Airport ventral region identification method and system

Abstract

The invention provides an airport ventral region identification method and system. The method comprises the following steps: acquiring airport operation comprehensive data of a target airport, and inter-region association degree data and traffic data between the target airport and each peripheral region; airport point source intensity data, the inter-region correlation data and the traffic data are input into an airport ventral region identification model, the influence degree value of the target airport on each surrounding region is obtained, and the airport ventral region identification model is constructed based on a smoke plume model; the airport point source intensity data is obtained through calculation of the airport operation comprehensive data; and according to the influence degree value, determining a ventral region range corresponding to the target airport from the plurality of peripheral regions. According to the method, the abdominal land range of the airport is identified and divided more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of air traffic management, and in particular to an airport hinterland identification method and system. Background Art

[0002] With the rapid expansion of the civil aviation transportation system, the number of airports continues to rise, resulting in increasingly fierce competition among airports within urban agglomerations. This competition is mainly reflected in the competition for airport hinterlands in terms of spatial layout. Accurately defining the hinterland scope of each airport within an urban agglomeration can effectively prevent unnecessary consumption of aviation resources and duplication of airport facilities, thereby promoting the coordinated development of airports within the urban agglomeration and achieving complementary advantages between them.

[0003] However, the boundaries of existing airport hinterlands are vague and are difficult to accurately define through a single standard or simple model due to multiple factors such as complex terrain barriers, transportation network layout and regional economic vitality.

[0004] Therefore, there is an urgent need for an airport hinterland identification method and system to solve the above problems. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides an airport hinterland identification method and system.

[0006] The present invention provides an airport hinterland identification method, comprising: Obtaining comprehensive airport operation data of a target airport, as well as inter-regional correlation data and traffic data between the target airport and various surrounding areas; Input the airport point source intensity data, the inter-regional correlation data and the traffic data into the airport hinterland identification model to obtain the impact degree value of the target airport on each of the surrounding areas, wherein the airport hinterland identification model is constructed based on the smoke plume model; the airport point source intensity data is calculated using the airport operation comprehensive data; According to the impact degree value, the hinterland area range corresponding to the target airport is determined from the multiple surrounding areas.

[0007] According to an airport hinterland identification method provided by the present invention, the airport operation comprehensive data includes airport construction data, airport operation scale data, airport service quality data and airport external environment data, and the traffic data includes the shortest ground traffic distance data and the shortest traffic time data between the target airport and the surrounding area, wherein: The airport construction data includes airport flight zone level information and airport runway quantity information; The airport operation scale data includes passenger throughput information, cargo and mail throughput information, aircraft take-off and landing information, and the number of operating airlines; The airport service quality data includes the number of airport complaints and the number of cities served by flights; The airport external environment data includes the total value generated in the area where the airport is located and the fixed asset investment information in the area where the airport is located.

[0008] According to an airport hinterland identification method provided by the present invention, the calculation process of the airport point source intensity data specifically includes: Constructing an initial matrix of point source intensity evaluation corresponding to the plurality of target airports according to the airport operation comprehensive data of the plurality of target airports; Standardizing the initial matrix for point source intensity evaluation to obtain a standard matrix for point source intensity evaluation; Based on the entropy weight method, the entropy value corresponding to each type of indicator data in the point source intensity evaluation standard matrix is ​​calculated, and the weight information of each type of indicator data is determined according to the entropy value; According to the point source intensity evaluation standard matrix and the weight information, a weighted decision evaluation matrix is ​​constructed, and the maximum value and the minimum value of each type of the indicator data in the weighted decision evaluation matrix are obtained; According to the maximum value of the indicator and the minimum value of the indicator, a first Euclidean distance and a second Euclidean distance are obtained, wherein the first Euclidean distance is the Euclidean distance between the indicator data in the weighted decision evaluation matrix and the corresponding maximum value of the indicator; the second Euclidean distance is the Euclidean distance between the indicator data in the weighted decision evaluation matrix and the corresponding minimum value of the indicator; The airport point source intensity data corresponding to each of the target airports is calculated based on the first Euclidean distance and the second Euclidean distance.

[0009] According to an airport hinterland identification method provided by the present invention, the inter-region correlation data is obtained by the following steps: Acquire first permanent population data and second permanent population data, wherein the first permanent population data is the permanent population data of the area where the target airport is located; and the second permanent population data is the permanent population data of the surrounding area; Acquire first industrial output value data and second industrial output value data, wherein the first industrial output value data is the industrial output value data of the area where the target airport is located; and the second permanent population data is the industrial output value data of the surrounding area; The inter-regional correlation data between the target airport and the surrounding areas is obtained based on the first permanent population data, the second permanent population data, the first industrial output value data, the second industrial output value data and the shortest travel time data.

[0010] According to an airport hinterland identification method provided by the present invention, the formula of the airport hinterland identification model is: ; ; in, Indicates The target airport is the influence degree value of the surrounding area, Indicates The airport point source intensity data of the target airport, Indicates The target airport and The inter-region correlation data between the surrounding regions, Indicates The target airport and The shortest travel time data between the surrounding areas, Indicates The target airport and The shortest ground transportation distance data between the surrounding areas, Indicates Permanent population data of the area where the target airport is located, Indicates Industrial output value data of the area where the target airport is located, Indicates Permanent population data of the surrounding areas, Indicates Industrial output value data of the surrounding areas.

[0011] According to an airport hinterland identification method provided by the present invention, determining the hinterland area range corresponding to the target airport from the plurality of surrounding areas according to the impact degree value includes: The impact values ​​of the target airport on each of the surrounding areas are sorted in order from large to small, and based on the sorting result of the impact values, the surrounding area corresponding to the maximum impact value is determined as the hinterland area range corresponding to the target airport.

[0012] According to a method for identifying an airport hinterland provided by the present invention, the method further comprises: Obtaining other impact degree values ​​except the maximum impact degree value from the impact degree value sorting result of the target airport; It is determined whether there is an overlapping area between the surrounding areas corresponding to the other impact degree values ​​of each target airport. If so, the overlapping area is determined as the airport competition hinterland area.

[0013] The present invention also provides an airport hinterland identification system, comprising: A data collection module, used to obtain comprehensive airport operation data of a target airport, as well as inter-regional correlation data and traffic data between the target airport and various surrounding areas; A model calculation module is used to input the airport point source intensity data, the inter-regional correlation data and the traffic data into the airport hinterland identification model to obtain the impact degree value of the target airport on each of the surrounding areas, wherein the airport hinterland identification model is constructed based on the smoke plume model; the airport point source intensity data is calculated using the airport operation comprehensive data; The hinterland result generating module is used to determine the hinterland area range corresponding to the target airport from the plurality of surrounding areas according to the impact degree value.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the airport hinterland identification method as described above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the airport hinterland identification method as described in any one of the above is implemented.

