A method for predicting bird flight routes around airports based on Dijkstra algorithm

Abstract

The present invention provides a method for predicting the flight routes of birds around airports based on the Dijkstra algorithm. For different high-risk bird species in the airport area, the resistance factor affecting the flight of the bird species and the land use data around the airport are first obtained, and the key habitats of the bird species are identified using spatial analysis technology to determine the starting point and end point of the bird flight route. After that, a bird flight resistance coefficient index system is constructed, and Matlab software is used to complete the processing of land use data and the calculation of the flight resistance value of the bird species on different plots around the airport. Finally, the Dijkstra algorithm is used to predict the flight routes of birds. The present invention is suitable for predicting the flight routes of most high-risk birds around airports, and is not limited to a single bird species, and has scalability. The present invention can provide a method for obtaining surrounding bird flight data for airports in my country that do not yet have radar detection technology, thereby achieving the effect of reducing the probability of bird strikes.

Description

Technical Field

[0001] The invention relates to the field of bird strike prevention and control, and more specifically to a method for predicting bird flight routes around an airport based on a Dijkstra algorithm. Background Art

[0002] The collision between birds and aircraft is a global problem, which endangers human flight safety and causes serious economic losses and psychological pressure on passengers. In recent years, with the improvement of the ecological environment, the increase in aircraft speed, the reduction of engine noise and the increase in the number of flights, the number of bird strikes between birds and aircraft in the same space has increased year by year. Not only civil aviation, but also the development of military aviation has been rapid, with new aircraft models springing up like mushrooms after rain. With the increase in flight subjects and flight volume, bird strikes have also occurred frequently. There are several bird strikes on aircraft in my country's military and civil aviation every year, causing major property losses and even casualties. The direct cause of bird strikes is the intersection of the flight trajectories of birds and aircraft at the same time. Indirect causes include the increased attraction of the surrounding habitat to birds, the lack of understanding of the laws of bird flight activities at the airport, and the inadequate bird strike prevention work. Compared with aircraft routes, bird flight routes are difficult to predict and irregular. my country's radar bird detection technology has not yet been effectively applied in the prevention of bird strikes at airports, and there are still defects in the field of obtaining bird flight data around airports. Therefore, understanding the bird situation in the airport area and the bird's activity routes is of great practical significance for the airport to carry out bird strike prevention and comprehensive management work. Summary of the invention

[0003] The present invention overcomes the deficiencies in the prior art and provides a method for predicting bird flight routes around airports based on the Dijkstra algorithm. By using Matlab software, the flight routes of high-risk bird species around airports are determined, providing a practical method for airports to obtain surrounding bird flight data, thereby helping to reduce the probability of bird strikes.

[0004] The purpose of the present invention is achieved through the following technical solutions.

[0005] A method for predicting bird flight paths around airports based on Dijkstra algorithm is carried out in the following steps:

[0006] S1. Based on the ecological characteristics of birds, select the habitat suitability evaluation factor of a certain bird species in the study area, and obtain land use data in the study area;

[0007] S2, using spatial analysis technology to perform weighted superposition of all evaluation factors in S1 to obtain the habitat suitability evaluation results of a certain bird species in the study area, and then determine the starting point and end point of the bird flight route;

[0008] S3. Based on the ecological habits of a certain bird species, obtain the resistance factor that affects the flight of the bird species, use the hierarchical analysis method to construct a bird flight resistance coefficient index system, and quantify the resistance of different ecological environment factors to bird flight;

[0009] S4. Use Matlab software to read the land use data of the study area, use image processing function to extract the location information of several plots, and use different colors to represent different land types;

[0010] S5. Use Matlab software to calculate the boundaries of different land use types and determine the radiation domains of different land types under different distance factors;

[0011] S6. Use Matlab software to organize the data information in the study area, and calculate the resistance values ​​of different plots in the study area according to the bird flight resistance coefficient index system constructed in S3;

[0012] S6. Using Matlab software, the bird flight path was calculated using the Dijkstra algorithm program based on the starting and ending points of the bird flight path determined in S2 and the resistance values ​​of different plots in the study area calculated in S6.

[0013] S7. Use Matlab software to repeat the work in S6 until the calculation of the flight routes of all the bird species in the study area is completed.

[0014] In S1: various bird habitat suitability evaluation factors, including but not limited to forest condition factors, water source condition factors, human disturbance factors, food abundance factors, and main road impact factors.

