A comprehensive layout method for airport atmospheric monitoring stations

Abstract

The present invention provides a comprehensive layout method for airport atmospheric monitoring sites, which relates to the technical fields of airport construction and atmospheric environment monitoring. The method solves the limitations of current site selection methods, such as being too subjective and lacking relevant algorithms or theoretical support. The method comprises: combining the geographic coordinates of multiple key points of an airport with the Gauss-Krüger projection theory to construct a site selection coordinate system for the airport atmospheric monitoring sites, and converting the geographic coordinates of the key points into coordinates of the site selection coordinate system; dividing the area where the airport is located into gridded areas, and determining the optimization target of the comprehensive layout of the airport atmospheric monitoring sites based on the site selection coordinate system and the gridded areas, thereby constructing a site selection model; and using an elite genetic algorithm to solve the optimization problem of the site selection model for the airport atmospheric monitoring sites, thereby obtaining a comprehensive layout plan for the airport atmospheric monitoring sites. The present invention overcomes the "island" effect, such as the limited coverage of a single monitoring point, thereby making the layout of the monitoring sites more scientific and effective, and the monitoring results more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of airport construction and atmospheric environment monitoring, and in particular to a comprehensive layout method for airport atmospheric monitoring sites. Background Art

[0002] With the rapid development of my country's civil aviation industry, demand for air transport is showing a significant growth trend. Faced with this challenge, airports, as key infrastructure in the air transport system, must not only meet the growing demand for aircraft operations but also actively address the resulting environmental pressures and promote the construction of green airports. Against this backdrop, effective monitoring of the airport atmosphere has become a crucial means of ensuring flight safety, improving air quality, and promoting sustainable development.

[0003] The proper siting of airport atmospheric monitoring stations is directly related to the effectiveness and cost-effectiveness of monitoring results. Traditional manual experience-based methods rely on the subjective judgment of airport operators, taking into account factors such as topography and meteorological conditions, to select monitoring station locations. While this method can meet basic needs to a certain extent, its high degree of subjectivity and low efficiency make it difficult to adapt to the precise and intelligent management requirements of modern airports. Furthermore, due to the lack of a systematic scientific basis, manual experience-based methods are prone to irrational site selection when faced with complex and changing environmental conditions, affecting the accuracy and reliability of monitoring results.

[0004] To address these issues, researchers have proposed a knowledge-driven approach. This approach constructs a physical model to simulate atmospheric flow around airports, thereby determining the most suitable locations for monitoring stations. This approach not only considers the influence of natural factors such as topography and weather, but also incorporates aerodynamic principles, improving the scientific and rational nature of site selection. However, knowledge-driven approaches also face limitations, such as high model construction complexity and high computational resource consumption. In particular, in the pursuit of model accuracy, simplifying assumptions are often required, which can lead to deviations between the final site selection and actual conditions.

[0005] In recent years, with the development of big data technology and artificial intelligence, data-driven methods have gradually been applied to the site selection of airport air monitoring stations. Through deep learning and analysis of historical environmental data, this method can automatically identify the optimal monitoring point locations, achieving the goal of extracting valuable information from the data. Compared with the previous two methods, the data-driven method has greater flexibility and adaptability, can quickly respond to environmental changes, and provide more accurate decision support. However, existing data-driven methods are mainly targeted at the site selection of urban air monitoring stations. For application in specific environments such as airports, further research and optimization are still needed to ensure that the site selection plan is more in line with actual needs.

[0006] This suggests that while a variety of methods exist for siting airport air monitoring sites, each has its own scope of applicability and limitations. Future research should develop a comprehensive siting strategy tailored to airport environmental characteristics to improve the efficiency and quality of air monitoring and contribute to the green development of the civil aviation industry. Summary of the Invention

[0007] Based on the current status of the background technology, the purpose of the present invention is to solve the limitations of the current airport atmospheric monitoring site selection method, such as being too subjective and lacking relevant algorithms or theoretical support. Therefore, a comprehensive layout method for airport atmospheric monitoring sites is proposed. The present invention determines the optimal site location of the atmospheric monitoring site through theoretical calculation and grid coverage. In contrast to the traditional subjective site selection method, the present invention will break the "island" effect such as the limited coverage of a single monitoring point, significantly enhance the overall efficiency of the monitoring network, achieve blind spot filling for multiple monitoring points, strengthen the linkage between monitoring points, make the monitoring site layout more scientific and effective, and the monitoring results more accurate, thus having good application prospects.

