Aviation data-based airline passenger transportation fleet business index calculation method and system

Abstract

The invention discloses an airline passenger transport fleet business index calculation method and system based on aviation data, and the method comprises the steps: obtaining the passenger transport information of an airline passenger transport fleet in a preset period, and carrying out the screening and cleaning of the passenger transport information, so as to form a passenger transport data set which can be used for analysis; constructing four-level index data of the airline passenger transportation fleet business index based on the passenger transportation data set, and determining the weight of the four-level index data by adopting a CRITIC method; performing season adjustment and standardization processing on the fourth-level index data, and performing weighted calculation according to the determined weight of the fourth-level index data to obtain third-level index data; carrying out abnormal value detection and processing on the third-level index data, and carrying out weighting calculation to obtain second-level index data; and calculating the second-level index data by adopting an entropy weight method to obtain the airline passenger transportation fleet prosperity index. According to the method and the system, the operation efficiency and the economic benefit of the airline are improved.

Description

Technical Field

[0001] This invention relates to the field of civil aviation fleet data analysis and processing technology, and in particular to a method and system for calculating the airline passenger fleet prosperity index based on aviation data. Background Technology

[0002] With the rapid development of my country's civil aviation industry, airline fleets are constantly expanding, resulting in massive amounts of civil aviation data scattered across multiple systems, leading to issues such as inconsistent data formats and definitions. Traditional data processing methods are outdated and unable to transform complex fleet operations into intuitive quantitative indicators, making it difficult for airlines to quickly grasp the overall operational status of their fleets and affecting the scientific rigor and timeliness of fleet management decisions. Currently, the civil aviation sector lacks a unified standard for calculating fleet prosperity indices, necessitating a method and system capable of integrating multi-source data and scientifically quantifying fleet operational status. Summary of the Invention

[0003] To address the technical problems existing in the background art, this invention proposes a method and system for calculating the airline passenger fleet prosperity index based on aviation data.

[0004] The method for calculating the airline passenger fleet prosperity index based on aviation data proposed in this invention includes the following steps:

[0005] S1. Obtain passenger information of airline passenger fleets within a preset period, and filter and clean the passenger information to form a passenger data set that can be used for analysis; the passenger information includes, but is not limited to, flight data, aircraft information and operational data;

[0006] S2. Construct a four-level indicator data for the airline passenger fleet prosperity index based on the passenger data set, and use the CRITIC method to determine the weight of the four-level indicator data.

[0007] S3. Seasonally adjust and standardize the fourth-level indicator data, and then perform weighted calculations based on the determined weights of the fourth-level indicator data to obtain the third-level indicator data. The third-level indicators include capacity utilization index, operating efficiency index, revenue performance index, and fleet structure index.

[0008] S4. Perform outlier detection and processing on the tertiary indicator data, and then obtain the secondary indicator data through weighted calculation; the secondary indicators include the fleet efficiency index and the fleet development potential index;

[0009] S5. The airline passenger fleet prosperity index is obtained by using the entropy weight method to calculate the secondary indicator data.

[0010] Preferably, the four-level indicator data include load factor, aircraft utilization rate, flight execution rate, on-time rate, average turnaround time, unit fuel consumption, revenue per passenger kilometer, revenue per seat kilometer, aircraft age composition, aircraft type uniformity, ratio of wide-body to narrow-body aircraft, new aircraft introduction rate, route matching degree, and seat class matching degree.

[0011] Preferably, the indicators requiring seasonal adjustment in the fourth-level indicator data include load factor, aircraft utilization rate, flight execution rate, on-time rate, average turnaround time, revenue per passenger kilometer, and revenue per seat kilometer; the specific process of seasonal adjustment in step S3 includes:

[0012] The seasonal factors for each indicator are calculated using the m-month moving average method. The calculation formula is as follows:

[0013]

[0014] Among them, X n Y represents the data for the nth month out of the past m months for the current indicator. n Z is the average of the current indicator over the past m months; A is the seasonal factor of the current indicator; Z n The current indicator requires calculating the data corresponding to the month for the seasonal factor.

[0015] Preferably, in step S3, the standardization process includes:

[0016] The four-level indicator data are distinguished into positive and negative indicators. Positive indicators indicate that the larger the indicator value, the better, while negative indicators indicate that the smaller the indicator value, the better.