[0015] In S3: The constructed bird flight resistance coefficient index system includes: target layer (resistance coefficient of a certain plot of land), intermediate layer (land suitability factors, distance factors, foraging conditions factors), and index layer (land use type, noise, distance from areas with different land use types, number of natural enemies, food abundance, and concealment).

[0016] The technical solution of the present invention targets different high-risk bird species in the airport area. First, the resistance factor affecting the flight of the bird species and the land use data around the airport are obtained, and the key habitats of the bird species are identified using spatial analysis technology to determine the starting point and end point of the bird flight route. Then, a bird flight resistance coefficient index system is constructed, and Matlab software is used to complete the processing of land use data and the calculation of the flight resistance value of the bird species on different plots around the airport. Finally, the Dijkstra algorithm is used to predict the bird flight route. The present invention is suitable for predicting the flight routes of most high-risk birds around the airport, and is not limited to a single bird species, and is extensible. The present invention can provide a method for obtaining surrounding bird flight data for airports in my country that do not yet have radar detection technology, thereby achieving the effect of reducing the probability of bird strikes. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a flow chart of a method for predicting bird flight routes around an airport based on the Dijkstra algorithm.

[0018] Figure 2 This is a schematic diagram (1) of using the subplot function in the present invention to display multiple graphics in a drawing window.

[0019] Figure 3 This is a schematic diagram (2) of using the subplot function in the present invention to display multiple graphics in a drawing window.

[0020] Figure 4 It is a schematic diagram showing the radiation area of ​​the built-up area in the present invention.

[0021] Figure 5 It is a schematic diagram of the matrix of the calculation results of the overall resistance matrix in the present invention.

[0022] Figure 6 It is an undirected graph based on Dijkstra algorithm of the present invention.

[0023] Figure 7 It is a schematic diagram of type 1 of the heron flight path in the present invention.

[0024] Figure 8 It is a schematic diagram of type 2 of the heron flight path in the present invention.

[0025] Fig. 9 It is a schematic diagram of type 3 of heron flight path in the present invention.

[0026] Fig.10 It is a path diagram repeatedly defined for egret flight in the present invention.

[0027] Fig.11 It is a simplified resistance matrix schematic diagram of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention is further described below through specific embodiments.

[0029] Take an airport in Tianjin as an example. It is located on the East Asian-Australasian Flyway, one of the nine major bird migration routes in the world.

[64] . Tianjin is located in the eastern part of the North China Plain. It has a gentle terrain, is backed by the Yanshan Mountains to the north, and faces the Bohai Sea to the east. The altitude gradually decreases from north to south. It is the confluence of many rivers in the Haihe River Basin. Tianjin has a temperate semi-humid continental monsoon climate with four distinct seasons. It is an important stopover and transit station for birds migrating from north to south. Every year, tens of billions of birds migrate around the world, traveling back and forth between breeding grounds and wintering grounds thousands or even tens of thousands of kilometers away. Tianjin has a variety of natural environments suitable for birds to inhabit, reproduce and forage. Among them, Beidagang Wetland, Qilihai Wetland, Tuanbowa Wetland, Dahuangbao Wetland, etc. attract a large number of birds every year, especially in the spring and autumn bird migration seasons, attracting millions of migratory birds to stop here. Birds need to consume a lot of energy during migration. In order to complete long-distance migration, most birds need to replenish food and accumulate energy in a series of stopovers to prepare for the next stage of migration. Therefore, stopovers with abundant food sources and excellent habitats play an important role for migratory birds. The habitats in the airport flight area are mainly runways, lawns, buildings, and an 80m wide emergency landing road on the west side of the runway. Among them, the grassland has the largest area and is distributed on both sides of the runway and between the buildings. It is the main area for bird activities in the area. The emergency landing runway is padded with soil, and this part is often compacted by a road roller to maintain the hardness of the topsoil. The other parts are exposed to the ground and the soil is loose. Further to the west, the vegetation is richer, and there is a pond at the north and south ends of the airport. After raining, some surface areas will form waterlogging, which is easy to attract sandpipers and herons. The surrounding environment of the airport mainly includes urban areas, villages, woodlands, water systems and farmlands. 4.5km from the north end of the airport is a country park, formed by the confluence of two rivers running from east to west and from north to south. There are cultivated land, ponds, villages and small urban areas around it. The west side of the airport is an urban area with a canal running through it. About 6km from the west side of the airport is a national wetland park. The east side of the airport is mainly large areas of cultivated land, forest land and a large number of villages distributed in it. There are two artificial lakes on the south side of the airport, surrounded by forest land and a small urban area. In addition, there are a large number of ponds and fish ponds scattered around the airport, with irregular distribution. The crops planted outside the site are mainly common crops such as corn and wheat, and rice is planted in a few areas.