[0008] The present invention adopts the following technical solutions to achieve the purpose:

[0009] A comprehensive layout method for airport atmospheric monitoring stations comprises: combining the geographical coordinates of multiple key points of an airport with Gauss-Krüger projection theory to construct a site selection coordinate system for the airport atmospheric monitoring stations, and converting the geographical coordinates of the key points into coordinates of the atmospheric monitoring station site selection coordinate system; dividing the area where the airport is located into gridded areas containing multiple grids, determining an optimization target for the comprehensive layout of the airport atmospheric monitoring stations based on the site selection coordinate system and the gridded areas, and constructing a site selection model for the airport atmospheric monitoring stations; and using an elite genetic algorithm to solve the optimization problem corresponding to the site selection model for the airport atmospheric monitoring stations, and obtaining a comprehensive layout plan for the airport atmospheric monitoring stations.

[0010] Furthermore, the method comprises the following steps:

[0011] S1. Based on the airport surface structure, airport oil depot area, and the distribution of typical air pollution sources, identify multiple key points and their geographic coordinates at the airport. Combined with the Gauss-Krüger projection theory, construct a coordinate system for the site selection of airport air monitoring stations. Convert the geographic coordinates of the key points into coordinates for the site selection of air monitoring stations.

[0012] S2. Construct a site selection model for airport atmospheric monitoring stations: Divide the airport area into a gridded area containing multiple non-overlapping grids. Grids whose center points are located within and on the edges of a graph formed by multiple key points, as well as grids where key points are located, are considered to be grids for station deployment. The optimization goal is to minimize the number of grids for station deployment and the distance between key points and the grid centers.

[0013] S3. Formulate coding rules for the site selection plan, and initialize different coding solutions for the optimization problem corresponding to the site selection plan in the elite genetic algorithm;

[0014] S4. Decode the generated different coding solutions, calculate the objective function value of each coding solution and use it as fitness;

[0015] S5. Select individuals with different coding solutions in the population to form the parent population;

[0016] S6. Perform crossover and mutation operations on the coding solution chromosomes between the parent populations to obtain the offspring population, and similarly calculate the fitness of each coding solution individual in the offspring population;

[0017] S7. Merge the offspring population with the parent population to form a new population; retain the elite population in the new population based on the fitness of each coding solution individual in the new population;

[0018] S8, return to step S5, reselect and form the parent population, repeat the crossover inheritance and mutation operations until the maximum number of iterations is reached;

[0019] S9, output the individual with the best fitness in the current population;

[0020] S10. After decoding the best individual, the site selection location of the airport atmospheric monitoring station is obtained and the corresponding comprehensive layout plan is output.

[0021] In summary, due to the adoption of this technical solution, the beneficial effects of the present invention are as follows:

[0022] The proposed method aims to overcome the limitations of existing site selection methods, such as their subjectivity and lack of scientific algorithms or theoretical support. By incorporating theoretical calculations and grid-based overlay technology, the proposed method can more accurately and intuitively determine the optimal locations for atmospheric monitoring stations, demonstrating greater scientificity and effectiveness compared to traditional subjective site selection methods.

[0023] Specifically, the present invention first collects detailed data on the airport surface structure parameters, the distribution of the oil depot area, and typical air pollution areas, including but not limited to the runway length, width, direction, airport reference point, and the specific location of the oil depot area, and clarifies the key points and their geographical coordinates. On this basis, the airport reference point is used as the origin O, and the Gauss-Krüger projection theory is combined to construct a coordinate system for the site selection of the airport atmospheric monitoring station. Through this coordinate system, key monitoring points such as the airport runway head end point and the center point of the oil depot area can be accurately located and projected onto plane coordinates. Next, the present invention uses a gridding method to comprehensively cover the entire airport area. By comprehensively evaluating the environmental factors in different grid cells, the suitability of each grid cell as a monitoring site is calculated, and finally a layout plan that can achieve optimal coverage of all key points is determined.

