An airport site selection evaluation optimization method

Abstract

The application relates to an airport site selection evaluation optimization method, which comprises the following steps: inputting a candidate take-off and landing point set and a destination point set, and obtaining geographical space data, airspace restriction data, population distribution data and meteorological environment data related to the candidate take-off and landing point and a flight path, and performing standardization processing; for each candidate take-off and landing point, calculating a topography and geomorphology suitability score, a population density influence score, a flight efficiency score and a safety compliance score; assigning configurable weight coefficients to the four predetermined dimensions, and performing weighted summation to obtain a comprehensive score of each candidate take-off and landing point; and according to the comprehensive scores of all the candidate take-off and landing points, performing sorting and outputting a site selection recommendation ranking. The application can solve the technical defects of strong subjectivity, single evaluation dimension, difficulty in adapting to dynamic task requirements and neglecting real three-dimensional airspace environment constraints in the existing low-altitude aircraft take-off and landing site selection method, and can perform automatic, quantifiable and comprehensive site selection decision-making under multiple constraint conditions.

Description

An Airport Site Selection Evaluation and Optimization Method Technical Field

[0001] This application relates to the field of low-altitude airspace planning technology, specifically to an airport site selection evaluation and optimization method. Background Technology

[0002] Existing methods for selecting take-off and landing sites for low-altitude aircraft in cities mostly rely on single-dimensional indicators or simple geographical radius screening, lacking systematic and comprehensive decision-making capabilities under multiple constraints. Common practices involve coarse-grained screening based on manual on-site survey experience or by simply defining service radii in geographic information systems. While this method is easy to operate, it is difficult to meet the refined site selection needs in the complex urban airspace environment.

[0003] Specifically, existing technologies suffer from the following main shortcomings: First, they rely excessively on single indicators such as distance and airspace, neglecting the coupled influence of multi-dimensional factors such as terrain conditions, population distribution, and meteorological environment, leading to safety risks or efficiency bottlenecks in actual operation of site selection schemes. Second, they lack quantitative assessment of multi-destination collaborative service capabilities, making it difficult to optimize the networked layout of take-off and landing points from a global perspective. Third, the decision-making process has a low degree of automation and involves significant subjective intervention, resulting in low efficiency and difficulty in ensuring the consistency and repeatability of assessment results. These limitations severely restrict the efficient and safe deployment of low-altitude economic infrastructure. Summary of the Invention

[0004] The purpose of this invention is to provide an airport site selection evaluation and optimization method that can solve the technical defects of existing low-altitude aircraft take-off and landing site selection methods, such as strong subjectivity, single evaluation dimension, difficulty in adapting to dynamic mission requirements, and neglect of real three-dimensional airspace environment constraints, and to make automated, quantifiable, and multi-constraint comprehensive site selection decisions.

[0005] The technical solution adopted in this invention is: an airport site selection evaluation and optimization method, comprising the following steps:

[0006] S1: Data Input and Preprocessing: Input the candidate take-off and landing point set and the destination point set, and obtain geospatial data, airspace restriction data, population distribution data, and meteorological environmental data related to the candidate take-off and landing points and flight paths, and perform standardization processing. Standardization processing includes coordinate unification, format conversion, and missing value handling. Coordinate unification uses a seven-parameter coordinate transformation method to unify the data to the preset world geodetic coordinate system and convert it into a structured data model. For missing spatial data, spatial interpolation is performed using the inverse distance weighted (IDW) interpolation method, with the specific formula as follows: ,in, The elevation of the point to be interpolated. For the first The elevation of a known point For the first The distance from a known point to the point to be interpolated The parameter is an exponential function; for missing population data in the attribute data, the average or median of data from geographically adjacent areas within the same administrative level is used to fill the gaps.

[0007] S2: Multi-factor parallel quantitative evaluation: For each candidate take-off and landing point, the quantitative score of each candidate take-off and landing point in four predetermined dimensions is calculated in parallel, including: terrain suitability score based on digital elevation model (DEM) and meteorological data, population density impact score based on population heat map query, flight efficiency score based on the planned path length to each destination, and safety compliance score based on the detection of spatial conflicts between the path and no-fly zones and obstacles.

[0008] S3: Dynamic weighted comprehensive decision: Assign configurable weight coefficients to four predetermined dimensions. For each candidate take-off and landing point, the quantitative scores of each candidate take-off and landing point in the four predetermined dimensions are weighted and summed with the corresponding weight coefficients to obtain the comprehensive score of each candidate take-off and landing point.

[0009] S4: Result Generation and Output: Sort all candidate take-off and landing points according to their comprehensive scores and output the site selection recommendation ranking.

