Airport green development supply and demand coordination evolution analysis method based on index screening optimization

Abstract

The application discloses an airport green development management technical field airport green development supply and demand coordination evolution analysis method based on index screening optimization, and aims to solve the problem that the existing technology lacks analysis of airport green development supply and demand subsystem coordination evolution from the supply and demand sides. It includes obtaining index data set of the airport green development demand subsystem and index data set of the airport green development supply subsystem, obtaining the airport green development demand coordination index set and the airport green development supply coordination index set, obtaining the order degree of both, and inputting into the previously constructed Haken model to analyze the airport green development supply and demand coordination evolution; the method fills the technical gap of analyzing the supply and demand evolution relationship of the airport in the green development process from the overall perspective of the supply and demand system, and has important research significance for forming high-level dynamic balance of demand leading supply and supply creating demand and realizing the sustainable development of the airport.

Description

Technical Field

[0001] This invention relates to a method for analyzing the supply and demand synergistic evolution of airport green development based on indicator screening and optimization, and belongs to the field of airport green development management technology. Background Technology

[0002] In recent years, the development of the aviation industry has promoted the development of my country's air transport industry. As a transportation service industry, the supply and demand relationship of air transport has become increasingly complex with the continuous improvement of the transportation system. Faced with the diversity and dynamism of transportation demand, the inherent finiteness and time lag of transportation supply have led to an acute contradiction between air transport supply and demand, manifested in a series of problems such as airspace resource shortages, increased workload of air traffic controllers, and serious environmental pollution, which in turn hinder the sustainable development of the air transport industry.

[0003] As a crucial infrastructure of the air transport industry, the green development of airports plays a vital role in promoting the green transformation of civil aviation. While airports drive trade and promote economic prosperity, the air and noise pollution they generate also pose significant threats to the environment, failing to meet the demands of green development that prioritizes resource conservation and environmental protection, and is guided by efficient, convenient, and human-centered services. Current research on the supply and demand relationship in airport green development primarily focuses on the supply and demand side, analyzing the environmental capacity and carrying capacity of the supply side. Technical methods for analyzing the evolution of supply and demand relationships in the green development process from a holistic perspective of both sides are lacking. Therefore, in-depth analysis of the supply and demand relationship in the green development process of airports, revealing the interaction mechanism of the airport green development supply and demand subsystems, is of significant research importance for achieving a high-level dynamic balance where demand drives supply and supply adapts to demand, thus realizing the sustainable development of airports. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for analyzing the coordinated evolution of supply and demand in airport green development based on indicator screening and optimization, thereby solving the problem of the lack of analysis of the coordinated evolution of the supply and demand subsystems of airport green development from both the supply and demand sides in the prior art.

[0005] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution:

[0006] In a first aspect, the present invention provides a method for analyzing the coordinated evolution of supply and demand in airport green development based on indicator screening and optimization, comprising the following steps:

[0007] Collect data on the green development needs of airports and construct an indicator dataset for the airport green development needs subsystem.

[0008] Collect supply data on airport green development and construct an indicator dataset for the airport green development supply subsystem based on the Pressure-State-Response model.

[0009] The indicator dataset of the airport green development demand subsystem is input into the pre-built airport green development supply and demand coordination indicator screening and optimization model to obtain the airport green development demand coordination indicator set; the indicator dataset of the airport green development supply subsystem is input into the pre-built airport green development supply and demand coordination indicator screening and optimization model to obtain the airport green development supply coordination indicator set.

[0010] The set of collaborative indicators for airport green development needs is input into a pre-built entropy weight TOPSIS model to obtain the orderliness of the airport green development demand subsystem; the set of collaborative indicators for airport green development supply is input into a pre-built entropy weight TOPSIS model to obtain the orderliness of the airport green development supply subsystem.

[0011] The orderliness of the airport green development demand subsystem and the orderliness of the airport green development supply subsystem are input into a pre-constructed Haken model. The Haken model outputs four parameters. Based on the values ​​of the four parameters and the state evaluation function, the interaction relationship and the degree of coordinated development between the airport green development demand subsystem and the airport green development supply subsystem are analyzed, so as to realize the co-evolution analysis of airport green development supply and demand.

[0012] Furthermore, the criteria layer of the indicator dataset of the airport green development demand subsystem includes: resource conservation layer indicators, environmental friendliness layer indicators, and operational efficiency layer indicators;

[0013] Among them, the resource conservation indicators include annual passenger throughput per unit area, annual takeoffs and landings per unit area, annual average comprehensive water consumption per passenger, and annual average comprehensive energy consumption per passenger.

[0014] Among them, the environmental friendliness level indicators include carbon dioxide emissions per unit of passenger throughput, annual emissions of various pollutants, and noise levels;

[0015] Among them, the indicators for efficient operation include annual passenger throughput, annual cargo and mail throughput, annual takeoffs and landings, airport on-time departure rate, and airport average taxiing time.

[0016] Furthermore, the criterion layer of the indicator dataset of the airport green development supply subsystem includes: supply pressure layer indicators, supply status layer indicators, and supply response layer indicators.

[0017] Among them, the supply pressure indicators include urban per capita GDP, urban population density, reduction in energy consumption per unit of GDP, and pollutant emission intensity per 10,000 yuan of industrial output.

[0018] Among them, the supply status indicators include the rate of decrease in energy consumption per passenger, the airport's public transportation facility support capacity, the airport service evaluation score, runway capacity, and the rate of improvement in on-time departure.

[0019] Among them, the supply-response level indicators include the per capita income contribution of airports, the green coverage rate of airports, the proportion of pollution control investment in GDP, and the proportion of urban air transport investment in GDP.

[0020] Furthermore, the method for constructing the Haken model includes:

[0021] Based on the indicator datasets of the airport green development demand subsystem and the airport green development supply subsystem, an optimization model for screening and selecting airport green development supply and demand coordination indicators is constructed based on the entropy weight TOPSIS model and the coordination degree model.

[0022] The genetic algorithm with elite retention strategy is used to solve the optimization model for screening and optimizing the supply and demand coordination indicators of airport green development. The set of coordination indicators of airport green development supply and demand subsystem is iterated with the optimal coordination degree as the guide.