[0024] The comprehensive layout method proposed in the present invention not only breaks the "island" effect such as the limited coverage of a single monitoring point, but also significantly enhances the overall efficiency of the monitoring network, realizes the blind spot filling of multiple monitoring points, and strengthens the linkage between monitoring points. Through scientific and reasonable site layout, the perception ability and response speed of changes in the airport's atmospheric environment can be effectively improved, providing strong data support for the airport's environmental protection management and flight safety. In addition, the application of the present invention is not limited to the planning stage of new facilities, but is also suitable for upgrading and renovating the atmospheric monitoring system of existing airports, showing broad application prospects and high practical value. Therefore, the method of the present invention is of great significance for promoting the green development of airports and protecting public health. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Schematic diagram of the overall process of the method of the present invention;

[0026] Figure 2 This is a schematic diagram of the geographical coordinates of key points of an airport according to an example of the present invention;

[0027] Figure 3 This is a schematic diagram of the coordinate system for selecting an airport atmospheric monitoring station according to an example of the present invention;

[0028] Figure 4 It is a schematic diagram of the iterative solution of the site selection optimization problem of the present invention;

[0029] Figure 5 This is a schematic diagram of the comprehensive layout of airport atmospheric monitoring stations according to the present invention. DETAILED DESCRIPTION

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Generally, the parts of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0031] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0032] Example 1

[0033] A comprehensive layout method for airport air monitoring stations is disclosed. The core content of the method is as follows: based on the airport surface structure, the airport oil depot area and the distribution of typical air pollution sources, multiple key points of the airport and their geographical coordinates are identified, and in combination with the Gauss-Krüger projection theory, a coordinate system for the site selection of the airport air monitoring stations is constructed, and the geographical coordinates of the key points are converted into coordinates of the site selection coordinate system for the air monitoring stations; the area where the airport is located is divided into gridded areas containing multiple grids, and based on the site selection coordinate system and the gridded areas, the optimization target of the comprehensive layout of the airport air monitoring stations is determined, and an airport air monitoring station site selection model is constructed; and an elite genetic algorithm is used to solve the optimization problem corresponding to the airport air monitoring station site selection model, and a comprehensive layout plan for the airport air monitoring stations is obtained.

[0034] In this embodiment, you can refer to Figure 1 The process diagram given is as follows:

[0035] S1. Identify key points and construct a coordinate system for the site selection of airport atmospheric monitoring stations: Based on the airport surface structure, the airport oil depot area, and the distribution of typical atmospheric pollution sources, the two ends of the airport runway head and the center point of the oil depot area are used as key points; the airport reference point is used as the origin O, and based on the Gauss-Krüger projection theory, the corresponding X-axis is established in the direction parallel to the y-axis in the Gauss-Krüger projection coordinate system, and the corresponding Y-axis is established in the direction parallel to the x-axis in the Gauss-Krüger projection coordinate system, thereby obtaining the site selection coordinate system of the airport atmospheric monitoring station, and the coordinates of the key points are converted into the corresponding coordinates in the site selection coordinate system;

[0036] S2. Construct a site selection model for airport atmospheric monitoring stations: Divide the airport area into a gridded area containing multiple non-overlapping grids. Grids whose center points are located within and on the edges of a graph formed by multiple key points, as well as grids where key points are located, are considered to be grids for station deployment. The optimization goal is to minimize the number of grids for station deployment and the distance between key points and the grid centers.

[0037] S3. Formulate coding rules for the site selection plan, and initialize different coding solutions for the optimization problem corresponding to the site selection plan in the elite genetic algorithm;

[0038] S4. Decode the generated different coding solutions, calculate the objective function value of each coding solution and use it as fitness;

[0039] S5. Select individuals with different coding solutions in the population to form the parent population;

[0040] S6. Perform crossover and mutation operations on the coding solution chromosomes between the parent populations to obtain the offspring population, and similarly calculate the fitness of each coding solution individual in the offspring population;

[0041] S7. Merge the offspring population with the parent population to form a new population; retain the elite population in the new population based on the fitness of each coding solution individual in the new population;

[0042] S8, return to step S5, reselect and form the parent population, repeat the crossover inheritance and mutation operations until the maximum number of iterations is reached;

[0043] S9, output the individual with the best fitness in the current population;

[0044] S10. After decoding the best individual, the site selection location of the airport atmospheric monitoring station is obtained and the corresponding comprehensive layout plan is output.