[0030] In order to fully understand the distribution of birds in the four seasons and key habitats in the surrounding area of ​​YC Airport, the survey scope was set up in accordance with the requirements of the survey scope in the "Guidelines for the Survey of Bird Ecological Environment at Civil Airports". The birds and ecological environment within 8 km of the YC Airport flight area were surveyed. The birds in the open area were surveyed by the transect method combined with the sampling method, the birds in the forest area were surveyed by the sampling method, and the water birds were surveyed by the sampling method. The species and number of birds were counted and the habitat conditions were recorded. In the survey around the airport, 4 to 6 transects were laid out according to the characteristics of the habitat around the airport; in the survey within the airport, transects and several sampling points were set up, and sampling and analysis were carried out. Herons were selected as the research object. The main reasons for selection were: (1) The heron breeding grounds outside the airport are close to the airport, and the bird prevention pressure is high during the summer breeding season every year; (2) The bird-scaring team frequently observed herons crossing the airport during daily patrols, and the time and route were relatively fixed; (3) The habitat in the airport flight area is complex, and there are many potential habitats for herons in the surrounding areas, which pose a great threat to the flight safety of the airport.

[0031] The method for predicting bird flight routes around airports based on the Dijkstra algorithm is carried out in the following steps:

[0032] S1. Based on the ecological characteristics of birds, select the habitat suitability evaluation factor of a certain bird species in the study area, and obtain land use data in the study area;

[0033] S2, using spatial analysis technology to perform weighted superposition of all evaluation factors in S1 to obtain the habitat suitability evaluation results of a certain bird species in the study area, and then determine the starting point and end point of the bird flight route;

[0034] S3. Based on the ecological habits of a certain bird species, obtain the resistance factor that affects the flight of the bird species, use the hierarchical analysis method to construct a bird flight resistance coefficient index system, and quantify the resistance of different ecological environment factors to bird flight;

[0035] S4. Use Matlab software to read the land use data of the study area, use image processing functions to extract the location information of several plots, and use different colors to represent different land types.

[0036] S5. Use Matlab software to calculate the boundaries of areas with different land use types and determine the radiation domains of different land types under different distance factors.

[0037] S6. Use Matlab software to organize the data information in the study area, and calculate the resistance values ​​of different plots in the study area based on the bird flight resistance coefficient index system constructed in S3.

[0038] S6. Using Matlab software, the bird flight path was calculated using the Dijkstra algorithm program based on the starting and ending points of the bird flight path determined in S2 and the resistance values ​​of different plots in the study area calculated in S6.

[0039] S7. Use Matlab software to repeat the work in S6 until the calculation of the flight routes of all the bird species in the study area is completed.

[0040] In S1, the evaluation factors of the suitability of bird habitats include forest condition factors, water source condition factors, human disturbance factors, food abundance factors, and main road impact factors. The weights of each factor were obtained by pairwise comparison using the hierarchical analysis method.

[0041] In S1, the methods for obtaining land use data include but are not limited to interpreting remote sensing images of the study area using ENVI software.

[0042] In S2, the methods for obtaining the suitability assessment results of bird habitats in the study area include but are not limited to the use of ArcGIS spatial analysis technology.

[0043] In S2, the starting and ending points of the bird flight routes can be determined based on the habitat suitability assessment results and certain bird habitat preferences, food preferences and other principles.

[0044] In S3, the bird flight resistance coefficient index system includes: target layer (resistance coefficient of a certain plot of land), intermediate layer (land suitability factors, distance factors, foraging conditions factors), and index layer (land use type, noise, distance from areas with different land use types, number of natural enemies, food abundance, and concealment).

[0045] In S4, S5, S6, and S7, the above contents can be implemented using Matlab software.

[0046] Based on the survey results of this paper and reference to relevant literature, nine evaluation indicators that affect the flight route selection of herons were determined.

[0047] Land suitability factors: Herons often gather in groups in areas such as river wetlands, rice fields, and ponds where human disturbance is low and where they can obtain abundant food. They also choose to breed and live in clean, moist woods with low average background noise. Therefore, this paper selects two evaluation indicators, land use type and noise, for scoring.