[0010] Furthermore, the geospatial data, airspace restriction data, population distribution data, and meteorological environment data in step S1 come from Geographic Information System (GIS), population database, and airspace management platform;

[0011] The standardization process includes coordinate unification, format conversion, and missing value handling. Coordinate unification involves using a seven-parameter coordinate transformation method to unify data from different sources and using different coordinate systems into a preset global geodetic coordinate system. Format conversion involves extracting, transforming, and loading various heterogeneous raw data, including vector, raster, and API-returned data, into a structured data model defined within the system, thus forming a standardized dataset with consistent spatial reference and a standardized data structure. Missing value handling involves filling or removing missing values ​​in the data using different strategies based on the data type.

[0012] For spatial data, the Inverse Distance Weighted (IDW) interpolation method is used to fill in the spatial gaps using the elevation values ​​of surrounding known points. The specific formula is as follows:

[0013] ;

[0014] in, The elevation of the point to be interpolated. For the first The elevation of a known point For the first The distance from a known point to the point to be interpolated For exponential parameters;

[0015] For missing population data in the attribute data, the average or median of data from geographically adjacent areas within the same administrative level is used to fill the gaps.

[0016] Data records that are severely missing or cannot be effectively filled are removed entirely and recorded in the log.

[0017] Furthermore, the specific calculation method for the terrain suitability score based on the digital elevation model (DEM) and meteorological data is as follows: the surface slope of the candidate take-off and landing points is calculated using the neighborhood analysis method based on the DEM, and the slope value is mapped to a standardized score according to a predefined piecewise linear function; at the same time, the density of surrounding obstacles and the stability of wind direction and speed are comprehensively evaluated to obtain obstacle scores and meteorological scores, and the standardized score, obstacle score, and meteorological score corresponding to the slope value are multiplied together to obtain the terrain suitability score;

[0018] The specific calculation method for the population density impact score based on the population heat map query is as follows: by spatial query, the density value corresponding to the candidate take-off and landing point coordinates on the population heat map is obtained, and the density value is mapped to a standardized score using a negative correlation function, and the standardized score corresponding to the density value is used as the population density impact score.

[0019] The specific calculation method for the flight efficiency score based on the planned path length to each destination is as follows: For each group of "candidate take-off and landing points - destination points", the A* algorithm is used to plan the optimal flight path and calculate the actual length corresponding to the optimal flight path; for each candidate take-off and landing point, based on the set of path lengths from the candidate point to all destinations, the average distance and distance variance are calculated, normalized and weighted and combined to obtain the flight efficiency score.

[0020] The specific calculation method for the safety compliance score based on the detection of spatial conflicts between the path and the no-fly zone and obstacles is as follows: detect spatial conflicts between all optimal flight paths and the no-fly zone polygon and the buffer zone of high-rise buildings, including counting the total number of times all optimal flight paths cross the no-fly zone and the total number of times all optimal flight paths approach high-risk obstacles, and calculate the safety compliance score based on the total number of times they cross the no-fly zone and the total number of times they approach high-risk obstacles.

[0021] Furthermore, the formula for calculating the standardized score by mapping the slope value to the piecewise linear function is as follows:

[0022] ;

[0023] in, Indicates the j-th candidate take-off and landing point The standardized score corresponding to the slope value Indicates the surface slope of the candidate take-off and landing points;

[0024] The obstacle score is calculated as follows: A safe radius of 50 meters is set around the candidate take-off and landing point, and all man-made or natural obstacles above the ground within this radius are extracted. Using a line-of-sight analysis algorithm, the maximum obstacle elevation angle is calculated at 1-degree intervals. The obstacle score is then calculated. The average of the maximum elevation angles at all azimuth angles The decision is made, and the specific calculation formula is as follows:

[0025] ;

[0026] in, Indicates the j-th candidate take-off and landing point The corresponding obstacle score, Indicates the j-th candidate take-off and landing point The average value of the maximum obstacle elevation angle in each azimuth direction. The preset maximum allowable average elevation angle threshold, average elevation angle When the angle is 0 degrees, the score is 1. Reaching or exceeding At that time, the score was 0;

[0027] The meteorological score is calculated by obtaining historical meteorological data for candidate take-off and landing points and extracting the annual average wind speed. Frequency of strong winds per year Two indicators, weather score The specific calculation formula is as follows:

[0028] ;

[0029] in, Indicates the j-th candidate take-off and landing point The corresponding meteorological score, Let represent the measured annual average wind speed at the j-th candidate point. This represents the annual frequency of strong winds at the j-th candidate point. The preset acceptable maximum annual average wind speed threshold. The preset threshold for the maximum acceptable daily frequency of strong winds. The weighting of the annual average wind speed, As the weight of the frequency of strong wind days, and + =1;

[0030] The formula for calculating the terrain suitability score is as follows:

[0031] ;

[0032] in, Indicates the j-th candidate take-off and landing point The terrain suitability score, Indicates the j-th candidate take-off and landing point The standardized score corresponding to the slope value. Indicates the j-th candidate take-off and landing point Obstacle score, Indicates the j-th candidate take-off and landing point The weather score.

[0033] Furthermore, the formula for calculating the population density impact score is as follows:

[0034] ;

[0035] in, Indicates the j-th candidate take-off and landing point The corresponding population density impact score, where k represents the adjustment coefficient. Indicates the j-th candidate take-off and landing point Population density of the location.