[0045] Example 2

[0046] Based on Example 1, this example introduces in detail the details contained in each specific step of the method and demonstrates the method process with specific data examples.

[0047] This embodiment uses theoretical calculations and analysis to scientifically determine the location of airport atmospheric monitoring stations, providing a decision-making basis for the comprehensive collection of airport atmospheric environmental data. First, during the data collection and processing process, relevant parameters such as the airport surface structure, the airport fuel depot area, and the distribution of typical atmospheric pollution sources are collected. The geographic coordinates of key points such as the airport reference point and the runway endpoint and fuel depot center are derived.

[0048] Sample data and airport scene are as follows Figure 2As shown, point 0 is the airport reference point, points 1 and 2 are at the ends of the existing runway, points 5 and 6 are at the ends of the new runway, and points 3 and 4 are the center points of the fuel depot area. The coordinates of each point are: point 0 (0, 0), point 1 (-848.2, -1067.7), point 2 (1459.2, 1107.3), point 3 (-718.6, 1284.4), point 4 (152.9, -3054.4), point 5 (2943, -523.9), and point 6 (2753.3, -1777).

[0049] First, in step S1, based on the Gauss-Krüger projection coordinate system, the airport reference point A is taken as the origin O, the corresponding X-axis is established in the direction parallel to the y-axis in the Gauss-Krüger projection coordinate system, and the corresponding Y-axis is established in the direction parallel to the x-axis in the Gauss-Krüger projection coordinate system, thereby obtaining the site selection coordinate system of the airport atmospheric monitoring station, as shown in FIG. Figure 3 As shown in the figure, multiple key points of the airport include the end points of the airport runway (points 1, 2, 5, and 6) and the center points of the oil depot area (points 3 and 4). The coordinates of the key points are converted into corresponding coordinates in the site selection coordinate system.

[0050] In step S2, the grid of this embodiment is a square grid; a point is randomly selected within a circle with the airport reference point A as the center and a radius of r1. in:

[0051]

[0052] θ1 is the polar angle of point M1; generate square C1 as follows: take point M1 as the diagonal center of square C1, the side length of square C1 is r2, select any vertex of the square, mark it as Based on this vertex, mark the other vertices in a clockwise or counterclockwise direction as Then the diagonal points are connected The angle between the positive X-axis and the atmospheric monitoring station's coordinate system is θ2;

[0053] Move point M1 along the edge In a certain direction parallel to the square, translate in sequence according to the spacing of r2, and after a total of n translations, obtain n new square diagonal centers, and generate squares C in sequence according to the method of generating square C1. k (k=2,3,...,n+1); then move point M1 along the edge Repeat the above operation on the other side of the square to obtain n new diagonal centers of the square, generating square C. k (k=n+2,n+3,...,2n+1).

[0054] Next, for the existing squares C1 to C2n+1 , first move the diagonal center point along the edge A certain direction parallel to the original direction is translated in sequence according to the spacing of r2. After a total of n translations, 2n is obtained. 2 +n new square diagonal centers, and generate squares C in the same way as square C1 k (k=2n+2,...,2n 2 +3n+1); the same applies to the squares C1 to C 2n+1 , and then align the diagonal center point along the edge Repeat the above operation on the other side of the parallel direction, and you will get 2n 2 +n new square diagonal centers, generating square C k (k=2n 2 +3n+2,...,4n 2 +4n+1).

[0055] Each square is considered as a grid, and the grids are numbered in sequence, denoted as W j (j=1,2,...,4n 2 +4n+1), the center point of the grid, that is, the diagonal center point of the corresponding square, is used as the monitoring station deployment point M j ; Multiple key points of the airport are recorded as K i Then, construct a set V whose elements are key points K i The number of the grid where the site is located is j; and the total number of grids to be deployed is recorded as N2.