[0016] The airport hinterland identification method and system provided by the present invention construct a model for airport hinterland identification based on the smoke plume model, and comprehensively consider factors such as airport point source intensity, ground transportation accessibility, and the degree of correlation between the airport and the target node area, so as to more accurately identify and divide the airport hinterland range. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of the flow chart of the airport hinterland identification method provided by the present invention; Figure 2 A schematic diagram summarizing the airport distribution in a certain airport cluster area provided by the present invention; Figure 3 A schematic diagram of the distribution of the hinterland of each airport obtained by identification based on the airport hinterland identification model provided by the present invention; Figure 4A schematic diagram of the distribution of the hinterland of major airports in a certain airport cluster area provided by the present invention; Figure 5 A schematic diagram of the distribution of the airport hinterland of City A in the airport cluster area provided by the present invention; Figure 6 A schematic diagram of the distribution of airport hinterland in Province C in the airport cluster area provided by the present invention; Figure 7 A schematic diagram of the distribution of airport hinterland in Province B in the airport cluster area provided by the present invention; Figure 8 A schematic diagram of the distribution of airport hinterland in Province D in the airport cluster area provided by the present invention; Fig. 9 A schematic diagram of the distribution of the airport competition hinterland in the airport cluster area provided by the present invention; Fig.10 A schematic diagram of the identification result of the airport competition hinterland between the main airports provided by the present invention; Fig.11 A schematic diagram of the structure of the airport hinterland identification system provided by the present invention; Fig.12 This is a schematic structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] With the rapid development of the civil aviation transportation system, the number of airports continues to increase, and the competition between airports in urban agglomerations is becoming increasingly fierce, which is manifested in the competition of airport hinterlands in space. Accurately identifying the scope of airport hinterlands in urban agglomerations can avoid the waste of aviation resources and duplicate airport construction, and promote the coordinated development and complementary advantages of airports in urban agglomerations.

[0021] The current research techniques for airport hinterland can be summarized as circle division method, questionnaire method, graph theory method and mathematical model method.

[0022] 1. The circle division method is widely used in the preliminary identification and macro-analysis of the airport hinterland. The circle division method defines the influence range of the airport by setting different distance or time circles. It is often used in regional economic development planning, transportation network layout and policy formulation.

[0023] The main structure and principle of the circle division method are as follows: Distance circles: A simple division based on spatial distance. Usually, concentric circles are drawn with the airport as the center according to different straight-line distances (such as 50 kilometers, 100 kilometers, etc.) as a preliminary estimate of the airport hinterland. This division method is simple and intuitive, but it ignores the influence of actual factors such as terrain and transportation network.

[0024] Time circle: Considering the speed of transportation and travel time, with the airport as the center, set the area accessible within different time ranges (such as 30 minutes, 60 minutes drive, etc.) as the hinterland. This division method is closer to reality, but is affected by dynamic factors such as traffic conditions and changes in road conditions.

[0025] 2. Questionnaire method: Through direct surveys of passengers, residents or related enterprises, first-hand data is collected to understand their needs, preferences and actual use of airport services, and then the hinterland scope of the airport and its influencing factors are analyzed.

[0026] The main structure and principles of the questionnaire method are as follows: Questionnaire design: Based on the research purpose, a multi-dimensional questionnaire was designed that included travel habits, transportation mode selection, airport service satisfaction, etc. to ensure the comprehensiveness and representativeness of the data.

[0027] Data collection: Distribute questionnaires online or offline to collect feedback from the target group. Ensure sample diversity and sufficient sample size to improve data reliability.

[0028] Data analysis: Use mathematical and statistical methods (such as frequency analysis, cross analysis, regression analysis, etc.) to process and analyze the collected data, explore the objective laws behind the data, and identify the core and peripheral areas of the airport hinterland.

[0029] 3. Graph Theory Method uses graph theory and spatial analysis theory, combined with computer modeling technology, to carry out detailed identification and division of airport hinterland. It is suitable for the identification and analysis of airport hinterland under complex transportation networks.

[0030] The main structure and principles of graph theory are as follows: Spatial modeling: The airport and its surrounding areas are abstracted into a collection of nodes and edges to build a spatial network model. Nodes represent key points such as airports, cities, and transportation hubs, and edges represent transportation routes or connecting lines.

[0031] Weighted Voronoi diagram: Based on the traditional Voronoi diagram, the transportation network or transportation accessibility is introduced as a weight factor to construct a weighted Voronoi diagram, so as to divide the hinterland range according to the shortest path or optimal accessibility between nodes.

[0032] Thiessen polygon method: Use the perpendicular bisectors between airports to construct a closed plane and divide the space into multiple Thiessen polygon areas. The distance from each point in the polygon to the airport inside it is less than the distance to other airports, thus determining the hinterland range of the airport.

[0033] 4. Mathematical model method constructs a mathematical model to quantitatively describe the influencing factors and changing laws of the airport hinterland, which is suitable for in-depth analysis of the complexity and dynamics of the airport hinterland.

[0034] The main structure and principles of the mathematical model method are as follows: Location entropy model: measures the degree of specialization of a region in a certain economic activity relative to the whole, and evaluates the influence of the airport in the region by calculating the location entropy value.

[0035] Gravity model: Drawing on the concept of gravity in physics, the gravitational force between the two places is calculated based on factors such as the economic scale, distance and transportation convenience between the two places, thereby determining the scope and strength of the airport hinterland.

[0036] Density model: By analyzing the density distribution of population and economic activities in the region and combining the attractiveness and radiation capacity of the airport, the reasonable scope and density threshold of the airport hinterland are calculated.

[0037] Huff model: This model considers multiple factors such as distance, facility size, and attractiveness, and divides the airport hinterland by calculating the probability of consumers choosing different facilities. This method can more comprehensively reflect the actual situation and changing trends of the airport hinterland.

[0038] However, in the above prior art, the following problems still exist: (1) The circle division method is a qualitative research method, which is usually divided according to administrative regions, travel distance or time. It is simple and easy to implement, but this method is relatively idealistic and fails to consider the impact of factors such as geography and transportation on the hinterland.

[0039] (2) The questionnaire method is to obtain demand data between the two places through first-hand information and to explore the objective laws behind the data through mathematical statistics. However, data acquisition is difficult and the survey cost is high.

[0040] (3) Graph theory is usually combined with operations research theory, spatial analysis and computer modeling to identify the hinterland. Conventional Voronoi diagrams treat spatial targets as equal particles for research, ignoring their own properties. Although weighted Voronoi diagrams can be constructed based on transportation networks or transportation accessibility to obtain more realistic results, they are only suitable for small-scale use due to the large amount of data and complex operations.

[0041] (4) Mathematical model methods generally lack consideration of the airport’s own strength. The model is highly complex and difficult to operate, and is difficult to apply to the identification of airport hinterlands within large-scale urban agglomerations.