[0023] The orderliness of the airport green development supply and demand subsystem is updated based on the iterative results of the genetic algorithm, and a Haken model that can characterize the co-evolution of airport green development supply and demand is constructed based on the orderliness of the airport green development supply and demand subsystem.

[0024] Furthermore, methods for constructing a model for screening and optimizing supply and demand synergy indicators for airport green development include:

[0025] Set up 0-1 decision variables for screening indicators of the airport green development supply and demand subsystem, and screen the indicator datasets of the airport green development demand subsystem and the airport green development supply subsystem:

[0026]

[0027]

[0028] In the formula, x j x is the result of the indicator selection for the airport green development demand subsystem. j =1 indicates that the indicator will be selected into the collaborative indicator set X of the airport green development demand subsystem, and 0 indicates otherwise; where X is the pre-defined collaborative indicator set in the indicator data of the airport green development demand subsystem.

[0029] In the formula, y j The results of the indicator selection for the airport green development supply subsystem, y j=1 indicates that the indicator will be selected into the collaborative indicator set Y of the airport green development supply subsystem, and 0 indicates otherwise; where Y is the pre-defined collaborative indicator set in the indicator data of the airport green development supply subsystem.

[0030] Based on the selected indicators of the airport green development supply and demand subsystems, an entropy-weighted TOPSIS model is constructed to obtain the orderliness U of the airport green development demand subsystem. X (t) and the orderliness of the airport green development supply subsystem U Y (t);

[0031] Based on the calculation results of the orderliness of the airport green development demand subsystem and the orderliness of the airport green development supply subsystem, a synergy model is constructed to calculate the synergy SD of the airport green development supply and demand subsystems:

[0032]

[0033] In the formula, SD(t) represents the degree of coordination between the supply and demand subsystems for airport green development in year t.

[0034]

[0035] In the formula, α and β represent the weights of the subsystem's degree of order and the rate of change of its degree of order, respectively, in influencing the overall level of coordinated development of the system; θ is the discriminant coefficient: when the two subsystems develop in opposite directions or one of them is stagnant, the product of their rates of change is less than or equal to 0, the synergistic effect weakens, and θ is 0; conversely, when the two subsystems develop in the same direction, the product of their rates of change is greater than 0, the synergistic effect strengthens, and θ is 1.

[0036]

[0037] Using the maximization of the synergy between the supply and demand system for airport green development as the objective function, and the number of selected indicators and the correlation between the selected indicators as constraints, an optimization model for selecting synergy indicators for airport green development supply and demand is constructed:

[0038]

[0039]

[0040] In the formula, and These represent the lower limits of the number of indicators in the collaborative indicator set of the airport green development demand subsystem and the collaborative indicator set of the airport green development supply subsystem, respectively; n x and n y These represent the total number of indicators for the airport green development demand subsystem and the airport green development supply subsystem, respectively; SP max The threshold for the correlation between indicators; SP MNSpearman correlation coefficients of two indicators M and N in the set of collaborative indicators for the airport green development demand subsystem:

[0041]

[0042] In the formula, R(x) iM ) and R(x iN ) represent x respectively iM and x iN Let (x) be the rank of x. 1M ,x 2M ,...,x mM ) T It consists of m samples from M, where x iM Sorted in ascending order as x (1)M <x (2)M <...<x (m)M ,like Then it is believed It is x iM rank, x iN The same applies to the rank;

[0043] Similarly, the Spearman correlation coefficient SP between two indicators in the collaborative indicator set of the computer field green development supply subsystem is... PQ :

[0044]

[0045] Furthermore, based on the genetic algorithm with an elite retention strategy, an optimization model for screening and selecting synergistic indicators for airport green development supply and demand is solved. Guided by the optimal synergy degree, the set of synergistic indicators for the airport green development supply and demand subsystems is iteratively derived, including:

[0046] Step A: Set algorithm parameters, including population size, number of iterations, elite retention rate, crossover and mutation probabilities;

[0047] Step B: Encode the index screening results with 0-1 and randomly initialize the population according to the population size;

[0048] Step C: Calculate the supply and demand system synergy degree for each individual, and set a penalty function based on the constraints of the number of indicators and the correlation constraints to calculate the fitness of the individual;

[0049] Step D: Generate a new generation of population through selection, crossover, mutation, and elite retention operations, and update the fitness value of the population;

[0050] Step E: Repeat step D until the number of iterations is met, calculate the optimal synergy in the current solution, and output the corresponding set of supply and demand indicators for airport green development through decoding operations.

[0051] Furthermore, the orderliness of the airport green development supply and demand subsystem is updated based on the iterative results of the genetic algorithm. A Haken model capable of characterizing the co-evolution of airport green development supply and demand is constructed based on this orderliness, including:

[0052] Based on the coordination index of the airport green development supply and demand subsystems obtained through genetic algorithm iteration, the orderliness U of the airport green development demand subsystem and supply subsystem is calculated using the entropy-weighted TOPSIS model. x (t) and U y (t);

[0053] Constructing a Haken model to analyze the coordinated development process and evolution mechanism of the supply and demand subsystems for airport green development:

[0054]

[0055]

[0056] Discretize the model:

[0057] U X (t)=(1-γ1)U X (t-1)-aU X (t-1)U Y (t-1)

[0058] U Y (t)=(1-γ2)U Y (t-1)+bU X (t-1) 2

[0059] In the formula, a and b represent U X and U Y The control parameters for the intensity of interaction between them; γ1 and γ2 represent the damping coefficients of the airport green development demand subsystem and supply subsystem, respectively;

[0060] The adiabatic approximation method is used to identify the order parameters of the supply and demand system. When γ2>0 and γ2>>|γ1|, it is said that the system satisfies the "adiabatic approximation assumption", indicating that the order parameter of the system is U. X At this moment, the U was instantly withdrawn. Y ,make Order parameter U X Keeping them constant, we can obtain the system's order parameter evolution equations:

[0061]

[0062] right The system potential function is obtained by integrating the opposite of the original value:

[0063]

[0064] make By combining the potential function, the stability point of the coordinated development of the supply and demand subsystems for airport green development can be solved [U]. X * (t),ν(U X * (t))];When the product of a, b, γ1, and γ2 is positive, the evolution equation has one and only one unique solution U. X * (t) = 0, corresponding to a stable point on the potential function; when the product of a, b, γ1, and γ2 is negative, the evolution equation has three solutions: U X * (t)=0、 These correspond to three stable points on the potential function. When a point on the potential function is in a non-equilibrium position, it tends to revert to a stable state. Simultaneously, the state of this point is also affected by its distance from the stable points. Therefore, the system's state evaluation function can be defined as follows:

[0065]

[0066] The smaller d is, the more stable the current state of the system is, and the higher the degree of coordination between the airport green development supply subsystem and the airport green development demand subsystem.