[0017] The standardization process for positive indicators is as follows:

[0018] The standardized value of the current indicator = (actual value of the current indicator - minimum value of the current indicator) ÷ (maximum value of the current indicator - minimum value of the current indicator) × 100;

[0019] The standardization process for contrarian indicators is as follows:

[0020] The standardized value of the current indicator = (the maximum value of the current indicator - the actual value of the current indicator) ÷ (the maximum value of the current indicator - the minimum value of the current indicator) × 100.

[0021] Preferably, in step S4, the outlier detection and processing of the tertiary indicator data specifically includes:

[0022] If any indicator data in the three-level indicator data does not conform to the corresponding indicator data range in any month within the preset period, then that month is defined as an abnormal month. At the same time, based on the abnormal month, the indicator data of the previous month and the indicator data of the next month are taken to calculate the indicator data difference Δ.

[0023] The indicator data corresponding to the abnormal month is calculated based on the difference Δ between the indicator data and the indicator data of the previous month.

[0024] Preferably, the step of calculating the indicator data corresponding to the abnormal month based on the indicator data difference Δ and the indicator data of the previous month specifically involves:

[0025] The indicator data for the abnormal month = the indicator data of the previous month for the current indicator + (the difference in indicator data Δ / 2).

[0026] Preferably, in step S4, the weighting calculation of the secondary indicators is as follows:

[0027] Fleet efficiency index = capacity utilization index × 40% + operational efficiency index × 35% + profitability index × 25%; Fleet development potential index = fleet structure index × 60% + market adaptability index × 40%.

[0028] Preferably, step S5 specifically includes:

[0029] List the secondary indicator data for m months, and calculate the proportion of each month's data to the total of the indicator over m months for each secondary indicator data;

[0030] Determine the highest and lowest values ​​of the proportions corresponding to each indicator data in the secondary indicator data, and calculate the corresponding proportion differences;

[0031] The amount of information is set according to the proportional difference, and the weight is determined by the proportion of each indicator's information to the total information. The airline passenger fleet prosperity index is calculated by weighting, that is, the airline passenger fleet prosperity index = fleet efficiency index × ω1 + fleet development potential index × ω2.

[0032] Wherein, ω1 is the weight of the fleet effectiveness index; ω2 is the weight of the fleet development potential index.

[0033] Preferably, it also includes storing the fourth-level indicator data, the third-level indicator data, the second-level indicator data, and the airline passenger fleet prosperity index in a preset database, and displaying them synchronously in the corresponding query system.

[0034] The present invention proposes an airline passenger fleet prosperity index calculation system based on aviation data, comprising:

[0035] The data acquisition module is used to acquire passenger information of the airline's passenger fleet within a preset period, and to filter and clean the passenger information to form a passenger data set that can be used for analysis; the passenger information includes, but is not limited to, flight data, aircraft information and operational data;

[0036] The first processing module is used to construct the fourth-level indicator data of the airline passenger fleet prosperity index based on the passenger data set, and to determine the weight of the fourth-level indicator data using the CRITIC method.

[0037] The second processing module is used to perform seasonal adjustment and standardization on the fourth-level indicator data, and then perform weighted calculations based on the determined weights of the fourth-level indicator data to obtain the third-level indicator data. The third-level indicators include capacity utilization index, operating efficiency index, revenue performance index, and fleet structure index.

[0038] The third processing module is used to detect and process outliers in the tertiary indicator data, and then obtain the secondary indicator data through weighted calculation; the secondary indicators include the fleet efficiency index and the fleet development potential index.

[0039] The output module is used to calculate the airline passenger fleet prosperity index by using the entropy weight method on the secondary indicator data.

[0040] This invention presents a method and system for calculating the airline passenger fleet prosperity index based on aviation data. By constructing a multi-level indicator system, it comprehensively covers all key aspects of airline passenger fleet operation, enabling a comprehensive assessment of fleet operational status. Employing scientific data analysis methods such as the CRITIC method, seasonal adjustment, standardization, and entropy weighting, the objectivity and accuracy of the indicator calculations are ensured, making the fleet operational status of different airlines and at different times comparable. Transforming complex aviation data into an intuitive prosperity index facilitates airlines' rapid understanding of the overall fleet operation, providing clear reference for management decisions such as fleet scheduling, flight schedule adjustments, and revenue analysis, thus contributing to improved operational efficiency and economic benefits. The system's construction achieves automated data processing and real-time index display, meeting the needs of airlines' dynamic management decisions. Attached Figure Description

[0041] Figure 1 This is a schematic diagram illustrating the workflow of the airline passenger fleet prosperity index calculation method based on aviation data proposed in this invention.