[0042] The airport hinterland is a relatively abstract concept, which is difficult to define clearly due to the influence of multiple factors such as terrain, transportation, and economic development level. Aiming at the identification needs of airport hinterlands in large-scale urban agglomerations, the present invention improves the plume model and redefines the model parameters to make it more suitable for the identification scenario of the airport hinterland. At the same time, it reduces the complexity of model operation, fully considers the impact of the airport's own strength (such as infrastructure construction, operation scale, service quality, etc.), ground transportation accessibility (such as the shortest travel time) and the degree of connection between cities (such as economic and trade exchanges, cultural exchanges, etc.) on the airport hinterland, and improves the accuracy and scientificity of airport hinterland identification.

[0043] Figure 1 A schematic diagram of the flow chart of the airport hinterland identification method provided by the present invention, as shown in FIG. Figure 1 As shown, the present invention provides an airport hinterland identification method, comprising: Step 101, obtaining comprehensive airport operation data of a target airport, as well as inter-regional correlation data and traffic data between the target airport and various surrounding areas.

[0044] In the present invention, the comprehensive airport operation data mainly includes the airport construction data (such as the airfield grade and the number of runways, etc.), the airport operation scale data (such as passenger throughput, cargo and mail throughput and aircraft take-offs and landings, etc.), the airport service quality data (such as the number of operating airlines, the number of airport complaints and the number of cities with flights, etc.) and regional economic data.

[0045] The inter-regional correlation data is used to quantify the degree of correlation between the airport and surrounding cities or regions through indicators such as economic connections and interactions between people and logistics, including data based on population flow, cargo circulation, tourism cooperation and other aspects.

[0046] The traffic data focuses on the transportation network, including the connection between the airport and the surrounding areas by road, rail and water, as well as the corresponding traffic flow, number of passengers and travel time. These data help analyze the airport's accessibility and convenience, and then evaluate its influence and attractiveness to the surrounding areas.

[0047] Step 102, input the airport point source intensity data, the inter-regional correlation data and the traffic data into the airport hinterland identification model to obtain the impact degree value of the target airport on each of the surrounding areas, wherein the airport hinterland identification model is constructed based on the smoke plume model; the airport point source intensity data is calculated using the comprehensive airport operation data.

[0048] In the present invention, the airport point source intensity data is a quantitative indicator that reflects the impact of airport operating activities on the surrounding environment or economy. The airport point source intensity data can be calculated through the airport operation comprehensive data (such as airport construction data, airport operation scale data, airport service quality data and airport external environment data, etc.) through corresponding algorithms or models. The specific content can refer to the subsequent calculation process of airport point source intensity data.

[0049] Inter-regional connection data measures the degree of economic, social or transportation connection between an airport and its surrounding areas. These data may include indicators of population mobility, cargo circulation and transportation network connectivity. Inter-regional connection data is mainly used to reflect the scope and degree of influence of an airport on its surrounding areas. By understanding the degree of connection between an airport and its surrounding areas, we can more accurately evaluate the economic driving effect and transportation improvement effect of the airport on the surrounding areas.

[0050] Traffic data is used to reflect the traffic flow between the airport and the surrounding areas, including the connection and travel time of roads, railways, waterways and other transportation modes. By analyzing the traffic data, we can understand the traffic bottlenecks and potential improvement space between the airport and the surrounding areas, and provide data support for optimizing the transportation network and improving the airport's traffic efficiency.

[0051] The airport hinterland identification model is used to identify the scope and degree of the airport's impact on the surrounding areas. The model is constructed based on the plume model and evaluates its impact by simulating the diffusion effect of airport operations on the surrounding areas. When constructing the airport hinterland identification model, it is necessary to first determine the input parameters of the model (such as airport point source intensity data, inter-regional correlation data, and traffic data, etc.), and select a suitable model structure and algorithm. Then, the prediction performance of the model is optimized by adjusting the parameters. Furthermore, after the airport point source intensity data, inter-regional correlation data, and traffic data are input into the airport hinterland identification model, the model will calculate the impact degree of the target airport on each surrounding area based on these data.

[0052] Step 103: Determine the hinterland area range corresponding to the target airport from the plurality of surrounding areas according to the impact degree value.

[0053] In the present invention, the surrounding area is a geographical unit that is geographically close to the target airport, which may be a city, a province, or a specific economic zone. Which areas are selected as analysis objects depends on the purpose and accuracy requirements of the analysis. Next, based on the influence degree value obtained in the above embodiment, the association strength or dependence degree between the surrounding area and the target airport is determined.

[0054] After the influence values ​​of all surrounding areas are calculated, one or more thresholds can be set to distinguish which areas should be regarded as the hinterland area of ​​the target airport. This threshold can be a ratio relative to the highest or average influence value. Finally, according to the set standard, those areas whose influence values ​​reach or exceed the threshold are screened out from all surrounding areas. These areas constitute the hinterland area range corresponding to the target airport. Optionally, in the present invention, all calculated influence values ​​can also be sorted from large to small, and then according to the sorting result, the surrounding area corresponding to the maximum influence value is determined as the hinterland area range of the target airport.

[0055] The airport hinterland identification method provided by the present invention constructs a model for airport hinterland identification based on the smoke plume model, and comprehensively considers factors such as airport point source intensity, ground transportation accessibility, and the degree of correlation between the airport and the target node area, so as to more accurately identify and divide the airport hinterland range.

[0056] On the basis of the above embodiment, the airport operation comprehensive data includes airport construction data, airport operation scale data, airport service quality data and airport external environment data, and the traffic data includes the shortest ground transportation distance data and the shortest traffic time data between the target airport and the surrounding area, wherein: The airport construction data includes airport flight zone level information and airport runway quantity information; The airport operation scale data includes passenger throughput information, cargo and mail throughput information, aircraft take-off and landing information, and the number of operating airlines; The airport service quality data includes the number of airport complaints and the number of cities served by flights; The airport external environment data includes the total value generated in the area where the airport is located and the fixed asset investment information in the area where the airport is located.

[0057] In the present invention, the airport flight zone grade information refers to the maximum takeoff and landing weight grade of the aircraft that can be guaranteed and allowed to be used by the airport flight zone runway and its related facilities. The higher the flight zone grade, the larger the aircraft that can take off and land at the airport, such as large passenger aircraft, cargo aircraft, etc. This information is crucial for evaluating the airport's capacity and development potential.

[0058] The number of airport runways directly affects the airport's flight handling capacity. Multiple runways can support more flights taking off and landing at the same time, improving the airport's operational efficiency, especially during peak hours.

[0059] Passenger throughput information refers to the number of passengers handled by an airport in a certain period of time (such as a year). It is an important indicator for measuring the scale and service capacity of an airport. A high passenger throughput means that the airport is busy and has a wider service scope.