[0036] Furthermore, the formulas for calculating the average distance and the distance variance are as follows:

[0037] ;

[0038] ;

[0039] in, Indicates the j-th candidate take-off and landing point The corresponding average distance, Indicates the j-th candidate take-off and landing point To the i-th destination The actual length corresponding to the optimal flight path , Indicates the j-th candidate take-off and landing point The corresponding distance variance, Indicates the total number of destinations;

[0040] The normalized formulas for the mean distance and the variance of the distance are:

[0041] ;

[0042] ;

[0043] in, Indicates the j-th candidate take-off and landing point The corresponding normalized result of the average distance, The minimum average distance among all candidate takeoff and landing points. The maximum value of the average distance among all candidate takeoff and landing points. Indicates the j-th candidate take-off and landing point The corresponding normalized result of the distance variance, Indicates the j-th candidate take-off and landing point The corresponding distance variance, The minimum of the distance variances of all candidate takeoff and landing points. This represents the maximum value of the distance variance among all candidate takeoff and landing points;

[0044] The formula for calculating flight efficiency score is: ,in, + =1, Weighted by average distance. This represents the distance variance weight.

[0045] Furthermore, the formula for calculating the security compliance score is as follows:

[0046] ;

[0047] in, Indicates the j-th candidate take-off and landing point The corresponding security compliance score, Indicates the j-th candidate take-off and landing point The total number of times all optimal flight paths cross the no-fly zone. Indicates the j-th candidate take-off and landing point The total number of times all optimal flight paths approach high-risk obstacles. The acceptable threshold for the number of violations.

[0048] Furthermore, in step S3, configurable weight coefficients are assigned to four predetermined dimensions according to the task mode. The task modes include efficiency-first mode, safety-first mode, and comprehensive balance mode. In the efficiency-first mode, the weight coefficient of flight efficiency score is the largest. In the safety-first mode, the weight coefficient of safety compliance score is the largest. In the comprehensive balance mode, the weight coefficients of the four predetermined dimensions are the same.

[0049] The formula for calculating the overall score for each candidate takeoff and landing point is as follows:

[0050] ;

[0051] ;

[0052] in, Indicates the j-th candidate take-off and landing point The corresponding overall score, Indicates the j-th candidate take-off and landing point The corresponding terrain suitability score, The weighting coefficients represent the topographic suitability score. Indicates the j-th candidate take-off and landing point The corresponding population density impact score, The weighting coefficients representing the impact score of population density Indicates the j-th candidate take-off and landing point The corresponding flight efficiency score, The weighting coefficients representing the flight efficiency score. Indicates the j-th candidate take-off and landing point The corresponding security compliance score, The weighting coefficients represent the safety compliance score.

[0053] Further, in step S4, the candidate take-off and landing points are sorted in descending order based on their comprehensive scores to generate a final site selection recommendation ranking, and verification logic is set up: verification is performed sequentially according to the site selection recommendation ranking, and the comprehensive score of the candidate take-off and landing points is considered when the ranking is reached. Above the qualified threshold And the corresponding security compliance score Above the safety threshold If the candidate take-off and landing point is selected as the optimal address, output that the candidate take-off and landing point is the optimal address; otherwise, prompt that the set of candidate take-off and landing points or decision parameters need to be adjusted.

[0054] The beneficial technical effects of this invention are as follows:

[0055] (1) This invention introduces technologies such as digital elevation model (DEM) analysis, spatial overlay analysis, and path planning algorithms to transform abstract factors such as topography and safety compliance into calculable mathematical indicators, such as slope values ​​and the number of incursions into no-fly zones. This achieves a fundamental shift in the assessment process from "experience-based judgment" to "algorithm calculation," ensuring the objectivity, consistency, and reproducibility of the assessment results. It overcomes the shortcomings of traditional methods, such as the analytic hierarchy process (AHP), which heavily rely on expert experience for subjective weighting, and where different experts may arrive at significantly different conclusions.

[0056] (2) This invention breaks through the limitations of traditional location selection models that often pursue a single objective, such as the shortest distance or fixed weights, and introduces a configurable dynamic weight comprehensive decision-making mechanism. This mechanism allows users to flexibly adjust the weight ratio of each dimension according to the specific task type, so that the merits and demerits of a candidate point in different task scenarios can be dynamically reflected, realizing the leap from "static optimal" to "scenario-adaptive optimal", which greatly improves the practicality and flexibility of the algorithm in complex application scenarios. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 is a flowchart of a method according to an embodiment of the present invention;

[0059] Figure 2 is a schematic diagram of candidate point evaluation according to an embodiment of the present invention. Detailed Implementation

[0060] To better understand the above-described objects, features, and advantages of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Many specific details are set forth in the following description to provide a thorough understanding of the invention; however, the invention may be practiced in other ways different from those described herein, and therefore, the invention is not limited to the specific embodiments disclosed below.