[0042] Figure 2 This is a schematic diagram of the system architecture of the airline passenger fleet prosperity index calculation system based on aviation data proposed in this invention. Detailed Implementation

[0043] Reference Figure 1 The method for calculating the airline passenger fleet prosperity index based on aviation data proposed in this invention includes the following steps:

[0044] S1. Obtain passenger information of airline passenger fleets within a preset period, and filter and clean the passenger information to form a passenger data set that can be used for analysis; passenger information includes, but is not limited to, flight data, aircraft information and operational data;

[0045] S2. Construct a four-level indicator data for the airline passenger fleet prosperity index based on the passenger data set, and use the CRITIC method to determine the weight of the four-level indicator data.

[0046] In this embodiment, the four-level indicator data include load factor, aircraft utilization rate, flight execution rate, on-time rate, average turnaround time, unit fuel consumption, revenue per passenger kilometer, revenue per seat kilometer, aircraft age composition, aircraft type uniformity, ratio of wide-body to narrow-body aircraft, new aircraft introduction rate, route matching degree, and seat class matching degree.

[0047] Specifically, the calculation process for each indicator in the fourth-level indicator data is shown in the table below:

[0048]

[0049]

[0050] Specifically, the weights are calculated using the CRITIC method. For example, since daily utilization hours are highly correlated with flight execution rate, their weights are relatively low; since flight punctuality rate fluctuates greatly and the indicators are sensitive, their weights are appropriately increased; and since aircraft age composition is highly correlated with new aircraft introduction rate, the weights corresponding to the indicator data are relatively low.

[0051] The adjusted Level 4 indicator data are as follows:

[0052]

[0053]

[0054] S3. Seasonally adjust and standardize the fourth-level indicator data, and then perform weighted calculations based on the determined weights of the fourth-level indicator data to obtain the third-level indicator data. The third-level indicators include capacity utilization index, operational efficiency index, revenue performance index, and fleet structure index.

[0055] In this embodiment, the indicators requiring seasonal adjustment in the fourth-level indicator data include load factor, aircraft utilization rate, flight execution rate, on-time rate, average turnaround time, revenue per passenger kilometer, and revenue per seat kilometer; the specific process of seasonal adjustment in step S3 includes:

[0056] The seasonal factors for each indicator are calculated using the m-month moving average method. The calculation formula is as follows:

[0057]

[0058] Among them, X n Y represents the data for the nth month out of the past m months for the current indicator. n Z is the average of the current indicator over the past m months; A is the seasonal factor of the current indicator; Z n This refers to the data for the month in which the seasonal factor needs to be calculated for the current indicator.

[0059] Specifically, let's assume an airline's load factor data: March load factor of the current year: 70%, average load factor of the previous year: 73%, seasonal factor: 0.95; adjusted load factor: 70% ÷ 0.95 = 73.7%.

[0060] In this embodiment, the standardization process in step S3 includes:

[0061] The fourth-level indicator data is divided into positive and negative indicators. Positive indicators mean that the larger the indicator value, the better, while negative indicators mean that the smaller the indicator value, the better.

[0062] The standardization process for positive indicators is as follows:

[0063] The standardized value of the current indicator = (actual value of the current indicator - minimum value of the current indicator) ÷ (maximum value of the current indicator - minimum value of the current indicator) × 100;

[0064] The standardization process for contrarian indicators is as follows:

[0065] The standardized value of the current indicator = (the maximum value of the current indicator - the actual value of the current indicator) ÷ (the maximum value of the current indicator - the minimum value of the current indicator) × 100.

[0066] Specifically, for example, the worst-case scenario for the load factor is 50%, and the best-case scenario is 100%. The load factor is a positive indicator. The original load factor is 73.7%, and the standardized load factor is:

[0067] (73.7% - 50%) ÷ (100% - 50%) × 100 = 47.4.

[0068] Specifically, regarding the aircraft age composition in the Level 4 indicator data, the worst value is 20 and the best value is 1; aircraft age is a reverse cause indicator, and the original value of aircraft age is 12, so the standardization of aircraft age is as follows:

[0069] (20-12)÷(20-1)×100=42.