[0060] Cargo and mail throughput information is similar to passenger throughput, but it is for cargo and mail transported through the airport. This data reflects the airport's service capabilities in the logistics field and its contribution to the regional economy.

[0061] Aircraft takeoff and landing information refers to the number of flight takeoffs and landings handled by an airport within a certain period of time. Aircraft takeoff and landing information directly reflects the flight density and operational efficiency of the airport.

[0062] The information on the number of operating airlines reflects the number of airlines operating at the airport. Multiple airlines operating means passengers have more flight options and more convenient connecting services.

[0063] The number of airport complaints reflects passengers' feedback on the quality of airport services. A large number of complaints may mean that the service quality needs to be improved, while a small number of complaints indicates that the airport service is relatively satisfactory.

[0064] The information on the number of cities with flights refers to the number of domestic and foreign cities that the airport can directly reach. This data reflects the coverage and connectivity of the airport's route network and is an important indicator for evaluating the airport's service scope.

[0065] The gross domestic product information of the airport region reflects the total economic volume and development level of the region where the airport is located. As an important part of the regional economy, the development of the airport is closely related to the regional economy.

[0066] The fixed asset investment information of the airport area refers to the long-term investment activities carried out in the airport area, including infrastructure construction, industrial upgrading, etc. This data can predict the future development space and potential of the airport.

[0067] The shortest ground transportation distance data refers to the shortest ground transportation distance from the target airport to surrounding areas (such as cities, towns, etc.). This data is crucial for evaluating the transportation convenience and accessibility between the airport and surrounding areas.

[0068] The shortest travel time data, corresponding to the shortest ground transportation distance, refers to the shortest travel time from the airport to the surrounding area. It should be noted that in the present invention, the shortest ground transportation distance data and the shortest travel time data can be obtained based on the travel data between the airport and the transportation hubs in the surrounding area (such as bus hubs, railway transportation hubs, and highway transportation hubs, etc.).

[0069] In the present invention, multiple data are used to identify the hinterland area of ​​the airport, which helps to more accurately identify the hinterland area of ​​the airport and provide strong support for the coordinated development of the airport and surrounding areas.

[0070] Based on the above embodiment, the calculation process of the airport point source intensity data specifically includes: Constructing an initial matrix of point source intensity evaluation corresponding to the plurality of target airports according to the airport operation comprehensive data of the plurality of target airports; Standardizing the initial matrix for point source intensity evaluation to obtain a standard matrix for point source intensity evaluation; Based on the entropy weight method, the entropy value corresponding to each type of indicator data in the point source intensity evaluation standard matrix is ​​calculated, and the weight information of each type of indicator data is determined according to the entropy value; According to the point source intensity evaluation standard matrix and the weight information, a weighted decision evaluation matrix is ​​constructed, and the maximum value and the minimum value of each type of the indicator data in the weighted decision evaluation matrix are obtained; According to the maximum value of the indicator and the minimum value of the indicator, a first Euclidean distance and a second Euclidean distance are obtained, wherein the first Euclidean distance is the Euclidean distance between the indicator data in the weighted decision evaluation matrix and the corresponding maximum value of the indicator; the second Euclidean distance is the Euclidean distance between the indicator data in the weighted decision evaluation matrix and the corresponding minimum value of the indicator; The airport point source intensity data corresponding to each of the target airports is calculated based on the first Euclidean distance and the second Euclidean distance.

[0071] The airport point source strength is an indicator to measure the airport's own strength. The present invention constructs an airport point source strength evaluation index system from four aspects: airport construction data, airport operation scale data, airport service quality data and airport external environment data. For details, please refer to Table 1: Table 1 Airport point source intensity evaluation index system

[0072] In the present invention, the airport hinterland identification process of multiple target airports is used for illustration. First, considering the dimensional differences between different indicators, the original data of each indicator needs to be standardized; then, the entropy weight method is used to calculate the weights of each evaluation indicator of airport point source intensity; finally, the TOPSIS method is used to establish an airport point source intensity evaluation model, thereby determining the point source intensity value of each target airport, that is, the airport point source intensity data.

[0073] Specifically, in the present invention, the airport point source intensity evaluation model is set to have evaluation object (i.e. target airport), The initial matrix of point source intensity evaluation is constructed by using evaluation indicators (i.e. comprehensive airport operation data). for: ; in, Indicates The first evaluation object Evaluation indicators.

[0074] Then, the evaluation indexes in the initial matrix of point source intensity evaluation are forward standardized to obtain the evaluation indexes after forward standardization. The forward standardization formula is: ; in, Indicates Airport No. The initial value of the indicator, , .

[0075] The initial matrix after forward normalization is converted into a point source intensity evaluation standard matrix : ; Furthermore, the entropy value corresponding to each type of indicator data in the point source intensity evaluation standard matrix is ​​calculated by the entropy weight method. : ; .

[0076] Then, according to the calculated entropy value, the weight information of each type of indicator data is determined. : .

[0077] Furthermore, the point source intensity evaluation standard matrix and the corresponding Multiply to construct a weighted decision evaluation matrix : ; Then, select the maximum value of each type of indicator data in the weighted decision evaluation matrix (i.e. the maximum value of the indicator) and the minimum value (i.e. the minimum value of the index), and then determine the positive ideal solution of the airport point source intensity and negative ideal solution .

[0078] Furthermore, the relative closeness of each airport to the positive and negative ideal solutions is calculated, that is, the greater the relative closeness, the better the airport. Point source intensity data The larger the value, the smaller the value. The specific calculation formula of airport point source intensity data is: ; ; ; in, Indicates The Euclidean distance between the target airport and the positive ideal solution, that is, the first Euclidean distance; Indicates The Euclidean distance between the target airport and the negative ideal solution is the second Euclidean distance.

[0079] Based on the above embodiment, the inter-region correlation data is obtained by the following steps: Acquire first permanent population data and second permanent population data, wherein the first permanent population data is the permanent population data of the area where the target airport is located; and the second permanent population data is the permanent population data of the surrounding area; Acquire first industrial output value data and second industrial output value data, wherein the first industrial output value data is the industrial output value data of the area where the target airport is located; and the second permanent population data is the industrial output value data of the surrounding area; The inter-regional correlation data between the target airport and the surrounding areas is obtained based on the first permanent population data, the second permanent population data, the first industrial output value data, the second industrial output value data and the shortest travel time data.

[0080] The connection between the airport and the surrounding areas is not only related to the traffic conditions between the two, but also affected by factors such as economic and trade exchanges, cultural exchanges and industrial cooperation. The present invention is based on the quantifiable economic effect intensity and calculates the regional correlation data between the airport and the surrounding areas. The calculation formula of the regional correlation data is as follows: ; in, Indicates The target airport and Inter-regional correlation data between surrounding areas; Indicates The number of permanent residents in the area where the target airport is located, i.e. the first permanent population data; Indicates The number of permanent residents in the surrounding areas is the second permanent population data; Indicates The industrial output value of the area where the target airport is located, that is, the first industrial output value data; Indicates The industrial output value of the surrounding areas, that is, the secondary industrial output value data; Indicates The target airport and The shortest ground transportation distance data between surrounding areas.