[0048] Distance factor: The ecological environment of the study area is complex. The west side of the airport is mainly a large urban area with a national wetland park inside; the east side is mainly a large farmland area and villages with a large number of ponds; the north side is mainly the confluence of two rivers, with a small number of villages and farmland; the south side is mainly distributed with two artificial lakes. Therefore, in order to accurately judge the flight route of herons, this paper selects the distance from the water system, the distance from the farmland, the distance from the forest, and the distance from the built-up area to evaluate the impact of the heron flight route, which is divided into 4 levels: <100m, 100-500m, 500-1000m, >1000m.

[0049] Foraging conditions:

[0050] (1) Food abundance: During the breeding season, herons need a lot of food to ensure the survival of their young. Foraging areas with rich food sources can provide the herons with the energy they need during the breeding season and are very attractive to them.

[0051] (2) Number of natural enemies: The natural enemies of egrets mainly include birds of prey, such as white-tailed harriers, magpies, and snakes. In addition, there is also predation among egrets. In the same nesting site, night herons have been found preying on and eating egret chicks that have not left the nest. The natural enemies of egrets often prey on their chicks during the egg-laying, hatching, and brooding periods, and when the parents leave the nest to feed.

[0052] (3) Concealment: Herons often build nests, breed, and rest in clusters on trees. A forest environment with little human interference can provide herons with a good concealed place for nesting, breeding, laying eggs, and raising chicks, while also avoiding natural enemies and providing a partial food source.

[0053] For the determined drag coefficient evaluation index system, three experts in the fields of airport bird research and airport bird strike prevention (two from the School of Environment of Tianjin University and one from the School of Geography and Environmental Sciences of Tianjin Normal University) were invited to score the index system. Based on the expert experience, a scale judgment was made on the relative importance of indicators at each level. Finally, the quantitative results provided by the experts were averaged and summarized, and the weight of each indicator was determined using the hierarchical analysis method.

[0054] Table 1 Drag coefficient evaluation table

[0055]

[0056]

[0057] Note: The higher the score, the greater the resistance to egret flight.

[0058] Table 1 Resistance coefficient evaluation table (continued)

[0059]

[0060] Note: The higher the score, the greater the resistance to egret flight.

[0061] Table 2 Intermediate layer judgment matrix

[0062]

[0063] Note: CI=0.0268, CR=0.0515<0.1

[0064] Table 3 Indicator layer judgment matrix 1

[0065]

[0066]

[0067] Note: Comply with consistency test

[0068] Table 4 Indicator layer judgment matrix 2

[0069]

[0070] Note: CI=0.0553, CR=0.0621<0.1

[0071] Table 5 Indicator layer judgment matrix 3

[0072]

[0073] Note: CI=0.0009, CR=0.0019<0.1

[0074] The way to obtain land use data around the airport is to interpret remote sensing images. The method selected is the maximum likelihood method, and the remote sensing image data used comes from the China Geospatial Data Cloud. In combination with the ecological habits of herons, this paper divides the land features in the study area into four categories, including construction land, water bodies, cultivated land and forest land. According to the requirements of herons for habitat environment, it is necessary to first determine the evaluation factors, that is, certain elements that have a significant impact on the habitat selection of herons. Secondly, the hierarchical analysis method is used to assign weights to each influencing factor using YAAHP software. On this basis, ArcGIS spatial analysis technology is used for weighted superposition to obtain the evaluation results of the suitability of heron habitats around Tianjin YC Airport. The focus of habitat suitability evaluation is the selection of evaluation factors. The selection principles of heron habitat suitability evaluation factors in this article are:

[0075] (1) Select factors that have a direct impact on egret activities, referring to the evaluation factors selected by Zhou Jiajun for the evaluation of egret habitat suitability in Xixi National Wetland: water area, shelter conditions, food abundance, and the evaluation factors selected by Dong Zhangyu et al. for the evaluation of waterfowl habitat suitability in Panjin Wetland: water source conditions, interference conditions (road density and residential concentration), food abundance, and shelter conditions (land use);

[0076] (2) Consider factors such as human disturbance and building disturbance that have a significant inhibitory effect on heron activities;

[0077] (3) Due to the small size of the study area, the impact of disaster risk, climate, and other factors on habitat destruction were not considered as evaluation factors.