[0067] Secondly, this invention provides an airport green development supply and demand synergistic evolution analysis device based on indicator screening and optimization, comprising:

[0068] The first data acquisition module collects data on the airport's green development needs, and obtains the indicator dataset of the airport's green development needs subsystem based on the principles of resource conservation, environmental friendliness, and operational efficiency.

[0069] The second data acquisition module collects supply data for airport green development. Based on the Pressure-State-Response (PSR) model, and taking supply pressure, supply status, and supply response as criteria, it obtains the indicator dataset of the airport green development supply subsystem.

[0070] The first calculation module: inputs the obtained indicator dataset of the airport green development demand subsystem into the pre-built airport green development supply and demand coordination indicator screening and optimization model to obtain the airport green development demand coordination indicator set; inputs the obtained indicator dataset of the airport green development supply subsystem into the pre-built airport green development supply and demand coordination indicator screening and optimization model to obtain the airport green development supply coordination indicator set.

[0071] The second calculation module inputs the acquired set of airport green development demand coordination indicators into the pre-built entropy weight TOPSIS model to obtain the orderliness of the airport green development demand subsystem; it also inputs the acquired set of airport green development supply coordination indicators into the pre-built entropy weight TOPSIS model to obtain the orderliness of the airport green development supply subsystem.

[0072] The third calculation module inputs the obtained orderliness of the airport green development demand subsystem and the airport green development supply subsystem into a pre-constructed Haken model. The Haken model outputs four parameters. Based on the values ​​of the four parameters and the state evaluation function, the module analyzes the interaction relationship and the degree of coordinated development between the airport green development demand subsystem and the airport green development supply subsystem, and analyzes the co-evolution of airport green development supply and demand.

[0073] Thirdly, the present invention provides a terminal, including a processor and a storage medium;

[0074] The storage medium is used to store instructions;

[0075] The processor is configured to operate according to the instructions to perform the steps of the method described in the first aspect.

[0076] Fourthly, a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in the first aspect.

[0077] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0078] 1. This invention comprehensively considers four dimensions: economy, society, environment, and airport operation. It constructs indicator datasets for the airport green development demand subsystem and the airport green development supply subsystem, respectively. Through the constructed Haken model, it analyzes the synergistic evolution process of the airport green development supply and demand sides. This fills the technical gap in analyzing the supply and demand evolution relationship of airports in the green development process from the overall perspective of the supply and demand system. It has important research significance for forming a high-level dynamic balance of demand driving supply and supply creating demand, and realizing the sustainable development of airports.

[0079] 2. This invention constructs an optimization model for screening and optimizing the supply and demand coordination indicators of airport green development based on the entropy weight TOPSIS model and the coordination degree model, and solves the model by combining it with a genetic algorithm. The model selects the coordination indicators of the supply and demand subsystem of airport green development based on the optimal coordination degree result, which has certain innovative significance. Attached Figure Description

[0080] Figure 1 This is a flowchart illustrating an airport green development supply and demand synergistic evolution analysis method based on indicator screening and optimization, according to an embodiment of the present invention.

[0081] Figure 2 This is a schematic diagram of the process for constructing the Haken model according to an embodiment of the present invention;

[0082] Figure 3 This is a schematic diagram of the indicator dataset of the airport green development demand subsystem provided in an embodiment of the present invention;

[0083] Figure 4 This is a schematic diagram of the indicator dataset of the airport green development supply subsystem provided according to an embodiment of the present invention;

[0084] Figure 5 This is a flowchart illustrating the process of solving the airport green development supply and demand coordination index screening and optimization model based on a genetic algorithm with an elite retention strategy, according to an embodiment of the present invention. Detailed Implementation

[0085] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0086] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0087] Example 1:

[0088] like Figure 1 As shown, this invention provides a method for analyzing the coordinated evolution of supply and demand in airport green development based on indicator screening and optimization, including the following steps:

[0089] Collect data on the green development needs of airports, and obtain the indicator dataset of the airport green development needs subsystem based on the principles of resource conservation, environmental friendliness, and operational efficiency.

[0090] Collect supply data on airport green development, and based on the Pressure-State-Response (PSR) model, obtain the indicator dataset of the airport green development supply subsystem according to the criteria of supply pressure, supply status, and supply response.

[0091] The indicator dataset of the airport green development demand subsystem is input into the pre-built airport green development supply and demand coordination indicator screening and optimization model to obtain the airport green development demand coordination indicator set; the indicator dataset of the airport green development supply subsystem is input into the pre-built airport green development supply and demand coordination indicator screening and optimization model to obtain the airport green development supply coordination indicator set.

[0092] The set of collaborative indicators for airport green development needs is input into a pre-built entropy weight TOPSIS model to obtain the orderliness of the airport green development demand subsystem; the set of collaborative indicators for airport green development supply is input into a pre-built entropy weight TOPSIS model to obtain the orderliness of the airport green development supply subsystem.

[0093] The orderliness of the airport green development demand subsystem and the orderliness of the airport green development supply subsystem are input into a pre-constructed Haken model. The Haken model outputs four parameters. Based on the values ​​of the four parameters and the state evaluation function, the interaction relationship and the degree of coordinated development between the airport green development demand subsystem and the airport green development supply subsystem are analyzed, so as to realize the co-evolution analysis of airport green development supply and demand.

[0094] Specifically, referencing airport green development policies, an indicator dataset for the airport green development demand subsystem is constructed based on the principles of resource conservation, environmental friendliness, and operational efficiency. This dataset, built by referencing airport green development policies, directly reflects the requirements of policy objectives and facilitates the coordinated implementation of policies. Based on national sustainable development policies and airport green development strategies, the airport green development demand is broken down into three aspects: resource conservation, environmental friendliness, and operational efficiency. Following the principles of scientific rigor, comprehensiveness, and quantifiability, indicators are selected by reviewing relevant policy materials and literature to construct the airport green development demand subsystem indicator dataset.

