water data was converted to Shape format, with the spatial datum being WGS84 coordinates. The attribute fields of the water data mainly include code, water feature name, water feature type, and river code. Based on the influence of water areas on airdrop site selection, during data normalization (normalizing each indicator using a linear transformation formula), the water area was assigned a value of 0, and the background was assigned a value of 100. See Table 5 below for details. Figure 2 .

[0029] Table 5: Explanation of Water Area Data Format Since the water surface can be used for airdropping supplies after freezing in winter, seasonal factors should be considered when using water area data in practice, and values ​​should be assigned to various water area elements according to the actual situation. As can be seen from the table, perennial rivers have attributes including name and river code, while seasonal lakes have attributes including name, type, and season (month). Major perennial rivers include the Indus River, the Qiangchenmo River, the Karakash River, and the Yurungkash River, while perennial lakes include Pangong Lake, Hongshan Lake, and Saligilegan Nankule. Perennial lakes are categorized into two types: "saline" and "freshwater."

[0030] The following steps are all based on the data from the previous step for site selection calculation.

[0031] In this embodiment, based on the data obtained in step S3 above and the conditions for airdrop area selection, GIS spatial analysis, AHP decision analysis and K-Means clustering analysis methods are used in combination to calculate the location.

[0032] First, all candidate areas within the proposed airdrop zone are pre-selected using GIS spatial analysis. Then, an evaluation index weight matrix (obtained through AHP decision analysis) is constructed. The airdrop target location and analysis range are input, and candidate areas within the analysis range are selected using AHP decision analysis, with the index weights calculated. Finally, K-Means clustering analysis is used to categorize the candidate areas into four levels, completing the site selection calculation. The results assist in site selection decisions. The calculation process is shown below. Figure 4 As shown.

[0033] S4: Combining the regional indicators, terrain indicators, and obstacle avoidance indicators obtained in step S3, use GIS-related software to calculate all candidate areas within the analysis scope and their corresponding evaluation index values. Specifically, it includes the following steps: S4.1 Using GlobalMapper software, select areas based on the terrain indicators obtained in step S3. The results are shown below. Figure 4 As shown; S4.2 Exclude areas with obstructions such as isal water bodies and rivers. S4.2.1 Extract vector features of isometric water bodies and rivers in the topographic map obtained in step S1, and perform projection transformation by cropping according to the area to be airdropped in ArcGIS software; select water bodies with names or areas greater than 1 square kilometer; Specifically, vector features of isometric water bodies and rivers in the study area were extracted from the 1:250,000 topographic map, cropped according to the study area in ArcGIS software, and then projected and transformed; water bodies with names or areas greater than 1 square kilometer were selected.

[0034] S4.2.2 Convert the terrain relief index data obtained in step S3 into surface features; Specifically, first, using the Spatial Analyst tool – Reclassify – Reclassify, set 0-MAX(Z) to 100 and Nodata to Nodta, reassigning the topographic relief index data with values ​​≤50m to 100. Then, using the Data Management tool – Transformation tool – Convert from Raster to Polygon tool, convert the topographic relief index data obtained in the previous step into polygon features, as shown in the image. Figure 6 As shown.

[0035] The Spatial Analyst tool and data management tool are both modules within the ArcGIS software.

[0036] S4.2.2.3 Using the analysis tool module of ArcGIS software, the topographic relief index surface features are obtained by taking the surface features obtained in S4.2.2 as input features and the water area data selected in S4.2.1 as update features to obtain the area excluding water areas; Specifically, using the analysis tools - overlay analysis - intersection negation tool, select the area features obtained in S4.2.2 as the input features, and the result is the terrain relief index area features; update the water data selected in S4.2.1, and the result is the area excluding water. See the detailed operation instructions below. Figure 7 As shown.

[0037] S4.3 Generate candidate area Convert the multi-part component into a single component and recalculate the geometric area and perimeter; select a region of a certain area as a candidate area; eliminate small holes and generate the bounding rectangle of the candidate area; Specifically, convert multi-part components to single-part components using (Data Management Tools - Features - Multi-part - Single-part), and recalculate the area and perimeter using computational geometry. Then, select areas with an area greater than 2.25 square kilometers (1.5 × 1.5) to obtain all candidate areas. Use (Data Management Tools - Cartographic Generalization - Eliminate Surface Components, parameter 0.5 square kilometers) to eliminate small holes; use (Data Management Tools - Features - Feature Envelope Rectangle to Surface) to generate the bounding rectangle of the candidate areas. The results are shown in [link to results]. Figure 8 As shown.