[0081] Based on the above embodiment, the formula of the airport hinterland identification model is: ; ; in, Indicates The target airport is the influence degree value of the surrounding area, Indicates The airport point source intensity data of the target airport, Indicates The target airport and The inter-region correlation data between the surrounding regions, Indicates The target airport and The shortest travel time data between the surrounding areas, Indicates The target airport and The shortest ground transportation distance data between the surrounding areas, Indicates Permanent population data of the area where the target airport is located, Indicates Industrial output value data of the area where the target airport is located, Indicates Permanent population data of the surrounding areas, Indicates Industrial output value data of the surrounding areas.

[0082] In the present invention, based on the plume model in the atmospheric pollutant propagation simulation, the airport hinterland identification model is constructed by redefining the model parameters, so that it is suitable for the identification and analysis of the airport hinterland. The specific construction process of the airport hinterland identification model is as follows: The original pollutant concentration in the plume model , defined as an airport In the area The impact value ,If there are multiple airports that have an impact on the region, the hinterland competition scope is identified based on the impact degree value.

[0083] The original pollutant source intensity in the plume model , defined as an airport Airport point source intensity data , specifically the airport The absorption and radiation capacity of the surrounding areas. The field point source intensity data is affected by the airport infrastructure construction, operation capacity, service quality and regional economic level, and is a parameter to measure its own strength. The higher the field point source intensity data, the greater the impact on the surrounding areas.

[0084] The original average wind speed in the plume model , defined as an airport With surrounding areas The shortest travel time between , that is, the convenience of ground transportation. Wind speed will affect the distance of pollutant diffusion and the change of concentration. Similarly, the airport relies on ground transportation to achieve the radiation and attraction of its hinterland. In the present invention, the self-driving travel time and public transportation travel time from each surrounding area to the target airport can be obtained through the existing network map API platform, and then the minimum travel time is selected as the shortest travel time data between the surrounding area and the target airport: ; in, From the surrounding area To the Airport Choose the duration of your self-driving trip. From the surrounding area To the Airport Choose the duration of public transportation.

[0085] The diffusion parameters in the plume model are used to reflect the relationship between pollutants and diffusion areas. According to the infinite space diffusion principle and the properties of the diffusion system, , , that is, the initial model formula can be expressed as: ; Among them, the diffusion coefficient , is a proportional constant. Different from the law of pollutant diffusion, when the airport in the present invention expands its hinterland, its point source intensity remains unchanged, so , after transforming the initial model formula, we get the second model formula: .

[0086] Furthermore, since the diffusion of pollutants in the atmosphere in the plume model is a three-dimensional motion process, and the research on the airport hinterland in the present invention is located in two-dimensional space, it is not considered. The change of the axis direction, therefore, In addition, when identifying the airport hinterland, the airport and the surrounding area are studied in a point-to-point direction, that is, the original smoke plume model Axis direction, and does not consider The diffusion effect in the axial direction , after converting the second model formula, we get the third model formula: .

[0087] Furthermore, based on the original plume model, the present invention adds the regional correlation data between the airport and the surrounding areas. , which is used to describe the economic and trade exchanges and cultural exchanges between the airport area and the surrounding areas. The more frequent the economic and trade exchanges and cultural exchanges between the two places, the greater the impact of the airport on the surrounding area. Therefore, it is added to the numerator of the third model formula to construct the airport hinterland identification model. The specific formula is: ; .

[0088] On the basis of the above embodiment, determining the hinterland area range corresponding to the target airport from the plurality of surrounding areas according to the impact degree value includes: The impact values ​​of the target airport on each of the surrounding areas are sorted in order from large to small, and based on the sorting result of the impact values, the surrounding area corresponding to the maximum impact value is determined as the hinterland area range corresponding to the target airport.

[0089] In the present invention, the improved plume model (i.e., the airport hinterland identification model) is used in combination with the airport point source intensity data, the inter-regional correlation data, the traffic data, etc. to calculate the impact degree of the target airport on each surrounding area.

[0090] After obtaining the impact values ​​of the target airport and all surrounding areas, these values ​​need to be sorted. The purpose of sorting is to clarify which areas are most affected. The result of sorting will be a list of impact values ​​arranged from large to small, and each value corresponds to a specific surrounding area.

[0091] Furthermore, based on the sorted impact values, the present invention determines the surrounding area corresponding to the maximum impact value as the hinterland area of ​​the target airport, because the hinterland area is usually defined as the area that is most affected by the airport and most closely connected with the airport. When determining the hinterland area, some actual conditions need to be considered, such as whether the area has sufficient economic scale, population size, and transportation infrastructure, to ensure the rationality and feasibility of the hinterland area.

[0092] After determining the scope of the hinterland area, the present invention can use ArcGIS and other geographic information system software for visualization. By drawing maps, marking hinterland areas, etc., the hinterland area of ​​the target airport can be intuitively displayed. In addition, in order to verify the accuracy and reliability of the model, the calculation results can be compared with actual data or expert evaluation. After comparison, it can be seen that the calculation results are consistent with the actual situation or the error is within an acceptable range, verifying that the airport hinterland identification model constructed by the present invention is effective.

[0093] The present invention divides the hinterland area range based on the sorting results of the impact degree values, which can more accurately understand the impact degree of the target airport on the surrounding areas and provide strong support for the planning, operation and management of the airport.

[0094] Based on the above embodiment, the method further includes: Obtaining other impact degree values ​​except the maximum impact degree value from the impact degree value sorting result of the target airport; It is determined whether there is an overlapping area between the surrounding areas corresponding to the other impact degree values ​​of each target airport. If so, the overlapping area is determined as the airport competition hinterland area.

[0095] In the present invention, one or more influence values ​​are calculated for each airport, which reflect the influence or attraction of the airport in its surrounding area. Then, these influence values ​​are sorted to find the maximum influence value of each airport, indicating that the surrounding area corresponding to the maximum influence value is the airport hinterland of the airport.

[0096] Furthermore, in order to evaluate the competitive relationship between airports, other impact values ​​need to be considered, because these values ​​may reveal the performance of airports in different dimensions or fields, as well as their advantages and disadvantages compared with competitors. After determining the impact values ​​other than the maximum impact value in the impact value ranking results corresponding to each target airport, it is necessary to link these other impact values ​​with specific geographical areas, which represent the geographical space covered by the impact value.