[0078] Based on the above three selection principles, five evaluation factors were finally determined, namely forest conditions, water source conditions, human interference, food abundance and the impact of main roads. Then, the analytic hierarchy process was used and the YAAHP software was used to obtain the weights of each factor, as shown in the following table:

[0079] Table 6 Weight distribution of each impact factor

[0080]

[0081] ArcGIS spatial analysis technology is used to process all influencing factors. The processing methods mainly include: Euclidean distance analysis, interpolation analysis, overlay analysis, reclassification, etc.

[0082] (1) Forest conditions

[0083] During the breeding season, herons often gather in groups on trees. The forest environment with little human interference can provide herons with a good hidden place for nesting, reproduction, egg laying, and chick raising, while avoiding capture by natural enemies. The existing land use classification results were used to extract forest patches, and the Euclidean distance method was used in ArcGIS software to obtain the distribution of the impact level of forest conditions.

[0084] (2)Water source conditions

[0085] Water source is an important indicator for herons to choose habitats. Herons mostly feed on fish, shrimps, amphibians, etc., so ponds, rice fields, rivers and wetlands with little human interference in the airport area are very attractive to herons. The water system layer was extracted using the existing land use classification results, and the Euclidean distance method in ArcGIS software was used to obtain the distribution of the impact level of water source conditions.

[0086] (3) Food abundance

[0087] Food abundance is another important indicator for herons to choose habitats. By investigating the sampling points and referring to relevant literature, the distribution of food abundance in the study area can be obtained. The interpolation analysis method in ArcGIS software is used for interpolation processing to obtain accurate food abundance distribution.

[0088] (4) Degree of human interference

[0089] Human activities have a great impact on the habitat of herons. Tree felling, urban construction, industrial production, etc. have seriously affected the living space of herons.

[82] Based on the results of actual survey and sampling analysis, this paper uses the Euclidean distance method in ArcGIS software to obtain the range and level of human interference radiation.

[0090] (5) Impact of major roads

[0091] The road data comes from OSM (openstreetmap). The acquired data is preprocessed by ArcGIS software, and main roads such as trunk roads and highways are retained. Then spatial analysis is performed to obtain the impact range. The results show that the closer to the road, the less suitable the location of heron habitat.

[0092] According to the result map of each evaluation factor, each indicator factor is graded into suitable, high suitability, medium suitability, low suitability, and unsuitable. Using the weighted sum tool under ArcGIS spatial analysis-overlay analysis, the weighted sum tool can weight and combine multiple inputs to create an integrated analysis. Based on the calculation results of each suitability evaluation factor, all data are superimposed using the weighted sum tool to achieve the final evaluation result of heron suitability. Finally, the superimposed results are reclassified into 5 levels, namely suitable, high suitability, medium suitability, low suitability, and unsuitable. According to the evaluation results of the suitability of heron habitats, the habitat locations where herons are most likely to cluster in the study area can be roughly determined, with a total of 11 areas. The next step is to conduct field research based on the key survey area map to determine the distribution of heron habitats and foraging areas around the airport.

[0093] MATLAB is used to complete the organization of basic data information of the airport area and the calculation of resistance values, and the Dijkstra algorithm is combined to determine the flight routes of egrets around the airport. The color information of the image is extracted through relevant statements to characterize different land types, so as to achieve the purpose of refined calculation. After reading the picture using Matlab, 1368×1502 location information is extracted according to the land use classification results. The way Matlab stores image color information is to store different color information in a three-dimensional matrix related to RGB. The data saved after reading the picture is of uint8 type. The clarity of the color of the land use classification map greatly reduces the difficulty of software processing. Through relevant statements, the built-up areas, water systems, woodlands, and farmlands of different colors are separated, and the above four land types are extracted separately. For easy comparison, the subplot function is used to display multiple graphics in a drawing window, such as Figure 2 As shown. According to the distance factor in the resistance coefficient evaluation table, it is necessary to calculate the boundaries of different areas. Therefore, in the face of such complex graphics, extracting the boundaries is a relatively important task. This article refers to the concept of only one-sided derivatives at the boundary point when finding the derivative of a function in mathematics for program design. Taking the built-up area as an example, in the two-dimensional matrix, the area value of the red information is set to 1, and the area of ​​the black information is set to zero. The slopes between two adjacent points on the horizontal and vertical axes are calculated respectively. When the slope is 0, it indicates that the two points belong to the red area or the black area at the same time; when the slope is 1 or -1, it indicates that the two points are on the boundary. The point with the larger horizontal or vertical coordinate value of the two numbers is located as the boundary point, and the boundary is extracted to ensure the accuracy of the extraction of each area. The boundaries of the four different land use types are obtained respectively, with the aim of determining the radiation domains of the land in the next step of <100m, 100~500m, 500~1000m, and >1000m, such as Figure 3 As shown. In this embodiment, the distance from different types of land can affect the choice of bird routes, so it is necessary to find out the impact of each land type on bird flight resistance. The main task is to determine the radiation domain of the four ecological types of land at <100m, 100-500m, 500-1000m, and >1000m. After the boundary is calculated, the calculation of the radiation area is much simpler. You only need to add the corresponding distance on the basis of the boundary, such as Figure 4 The radiation area of ​​the built-up area is shown.