[0070] In this embodiment, the weighted index calculated according to the CRITIC method is included in the weighted calculation of the fourth-level indicator data to obtain the third-level indicator data. The specific calculation process is as follows:

[0071] Initial capacity utilization index = 0.6 * seasonally adjusted and standardized load factor + 0.2 * seasonally adjusted and standardized daily utilization hours + 0.2 * seasonally adjusted and standardized flight execution rate;

[0072] Initial operational efficiency index = 0.6 * seasonally adjusted and standardized on-time rate + 0.25 * seasonally adjusted and standardized average turnaround time + 0.15 * seasonally adjusted and standardized unit fuel consumption;

[0073] Initial revenue performance index = 0.5 * seasonally adjusted and standardized passenger-kilometer revenue level + 0.5 * seasonally adjusted and standardized seat-kilometer revenue;

[0074] Initial fleet structure index = 0.35 * Standardized aircraft age structure + 0.3 * Standardized aircraft type uniformity + 0.2 * Standardized wide-body / narrow-body aircraft ratio + 0.15 * Standardized new aircraft introduction rate.

[0075] S4. Perform outlier detection and processing on the tertiary indicator data, and then calculate the secondary indicator data through weighted calculation; the secondary indicators include the fleet efficiency index and the fleet development potential index.

[0076] In this embodiment, step S4 involves outlier detection and processing of the tertiary indicator data, specifically including:

[0077] If any indicator data in the three-level indicator data does not conform to the corresponding indicator data range in any month within the preset period, then that month is defined as an abnormal month. At the same time, based on the abnormal month, the indicator data of the previous month and the indicator data of the next month are taken to calculate the indicator data difference Δ.

[0078] The indicator data corresponding to the abnormal month is calculated based on the difference Δ between the indicator data and the indicator data of the previous month.

[0079] Specifically, the indicator data for the abnormal month = the indicator data of the previous month for the current indicator + (the difference in indicator data Δ / 2).

[0080] In this embodiment, in step S4, the weighting calculation of the secondary indicators is as follows:

[0081] Fleet efficiency index = capacity utilization index × 40% + operational efficiency index × 35% + profitability index × 25%; Fleet development potential index = fleet structure index × 60% + market adaptability index × 40%.

[0082] S5. The airline passenger fleet prosperity index is obtained by using the entropy weight method to calculate the secondary indicator data.

[0083] In this embodiment, step S5 specifically includes:

[0084] List the secondary indicator data for m months, and calculate the proportion of each month's data to the total of the indicator over m months for each secondary indicator data;

[0085] Determine the highest and lowest values ​​of the proportions corresponding to each indicator data in the secondary indicator data, and calculate the corresponding proportion differences;

[0086] The amount of information is set according to the proportional difference, and the weight is determined by the proportion of each indicator's information to the total information. The airline passenger fleet prosperity index is calculated by weighting, that is, the airline passenger fleet prosperity index = fleet efficiency index × ω1 + fleet development potential index × ω2.

[0087] Wherein, ω1 is the weight of the fleet effectiveness index; ω2 is the weight of the fleet development potential index.

[0088] Specifically, for example, if m = 12, the secondary indicator data for the 12 months are listed in the table below:

[0089] month Fleet efficiency index Fleet Development Potential Index January 75 82 February 78 83 March 72 81 April 80 82 May 69 83 June 74 82 July 85 83 August 88 84 September 71 82 October 82 83 November 76 82 December 79 83

[0090] Specifically, the process for calculating the proportion of each indicator is as follows:

[0091] Step 1: Calculation of fleet effectiveness index ratio:

[0092] Total: 75 + 78 + 72 + 80 + 69 + 74 + 85 + 88 + 71 + 82 + 76 + 79 = 929

[0093] Monthly percentage:

[0094] January: 75 ÷ 929 = 0.081 (8.1%)

[0095] February: 78 ÷ 929 = 0.084 (8.4%)

[0096] ...and so on;

[0097] Calculation of fleet development potential index ratio:

[0098] Total: 82 + 83 + 81 + 82 + 83 + 82 + 83 + 84 + 82 + 83 + 82 + 83 = 990

[0099] Monthly percentage:

[0100] January: 82 ÷ 990 = 0.083 (8.3%)

[0101] February: 83 ÷ 990 = 0.084 (8.4%)

[0102] ...and so on.

[0103] Step 2: Observe the degree of dispersion of the proportions:

[0104] The distribution of the fleet effectiveness index is as follows: highest percentage: 8.9% (August); lowest percentage: 7.4% (May); the percentage difference is 8.9% - 7.4% = 1.5%; the information content of the fleet effectiveness index is set based on the percentage difference.