[0095] like Figure 3 As shown in one embodiment, the criteria layer of the indicator dataset of the airport green development demand subsystem includes: resource conservation layer indicators, environmental friendliness layer indicators, and operational efficiency layer indicators.

[0096] Among them, the resource conservation indicators include annual passenger throughput per unit area, annual takeoffs and landings per unit area, annual average comprehensive water consumption per passenger, and annual average comprehensive energy consumption per passenger.

[0097] Among them, the environmental friendliness level indicators include carbon dioxide emissions per unit of passenger throughput, annual emissions of various pollutants, and noise levels;

[0098] Among them, the indicators for efficient operation include annual passenger throughput, annual cargo and mail throughput, annual takeoffs and landings, airport on-time departure rate, and airport average taxiing time.

[0099] Specifically, regarding the resource conservation indicators, the green development of airports emphasizes reducing resource consumption and improving resource utilization. These indicators are constructed from three aspects: land conservation, water conservation, and energy conservation. They include annual passenger throughput per unit area, annual takeoffs and landings per unit area, average annual water consumption per passenger, and average annual energy consumption per passenger. Regarding the environmental friendliness indicators, these indicators are constructed from two aspects: low-carbon construction and environmental governance. They include carbon dioxide emissions per unit passenger throughput, annual emissions of various pollutants (CO, HC, NOx, PM), and noise levels. Regarding the operational efficiency indicators, these indicators are constructed from two aspects: the airport's production system and the airport's operational efficiency. The airport's production system indicators include annual passenger throughput, annual cargo and mail throughput, annual takeoffs and landings, airport on-time departure rate, and average taxiing time.

[0100] like Figure 4 As shown in one embodiment, the criterion layer of the indicator dataset of the airport green development supply subsystem includes: supply pressure layer indicators, supply status layer indicators, and supply response layer indicators.

[0101] Among them, the supply pressure indicators include urban per capita GDP, urban population density, reduction in energy consumption per unit of GDP, and pollutant emission intensity per 10,000 yuan of industrial output.

[0102] Among them, the supply status indicators include the rate of decrease in energy consumption per passenger, the airport's public transportation facility support capacity, the airport service evaluation score, runway capacity, and the rate of improvement in on-time departure.

[0103] Among them, the supply-response level indicators include the per capita income contribution of airports, the green coverage rate of airports, the proportion of pollution control investment in GDP, and the proportion of urban air transport investment in GDP.

[0104] Specifically, the Pressure-State-Response (PSR) model follows a causal analysis approach of "supply status - supply deficiencies - solutions," constructing a dataset of indicators for the airport green development supply subsystem, with the criterion layer consisting of supply pressure, supply status, and supply response. Regarding the supply pressure layer indicators, considering that airport green development supply is not only related to the macro-environment such as the economy and society, but also closely related to the region's demand for green development, the supply pressure for airport green development mainly comes from two parts: one part is the supply pressure caused by economic and social development, including urban per capita GDP and urban population density; the other part is the supply pressure caused by the region's green development demand, including energy consumption per unit of GDP. Energy consumption reduction and industrial output value pollutant emission intensity; among them, for the supply status level indicators, the supply pressure of airport green development is considered to drive changes in the supply status. On the one hand, it is the supply status of airport environmental carrying capacity, including the rate of reduction in energy consumption per passenger and the airport public transportation facility support capacity; on the other hand, it is the supply status of airport operation support, including airport service evaluation score, runway capacity and on-time departure improvement rate; among them, for the supply response level indicators, measures such as adjusting human resources and increasing financial investment are used to alleviate supply pressure and adjust the supply status to achieve coordination between supply pressure, status and response, including airport per capita income contribution, airport green coverage rate, pollution control investment as a percentage of GDP and urban air transport industry investment as a percentage of GDP.

[0105] like Figure 2 As shown, in one embodiment, a method for constructing the Haken model includes:

[0106] Collect data on the green development needs of airports, and obtain the indicator dataset of the airport green development needs subsystem based on the principles of resource conservation, environmental friendliness, and operational efficiency.

[0107] Collect supply data on airport green development, and based on the Pressure-State-Response (PSR) model, obtain the indicator dataset of the airport green development supply subsystem according to the criteria of supply pressure, supply status, and supply response.

[0108] Based on the obtained indicator datasets of the airport green development demand subsystem and the airport green development supply subsystem, an optimization model for screening and selecting airport green development supply and demand coordination indicators is constructed based on the entropy weight TOPSIS model and the coordination degree model.

[0109] The genetic algorithm with elite retention strategy is used to solve the optimization model for screening and optimizing the supply and demand coordination indicators of airport green development. The set of coordination indicators of airport green development supply and demand subsystem is iterated with the optimal coordination degree as the guide.

[0110] The orderliness of the airport green development supply and demand subsystem is updated based on the iterative results of the genetic algorithm, and a Haken model that can characterize the co-evolution of airport green development supply and demand is constructed based on the orderliness of the airport green development supply and demand subsystem.

[0111] Specifically, the methods for constructing a screening and optimization model for supply and demand synergy indicators for airport green development include:

[0112] Based on the indicator datasets of the established airport green development demand subsystem and airport green development supply subsystem, collect and calculate indicator data.

[0113] Set up 0-1 decision variables for screening indicators of the airport green development supply and demand subsystem, and screen the indicator datasets of the airport green development demand subsystem and the airport green development supply subsystem:

[0114]

[0115]

[0116] In the formula, x j x is the result of the indicator selection for the airport green development demand subsystem. j =1 indicates that the indicator will be selected into the collaborative indicator set X of the airport green development demand subsystem, and 0 indicates otherwise; where X is the pre-defined collaborative indicator set in the indicator data of the airport green development demand subsystem.

[0117] In the formula, y j The results of the indicator selection for the airport green development supply subsystem, y j =1 indicates that the indicator will be selected into the collaborative indicator set Y of the airport green development supply subsystem, and 0 indicates otherwise; where Y is the pre-defined collaborative indicator set in the indicator data of the airport green development supply subsystem.