[0061] As shown in Figure 1, an airport site selection evaluation and optimization method includes the following steps:

[0062] S1: Data Input and Preprocessing: Input the candidate take-off and landing point set and the destination point set, and obtain geospatial data, airspace restriction data, population distribution data, and meteorological environmental data related to the candidate take-off and landing points and flight paths, and perform standardization processing. Standardization processing includes coordinate unification, format conversion, and missing value handling. Coordinate unification uses a seven-parameter coordinate transformation method to unify the data to the preset world geodetic coordinate system and convert it into a structured data model. For missing spatial data, spatial interpolation is performed using the inverse distance weighted (IDW) interpolation method, with the specific formula as follows: ,in, The elevation of the point to be interpolated. For the first The elevation of a known point For the first The distance from a known point to the point to be interpolated The parameter is an exponential function; for missing population data in the attribute data, the average or median of data from geographically adjacent areas within the same administrative level is used to fill the gaps.

[0063] The standardization process includes coordinate unification, format conversion, and missing value handling. Coordinate unification involves using a seven-parameter coordinate transformation method to unify data from different sources and using different coordinate systems into a preset global geodetic coordinate system. Format conversion involves extracting, transforming, and loading various heterogeneous raw data, including vector, raster, and API-returned data, into a structured data model defined within the system, thus forming a standardized dataset with consistent spatial reference and a standardized data structure. Missing value handling involves filling or removing missing values ​​in the data using different strategies based on the data type.

[0064] For spatial data, the Inverse Distance Weighted (IDW) interpolation method is used to fill in the spatial gaps using the elevation values ​​of surrounding known points. The specific formula is as follows:

[0065] ;

[0066] in, The elevation of the point to be interpolated. For the first The elevation of a known point For the first The distance from a known point to the point to be interpolated For exponential parameters;

[0067] For missing population data in the attribute data, the average or median of data from geographically adjacent areas within the same administrative level is used to fill the gaps.

[0068] Data records that are severely missing or cannot be effectively filled are removed entirely and recorded in the log.

[0069] S2: Multi-factor parallel quantitative assessment: For each candidate take-off and landing point, the quantitative score of each candidate take-off and landing point in four predetermined dimensions is calculated in parallel, including: terrain suitability score based on digital elevation model (DEM) and meteorological data, population density impact score based on population heat map query, flight efficiency score based on the planned path length to each destination, and safety compliance score based on the detection of spatial conflicts between the path and no-fly zones and obstacles.

[0070] The specific calculation method for the terrain suitability score based on the digital elevation model (DEM) and meteorological data is as follows: the surface slope of the candidate take-off and landing points is calculated using the neighborhood analysis method based on the DEM, and the slope value is mapped to a standardized score according to a predefined piecewise linear function; at the same time, the density of surrounding obstacles and the stability of wind direction and speed are comprehensively evaluated to obtain obstacle scores and meteorological scores, and the standardized score, obstacle score and meteorological score corresponding to the slope value are multiplied together to obtain the terrain suitability score.

[0071] The design principle of the piecewise linear function is that the smaller the slope, the higher the score; if the slope exceeds a safety threshold, the score is zero. In this embodiment of the invention, the piecewise linear function maps the slope value to the standardized score using the following formula:

[0072] ;

[0073] in, Indicates the j-th candidate take-off and landing point The standardized score corresponding to the slope value Indicates the surface slope of the candidate take-off and landing points.

[0074] The obstacle score is calculated as follows: A safe radius of 50 meters is set around the candidate take-off and landing point, and all man-made or natural obstacles above the ground within this radius are extracted. Using a line-of-sight analysis algorithm, the maximum obstacle elevation angle is calculated at 1-degree intervals. The obstacle score is then calculated. The average of the maximum elevation angles at all azimuth angles The decision is made, and the specific calculation formula is as follows:

[0075] ;

[0076] in, Indicates the j-th candidate take-off and landing point The corresponding obstacle score, Indicates the j-th candidate take-off and landing point The average value of the maximum obstacle elevation angle in each azimuth direction. The preset maximum allowable average elevation angle threshold, average elevation angle When the angle is 0 degrees, the score is 1. Reaching or exceeding When the score is 0, the maximum allowable average elevation angle threshold is preset. It is 15°.

[0077] The meteorological score is calculated by obtaining historical wind speed data for candidate take-off and landing points and calculating the annual average wind speed. and frequency of strong wind days Two indicators, weather score The specific calculation formula is as follows:

[0078] ;

[0079] in, Indicates the j-th candidate take-off and landing point The corresponding meteorological score, Let represent the measured annual average wind speed at the j-th candidate point. This represents the annual frequency of strong winds at the j-th candidate point. The preset acceptable maximum annual average wind speed threshold. The preset threshold for the maximum acceptable daily frequency of strong winds. The weighting of annual average wind speed is used to adjust the relative importance of annual average wind speed in the score. This is the weighting of the frequency of strong wind days, used to adjust the frequency of strong wind days within the range of strong wind days, and + =1.