[0038] S4.4 Evaluation Indicator Assignment Using the spatial analysis module of ArcGIS software, based on the candidate area data obtained in step S4.3, select the data to be assigned values, assign values ​​to the data after normalization in step S3 one by one, and finally generate an evaluation index table. Specifically, using the tool (Spatial Analysis - Regional Analysis - Displaying Zonal Statistics in Tables), select the candidate area data generated in ③ for the regional data, select FID for the regional field, and select the raster data to be assigned, such as annual average wind speed and temperature, etc., for the input assignment raster. Assign values ​​to the data modeled in the data model one by one, and finally generate an evaluation index table. For details, please refer to [reference needed]. Figure 9 and Figure 10 As shown.

[0039] S4.5 AHP Decision Analysis Evaluation The Alternating Hierarchical Analysis (AHP) method is an important approach for solving complex, unstructured geographical decision-making problems. This invention uses the candidate areas obtained in step S4.3 as evaluation objects, and constructs a two-layer judgment matrix using four types of indicators: regional indicators, topographic indicators, meteorological indicators, and transportation indicators (S3). AHP values ​​are calculated for the evaluation indicators in step S4.4, enabling the candidate areas to be ranked according to their AHP values.

[0040] S4.5.1 Establishing a Hierarchical Model A site selection evaluation index system for airdrop areas in high-altitude mountainous regions was established, categorizing site selection influencing indicators into four types: regional indicators, topographic indicators, meteorological indicators, and transportation indicators. Regional indicators include area and length, selecting areas meeting the area and length requirements of the airdrop site. Topographic indicators include elevation, slope, and terrain undulation, selecting areas meeting the topographic conditions based on the airdrop site's clearance conditions. Meteorological indicators include average wind speed, average annual precipitation, snow cover, and snow depth, selecting areas meeting the meteorological conditions of the airdrop site. Transportation indicators include the number of roads, total road length, and highest road grade, selecting areas meeting the transportation conditions of the airdrop site. Therefore, the constructed site selection evaluation index system for airdrop areas in high-altitude mountainous regions (AHP establishment) is established under the premise of comprehensively considering all specific requirements and ensuring feasibility. The indicators included in site selection are grouped, with each group forming a level, and arranged in the order of: target level - criterion level - indicator level, as detailed in Table 6. Figure 11 As shown.

[0041] Table 6: Evaluation Index System As shown in Table 6, the hierarchical model includes a target layer, an indicator layer, and a criterion layer; The target layer includes an evaluation index system, and the criteria layer includes regional indicators, topographic indicators, meteorological indicators, and transportation indicators. The indicator layer corresponding to the regional indicators includes regional area and regional length; the indicator layer corresponding to the topographic indicators includes elevation, slope, and topographic relief. The meteorological indicators include average wind speed, average annual precipitation, snow and ice cover, and snow and ice depth. The indicator layers corresponding to the traffic indicators include the number of roads, the total length of roads, and the highest level; S4.5.2 Constructing the judgment matrix Based on the hierarchical model constructed in step S4.5.1, a site selection judgment matrix for airdrop areas in plateau and mountainous regions is established to obtain the weight values ​​of the indicators. Specifically, the judgment matrix assesses the relative importance of relevant elements at a given level, with respect to a specific element at the previous level. Based on the introduction of the AHP decision analysis method and the established evaluation index system, a judgment matrix for the selection of airdrop zones for air transport logistics materials in plateau regions was established, and the results are shown in Tables 7 to 11.

[0042] Table 7: Target Layer Judgment Matrix CR = 0.0439 < 0.10. The judgment matrix has satisfactory consistency.

[0043] The results show that, for the constraint factor layer (B), the regional indicator has the greatest weight. This is because the regional indicator includes the area and length of the region, which are rigid requirements for the selection of the airdrop area. Improper handling of these two indicators can pose the greatest risk to the airdrop mission.

[0044] Table 8: Regional Indicator Judgment Matrix CR = 0.0000 < 0.10. The judgment matrix has satisfactory consistency.

[0045] The results show that for the indicator factor layer (C1-C2), the area and length of the region are equally important. Under the condition of meeting the area requirements, the length of the region is an important indicator because the shape of materials is generally regular. To prevent the materials from being damaged, a region that meets the requirements is needed.

[0046] Table 9: Terrain Index Judgment Matrix CR = 0.0565 < 0.10. The judgment matrix has satisfactory consistency.

[0047] The results show that, for the indicator factor layer (C3-C5), topographic relief has the highest weight because the parameter values ​​of topographic relief can reflect an important indicator of the undulation of the terrain. This is because topographic relief can distinguish between plains, plateaus, hills, and mountains.

[0048] Table 10: Meteorological Indicator Judgment Matrix CR = 0.0163 < 0.10. The judgment matrix has satisfactory consistency.

[0049] The results show that, for the indicator factor layer (C6-C9), snow and ice cover and snow and ice depth have the largest weights. Snow and ice cover mainly refers to the coverage area, and excessive snow and ice cover affects the site selection area. Because the region has snow all year round, excessive snow and ice depth makes the search and transportation of materials difficult.