[0118] Based on the selected indicators of the airport green development supply and demand subsystems, an entropy-weighted TOPSIS model is constructed to obtain the orderliness U of the airport green development demand subsystem. X (t) and the orderliness of the airport green development supply subsystem U Y (t), based on the airport green development demand subsystem U X Taking the measurement of the degree of order of (t) as an example;

[0119] 1) Construct a set of collaborative indicators for the airport's green development needs subsystem:

[0120]

[0121] In the formula, This refers to the data for the j-th indicator in year t.

[0122] 2) For positive and negative indicators, the data are standardized using positive and negative power functions respectively;

[0123] For positive indicators, the standardized formula is:

[0124]

[0125] For negative indicators, the standardized formula is:

[0126]

[0127] In the formula, r tj min t (x tj ), max t (x tj ) represent the standard value, maximum value, and minimum value of the j-th indicator in year t, respectively;

[0128] 3) Calculate the information entropy H of the j-th indicator. j :

[0129]

[0130] In the formula, k = 1 / lnm, and satisfy k > 0, H j ≥0;

[0131] 4) Calculate the entropy weight ω of the j-th index. j :

[0132]

[0133] 5) Construct the weighted matrix X':

[0134] X'=(x' tj ) m×n ,x' tj =ω j ×r tj

[0135] 6) Determine the optimal solution Z j + And worst-case scenario Z j - :

[0136] Z j + ={max(x' t1 ),max(x' t2 ),...,max(x' tn (j=1,2,...,n)

[0137] Z j- ={min(x' t1 ),min(x' t2 ),...,min(x' tn (j=1,2,...,n)

[0138] 7) Calculate the Euclidean distance D between each evaluation object and the optimal and worst solutions. t + D t - :

[0139]

[0140]

[0141] 8) Calculate the degree of order U of the airport green development demand subsystem for each year. X (t):

[0142]

[0143] Orderliness of Airport Green Development Supply Subsystems in Each Year (U) Y The measurement process of (t) is consistent with the above process.

[0144] Based on the calculation results of the orderliness of the airport green development demand subsystem and the orderliness of the airport green development supply subsystem, a synergy model is constructed to calculate the synergy SD of the airport green development supply and demand subsystems:

[0145]

[0146] In the formula, SD(t) represents the degree of coordination between the supply and demand subsystems for airport green development in year t.

[0147]

[0148] In the formula, α and β represent the weights of the subsystem's degree of order and the rate of change of the subsystem's degree of order, respectively, in influencing the overall level of coordinated development of the system; under normal circumstances, each is taken as 0.5. θ is the discriminant coefficient: when the two subsystems develop in opposite directions or one of the systems is stagnant, the product of the rates of change is less than or equal to 0, the synergistic effect weakens, and θ is 0; conversely, when the two subsystems develop in the same direction, the product of the rates of change is greater than 0, the synergistic effect strengthens, and θ is 1.

[0149]

[0150] Using the maximization of the synergy between the supply and demand system for airport green development as the objective function, and the number of selected indicators and the correlation between the selected indicators as constraints, an optimization model for selecting synergy indicators for airport green development supply and demand is constructed:

[0151]

[0152]

[0153] In the formula, and These represent the lower limits of the number of indicators in the collaborative indicator set of the airport green development demand subsystem and the collaborative indicator set of the airport green development supply subsystem, respectively; n x and n y These represent the total number of indicators for the airport green development demand subsystem and the airport green development supply subsystem, respectively; SP max The threshold for the correlation between indicators; SP MN Spearman correlation coefficients of two indicators M and N in the set of collaborative indicators for the airport green development demand subsystem:

[0154]

[0155] In the formula, R(x) iM ) and R(x iN ) represent x respectively iM and x iN Let (x) be the rank of x. 1M ,x 2M ,...,x mM ) T It consists of m samples from M, where x iM Sorted in ascending order as x (1)M <x (2)M <...<x (m)M ,like Then it is believed It is x iM rank, x iN The same applies to the rank;

[0156] Similarly, the Spearman correlation coefficient (SP) between two indicators in the set of collaborative indicators for the green development supply subsystem of the computer field can be calculated. PQ :

[0157]

[0158] like Figure 5 As shown in one embodiment, a genetic algorithm with an elite retention strategy is used to solve the optimization model for screening and optimizing the supply and demand coordination indicators for airport green development. The set of coordination indicators for the airport green development supply and demand subsystems is iteratively derived, guided by the optimal coordination degree, and includes:

[0159] Step A: Set algorithm parameters, including population size, number of iterations, elite retention rate, crossover and mutation probabilities;

[0160] Step B: Encode the index screening results with 0-1 and randomly initialize the population according to the population size;

[0161] Step C: Calculate the supply and demand system synergy degree for each individual, and set a penalty function based on the constraints of the number of indicators and the correlation constraints to calculate the fitness of the individual;

[0162] Step D: Generate a new generation of population through selection, crossover, mutation, and elite retention operations, and update the fitness value of the population;

[0163] Step E: Repeat step D until the number of iterations is met, calculate the optimal synergy in the current solution, and output the corresponding set of supply and demand indicators for airport green development through decoding operations.

[0164] In one embodiment, the orderliness of the airport green development supply and demand subsystem is updated based on the iterative results of a genetic algorithm. A Haken model capable of characterizing the co-evolution of airport green development supply and demand is constructed based on this orderliness, including:

[0165] Based on the coordination index of the airport green development supply and demand subsystems obtained through genetic algorithm iteration, the orderliness U of the airport green development demand subsystem and supply subsystem is calculated using the entropy-weighted TOPSIS model. x (t) and U y (t);

[0166] Constructing a Haken model to analyze the coordinated development process and evolution mechanism of the supply and demand subsystems for airport green development:

[0167]

[0168]

[0169] Discretize the model:

[0170] U X (t)=(1-γ1)U X (t-1)-aU X (t-1)U Y (t-1)