[0080] The formula for calculating the terrain suitability score is as follows:

[0081] ;

[0082] in, Indicates the j-th candidate take-off and landing point The terrain suitability score, Indicates the j-th candidate take-off and landing point The standardized score corresponding to the slope value. Indicates the j-th candidate take-off and landing point Obstacle score, Indicates the j-th candidate take-off and landing point The meteorological score. In the topographic suitability score, each sub-item score has a "one-vote veto" effect, that is, if any sub-item score is zero, the total score is zero.

[0083] The specific calculation method for the population density impact score based on the population heat map query is as follows: Through spatial query, the density values ​​corresponding to the candidate take-off and landing point coordinates on the population heat map are obtained. Then, a negative correlation function is used to map these density values ​​to standardized scores, and the standardized scores corresponding to the density values ​​are used as the population density impact score. The specific calculation formula is as follows:

[0084] ;

[0085] in, Indicates the j-th candidate take-off and landing point The corresponding population density impact score, where k represents the adjustment coefficient. Indicates the j-th candidate take-off and landing point Population density of the location.

[0086] The specific calculation method for the flight efficiency score based on the planned path length to each destination is as follows: For each group of "candidate take-off and landing points - destination points", the A* algorithm is used to plan the optimal flight path and calculate the actual length corresponding to the optimal flight path; for each candidate take-off and landing point, based on the set of path lengths from the candidate point to all destinations, the average distance and distance variance are calculated, and Min-Max normalization is performed respectively, mapped to the [0, 1] interval, and the cost-type indicators are converted into benefit-type indicators (the larger the value, the better), and then weighted and merged as the flight efficiency score.

[0087] The formulas for calculating the average distance and the variance of the distance are:

[0088] ;

[0089] ;

[0090] in, Indicates the j-th candidate take-off and landing point The corresponding average distance, Indicates the j-th candidate take-off and landing point To the i-th destination The actual length corresponding to the optimal flight path , Indicates the j-th candidate take-off and landing point The corresponding distance variance, This indicates the total number of destinations.

[0091] The normalized formulas for the mean distance and the variance of the distance are:

[0092] ;

[0093] ;

[0094] in, Indicates the j-th candidate take-off and landing point The corresponding normalized result of the average distance, The minimum average distance among all candidate takeoff and landing points. The maximum value of the average distance among all candidate takeoff and landing points. Indicates the j-th candidate take-off and landing point The corresponding normalized result of the distance variance, Indicates the j-th candidate take-off and landing point The corresponding distance variance, The minimum of the distance variances of all candidate takeoff and landing points. This represents the maximum variance of the distances to all candidate takeoff and landing points.

[0095] The formula for calculating flight efficiency score is: ,in, + =1, The average distance weight is used to adjust the importance of absolute path efficiency in the overall evaluation; the higher the weight, the more the algorithm tends to choose take-off and landing points with shorter overall average flight distances. The distance variance weight is used to adjust the importance of path service stability in the overall evaluation; the higher the weight, the more the algorithm tends to select take-off and landing points with smaller distance differences to each destination and more stable service performance.

[0096] The specific calculation method for the safety compliance score based on path and no-fly zone and obstacle spatial conflict detection is as follows: Detect spatial conflicts between all optimal flight paths and no-fly zone polygons and high-rise building buffer zones, including counting the total number of times all optimal flight paths cross no-fly zones and the total number of times all optimal flight paths approach high-risk obstacles. Calculate the safety compliance score based on the total number of times they cross no-fly zones and the total number of times they approach high-risk obstacles. The specific calculation formula is as follows:

[0097] ;

[0098] in, Indicates the j-th candidate take-off and landing point The corresponding security compliance score, Indicates the j-th candidate take-off and landing point The total number of times all optimal flight paths cross the no-fly zone. Indicates the j-th candidate take-off and landing point The total number of times all optimal flight paths approach high-risk obstacles. The acceptable threshold for the number of violations.

[0099] S3: Dynamic Weighted Comprehensive Decision: Assign configurable weight coefficients to four predetermined dimensions. For each candidate take-off and landing point, sum the quantitative scores of each candidate take-off and landing point in the four predetermined dimensions with the corresponding weight coefficients to obtain the comprehensive score of each candidate take-off and landing point.

[0100] In this embodiment of the invention, configurable weight coefficients are assigned to four predetermined dimensions according to the task mode. The task modes include efficiency-first mode, safety-first mode, and comprehensive balance mode. In the efficiency-first mode, the weight coefficient of flight efficiency score is the largest. In the safety-first mode, the weight coefficient of safety compliance score is the largest. In the comprehensive balance mode, the weight coefficients of the four predetermined dimensions are the same.