[0094] The following calculation part needs to be assigned according to the attributes of each area, and the resistance calculation is performed according to the size of the assigned value. The basic idea is to use the hierarchical analysis method to quantify the resistance-related indicators. The purpose of quantification is to convert subjective evaluation into more statistically significant scores, so that the actual effects of each influencing variable can be evaluated more objectively. Before assigning values, the variable names are extracted and sorted to make subsequent calculations clearer. Considering that different land types have different effects on bird flight trajectories, this part of the data is first extracted and assigned variable names. These variables correspond to remote sensing mapping images, and the extraction is relatively simple. Regarding the calculation of the distance factor, it should be pointed out that the area of ​​100 to 500m needs to be subtracted from the area within 500m by the area within 100m. The other calculation steps are similar. If the subtraction process is not performed, the attribute weights of different areas will become larger, resulting in the distortion of the weights calculated by the hierarchical analysis method.

[0095] (1) Arrangement of sub-indicators of land suitability factors

[0096] Table 7 Summary of sub-indicators of land suitability factors

[0097]

[0098] (2) Arrangement of distance factor sub-indicators

[0099] Table 8 Distance factor sub-indicators summary

[0100]

[0101] Table 8 Distance factor sub-indicators summary (continued)

[0102]

[0103] (3) Summary of sub-indicators of foraging conditions

[0104] Table 9 Summary of sub-indicators of foraging condition factors

[0105]

[0106] Table 9 Summary of sub-indicators of foraging conditions (continued)

[0107]

[0108] According to the resistance coefficient evaluation table scored by experts, the resistance values ​​of different locations in the study area are calculated. (1) Calculation of resistance values ​​of land suitability factor sub-indicators

[0109] Table 10 Calculation of resistance values ​​of sub-indicators of land suitability factors

[0110]

[0111]

[0112] Table 10 Calculation of resistance values ​​of sub-indicators of land suitability factors (continued)

[0113]

[0114] Among them, C11 represents a matrix (1502×1368), and each element in the matrix corresponds to a plot of land information. The non-zero elements in C11 represent the resistance values ​​of all water-based plots in the study area to heron flight, among which the 0 element represents other land type areas, which have not yet been calculated. The calculation method of the resistance value is: water-based plot resistance value = weight × expert score value, where the weight has been calculated, and the remaining resistance values ​​can be obtained in the same way.

[0115] Table 11 Calculation of resistance value of distance factor sub-indicator

[0116]

[0117]

[0118] The non-zero elements in C31 represent the resistance values ​​of all plots within 100m from the water system to the flight of egrets in the study area. The reason why other areas are 0 is the same as above. The calculation method of resistance value is: resistance value of plots within 100m from the water system = weight × expert score, where the weight has been calculated. The other resistance values ​​can be obtained in the same way.

[0119] (3) Calculation of resistance value of foraging condition factor sub-indicators

[0120] Table 12 Calculation of resistance values ​​of sub-indicators of foraging condition factors

[0121]

[0122] The non-zero elements in C71 represent the resistance value of the number of natural enemies in all water system plots in the study area to the flight of herons. The reason why other areas are 0 is the same as above. The calculation method of resistance value is: the resistance value of the number of natural enemies in the water system plot to the flight of herons = weight × expert score, where the weight has been calculated. The rest of the resistance values ​​can be obtained in the same way. In summary, by adding up all the calculated resistance matrices, we can get the overall resistance matrix (zuli) of the study area, where:

[0123] The overall resistance matrix (zuli) = C11 + C12 + C13 + C14 + C21 + C22 + C23 + C24 + C31 + C32 + C33 + C34 + C41 + C42 + C43 + C44 + C51 + C52 + C53 + C54 + C61 + C62 + C63 + C64 + C71 + C72 + C73 + C74 + C81 + C82 + C83 + C84 + C91 + C92 + C93 + C94. The value in each matrix is ​​the resistance encountered by egrets flying in the corresponding plot. The calculated resistance value does not have an absolute value. It reflects the relative resistance between different plots. In order to facilitate subsequent calculations, it needs to be normalized. In a color image, each color channel value (color identification) is between 0 and 255. The purpose is to classify all resistance values ​​in the overall resistance matrix (zuli) between 0 and 1. Each color of different depths corresponds to a resistance value, and all resistance values ​​are evenly distributed between 0 and 1, with a minimum value of 0 and a maximum value of 1, to ensure the accuracy of the image information.

[0124] Dijkstra's algorithm is a typical single-source shortest path algorithm, which is used to calculate the shortest path from one node to all other nodes. Dijkstra's algorithm first divides the set V of all vertices in the known graph G = (V, E) into two groups. The first group is the set of vertices whose shortest paths have been determined (represented by S). Initially, there is only one vertex in S. After each shortest path is obtained, the vertex is added to the set S until all vertices are added to S. The second group is the set of vertices whose shortest paths have not yet been determined (represented by U). The vertices of the second group are added to the set S in ascending order of the shortest path length. In the process of adding, the shortest path length from the source point v to each vertex in S is always kept no greater than the shortest path length from the source point v to any vertex in U. In addition, each vertex in the two sets corresponds to a distance. The distance of the vertex in the set S is the shortest path length from the source point v to the vertex, and the distance of the vertex in the set U is the current shortest path length from the source point v to the vertex, which only includes vertices in the set S as intermediate vertices.

[0125] (1) Initially, the set S contains only the source point v; the set U contains all vertices except v, and the distance of the vertex in the set U is the "distance from the source point v to the vertex"; at the same time, an array dis is declared to store the shortest distance from the source point v to each vertex. For example, if there is a vertex m that can be directly reached for vertex v, the edge is recorded as (v, m), then dis[m] is the weight of this edge, if v and m are not adjacent, then dis[m] is ∞.

[0126] (2) Select the vertex k with the shortest distance to the source point v from the set U and add the vertex k to the set S; at the same time, remove the vertex k from the set U.

[0127] (3) Update the distance from each vertex in set U to the source point v. Select a vertex k that is closest to the source point v from all the vertices in set U and add it to set S. Examine all the edges starting from point k and perform relaxation operations on each edge. For example, if there is an edge from k to m, then a path from v to m can be extended by adding the edge (k, m) to the tail. The length of this path is expressed as dis[k]+(v, m). If this value is smaller than the currently known value of dis[m], we can use this new value to replace the current value in dis[m].

[0128] (4) Repeat steps (2) and (3) until all vertices in the graph are added to the set S or there are no more vertices that can be added to the set S. The final value in the dis array is the shortest path from the source point v to all vertices in the graph.

[0129] Given an undirected graph such as Figure 6 As shown, the purpose is to find the shortest path from vertex A to the remaining vertices.

[0130] The algorithm example is shown in the following table

[0131]

[0132] The algorithm example is shown in the following table (continued)

[0133]

[0134]

[0135] The above algorithm is used to determine the flight path of herons:

[0136] First, assume that the starting point of the heron's flight is A(1,1) and the end point is B(4,4), and the side length of each square area is d. It is necessary to find all the paths from the starting point A to the end point B, which is determined by the basic information of the image. Then consider which grid you can go to from the starting point A. It is found that there are 3 paths to choose from, namely (1,1)→(1,2), (1,1)→(2,2), (1,1)→(2,1), and the weights of the 3 paths are d, d. The next step is to consider the starting point (1,2) and continue to determine the possible paths. We find that there are five paths to choose from: (1,2)→(1,3), (1,2)→(1,1), (1,2)→(2,1), (1,2)→(2,2), (1,2)→(2,3). The weights of the five paths are d, d, d. After determining all the paths, it is found that a path (1,2)→(1,1) is defined repeatedly. In the subsequent calculations, there is no need to define such a path repeatedly. The next step is to consider the starting point (2,3) and continue to determine the possible paths. It is found that there are 8 paths to choose from, namely (2,3)→(1,2), (2,3)→(2,2), (2,3)→(1,3), (2,3)→(1,4), (2,3)→(2,4), (2,3)→(3,2), (2,3)→(3,3), (2,3)→(3,4). The weights of the 8 paths are d, d, d. d. After determining all the paths, it is found that a path (2,3)→(1,2) is defined repeatedly. In the subsequent calculations, there is no need to define such a path repeatedly. The above describes the three types of paths that can be selected from any position on the graph, which are divided into three graphs to represent them, such as Figure 7 —9, it is found that 2 paths are defined repeatedly. Similarly, there are 2 more paths with such repeated definitions. Therefore, it is not necessary to define these 4 repeated paths in the subsequent calculations, which are represented by dotted lines. Only the 4 solid line paths need to be considered to represent all the paths, as shown in Fig.10 shown.