[0050] Table 11: Traffic Indicator Judgment Matrix CR = 0.0334 < 0.10. The judgment matrix has satisfactory consistency.

[0051] The results show that for the indicator factor layer (C10-C12), the total road length has the highest weight because logistical supplies need to be transported after airdrop, and the airdrop area is generally required to be close to a road. The roads in this area are all simple roads, mostly gravel and sandy surfaces, uneven, and many roads are relatively short. Therefore, the total road length is an important indicator.

[0052] S4.5.3 Ranking Evaluation The weight values ​​of each type of indicator obtained in step 4.5.2 and the evaluation index values ​​after data processing are calculated for overall evaluation. Then, the indicators are sorted in descending order according to the final overall weight values ​​to obtain the overall evaluation index table. The specific calculation formula is shown in equation (1): (1) In the formula: P is the overall weight value of the airdrop area for air transport logistics supplies in plateau regions; These are the values ​​of each evaluation index; The weights of each evaluation indicator.

[0053] S5 K-Means clustering grouping Taking the candidate area set from the AHP decision analysis results in step S4 as the object, and the AHP evaluation value as distance, the candidate areas are grouped through iterative calculation and used as the final calculation result to assist in site selection decision.

[0054] Specifically, K-Means clustering analysis is an effective algorithm for clustering based on object distance. Through iterative calculation, objects can be divided into specific groups according to cluster centers. This step uses the candidate area set from the AHP decision analysis results in step S4 as the object, and the AHP evaluation value P as the distance. Through iterative calculation, the candidate areas are divided into four groups: green, blue, orange, and red, providing a basis for site selection decisions as the final calculation result. See the clustering grouping diagram below. Figure 12 As shown, when making a decision, the green selection area group is the best choice, followed by the blue group, then the orange group, and the red group is the worst choice.

[0055] Example 2: This example is based on the airdrop area selection method for high-altitude mountain air transport in Example 1. Using Visual Studio 2012 and C#, it calls the GTMap (Gengtu Geographic Situation Information System) SDK to load basic geographic information data, display electronic maps and analysis results, and independently implements AHP and K-Means algorithms. It completes the AHP weight matrix calculation settings, analysis range input, report output, map output and other functions of the site selection module, and demonstrates its application through the above module.

[0056] Operation process: After loading data and setting calculation weights, users plot the airdrop target locations, set the analysis range, and then click the site selection analysis button. The system displays the site selection results in four levels: green, blue, orange, and red. Clicking the report output button allows users to browse the indicator information of the candidate areas in a window. The report can be exported as an HTML or XLS file. Double-clicking the row containing the selected area on the map locates the selected area. Selecting map output allows users to set the output scale and resolution to output the site selection result map as a BMP image (see...). Figure 13 (As shown), used for the next step.