[0105] Similarly, observe the dispersion of the computer team's development potential index: highest percentage: 8.48% (August); lowest percentage: 8.18% (March); the percentage difference is 8.48% - 8.18% = 0.3%.

[0106] The information content is set according to the proportional difference, so the information content of the fleet development potential index is 0.3%.

[0107] Step 3: Calculate the weights corresponding to each indicator data:

[0108] The weight of each indicator data point = the information content of that indicator data ÷ the sum of the information content of all indicators;

[0109] Total information content: 1.5% + 0.3% = 1.8%;

[0110] Fleet effectiveness index weight: 1.5% ÷ 1.8% = 0.833 = 83.3%;

[0111] Fleet development potential index weight: 0.3% ÷ 1.8% = 0.167 = 16.7%;

[0112] In summary, the fleet prosperity index = 83.3% * fleet efficiency index + 16.7% * fleet development potential index.

[0113] In this embodiment, the method further includes storing the fourth-level indicator data, the third-level indicator data, the second-level indicator data, and the airline passenger fleet prosperity index in a preset database and displaying them synchronously in the corresponding query system.

[0114] Reference Figure 1 and Figure 2 The present invention proposes an airline passenger fleet prosperity index calculation system based on aviation data, comprising:

[0115] The data acquisition module is used to acquire passenger information of airline passenger fleets within a preset period, and to filter and clean the passenger information to form a passenger data set that can be used for analysis; the passenger information includes, but is not limited to, flight data, aircraft information and operational data;

[0116] The first processing module is used to construct the fourth-level indicator data of the airline passenger fleet prosperity index based on the passenger data set, and to determine the weight of the fourth-level indicator data using the CRITIC method.

[0117] The second processing module is used to perform seasonal adjustment and standardization on the fourth-level indicator data, and then perform weighted calculations based on the determined weights of the fourth-level indicator data to obtain the third-level indicator data. The third-level indicators include capacity utilization index, operating efficiency index, revenue performance index, and fleet structure index.

[0118] The third processing module is used to detect and process outliers in the tertiary indicator data, and then obtain the secondary indicator data through weighted calculation; the secondary indicators include the fleet efficiency index and the fleet development potential index.

[0119] The output module is used to calculate the airline passenger fleet prosperity index by using the entropy weight method on the secondary indicator data.

[0120] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for calculating the airline passenger fleet prosperity index based on aviation data, characterized in that, Includes the following steps: S1. Obtain passenger information of airline passenger fleets within a preset period, and filter and clean the passenger information to form a passenger data set that can be used for analysis; the passenger information includes, but is not limited to, flight data, aircraft information and operational data; S2. Construct a four-level indicator data for the airline passenger fleet prosperity index based on the passenger data set, and use the CRITIC method to determine the weight of the four-level indicator data. S3. Seasonally adjust and standardize the fourth-level indicator data, and then perform weighted calculations based on the determined weights of the fourth-level indicator data to obtain the third-level indicator data. The third-level indicators include capacity utilization index, operating efficiency index, revenue performance index, and fleet structure index. S4. Perform outlier detection and processing on the tertiary indicator data, and then obtain the secondary indicator data through weighted calculation; the secondary indicators include the fleet efficiency index and the fleet development potential index; S5. The airline passenger fleet prosperity index is obtained by using the entropy weight method to calculate the secondary indicator data.

2. The method for calculating the airline passenger fleet prosperity index based on aviation data according to claim 1, characterized in that, The four-level indicator data include load factor, aircraft utilization rate, flight execution rate, on-time rate, average turnaround time, unit fuel consumption, revenue per passenger kilometer, revenue per seat kilometer, aircraft age composition, aircraft type uniformity, ratio of wide-body to narrow-body aircraft, new aircraft introduction rate, route matching degree, and seat class matching degree.

3. The method for calculating the airline passenger fleet prosperity index based on aviation data according to claim 2, characterized in that, The indicators requiring seasonal adjustment among the fourth-level indicator data include load factor, aircraft utilization rate, flight execution rate, on-time rate, average turnaround time, revenue per passenger kilometer, and revenue per seat kilometer; the specific process of seasonal adjustment in step S3 includes: The seasonal factors for each indicator are calculated using the m-month moving average method. The calculation formula is as follows: A=Z n / AND n ; Among them, X n Y represents the data for the nth month out of the past m months for the current indicator. n Z is the average of the current indicator over the past m months; A is the seasonal factor of the current indicator; Z n This refers to the data for the month in which the seasonal factor needs to be calculated for the current indicator.