[0137] In the process of information extraction, the information is extracted in the most accurate way, but it is not conducive to calculation, resulting in an exponential increase in calculation time. Therefore, appropriate simplification is performed. In this study, the 5×5 plot is simplified into a 1×1 plot to increase the calculation speed. The basic principle of simplification is to calculate the horizontal and vertical coordinates to determine the dark center grid as the final choice after simplification. The resistance value of the dark plot represents the resistance value of the area, such as Fig.11 Then, according to this method, all the matching elements in the matrix are found one by one, a new simplified matrix is ​​constructed, and the corresponding resistance values ​​are extracted for subsequent calculations.

[0138] According to the method of defining the path, relevant statements are used to determine whether any two points in the simplified matrix are adjacent, and the resistance value is calculated. The calculation method is: the resistance experienced from point a to point b = path weight × resistance value of point a; if the two points are not adjacent, the calculated resistance value is infinite. Then determine the starting point and end point of the route and call the Dijkstra algorithm program to depict the heron flight path.

[0139] The present invention is described above by way of example. It should be noted that, without departing from the core of the present invention, any simple deformation, modification or other equivalent replacement that can be made by those skilled in the art without inventive effort falls within the protection scope of the present invention.

Claims

1. A method for predicting bird flight routes around an airport based on the Dijkstra algorithm, characterized in that: Follow the steps below: S1. Based on the ecological characteristics of birds, select the habitat suitability evaluation factor of a certain bird species in the study area, and obtain land use data in the study area; S2, using spatial analysis technology to perform weighted superposition of all evaluation factors in S1 to obtain the habitat suitability evaluation results of a certain bird species in the study area, and then determine the starting point and end point of the bird flight route; S3. Based on the ecological habits of a certain bird species, obtain the resistance factor that affects the flight of the bird species, use the hierarchical analysis method to construct a bird flight resistance coefficient index system, and quantify the resistance of different ecological environment factors to bird flight; S4. Use Matlab software to read the land use data of the study area, use image processing function to extract the location information of several plots, and use different colors to represent different land types; S5. Use Matlab software to calculate the boundaries of different land use types and determine the radiation domains of different land types under different distance factors; S6. Use Matlab software to organize the data information in the study area, and calculate the resistance values ​​of different plots in the study area according to the bird flight resistance coefficient index system constructed in S3; S6, using Matlab software, calculate the bird flight path using the Djikstra algorithm program based on the starting and ending points of the bird flight path determined in S2 and the resistance values ​​of different plots in the study area calculated in S6; S7. Use Matlab software to repeat the work in S6 until the calculation of the flight routes of all the bird species in the study area is completed.

2. The method for predicting bird flight routes around an airport based on the Dijkstra algorithm according to claim 1, characterized in that: S1 includes: Various bird habitat suitability evaluation factors include, but are not limited to, forest condition factors, water source condition factors, human disturbance factors, food abundance factors, and main road impact factors.

3. The method for predicting bird flight routes around an airport based on the Dijkstra algorithm according to claim 1, characterized in that: S3 includes: The constructed bird flight drag coefficient index system includes: target layer, intermediate layer and index layer.

4. The method for predicting bird flight routes around an airport based on the Dijkstra algorithm according to claim 3, characterized in that: The target layer is the resistance coefficient of a certain plot of land.

5. The method for predicting bird flight routes around an airport based on the Dijkstra algorithm according to claim 3, characterized in that: The middle layer is land suitability factors, distance factors, and foraging condition factors.

6. The method for predicting bird flight routes around an airport based on the Dijkstra algorithm according to claim 3, characterized in that: The indicator layers are land use type, noise, distance to areas with different land use types, number of natural enemies, food abundance, and concealment.

Images