[0056] The above content is the data collection and processing process performed in this embodiment, and the location of the atmospheric monitoring station will be determined based on it.

[0057] Similarly, in step S2, the key coordinates of the airport runway end point, the center point of the oil depot area, and the grid parameters of the airport and its surrounding areas in the processed Gauss-Krüger projection coordinate system are input into the constructed airport atmospheric monitoring station site selection model. The optimization problem is as follows:

[0058]

[0059] st:

[0060]

[0061] N2=Q+k

[0062]

[0063] In the optimization problem, Q is the number of grids whose center points are located inside and on the edge of the graph surrounded by multiple key points, and k is the number of grids where the key points are located; Dij is the key point K i The distance from the center of the grid, that is, the monitoring station deployment point M j The distance between i is the artificial coefficient.

[0064] In step S3, the coding information in the coding rule includes the angle and distance of the monitoring site; the angle and distance parameters are encoded in the form of direct coding as follows:

[0065] Chromosome p i (i=1,2,...,N P ) consists of gene fragments [x], [f], N P is the total number of initial chromosomes; the gene segment [x] includes parameters r1, r2, θ1, θ2, where θ1 = 2π*randi[0,1], θ2 = 2π*randi[0,1], r1 = randi(0,w1], r2 = randi(0,w2], randi represents a random number in a specified interval, w1 and w2 are artificial parameters (w2 is set comprehensively considering the coverage of a single monitoring station), which are 1000 and 1500 respectively; the segment [f] is the parameter F, and F is the fitness value of the individual encoding solution; from this N P After the chromosomes form the initial population P0, the fitness values of all coding solution individuals in the initial population are set to 0.

[0066] In step S4, the fitness function of each coding solution individual is constructed as follows:

[0067]

[0068] In the formula, F is the fitness value of the individual encoding solution, λ1 is 1, λ2 is 100, and λ3 is 1; for chromosome p i , after calculating its fitness value, update the chromosome p i The corresponding gene fragment [f].

[0069] In step S5, after determining the capacity of the pairing pool (e.g., N / 2), two different encoding solution individuals are randomly selected from the initial population P0, and the encoding solution individual with the smaller fitness value is added to the pairing pool; if the fitness values of the two encoding solution individuals are the same, one of them is randomly added to the pairing pool; this operation is repeated until the pairing pool capacity is full, forming the parent population P1.

[0070] In step S6, the process of crossover genetic operation is: select chromosomes p in pairs from the parent population P1 v and p v+1 , if the crossover probability p c , then perform a cross operation on the two to enhance population diversity; the corresponding fragments [xv ] and [x v+1 ] are exchanged, where q is a random number in a preset interval; all coding solution individuals after the crossover operation are recorded as the offspring population P2, the fitness values of the corresponding coding solution individuals are calculated, and the corresponding gene fragments [f] in their chromosomes are updated.

[0071] Similarly, in step S6, the mutation operation process is as follows: for each coding solution individual in the offspring population P2, if the mutation probability p is satisfied, m , then perform mutation operation on it and change its chromosome p d Corresponding gene fragment [x d The s-th gene mutation on ] is the s-th row element of the matrix g, and the matrix g is as follows:

[0072]

[0073] All coding solution individuals after the mutation operation are recorded as the offspring population P3, the fitness value of the corresponding coding solution individual is calculated, and the corresponding gene fragment [f] in its chromosome is updated.

[0074] In step S7, the parent population P1 and the child population P3 are merged, and after being sorted in descending order of fitness value, the top N P The chromosomes corresponding to the coded solution individuals constitute a new population P4.

[0075] In step S8, the crossover and mutation operations are repeated until the maximum number of iterations G is reached. At this time, the newly obtained population P4 is the optimal solution set. Figure 4 The fitness decrease curve corresponding to the iteration situation of this embodiment.

[0076] In step S9, the individual with the best fitness is selected from the optimal solution set.