[0057] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for selecting airdrop zones in high-altitude mountainous air transport, characterized in that, It includes the following steps: S1: Obtain basic geographic information data of the airdrop target area as a base map for display. The basic geographic information data includes orthophotos, surface simulations, and topographic maps. S2: Obtain analysis data of the airdrop target area. The analysis data includes three types of data: regional data, terrain analysis data, environmental analysis data, and obstacle avoidance data. All of the analysis data are raster data. The terrain analysis data includes digital elevation model, slope, terrain relief, terrain slope index, and landform type; Environmental analysis data includes meteorological data and traffic data; The obstacle avoidance data includes disaster data and water area data; S3: The attributes of the analytical data obtained in step S2 are normalized to dimensionless using the linear transformation method, and index values ​​are assigned to them to obtain index data. S4: Combining the regional indicators, terrain indicators, and obstacle avoidance indicators obtained in step S3, use GIS-related software to calculate all candidate areas within the airdrop target area and their corresponding evaluation indicator values. Specifically, it includes the following steps: S4.1 Use GlobalMapper software to select the area based on the terrain indicators obtained in step S3; S4.2 Exclude areas with obstructions such as isal water bodies and rivers. S4.2.1 Extract vector features of isometric water bodies and rivers in the topographic map obtained in step S1, and perform projection transformation by cropping according to the area to be airdropped in ArcGIS software; select water bodies with names or areas greater than 1 square kilometer; S4.2.2 Convert the terrain relief index data obtained in step S3 into surface features; S4.2.2.3 Using the analysis tool module of ArcGIS software, the topographic relief index surface features are obtained by taking the surface features obtained in S4.2.2 as input features and the water area data selected in S4.2.1 as update features to obtain the area excluding water areas; S4.3 Generate candidate area Convert the multi-part component to a single-part component and recalculate the area and perimeter using computational geometry; select a region of a certain area as a candidate region; eliminate small holes and generate the bounding rectangle of the candidate region; S4.4 Evaluation Indicator Assignment Using the spatial analysis module of ArcGIS software, based on the candidate area data obtained in step S4.3, select the data to be assigned values, assign values ​​to the data after normalization in step S3 one by one, and finally generate an evaluation index table. S4.5 AHP Decision Analysis Evaluation S4.5.1 Establishing a Hierarchical Model The hierarchical model includes a target layer, an indicator layer, and a criterion layer; The target layer includes an evaluation index system, and the criteria layer includes regional indicators, topographic indicators, meteorological indicators, and transportation indicators. The indicator layer corresponding to the regional indicators includes regional area and regional length; the indicator layer corresponding to the topographic indicators includes elevation, slope, and topographic relief. The meteorological indicators include average wind speed, average annual precipitation, snow and ice cover, and snow and ice depth. The indicator layers corresponding to the traffic indicators include the number of roads, the total length of roads, and the highest level; S4.5.2 Constructing the judgment matrix Based on the hierarchical model constructed in step S4.5.1, a decision matrix for the selection of airdrop areas for air transport logistics materials in plateau regions is established to obtain the weight values ​​of the indicators. S4.5.3 Ranking Evaluation The weight values ​​of each type of indicator obtained in step 4.5.2 and the evaluation index values ​​after data processing are calculated for overall evaluation. Then, the indicators are sorted in descending order according to the final overall weight values ​​to obtain the overall evaluation index table. The specific calculation formula is shown in equation (1): 　　　（1） In the formula: P is the overall weight value of the airdrop area for air transport logistics supplies in plateau regions; These are the values ​​of each evaluation index; Weights for each evaluation indicator; S5: K-Means Clustering Grouping Taking the candidate area set from the AHP decision analysis results in step S4 as the object, and the AHP evaluation value as the distance, through iterative calculation, the candidate areas are finally divided into four levels based on the AHP decision analysis results and grouped as the final calculation result to assist in site selection decision.

2. The method for selecting airdrop areas in plateau and mountainous air transport as described in claim 1, characterized in that, The operation of normalizing and dimensionless processing of the topographic relief in the analysis data in step S3 is as follows: The topographic relief (Range) reflects the undulation of the earth's surface. It is characterized by the difference (relative elevation difference) between the elevation of a point and the average elevation of its surrounding area. It is expressed by equation (2): 　　　（2） In the formula: Range represents the topographic relief, and DEM represents the elevation of a point on the Earth's surface. The average elevation of a certain area surrounding that point; The topographic relief is normalized using the following formula (3) through a linear transformation method: 　　　（3） In the formula: F represents the slope index value in the comprehensive evaluation, which is dimensionless and has a value range of 0-100; R represents the topographic relief, in meters. This represents the maximum topographic relief during linear transformation and is dimensionless. This represents the minimum topographic relief during linear transformation and is dimensionless.

3. The method for selecting airdrop areas in plateau and mountainous air transport as described in claim 1, characterized in that, The normalization and dimensionless processing of snow cover in the analysis data in step S3 is as follows: The source data for the snow cover is the ERA5 reanalysis dataset, which is then normalized using the linear transformation method according to the following equation (4): 　　　（4） In the formula: F represents the comprehensive evaluation index value of annual snow cover, which is dimensionless and has a value range of 0-100; Indicates annual snow cover; This represents the maximum annual snow cover under a linear transformation and is dimensionless. This represents the minimum annual snow cover under a linear transformation and is dimensionless.

4. The method for selecting airdrop areas in plateau and mountainous air transport as described in claim 1, characterized in that, The operation of normalizing and dimensionlessly processing the water area data in step S3 is as follows: First, areal water features are extracted from the source data. Then, the water data is converted to the military standard exchange format. Finally, DataBUS 3.0 software is used to convert the military standard exchange format data to Shape format data, with the spatial reference being WGS84 coordinates. The attribute fields of water area data mainly include code, water area feature name, water area feature type, and river code. Based on the influence of water area on airdrop site selection, the water area part is assigned a value of 0 and the background part is assigned a value of 100 during data normalization.

5. The method for selecting airdrop areas in plateau mountainous air transport according to claim 4, characterized in that, Since water surfaces can be used for airdrops after freezing in winter, seasonal factors should be considered when using water area data in practice. Values ​​should be assigned to various water area elements according to the actual situation. The attributes of perennial rivers are name and river code, the attributes of seasonal rivers are name, season and river code, the attributes of perennial lakes are name and type, and the attributes of seasonal lakes are name, type and season.

6. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. Preface, characterized in that, When the processor executes the computer program, it implements a method for selecting airdrop areas for air transport in plateau and mountainous regions as described in any one of claims 15.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for selecting airdrop areas for air transport in high-altitude mountainous areas as described in any one of claims 15.

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