[0171] U Y (t)=(1-γ2)U Y (t-1)+bU X (t-1) 2

[0172] In the formulas, the above two equations reflect the basic relationship of the supply and demand subsystem of airport green development. The interaction relationship of the supply and demand subsystem of airport green development can be analyzed based on the values ​​of the four parameters a, b, γ1, and γ2: a and b represent U X and U YThe control parameter for the strength of the interaction between them, when a > 0, is the subsystem U X and U Y No positive synergy was formed between them, U Y Hindered U X The evolutionary process; when a < 0, subsystem U X and U Y A positive synergistic effect is formed between them, U Y Promote U X Evolving to a higher level; when b > 0, U X Promote U Y Evolution; when b < 0, U X For U Y This creates an obstructive effect, hindering the generation of synergistic effects; γ1 and γ2 represent the damping coefficients of the airport green development demand subsystem and supply subsystem, respectively: when γ1 < 1, subsystem U X A stable and ordered dissipative structure is formed, which is conducive to the further evolution of the system; conversely, subsystem U... X The structure is in a disordered and chaotic state, making it difficult to generate synergistic effects; when γ2 < 1, the subsystem U Y The established dissipative structure can drive its own cooperative evolution; conversely, subsystem U... Y The evolution is negatively affected by disordered structures;

[0173] The adiabatic approximation method is used to identify the order parameters of the supply and demand system. When γ2>0 and γ2>>|γ1| (at least one order of magnitude), it is said that the system satisfies the "adiabatic approximation assumption", indicating that the order parameter of the system is U. X At this moment, the U was instantly withdrawn. Y ,make Order parameter U X Since there is no time for changes, the order parameter evolution equation of the system can be obtained:

[0174]

[0175] right The system potential function is obtained by integrating the opposite of the original value:

[0176]

[0177] make By combining the potential function, the stability point of the coordinated development of the supply and demand subsystems for airport green development can be solved [U]. X * (t),ν(U X * (t))];When the product of a, b, γ1, and γ2 is positive, the evolution equation has one and only one unique solution U. X *(t) = 0, corresponding to a stable point on the potential function; when the product of a, b, γ1, and γ2 is negative, the evolution equation has three solutions: U X * (t)=0、 These correspond to three stable points on the potential function. When a point on the potential function is in a non-equilibrium position, it tends to revert to a stable state. Simultaneously, the state of this point is also affected by its distance from the stable points. Therefore, the system's state evaluation function can be defined as follows:

[0178]

[0179] The smaller d is, the more stable the current state of the system is, and the higher the degree of coordination between the airport green development supply subsystem and the airport green development demand subsystem.

[0180] This study analyzes the co-evolution of supply and demand in airport green development based on a discretized Haken model. By determining whether the fitted damping coefficients γ1 and γ2 satisfy the adiabatic approximation assumption, it further determines whether the assumed order parameter quantum system is the dominant subsystem governing the co-evolution of supply and demand in airport green development during the research phase. Furthermore, by solving the potential function, it effectively grasps the stage evolution law of the airport green development supply and demand system, thereby providing targeted suggestions for promoting high-level co-evolution of supply and demand in airport green development and realizing the green transformation of airports.

[0181] This invention, based on relevant airport green development policies and the causal analysis approach of the Pressure-State-Response (PSR) model, comprehensively considers four dimensions: economic, social, environmental, and airport operation. It constructs indicator datasets for both the airport green development demand subsystem and the airport green development supply subsystem. Through the constructed Haken model, it analyzes the synergistic evolution of the airport green development supply and demand sides, filling the technical gap in analyzing the supply and demand evolution relationship of airports in the green development process from the overall perspective of the supply and demand system. This has significant research value for forming a high-level dynamic balance where demand drives supply and supply creates demand, thus achieving sustainable airport development. Furthermore, based on the entropy-weighted TOPSIS model and the synergy model, it constructs an optimization model for screening and optimizing airport green development supply and demand synergy indicators, and solves it using a genetic algorithm. The optimal synergy result guides the screening of synergy indicators for the airport green development supply and demand subsystem, demonstrating certain innovative significance.

[0182] Based on the screening results of supply and demand coordination indicators for airport green development, this invention constructs a Haken model to further reveal the interaction mechanism of the supply and demand subsystems of airport green development, analyzes the synergistic evolution process of the supply and demand sides of airport green development, fills the technical gap in analyzing the supply and demand evolution relationship of airports in the green development process from the overall perspective of the supply and demand system, and has important research significance for forming a high-level dynamic balance of demand driving supply and supply creating demand, and realizing the sustainable development of airports.

[0183] This invention analyzes the coordinated evolution of supply and demand in airport green development. Based on the entropy-weighted TOPSIS model and the coordination degree model, and with the optimal coordination degree as the objective and the number and correlation of indicators as constraints, an optimization model for screening and selecting coordinated indicators for airport green development supply and demand is constructed. This model is then solved using a genetic algorithm with an elite retention strategy, iteratively yielding the set of coordinated indicators for airport green development supply and demand corresponding to the optimal coordination degree. Based on the results of the genetic algorithm, a Haken model is constructed to analyze the coordinated evolution of the supply and demand sides of airport green development. This fills the technical gap in analyzing the supply and demand evolution relationship of airports in the green development process from the overall perspective of the supply and demand system. It has significant research value for forming a high-level dynamic balance where demand drives supply and supply creates demand, thus achieving sustainable development of airports.

[0184] Example 2:

[0185] This invention also provides a device for analyzing the coordinated evolution of supply and demand in airport green development based on indicator screening and optimization, comprising:

[0186] The first data acquisition module collects data on the airport's green development needs, and obtains the indicator dataset of the airport's green development needs subsystem based on the principles of resource conservation, environmental friendliness, and operational efficiency.

[0187] The second data acquisition module collects supply data for airport green development. Based on the Pressure-State-Response (PSR) model, and taking supply pressure, supply status, and supply response as criteria, it obtains the indicator dataset of the airport green development supply subsystem.

[0188] The first calculation module: inputs the obtained indicator dataset of the airport green development demand subsystem into the pre-built airport green development supply and demand coordination indicator screening and optimization model to obtain the airport green development demand coordination indicator set; inputs the obtained indicator dataset of the airport green development supply subsystem into the pre-built airport green development supply and demand coordination indicator screening and optimization model to obtain the airport green development supply coordination indicator set.