[0101] The formula for calculating the overall score for each candidate takeoff and landing point is as follows:

[0102] ;

[0103] ;

[0104] in, Indicates the j-th candidate take-off and landing point The corresponding overall score, Indicates the j-th candidate take-off and landing point The corresponding terrain suitability score, The weighting coefficients represent the topographic suitability score. Indicates the j-th candidate take-off and landing point The corresponding population density impact score, The weighting coefficients representing the impact score of population density Indicates the j-th candidate take-off and landing point The corresponding flight efficiency score, The weighting coefficients representing the flight efficiency score. Indicates the j-th candidate take-off and landing point The corresponding security compliance score, The weighting coefficients represent the safety compliance score.

[0105] S4: Result Generation and Output: Sort all candidate take-off and landing points according to their comprehensive scores and output the site selection recommendation ranking. In this embodiment of the invention, the comprehensive scores are sorted in descending order to generate the final site selection recommendation ranking, and verification logic is set: verification is performed sequentially according to the site selection recommendation ranking, and the comprehensive scores of the candidate take-off and landing points are considered when the ranking is reached. Above the qualified threshold And the corresponding security compliance score Above the safety threshold If the candidate take-off and landing point is selected as the optimal address, output that the candidate take-off and landing point is the optimal address; otherwise, prompt that the set of candidate take-off and landing points or decision parameters need to be adjusted.

[0106] In this embodiment of the invention, five destinations D1 to D5 are set in the simulation test scenario, and three candidate take-off and landing points S1 to S3 with very different characteristics are selected. Among them, candidate take-off and landing point S1 is characterized as safe and efficient, located in an open area on the outskirts of the city with good airspace conditions. Candidate take-off and landing point S2 is characterized as economic risk, located on the edge of the city, close to some no-fly zones, but with the shortest average distance to the destination. Candidate take-off and landing point S3 is characterized as balanced, with no outstanding advantages in any indicator, but no obvious disadvantages either.

[0107] To demonstrate that this invention can automatically recommend the most suitable take-off and landing points based on different mission objectives by adjusting weight parameters, the comprehensive scores of candidate take-off and landing points S1~S3 are calculated in efficiency-first mode, safety-first mode, and comprehensive balance mode, resulting in the candidate point evaluation diagram shown in Figure 2. Specifically, in efficiency-first mode, the weighting coefficients are set as follows: terrain suitability score: 0.1; population density impact score: 0.1; flight efficiency score: 0.5; safety compliance score: 0.3. In safety-first mode, the weighting coefficients are set as follows: terrain suitability score: 0.2; population density impact score: 0.1; flight efficiency score: 0.1; safety compliance score: 0.6. In comprehensive balance mode, the weighting coefficients are set as follows: terrain suitability score: 0.25; population density impact score: 0.25; flight efficiency score: 0.25; safety compliance score: 0.25. In the safety-first mode, the output site selection recommendation ranking is shown in Table 1.

[0108] Table 1. Ranking of Output Site Selection Recommendations

[0109]

[0110] The following experiments illustrate the differences between the embodiments of the present invention and traditional methods, namely the Analytic Hierarchy Process (AHP), to demonstrate the convenience of the embodiments of the present invention. The Analytic Hierarchy Process (AHP) is a multi-criteria decision analysis method that combines qualitative and quantitative approaches. This method quantifies the decision-maker's subjective judgment through steps such as establishing a hierarchical structure, constructing a judgment matrix, and performing consistency checks, thereby providing a basis for multi-objective decision-making.

[0111] For candidate take-off and landing points S1~S 3， There are two task scenarios, namely Scenario A and Scenario B. Scenario A is an emergency logistics task with efficiency as the core requirement, while Scenario B is a passenger transportation task with safety as the core requirement.

[0112] The weight settings of the embodiments of the present invention and the Analytic Hierarchy Process (AHP) are shown in Table 2. The embodiments of the present invention set the weights directly and transparently according to the task objectives; the AHP, on the other hand, simulates inviting an expert group to obtain a fixed set of "average weights" through scoring and calculation.

[0113] Table 2. Weight settings for embodiments of the present invention and the Analytic Hierarchy Process (AHP).

[0114]

[0115] Substitute the scores of each candidate point into the weighting system of the two methods, calculate the comprehensive score and rank them, and obtain the final ranking results as shown in Table 3.

[0116] Table 3. Final ranking results of the embodiments of the present invention and the Analytic Hierarchy Process (AHP).

[0117]

[0118] As shown in Table 3, in scenario A, this embodiment of the invention correctly recommended the most efficient candidate take-off and landing point S2. The Analytic Hierarchy Process (AHP), due to its fixed and dispersed weights, cannot maximize the "efficiency" factor, resulting in the recommendation of a more balanced but not optimally efficient candidate take-off and landing point S1, leading to inaccurate decision-making. In scenario B, this embodiment of the invention, due to its extremely high safety weight, ranked the high-risk candidate take-off and landing point S2 last. The AHP did not adequately penalize the safety risk of candidate take-off and landing point S2, and its ranking remained higher than candidate take-off and landing point S3, failing to effectively avoid risk.