[0077] In step S10, the key parameters of the individual with the best fitness, i.e., r1, r2, θ1, θ2, are output. Combined with the generation rules of the gridded area, the recommended site selection of the airport atmospheric monitoring station is determined, and then the monitoring network is drawn to form a comprehensive layout plan of the airport atmospheric monitoring station for reference by the airport decision-making management and operation departments. In this embodiment, a total of 7 monitoring points are selected, and the final site selection of the monitoring points is as follows: Figure 5 As shown, their coordinates in the atmospheric monitoring site selection coordinate system are: (-18.852, 974.328), (1459.18, 1107.31), (1421.55, 237.321), (2124.94, -1940.08), (684.538, -1203.08), (-52.469, -2643.47), (-755.859, -466.07).

Claims

1. A comprehensive layout method for airport atmospheric monitoring stations, characterized by: The geographic coordinates of multiple key points at the airport are combined with the Gauss-Krüger projection theory to construct a site selection coordinate system for airport atmospheric monitoring stations. The geographic coordinates of the key points are then converted into coordinates of the site selection coordinate system for the atmospheric monitoring stations. The airport area is divided into a gridded region containing multiple grids. Based on the site selection coordinate system and the gridded region, the optimization target for the comprehensive layout of the airport atmospheric monitoring stations is determined, and a site selection model for the airport atmospheric monitoring stations is constructed. An elite genetic algorithm is used to solve the optimization problem corresponding to the site selection model for the airport atmospheric monitoring stations, resulting in a comprehensive layout plan for the airport atmospheric monitoring stations. The method comprises the following steps: S1. Based on the airport surface structure, airport oil depot area, and the distribution of typical air pollution sources, identify multiple key points of the airport and their geographic coordinates. Combined with the Gauss-Krüger projection theory, construct a coordinate system for the site selection of airport air monitoring stations. Then, convert the geographic coordinates of multiple key points of the airport into the coordinates of the site selection coordinate system for airport air monitoring stations. S2. Construct an airport atmospheric monitoring station site selection model: Divide the airport area into a gridded area containing multiple non-overlapping grids. Grids whose center points are located inside and on the edge of a graph surrounded by multiple key points, as well as grids where key points are located, are considered to be the grids for station deployment. The optimization goal is to minimize the number of grids to be deployed and the distance between key points and the center of the grids. S3. Formulate coding rules for the site selection plan, and initialize different coding solutions for the optimization problem corresponding to the site selection plan in the elite genetic algorithm; S4. Decode the generated different coding solutions, calculate the objective function value of each coding solution and use it as fitness; S5. Select individuals with different coding solutions in the population to form the parent population; S6. Perform crossover and mutation operations on the coding solution chromosomes between the parent populations to obtain the offspring population, and similarly calculate the fitness of each coding solution individual in the offspring population; S7. Merge the offspring population with the parent population to form a new population; retain the elite population in the new population based on the fitness of each coding solution individual in the new population; S8, return to step S5, reselect and form the parent population, repeat the crossover inheritance and mutation operations until the maximum number of iterations is reached; S9, output the individual with the best fitness in the current population; S10. After decoding the best individual, the site selection location of the airport atmospheric monitoring station is obtained and the corresponding comprehensive layout plan is output.

2. The method for comprehensive layout of airport atmospheric monitoring sites according to claim 1, characterized in that: In step S1, based on the airport surface structure, the airport oil depot area, and the distribution of typical air pollution sources, the two ends of the airport runway and the center point of the oil depot area are selected as key points; The airport reference point is used as the origin Based on the Gauss-Krüger projection theory, the corresponding Axis, parallel to the x-axis in the Gauss-Krüger projection coordinate system, establishes the corresponding Axis, thus obtaining the site selection coordinate system of the airport atmospheric monitoring station, and transforming the key point coordinates into the corresponding coordinates in the site selection coordinate system.