[0189] The second calculation module inputs the acquired set of airport green development demand coordination indicators into the pre-built entropy weight TOPSIS model to obtain the orderliness of the airport green development demand subsystem; it also inputs the acquired set of airport green development supply coordination indicators into the pre-built entropy weight TOPSIS model to obtain the orderliness of the airport green development supply subsystem.

[0190] The third calculation module inputs the obtained orderliness of the airport green development demand subsystem and the airport green development supply subsystem into a pre-constructed Haken model. The Haken model outputs four parameters. Based on the values ​​of the four parameters and the state evaluation function, the module analyzes the interaction relationship and the degree of coordinated development between the airport green development demand subsystem and the airport green development supply subsystem, and analyzes the co-evolution of airport green development supply and demand.

[0191] Example 3:

[0192] This invention provides a terminal, including a processor and a storage medium;

[0193] The storage medium is used to store instructions;

[0194] The processor is configured to operate according to the instructions to perform the steps of the method according to Embodiment 1.

[0195] Since the terminal provided in this embodiment of the invention can execute the method provided in Embodiment 1 of the invention, the terminal provided in this embodiment of the invention has the corresponding functional modules and beneficial effects for executing the method.

[0196] Example 4:

[0197] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0198] Since the storage medium provided in this embodiment of the invention can execute the method provided in Embodiment 1 of the invention, it has the corresponding functional modules and beneficial effects for executing the method.

[0199] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0200] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0201] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0202] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0203] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for analyzing the coordinated evolution of supply and demand in airport green development based on indicator selection and optimization, characterized in that: Includes the following steps: Collect data on the green development needs of airports and construct an indicator dataset for the airport green development needs subsystem. Collect supply data on airport green development and construct an indicator dataset for the airport green development supply subsystem based on the Pressure-State-Response model. The indicator dataset of the airport green development demand subsystem is input into the pre-built airport green development supply and demand coordination indicator screening and optimization model to obtain the airport green development demand coordination indicator set; the indicator dataset of the airport green development supply subsystem is input into the pre-built airport green development supply and demand coordination indicator screening and optimization model to obtain the airport green development supply coordination indicator set. The set of collaborative indicators for airport green development needs is input into a pre-built entropy weight TOPSIS model to obtain the orderliness of the airport green development demand subsystem; the set of collaborative indicators for airport green development supply is input into a pre-built entropy weight TOPSIS model to obtain the orderliness of the airport green development supply subsystem. The orderliness of the airport green development demand subsystem and the orderliness of the airport green development supply subsystem are input into a pre-constructed Haken model. The Haken model outputs four parameters. Based on the values ​​of the four parameters and the state evaluation function, the interaction relationship and the degree of coordinated development between the airport green development demand subsystem and the airport green development supply subsystem are analyzed, thereby realizing the co-evolution analysis of airport green development supply and demand. The method for constructing the Haken model includes: Based on the indicator datasets of the airport green development demand subsystem and the airport green development supply subsystem, an optimization model for screening and selecting airport green development supply and demand coordination indicators is constructed based on the entropy weight TOPSIS model and the coordination degree model. The genetic algorithm with elite retention strategy is used to solve the optimization model for screening and optimizing the supply and demand coordination indicators of airport green development. The set of coordination indicators of airport green development supply and demand subsystem is iterated with the optimal coordination degree as the guide. The orderliness of the airport green development supply and demand subsystem is updated based on the iterative results of the genetic algorithm, and a Haken model that can characterize the co-evolution of airport green development supply and demand is constructed based on the orderliness of the airport green development supply and demand subsystem. The orderliness of the airport green development supply and demand subsystem is updated based on the iterative results of the genetic algorithm. A Haken model capable of characterizing the co-evolution of airport green development supply and demand is constructed based on this orderliness, including: Based on the coordination index of the airport green development supply and demand subsystems obtained through genetic algorithm iteration, the orderliness of the airport green development demand subsystem and supply subsystem is calculated using the entropy-weighted TOPSIS model. and ; Constructing a Haken model to analyze the coordinated development process and evolution mechanism of the supply and demand subsystems for airport green development: Discretize the model: In the formula, , express and Control parameters for the strength of the interaction between them; , These represent the damping coefficients of the airport's green development demand subsystem and supply subsystem, respectively. The adiabatic approximation method is used to identify the order parameters of the supply and demand system when and When this is true, it is said to satisfy the "adiabatic approximation" of the system, indicating that the order parameter of the system is... At this moment, they instantly withdrew. ,make , sequence parameter By keeping these parameters constant, we can obtain the system's order parameter evolution equations: right The system potential function is obtained by integrating the opposite of the original value: make By combining the potential function, the stability point of the coordinated development of the supply and demand subsystems for airport green development can be solved. ;when , , , When the product of and is positive, the evolution equation has one and only one solution. This corresponds to a stable point on the potential function; when , , , When the product of and is negative, the evolution equation has three solutions: , , These correspond to three stable points on the potential function. When a point on the potential function is in a non-equilibrium position, it tends to regress to a stable state. Simultaneously, the state of this point is also affected by its distance from the stable points. Therefore, the state evaluation function of the system can be defined as follows: The smaller the value, the more stable the current state of the system, and the higher the degree of coordination between the airport green development supply subsystem and the airport green development demand subsystem.

2. The airport green development supply and demand synergistic evolution analysis method based on indicator screening and optimization as described in claim 1, characterized in that, The criteria layer of the indicator dataset of the airport green development demand subsystem includes: resource conservation layer indicators, environmental friendliness layer indicators, and operational efficiency layer indicators. Among them, the resource conservation indicators include annual passenger throughput per unit area, annual takeoffs and landings per unit area, annual average comprehensive water consumption per passenger, and annual average comprehensive energy consumption per passenger. Among them, the environmental friendliness level indicators include carbon dioxide emissions per unit of passenger throughput, annual emissions of various pollutants, and noise levels; Among them, the indicators for efficient operation include annual passenger throughput, annual cargo and mail throughput, annual takeoffs and landings, airport on-time departure rate, and airport average taxiing time.