[0097] Next, compare these geographic areas to see if there is any overlap between them. Overlap means that two or more airports have similar influence or appeal in the same or adjacent geographic areas, which may lead to passengers or cargo in these areas facing choices when choosing airports, thus forming competition between airports. If overlapping areas are found, then these areas are considered to be the competitive hinterland areas between airports. This is because passengers or cargo in these areas may weigh factors such as price, convenience, and service quality when choosing an airport, and thus choose the airport that is most attractive to them.

[0098] The present invention determines the competitive hinterland areas among airports by analyzing the impact degree values ​​of target airports and their corresponding geographical areas. This is of great significance to airport managers and airlines, as it can help relevant personnel understand in which areas they face competition and how to enhance their competitiveness by improving services, adding routes or providing more attractive pricing strategies.

[0099] In one embodiment, the process of drawing and marking the hinterland area of ​​an airport existing in a 1:4 million standard map of a certain airport cluster area is described. Figure 2 The schematic diagram of the airport distribution summary of a certain airport cluster area provided by the present invention can be referred to Figure 2 As shown, the region includes City A, Province B, Province C and Province D, involving 23 civil aviation transport airports, namely SH Airport and PV Airport in City A, NK Airport, WU Airport, XU Airport, CZ Airport, NT Airport, LY Airport, HI Airport, YN Airport and YT Airport in Province B, HG Airport, NG Airport, WN Airport, YI Airport, UZ Airport, HS Airport and HY Airport in Province C, and HF Airport, AQ Airport, TX Airport, FU Airport and UH Airport in Province D. The hinterland to be identified (i.e., the surrounding area) was divided using the county (district) as the basic analysis unit, and a total of 307 county (district) analysis units were obtained.

[0100] In this embodiment, the identification range of the hinterland of each airport in the airport cluster area is basically related to the size of the airport point source intensity, but is also affected by the accessibility of ground transportation and the degree of connection between cities. The airports are unevenly distributed in the hinterland of this airport cluster area. Figure 3 This is a schematic diagram of the distribution of the hinterland of each airport obtained based on the airport hinterland identification model provided by the present invention. The airport hinterland distribution results of the airport cluster area are calculated based on the improved smoke plume model of the present invention. Figure 3 As shown. Figure 3 In the figure, areas of different colors correspond to the hinterland area of ​​the airport (calculated based on the airport point source intensity data, inter-regional correlation data and traffic data in the airport cluster area in 2019).

[0101] Furthermore, five major airports in the airport cluster area were selected for specific analysis, including international aviation hubs (PV, SH) and regional aviation hubs (HG, NK and HF). Figure 4 The schematic diagram of the distribution of the main airport hinterland in a certain airport cluster area provided by the present invention can be referred to Figure 4As shown in the figure, the hinterland of these five airports is centered on the area where the airport is located and extends outward along the railway or highway lines. Among them, the hinterland of PV airport is mainly located in the eastern coastal area, and shows a trend of weakening intensity from east to west. Some areas in the south of Province C are also identified as the hinterland of PV airport. This is because when the distance advantage of these areas to HG is not obvious, PV has a higher attraction to them due to its high airport competitiveness. SH is located between PV, HG and NK, so its airport hinterland is relatively small, mainly concentrated in the west of City A and the south of Province B. The hinterland of HG airport occupies most of the area of ​​Province C and extends its hinterland to the south of Province D, which has less competitiveness. NK airport is located near the geographical center of the airport cluster area. Affected by other airports, its hinterland is concentrated in the west of Province B and the east of Province D. The hinterland of HF airport is concentrated in the central and western parts of Province D. Thanks to its geographical advantages and the regional ground transportation network, the hinterland of HF airport also covers some areas in the north of Province B.

[0102] Specifically, firstly, the weights of indicators at all levels are calculated according to the relevant formulas of the airport point source intensity evaluation index weights. For details, please refer to Table 2: Table 2 Weights of airport point source intensity evaluation indicators

[0103] According to the weight calculation results, in the airport point source intensity evaluation study, the airport external environment indicators occupy a major position, that is, the economic development of the airport area has a great impact on the airport hinterland. From the perspective of the airport itself, the core indicators that determine the airport's competitiveness include the number of cities with flights, cargo and mail throughput, passenger throughput, and aircraft takeoffs and landings. The final results of the airport point source intensity evaluation study can be found in Table 3: Table 3 Calculation results and ranking of point source intensity of each airport in the airport cluster area

[0104] According to the ranking results in Table 3, PV Airport has the highest airport point source intensity in the airport cluster area. This result is mainly due to the obvious advantages of PV Airport in airport infrastructure, airport operation scale, airport service quality and airport external environment. Although facing the diversion pressure of PV Airport, SH Airport still ranks second with its superior geographical location and excellent operation scale. As regional aviation hub airports, HG, NK and HF airports have abundant domestic and international route resources in the region and rank closely behind. In terms of administrative regions, the two airports in City A have the highest point source intensity, taking the top two places. The overall competitiveness of airports in Province B is relatively strong, with a total of 4 airports in the top ten (NK, WU, NT, XU), and the remaining 5 airports are all ranked from 11th to 15th. The overall competitiveness of airports in Province C is slightly weaker, with a total of 3 airports in the top ten (HG, NG, WN), but the remaining 4 airports are relatively weak and ranked after 15th. The competitiveness of airports in Province D needs to be improved. Except for HF Airport, the point source intensity rankings of other airports are all ranked after 15th.

[0105] Furthermore, the hinterland of major airports is identified. The hinterland of SH and PV airports is mainly centered on City A and radiates outward along railways and highways. Figure 5 The schematic diagram of the distribution of the airport hinterland of City A in the airport cluster area provided by the present invention can be referred to Figure 5 As shown in the figure, the airport hinterland of PV is mainly concentrated in the eastern coastal area of ​​City A and extends along (railways and highways) to the northern area of ​​Province C; the airport hinterland of SH is concentrated in the city center and western area of ​​A and extends along (railways and highways) to the eastern area of ​​Province B. It is worth noting that as the two airports with the highest point source intensity in the airport cluster area, the hinterland range of SH and PV airports is not outstanding. The reason is that the airports in the airport cluster area are densely populated and the competition is fierce.

[0106] The hinterland of the airport in Province C is relatively uniform. Figure 6 The schematic diagram of the distribution of airport hinterland in Province C in the airport cluster area provided by the present invention can be referred to Figure 6 As shown, the hinterland of HG Airport covers the largest number of areas (24 areas), which are mainly concentrated in the northern part of Province C and extend to the southern part of Province D. The hinterland of WN Airport covers 19 areas, ranking second in Province C in terms of the number of covered areas, and is mainly concentrated in the southern part of Province C. The hinterland of NG Airport covers 13 areas, which are mainly concentrated in the eastern coastal area of ​​Province C. The main reason why the number of WN Airport hinterlands exceeds that of NG Airports is that there are few airports in the southern part of Province C and there is a lack of airports that can compete with them. The airport hinterlands of UZ, YI and HY are concentrated in the central area of ​​Province C, and are distributed in a belt from west to east. Due to its geographical location, the airport hinterland of HS is concentrated near coastal islands.