[0119] By comparison, it can be seen that the embodiments of the present invention adopt dynamic weights, which can instantly switch between modes such as "efficiency" and "safety" according to the task mode, always giving the decision that best matches the current highest priority. It has the advantages of accuracy, flexibility, and high risk sensitivity. The Analytic Hierarchy Process (AHP) adopts fixed weights, and the weights are set based on a compromise of the opinions of various experts. This makes the decision-making process heavily dependent on the knowledge and preferences of the expert group. Different expert groups may reach significantly different conclusions, making it difficult to guarantee objectivity. At the same time, once the weights are determined, their adjustment and updating require reorganizing the entire expert evaluation process, which is time-consuming and laborious, resulting in a slow response of the decision-making system, rigid strategies, and difficulty in quickly adapting to dynamic and changing task requirements.

[0120] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. 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. An airport site selection evaluation and optimization method, characterized in that, The process includes the following steps: S1: Data Input and Preprocessing: Input the candidate take-off and landing point set and the destination point set, and obtain geospatial data, airspace restriction data, population distribution data, and meteorological environment data related to the candidate take-off and landing points and flight paths. Perform standardization processing, which includes coordinate unification, format conversion, and missing value handling. Coordinate unification uses a seven-parameter coordinate transformation method to unify the data to the preset world geodetic coordinate system and convert it into a structured data model. For missing spatial data, the inverse distance weighted interpolation method (IDW) is used for spatial interpolation imputation. The specific formula is as follows: ,in, The elevation of the point to be interpolated. For the first The elevation of a known point For the first The distance from a known point to the point to be interpolated S1: Power parameter; For missing population data in the attribute data, the average or median of data from adjacent geographical areas within the same administrative level is used to fill in the gaps; S2: Multi-factor parallel quantitative evaluation: For each candidate take-off and landing point, the quantitative score of each candidate take-off and landing point in four predetermined dimensions is calculated in parallel, including: terrain suitability score based on digital elevation model (DEM) and meteorological data, population density impact score based on population heat map query, flight efficiency score based on the planned path length to each destination, and safety compliance score based on spatial conflict detection between the path and no-fly zones and obstacles; S3: Dynamic weighted comprehensive decision: Configurable weight coefficients are assigned to the four predetermined dimensions. For each candidate take-off and landing point, the quantitative score of each candidate take-off and landing point in the four predetermined dimensions is weighted and summed with the corresponding weight coefficients to obtain the comprehensive score of each candidate take-off and landing point; S4: Result generation and output: The comprehensive scores of all candidate take-off and landing points are sorted, and the site selection recommendation ranking is output.

2. The airport site selection evaluation and optimization method according to claim 1, characterized in that, The specific calculation method for the terrain suitability score based on the Digital Elevation Model (DEM) and meteorological data in step S2 is as follows: The surface slope of the candidate take-off and landing points is calculated using neighborhood analysis based on the DEM, and the slope value is mapped to a standardized score according to a predefined piecewise linear function. Simultaneously, the density of surrounding obstacles and the stability of wind direction and speed are comprehensively evaluated to obtain obstacle scores and meteorological scores. The standardized score corresponding to the slope value, the obstacle score, and the meteorological score are multiplied together to obtain the terrain suitability score. The specific calculation method for the population density impact score based on the population heat map query is as follows: Through spatial query, the density value corresponding to the coordinates of the candidate take-off and landing points on the population heat map is obtained, and the density value is mapped to a standardized score using a negative correlation function. The standardized score corresponding to the density value is then used as the population density impact score. The specific calculation method for the flight efficiency score based on the planned path length to each destination is as follows: For each group of "candidate take-off and landing points - destination points", the A* algorithm is used to plan the optimal flight path, and the actual length corresponding to the optimal flight path is calculated; for each candidate take-off and landing point, based on the set of path lengths from the candidate point to all destinations, the average distance and distance variance are calculated, normalized, and then weighted and combined as the flight efficiency score; The specific calculation method for the safety compliance score based on the detection of spatial conflicts between the path and no-fly zones and obstacles is as follows: detect the spatial conflicts between all optimal flight paths and no-fly zone polygons and high-rise building buffer zones, including counting the total number of times all optimal flight paths cross no-fly zones and the total number of times all optimal flight paths approach high-risk obstacles, and calculate the safety compliance score based on the total number of times they cross no-fly zones and the total number of times they approach high-risk obstacles.