3. The airport green development supply and demand synergistic evolution analysis method based on indicator screening and optimization as described in claim 1, characterized in that, The criteria layer of the indicator dataset of the airport green development supply subsystem includes: supply pressure layer indicators, supply status layer indicators, and supply response layer indicators. Among them, the supply pressure indicators include urban per capita GDP, urban population density, reduction in energy consumption per unit of GDP, and pollutant emission intensity per 10,000 yuan of industrial output. Among them, the supply status indicators include the rate of decrease in energy consumption per passenger, the airport's public transportation facility support capacity, the airport service evaluation score, runway capacity, and the rate of improvement in on-time departure. Among them, the supply-response level indicators include the per capita income contribution of airports, the green coverage rate of airports, the proportion of pollution control investment in GDP, and the proportion of urban air transport investment in GDP.

4. The airport green development supply and demand synergistic evolution analysis method based on indicator screening and optimization according to claim 1, characterized in that, Methods for constructing a supply-demand synergy indicator screening and optimization model for airport green development include: Set up 0-1 decision variables for screening indicators of the airport green development supply and demand subsystem, and screen the indicator datasets of the airport green development demand subsystem and the airport green development supply subsystem: In the formula, The results of the indicator selection for the airport's green development needs subsystem. This indicates that the indicator will be selected and included in the collaborative indicator set of the airport green development demand subsystem. In the middle, the opposite is 0; among them, A set of collaborative indicators pre-defined in the indicator data of the airport green development demand subsystem; In the formula, The results of indicator selection for the airport green development supply subsystem. This indicates that the indicator will be selected and included in the set of collaborative indicators for the airport green development supply subsystem. In the middle, the opposite is 0; among them, A set of pre-defined collaborative indicators is provided for the indicator data of the airport green development supply subsystem. Based on the selected indicators of the airport green development supply and demand subsystems, an entropy-weighted TOPSIS model is constructed to obtain the orderliness of the airport green development demand subsystem. Orderliness of the airport green development supply subsystem ; Based on the calculation results of the orderliness of the airport green development demand subsystem and the orderliness of the airport green development supply subsystem, a synergy model is constructed to calculate the synergy SD of the airport green development supply and demand subsystems: In the formula, For the first Annual coordination degree of airport green development supply and demand subsystems; In the formula, These represent the weights of the subsystem's degree of order and the rate of change of the subsystem's degree of order, respectively, in influencing the overall level of coordinated development of the system. The discriminant coefficient is used when the two subsystems develop in opposite directions or one of the systems is stagnant, and the product of their rates of change is less than or equal to 0, indicating a weakening of the synergistic effect. If the product of the rates of change is 0, then the synergistic effect is enhanced; conversely, if the two subsystems develop in the same direction, the product of their rates of change is greater than 0. 1: Using the maximization of the synergy between the supply and demand system for airport green development as the objective function, and the number of selected indicators and the correlation between the selected indicators as constraints, an optimization model for selecting synergy indicators for airport green development supply and demand is constructed: In the formula, and These represent the lower limits of the number of indicators in the set of collaborative indicators for the airport green development demand subsystem and the set of collaborative indicators for the airport green development supply subsystem, respectively. and These represent the total number of indicators for the airport green development demand subsystem and the airport green development supply subsystem, respectively. This is the threshold for the correlation between indicators; Two indicators in the set of collaborative indicators for the airport's green development needs subsystem and Spearman correlation coefficient: In the formula, and They represent and The rank, let It comes from of One sample, Sort in ascending order. ,like Then it is believed yes rank, The same applies to the rank; Similarly, the Spearman correlation coefficient between two indicators in the collaborative indicator set of the green development supply subsystem of the computer field is... : 。 5. The airport green development supply and demand synergistic evolution analysis method based on indicator screening and optimization according to claim 4, characterized in that, A genetic algorithm with an elite retention strategy is used to solve the optimization model for screening and selecting synergistic indicators of airport green development supply and demand. Guided by the optimal synergy degree, the set of synergistic indicators for the airport green development supply and demand subsystems is iteratively derived, including: Step A: Set algorithm parameters, including population size, number of iterations, elite retention rate, crossover and mutation probabilities; Step B: Encode the index screening results with 0-1 and randomly initialize the population according to the population size; Step C: Calculate the supply and demand system synergy degree for each individual, and set a penalty function based on the constraints of the number of indicators and the correlation constraints to calculate the fitness of the individual; Step D: Generate a new generation of population through selection, crossover, mutation, and elite retention operations, and update the fitness value of the population; Step E: Repeat step D until the number of iterations is met, calculate the optimal synergy in the current solution, and output the corresponding set of supply and demand indicators for airport green development through decoding operations.

6. An airport green development supply and demand synergistic evolution analysis device based on indicator screening and optimization, based on the airport green development supply and demand synergistic evolution analysis method based on indicator screening and optimization as described in any one of claims 1 to 5, characterized in that, include: The first data acquisition module collects data on the airport's green development needs, and obtains the indicator dataset of the airport's green development needs subsystem based on the principles of resource conservation, environmental friendliness, and operational efficiency. The second data acquisition module collects supply data for airport green development. Based on the Pressure-State-Response (PSR) model, and taking supply pressure, supply status, and supply response as criteria, it obtains the indicator dataset of the airport green development supply subsystem. The first calculation module: inputs the obtained indicator dataset of the airport green development demand subsystem into the pre-built airport green development supply and demand coordination indicator screening and optimization model to obtain the airport green development demand coordination indicator set; inputs the obtained indicator dataset of the airport green development supply subsystem into the pre-built airport green development supply and demand coordination indicator screening and optimization model to obtain the airport green development supply coordination indicator set. The second calculation module inputs the acquired set of airport green development demand coordination indicators into the pre-built entropy weight TOPSIS model to obtain the orderliness of the airport green development demand subsystem; it also inputs the acquired set of airport green development supply coordination indicators into the pre-built entropy weight TOPSIS model to obtain the orderliness of the airport green development supply subsystem. The third calculation module inputs the obtained orderliness of the airport green development demand subsystem and the airport green development supply subsystem into a pre-constructed Haken model. The Haken model outputs four parameters. Based on the values ​​of the four parameters and the state evaluation function, the module analyzes the interaction relationship and the degree of coordinated development between the airport green development demand subsystem and the airport green development supply subsystem, and analyzes the co-evolution of airport green development supply and demand.

7. A terminal, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 5.

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