4. The method for calculating the airline passenger fleet prosperity index based on aviation data according to claim 2, characterized in that, In step S3, the specific process of standardization includes: The four-level indicator data are distinguished into positive and negative indicators. Positive indicators indicate that the larger the indicator value, the better, while negative indicators indicate that the smaller the indicator value, the better. The standardization process for positive indicators is as follows: The standardized value of the current indicator = (actual value of the current indicator - minimum value of the current indicator) ÷ (maximum value of the current indicator - minimum value of the current indicator) × 100; The standardization process for contrarian indicators is as follows: The standardized value of the current indicator = (the maximum value of the current indicator - the actual value of the current indicator) ÷ (the maximum value of the current indicator - the minimum value of the current indicator) × 100.

5. The method for calculating the airline passenger fleet prosperity index based on aviation data according to claim 1, characterized in that, In step S4, the outlier detection and processing of the tertiary indicator data specifically includes: If any indicator data in the three-level indicator data does not conform to the corresponding indicator data range in any month within the preset period, then that month is defined as an abnormal month. At the same time, based on the abnormal month, the indicator data of the previous month and the indicator data of the next month are taken to calculate the indicator data difference Δ. The indicator data corresponding to the abnormal month is calculated based on the difference Δ between the indicator data and the indicator data of the previous month.

6. The method for calculating the airline passenger fleet prosperity index based on aviation data according to claim 5, characterized in that, The step of calculating the indicator data corresponding to the abnormal month based on the difference Δ in the indicator data and the indicator data of the previous month is as follows: The indicator data for the abnormal month = the indicator data of the previous month for the current indicator + (the difference in indicator data Δ / 2).

7. The method for calculating the airline passenger fleet prosperity index based on aviation data according to claim 1, characterized in that, In step S4, the weighting calculation of the secondary indicators is as follows: Fleet efficiency index = capacity utilization index × 40% + operational efficiency index × 35% + profitability index × 25%; Fleet development potential index = fleet structure index × 60% + market adaptability index × 40%.

8. The method for calculating the airline passenger fleet prosperity index based on aviation data according to claim 1, characterized in that, Step S5 specifically includes: List the secondary indicator data for m months, and calculate the proportion of each month's data to the total of the indicator over m months for each secondary indicator data; Determine the highest and lowest values ​​of the proportions corresponding to each indicator data in the secondary indicator data, and calculate the corresponding proportion differences; The amount of information is set according to the proportional difference, and the weight is determined by the proportion of each indicator's information to the total information. The airline passenger fleet prosperity index is calculated by weighting, that is, the airline passenger fleet prosperity index = fleet efficiency index × ω1 + fleet development potential index × ω2. Wherein, ω1 is the weight of the fleet effectiveness index; ω2 is the weight of the fleet development potential index.

9. The method for calculating the airline passenger fleet prosperity index based on aviation data according to claim 1, characterized in that, Also includes: The data of the fourth-level indicators, the third-level indicators, the second-level indicators, and the airline passenger fleet prosperity index are stored in a pre-set database and displayed synchronously in the corresponding query system.

10. A system for calculating the airline passenger fleet prosperity index based on aviation data, characterized in that, include: The data acquisition module is used to acquire passenger information of the airline's passenger fleet within a preset period, and to filter and clean the passenger information to form a passenger data set that can be used for analysis; the passenger information includes, but is not limited to, flight data, aircraft information and operational data; The first processing module is used to construct the fourth-level indicator data of the airline passenger fleet prosperity index based on the passenger data set, and to determine the weight of the fourth-level indicator data using the CRITIC method. The second processing module is used to perform seasonal adjustment and standardization on the fourth-level indicator data, and then perform weighted calculations based on the determined weights of the fourth-level indicator data to obtain the third-level indicator data. The third-level indicators include capacity utilization index, operating efficiency index, revenue performance index, and fleet structure index. The third processing module is used to detect and process outliers in the tertiary indicator data, and then obtain the secondary indicator data through weighted calculation; the secondary indicators include the fleet efficiency index and the fleet development potential index. The output module is used to calculate the airline passenger fleet prosperity index by using the entropy weight method on the secondary indicator data.

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