3. The airport site selection evaluation and optimization method according to claim 2, characterized in that, The formula for calculating the standardized score by mapping the slope value to the piecewise linear function is as follows: ;in, Indicates the j-th candidate take-off and landing point The standardized score corresponding to the slope value The surface slope of the candidate take-off and landing point is represented. The obstacle score is calculated as follows: A safety radius of 50 meters is set around the candidate take-off and landing point, and all man-made or natural obstacles higher than the ground surface within this radius are extracted. Using a line-of-sight analysis algorithm, the maximum obstacle elevation angle is calculated at 1-degree intervals. The obstacle score is then calculated. The average of the maximum elevation angles at all azimuth angles The decision is made, and the specific calculation formula is as follows: ;in, Indicates the j-th candidate take-off and landing point The corresponding obstacle score, Indicates the j-th candidate take-off and landing point The average value of the maximum obstacle elevation angle in each azimuth direction. The preset maximum allowable average elevation angle threshold, average elevation angle When the angle is 0 degrees, the score is 1. Reaching or exceeding When the time is right, the score is 0; the meteorological score is calculated by obtaining historical meteorological data of the candidate take-off and landing points and extracting the annual average wind speed. Frequency of strong winds per year Two indicators, weather score The specific calculation formula is as follows: ;in, Indicates the j-th candidate take-off and landing point The corresponding meteorological score, Let represent the measured annual average wind speed at the j-th candidate point. This represents the annual frequency of strong winds at the j-th candidate point. The preset acceptable maximum annual average wind speed threshold. The preset threshold for the maximum acceptable daily frequency of strong winds. The weighting of the annual average wind speed, As the weight of the frequency of strong wind days, and + =1; The formula for calculating the terrain suitability score is: ;in, Indicates the j-th candidate take-off and landing point The terrain suitability score, Indicates the j-th candidate take-off and landing point The standardized score corresponding to the slope value. Indicates the j-th candidate take-off and landing point Obstacle score, Indicates the j-th candidate take-off and landing point The weather score.

4. The airport site selection evaluation and optimization method according to claim 1, characterized in that, The formula for calculating the population density impact score in step S2 is as follows: ;in, Indicates the j-th candidate take-off and landing point The corresponding population density impact score, where k represents the adjustment coefficient. Indicates the j-th candidate take-off and landing point Population density of the location.

5. The airport site selection evaluation and optimization method according to claim 3, characterized in that, The formulas for calculating the average distance and the variance of the distance are: ； ;in, Indicates the j-th candidate take-off and landing point The corresponding average distance, Indicates the j-th candidate take-off and landing point To the i-th destination The actual length corresponding to the optimal flight path ， Indicates the j-th candidate take-off and landing point The corresponding distance variance, This represents the total number of destination points; the normalized formulas for the average distance and the variance of the distance are: ； ;in, Indicates the j-th candidate take-off and landing point The corresponding normalized result of the average distance, The minimum average distance among all candidate takeoff and landing points. The maximum value of the average distance among all candidate takeoff and landing points. Indicates the j-th candidate take-off and landing point The corresponding normalized result of the distance variance, Indicates the j-th candidate take-off and landing point The corresponding distance variance, The minimum of the distance variances of all candidate takeoff and landing points. The maximum variance of distances to all candidate takeoff and landing points; the formula for calculating the flight efficiency score is: ,in, + =1， Weighted by average distance. This represents the distance variance weight.

6. The airport site selection evaluation and optimization method according to claim 1, characterized in that, The formula for calculating the security compliance score in step S2 is as follows: ;in, Indicates the j-th candidate take-off and landing point The corresponding security compliance score, Indicates the j-th candidate take-off and landing point The total number of times all optimal flight paths cross the no-fly zone. Indicates the j-th candidate take-off and landing point The total number of times all optimal flight paths approach high-risk obstacles. The acceptable threshold for the number of violations.

7. The airport site selection evaluation and optimization method according to claim 1, characterized in that, In step S3, configurable weight coefficients are assigned to four predetermined dimensions according to the mission mode. The mission modes include efficiency-first mode, safety-first mode, and comprehensive balance mode. In efficiency-first mode, the flight efficiency score has the highest weight coefficient; in safety-first mode, the safety compliance score has the highest weight coefficient; and in comprehensive balance mode, the weight coefficients for all four predetermined dimensions are equal. The formula for calculating the comprehensive score for each candidate takeoff and landing point is: ； ;in, Indicates the j-th candidate take-off and landing point The corresponding overall score, Indicates the j-th candidate take-off and landing point The corresponding terrain suitability score, The weighting coefficients represent the topographic suitability score. Indicates the j-th candidate take-off and landing point The corresponding population density impact score, The weighting coefficients representing the impact score of population density Indicates the j-th candidate take-off and landing point The corresponding flight efficiency score, The weighting coefficients representing the flight efficiency score. Indicates the j-th candidate take-off and landing point The corresponding security compliance score, The weighting coefficients represent the safety compliance score.

8. The airport site selection evaluation and optimization method according to claim 1, characterized in that, In step S4, the candidate take-off and landing points are sorted in descending order based on their comprehensive scores to generate a final site selection recommendation ranking. Verification logic is then set up: verification is performed sequentially based on the site selection recommendation ranking, with the comprehensive score of each candidate take-off and landing point determining the ranking. Above the qualified threshold And the corresponding security compliance score Above the safety threshold If the candidate take-off and landing point is selected as the optimal address, output that the candidate take-off and landing point is the optimal address; otherwise, prompt that the set of candidate take-off and landing points or decision parameters need to be adjusted.

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