3. The comprehensive layout method of airport atmospheric monitoring stations according to claim 1, characterized in that: In step S2, the grid is a square grid; is the center of the circle and the radius is Randomly pick a point in the circle ,in: for point The polar angle of ; the square is generated as follows :Point As a square The diagonal center of the square The side length is , select the square Any vertex of Based on this vertex, other vertices are recorded in clockwise or counterclockwise direction as , then the diagonal points are connected Coordinate system for site selection of atmospheric monitoring stations The angle between the positive axis and the ; Point Along the edge Parallel to a certain direction, according to The spacing of After times, get A new square diagonal center, and generate a square The squares are generated in sequence ; Then point Along the edge Repeat the above operation on the other side of the parallel direction, and you will get New square diagonal centers, generating squares ; Next, for the existing square to , first move the diagonal center point along the edge Parallel to a certain direction, according to The spacing of After times, get A new square diagonal center, and generate a square The squares are generated in sequence ; Same for square to , and then align the diagonal center point along the edge Repeat the above operation on the other side of the parallel direction, and you will get New square diagonal centers, generating squares ; Each square is considered as a grid, and the grids are numbered in sequence, recorded as , the center point of the grid, that is, the diagonal center point of the corresponding square, is used as the monitoring station deployment point ; Record multiple key points of the airport as Then, construct the set , whose elements are key points The number of the grid ; Then record the total number of site grids to be deployed as Finally, the optimization problem of the constructed airport atmospheric monitoring station site selection model is as follows: In the optimization problem, The number of grids whose center points are located inside and on the edge of the graph surrounded by multiple key points. is the number of grids where the key points are located; For key points The center point of the grid, that is, the monitoring station deployment point the distance between them; is the artificial coefficient.

4. The method for comprehensive layout of airport atmospheric monitoring sites according to claim 3, characterized in that: In step S3, the coding information in the coding rule includes the angle and distance of the monitoring site; the angle and distance parameters are encoded in the form of direct coding as follows: Chromosome By gene fragment 、 composition, is the total number of initial chromosomes; Include parameters 、 、 、 ,in, , , 、 , Represents a random number in the specified interval, 、 is an artificial parameter; For parameters , is the fitness value of the individual solution of the code; The initial population consists of chromosomes Finally, the fitness values of all coding solution individuals in the initial population are set to 0.

5. The method for comprehensive layout of airport atmospheric monitoring sites according to claim 3, characterized in that: In step S4, the fitness function of each coding solution individual is constructed as follows: Where, To encode the fitness value of the individual; for chromosome , after calculating its fitness value, update the chromosome The corresponding gene fragment .

6. The method for comprehensive layout of airport atmospheric monitoring sites according to claim 1, characterized in that: In step S5, after determining the capacity of the pairing pool, randomly select Select two different coding solution individuals, and add the coding solution individual with the smaller fitness value to the pairing pool; if the fitness values of the two coding solution individuals are the same, select one of them and add it to the pairing pool; repeat this operation until the pairing pool capacity is full, and the parent population is formed. .

7. The method for comprehensive layout of airport atmospheric monitoring sites according to claim 1, characterized in that: In step S6, the process of crossover genetic operation is: from the parent population Select chromosomes in pairs and , if the crossover probability is satisfied , then perform a cross operation on the two and divide the corresponding fragments and No. The genes are exchanged, is a random number in the preset interval; all the coding solution individuals after the crossover operation are recorded as the offspring population , calculate the fitness value of the corresponding coding solution individual and update the corresponding gene fragment in its chromosome ; The process of mutation operation is: for the offspring population For each coding solution individual in , if it satisfies the mutation probability , then perform mutation operation on it and change its chromosome Corresponding gene fragment On the Gene mutation matrix No. Row elements, matrix as follows: All the coding solution individuals after the mutation operation are recorded as the offspring population , calculate the fitness value of the corresponding coding solution individual and update the corresponding gene fragment in its chromosome .

8. The method for comprehensive layout of airport atmospheric monitoring sites according to claim 7, characterized in that: In step S7, the parent population With offspring population After fusion, sort the fitness values from large to small, and then sort the top The chromosomes corresponding to the coded individuals constitute a new population .

9. The method for comprehensive layout of airport atmospheric monitoring sites according to claim 8, characterized in that: In step S8, the crossover and mutation operations are repeated until the maximum number of iterations is reached. After that, the newly obtained population The optimal solution set In step S9, the individual with the best fitness is selected from the optimal solution set; In step S10, the optimal individual parameters are output and combined with the generation rules of the gridded area to determine the recommended site selection location of the airport air monitoring station, and then the monitoring network is drawn to form a comprehensive layout plan for the airport air monitoring station.

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