[0107] The airport hinterland of Province B is relatively scattered, which is related to the fierce competition among airports within Province B. Figure 7 The schematic diagram of the distribution of airport hinterland in Province B in the airport cluster area provided by the present invention can be referred to Figure 7 As shown in the figure, NK's airport hinterland is concentrated in the southwest of Province B, and with the capital city of Province B as the center, it extends to the eastern part of Province D along the (railway line), where the competitive pressure is less. XU has a vast airport hinterland with its developed railway and highway network, covering 27 areas in the north of Province B and Province D. Although WU, NT and CZ have good airport competitive advantages, the size of their airport hinterland is limited by the fact that their airports are located around international aviation hubs and regional aviation hubs such as PV, SH, NK and HG. The airport hinterlands of YN, YT, HI and LY are mainly concentrated in the central and eastern parts of Province B, and they have certain selective advantages in their regions.

[0108] Within the airport cluster area, the airport construction level of Province D is relatively weak, and its areas are mostly "eroded" by airports in other provinces and cities. Figure 8 The schematic diagram of the distribution of airport hinterland in Province D in the airport cluster area provided by the present invention can be referred to Figure 8 As shown in the figure, NK and XU have the most obvious influence on Province D, occupying almost most of the eastern and northern areas of Province D. HF, located in the provincial capital, has a greater selection advantage in the central and western areas of Province D, and its airport hinterland covers 35 areas of Province D. Due to the limited competitiveness of the airports themselves and the pressure from the superior airports in the east, the airport hinterlands of AQ, FU, TX and UH are also mostly concentrated in the west of Province D, and the range of hinterland advantages is not obvious.

[0109] Furthermore, the airport competition hinterland between the above airports is identified. The airport competition hinterland refers to the area whose identification is easily transferred by other airports due to the limited influence of a certain airport. By analyzing the airport influence degree of all areas in the above airport cluster area, the distribution of the airport competition hinterland in the airport cluster area is drawn. Fig. 9 The schematic diagram of the distribution of the airport competition hinterland in the airport cluster area provided by the present invention can be referred to Fig. 9 As shown in the figure, the darker color indicates that the area is more affected by a certain airport, and the area is not easily affected by other airports. However, the lighter color indicates that the area is less affected by a single airport, that is, the airports compete for the hinterland. There may be two reasons for this result: first, the region has unique geographical advantages and a high level of economic development, which leads to fierce competition from multiple airports, making the airports evenly matched in the fight for the hinterland; second, the region is geographically remote and has a limited level of economic development, so the surrounding airports have little influence on it. Fig.10This is a schematic diagram of the recognition results of the airport competition hinterland between the main airports provided by the present invention. Based on the recognition accuracy of different airport competition hinterlands, the recognition results of the airport competition hinterlands between PV, SH, HG, NK and HF airports can be referred to Fig.10 shown.

[0110] The present invention introduces the plume model and redefines and modifies it in combination with actual factors such as airport point source intensity and ground transportation convenience, making the identification of the airport hinterland more accurate and scientific. This method not only takes into account internal factors such as airport infrastructure construction, operating scale, and service quality, but also fully considers the impact of external factors such as regional economic level and ground transportation convenience on the airport hinterland, making the identification results more in line with the actual situation, thereby improving the accuracy and reliability of the identification.

[0111] Compared with traditional qualitative research methods (such as circle division method) and costly quantitative research methods (such as questionnaire method), the present invention uses mathematical models for quantitative analysis, which has clear logic and is easy to operate, greatly improving research efficiency. At the same time, the weights of evaluation indicators are determined by the entropy weight method, which reduces the interference of human factors and makes the results more objective and fair. In addition, the airport hinterland distribution results calculated by the plume model of the present invention can be intuitively displayed on the map, which is convenient for decision makers to conduct spatial analysis and planning. This visual display not only improves the efficiency of information transmission, but also makes the planning process more intuitive and easy to understand.

[0112] At the same time, the application of the present invention helps to clarify the hinterland scope of each airport, providing a scientific basis for competition and cooperation between airports. Through reasonable planning and layout, disorderly competition and duplicate construction between airports can be avoided, the coordinated development of regional aviation transportation industry can be promoted, and it is also helpful to optimize the layout of regional transportation network and improve overall transportation efficiency.

[0113] The airport hinterland identification system provided by the present invention is described below. The airport hinterland identification system described below and the airport hinterland identification method described above can be referenced to each other.

[0114] Fig.11 The schematic diagram of the structure of the airport hinterland identification system provided by the present invention is as follows: Fig.11As shown, the present invention provides an airport hinterland identification system, including a data acquisition module 1101, a model calculation module 1102 and a hinterland result generation module 1103, wherein the data acquisition module 1101 is used to obtain the airport operation comprehensive data of the target airport, as well as the inter-regional correlation data and the passage data between the target airport and each surrounding area; the model calculation module 1102 is used to input the airport point source intensity data, the inter-regional correlation data and the passage data into the airport hinterland identification model to obtain the impact degree value of the target airport on each of the surrounding areas, wherein the airport hinterland identification model is constructed based on the smoke plume model; the airport point source intensity data is calculated through the airport operation comprehensive data; the hinterland result generation module 1103 is used to determine the hinterland area range corresponding to the target airport from a plurality of the surrounding areas according to the impact degree value.

[0115] The airport hinterland identification system provided by the present invention constructs a model for airport hinterland identification based on the smoke plume model, and comprehensively considers factors such as airport point source intensity, ground transportation accessibility, and the degree of correlation between the airport and the target node area, so as to more accurately identify and divide the airport hinterland range.

[0116] The system provided in the embodiment of the present invention is used to execute the above-mentioned method embodiments. Please refer to the above-mentioned embodiments for the specific process and detailed contents, which will not be repeated here.

[0117] Fig.12 A schematic diagram of the structure of an electronic device provided by the present invention, such as Fig.12 As shown, the electronic device may include: a processor 1201, a communication interface 1202, a memory 1203 and a communication bus 1204, wherein the processor 1201, the communication interface 1202 and the memory 1203 communicate with each other through the communication bus 1204. The processor 1201 may call the logic instructions in the memory 1203 to execute the airport hinterland identification method, which includes: obtaining the airport operation comprehensive data of the target airport, and the inter-regional correlation data and the passage data between the target airport and each surrounding area; inputting the airport point source intensity data, the inter-regional correlation data and the passage data into the airport hinterland identification model to obtain the impact degree value of the target airport on each of the surrounding areas, wherein the airport hinterland identification model is constructed based on the smoke plume model; the airport point source intensity data is calculated by the airport operation comprehensive data; according to the impact degree value, determining the hinterland area range corresponding to the target airport from the multiple surrounding areas.

[0118] In addition, the logic instructions in the above-mentioned memory 1203 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk.

[0119] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the airport hinterland identification method provided by the above-mentioned methods, and the method includes: obtaining comprehensive airport operation data of the target airport, as well as inter-regional correlation data and traffic data between the target airport and each surrounding area; inputting the airport point source intensity data, the inter-regional correlation data and the traffic data into an airport hinterland identification model to obtain the impact degree value of the target airport on each of the surrounding areas, wherein the airport hinterland identification model is constructed based on a smoke plume model; the airport point source intensity data is calculated using the comprehensive airport operation data; and according to the impact degree value, determining the hinterland area range corresponding to the target airport from multiple surrounding areas.

[0120] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the airport hinterland identification method provided in the above-mentioned embodiments, the method comprising: obtaining comprehensive airport operation data of the target airport, as well as inter-regional correlation data and traffic data between the target airport and each surrounding area; inputting the airport point source intensity data, the inter-regional correlation data and the traffic data into an airport hinterland identification model to obtain an impact degree value of the target airport on each of the surrounding areas, wherein the airport hinterland identification model is constructed based on a smoke plume model; the airport point source intensity data is calculated using the comprehensive airport operation data; and according to the impact degree value, determining the hinterland area range corresponding to the target airport from a plurality of the surrounding areas.

[0121] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0122] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying an airport hinterland, characterized in that: include: Obtaining comprehensive airport operation data of a target airport, as well as inter-regional correlation data and traffic data between the target airport and various surrounding areas; Input the airport point source intensity data, the inter-regional correlation data and the traffic data into the airport hinterland identification model to obtain the impact degree value of the target airport on each of the surrounding areas, wherein the airport hinterland identification model is constructed based on the smoke plume model; the airport point source intensity data is calculated using the airport operation comprehensive data; According to the impact degree value, the hinterland area range corresponding to the target airport is determined from the multiple surrounding areas.

2. The airport hinterland identification method according to claim 1, characterized in that: The airport operation comprehensive data includes airport construction data, airport operation scale data, airport service quality data and airport external environment data; the traffic data includes the shortest ground transportation distance data and the shortest traffic time data between the target airport and the surrounding area, wherein: The airport construction data includes airport flight zone level information and airport runway quantity information; The airport operation scale data includes passenger throughput information, cargo and mail throughput information, aircraft take-off and landing information, and the number of operating airlines; The airport service quality data includes the number of airport complaints and the number of cities served by flights; The airport external environment data includes the total value generated in the area where the airport is located and the fixed asset investment information in the area where the airport is located.

3. The airport hinterland identification method according to claim 2, characterized in that: The calculation process of the airport point source intensity data specifically includes: Constructing an initial matrix of point source intensity evaluation corresponding to the plurality of target airports according to the airport operation comprehensive data of the plurality of target airports; Standardizing the initial matrix for point source intensity evaluation to obtain a standard matrix for point source intensity evaluation; Based on the entropy weight method, the entropy value corresponding to each type of indicator data in the point source intensity evaluation standard matrix is ​​calculated, and the weight information of each type of indicator data is determined according to the entropy value; According to the point source intensity evaluation standard matrix and the weight information, a weighted decision evaluation matrix is ​​constructed, and the maximum value and the minimum value of each type of the indicator data in the weighted decision evaluation matrix are obtained; According to the maximum value of the indicator and the minimum value of the indicator, a first Euclidean distance and a second Euclidean distance are obtained, wherein the first Euclidean distance is the Euclidean distance between the indicator data in the weighted decision evaluation matrix and the corresponding maximum value of the indicator; the second Euclidean distance is the Euclidean distance between the indicator data in the weighted decision evaluation matrix and the corresponding minimum value of the indicator; The airport point source intensity data corresponding to each of the target airports is calculated based on the first Euclidean distance and the second Euclidean distance.

4. The airport hinterland identification method according to claim 3, characterized in that: The inter-regional correlation data is obtained by the following steps: Acquire first permanent population data and second permanent population data, wherein the first permanent population data is the permanent population data of the area where the target airport is located; and the second permanent population data is the permanent population data of the surrounding area; Acquire first industrial output value data and second industrial output value data, wherein the first industrial output value data is the industrial output value data of the area where the target airport is located; and the second permanent population data is the industrial output value data of the surrounding area; The inter-regional correlation data between the target airport and the surrounding areas is obtained based on the first permanent population data, the second permanent population data, the first industrial output value data, the second industrial output value data and the shortest travel time data.

5. The airport hinterland identification method according to claim 4, characterized in that: The formula of the airport hinterland identification model is: ； ； in, Indicates The target airport is the influence degree value of the surrounding area, Indicates The airport point source intensity data of the target airport, Indicates The target airport and The inter-region correlation data between the surrounding regions, Indicates The target airport and The shortest travel time data between the surrounding areas, Indicates The target airport and The shortest ground transportation distance data between the surrounding areas, Indicates Permanent population data of the area where the target airport is located, Indicates Industrial output value data of the area where the target airport is located, Indicates Permanent population data of the surrounding areas, Indicates Industrial output value data of the surrounding areas.

6. The airport hinterland identification method according to any one of claims 3 to 5, characterized in that: Determining the hinterland area range corresponding to the target airport from the plurality of surrounding areas according to the impact degree value includes: The impact values ​​of the target airport on each of the surrounding areas are sorted in order from large to small, and based on the sorting result of the impact values, the surrounding area corresponding to the maximum impact value is determined as the hinterland area range corresponding to the target airport.

7. The airport hinterland identification method according to claim 6, characterized in that: The method further comprises: Obtaining other impact degree values ​​except the maximum impact degree value from the impact degree value sorting result of the target airport; It is determined whether there is an overlapping area between the surrounding areas corresponding to the other impact degree values ​​of each target airport. If so, the overlapping area is determined as the airport competition hinterland area.

8. An airport hinterland identification system, characterized in that: include: A data collection module, used to obtain comprehensive airport operation data of a target airport, as well as inter-regional correlation data and traffic data between the target airport and various surrounding areas; A model calculation module is used to input the airport point source intensity data, the inter-regional correlation data and the traffic data into the airport hinterland identification model to obtain the impact degree value of the target airport on each of the surrounding areas, wherein the airport hinterland identification model is constructed based on the smoke plume model; the airport point source intensity data is calculated using the airport operation comprehensive data; The hinterland result generating module is used to determine the hinterland area range corresponding to the target airport from the plurality of surrounding areas according to the impact degree value.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the airport hinterland identification method as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the airport hinterland identification method according to any one of claims 1 to 7 is implemented.

Images