A method and system for intelligent analysis and processing of commercial data of a super-large international hub airport

Abstract

The present invention relates to a method and system for intelligent analysis and processing of commercial data of an ultra-large international hub airport. The method includes: obtaining short-term sales data from a commercial data center, and outputting short-term sales results through model analysis; obtaining operation data from the commercial data center, outputting the positions of commercial resources in a matrix through model analysis, and outputting periodic operation results by comparing the changes in the positions of the same commercial resources in the matrix and the change trends of each indicator in two adjacent specific periods; obtaining the sales data and market attention data of a brand from the commercial data center, and outputting brand suitability through model analysis; evaluating the rental value of commercial resources, and outputting the optimal allocation plan of commercial resources in combination with the short-term sales results, periodic operation results and brand suitability of commercial resources. The present invention will greatly improve the commercial data processing capacity, decision-making efficiency and resource allocation optimization level of the airport, and thus promote the significant improvement of the overall operation efficiency of the airport.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and analysis, and particularly to a method and system for intelligent analysis and processing of commercial data of ultra-large international hub airports. Background Art

[0002] With the rapid progress of the global aviation industry, ultra-large international hub airports have become key nodes connecting the world. These airports not only carry heavy air traffic tasks but also involve complex commercial operations and management activities. In daily operations, these airports need to process a huge amount of commercial data, which comes from diverse systems such as point-of-sale systems, flight information systems, and customer relationship management systems. Each type of data contains rich commercial information and operational insights. For airport managers, effectively integrating and analyzing this data is an important basis for improving commercial operation efficiency, accurately allocating resources, and enhancing customer satisfaction.

[0003] However, traditional data processing and analysis methods are overwhelmed when faced with such a large and complex dataset. The complexity of data source access, the low efficiency of data processing, and the inaccuracy of analysis results have become the main problems hindering the optimization of airport commercial operations. These problems not only affect the immediate analysis and decision support capabilities of data but also may lead to delays and mistakes in commercial operation decisions, ultimately having a negative impact on the overall operational efficiency of the airport. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] In view of the above-mentioned disadvantages and deficiencies of the prior art, the present invention provides a method and system for intelligent analysis and processing of commercial data of ultra-large international hub airports, which solves the technical problem that traditional data processing methods cannot meet the requirements of ultra-large international hub airports for efficient integration and accurate analysis of massive and diverse data sources.

[0006] (2) Technical Solutions

[0007] To achieve the above object, the main technical solutions adopted by the present invention include:

[0008] In the first aspect, an embodiment of the present invention provides a method for intelligent analysis and processing of commercial data of ultra-large international hub airports, including:

[0009] Automatically identifying and accessing airport commercial data from different data sources, performing fusion processing including vertical dimensions and / or horizontal dimensions on the accessed airport commercial data, and integrating it onto a data platform to form a commercial data mid-platform;

[0010] Obtain the short-term sales data of commercial resources within a specific time period from the commercial data center, analyze the short-term sales data through a pre-built business dynamic warning model, and output the short-term sales results;

[0011] Obtain the business operation data of commercial resources within two adjacent specific periods from the commercial data center, input at least one index value in the business operation data of commercial resources into a pre-built three-dimensional matrix model, output the position of the commercial resources in the matrix, and output the periodic operation results by comparing the position changes of the same commercial resources in the matrix and the change trends of each index within two adjacent specific periods;

[0012] Obtain the sales data and market attention data of the brands corresponding to commercial resources within a specific time period from the commercial data center, analyze the sales data and market attention data of the brands through a pre-built brand fitness model, and output the brand fitness;

[0013] Evaluate the rental value of commercial resources through at least one measurement model, and combine the short-term sales results, periodic operation results, and brand fitness of commercial resources to output the optimal allocation plan for each commercial resource.

[0014] Optionally, automatically identify and access airport commercial data from different data sources, perform fusion processing including vertical dimension and / or horizontal dimension on the accessed airport commercial data and integrate it onto a data platform to form a commercial data center, including:

[0015] Introduce a pre-trained natural language processing model to automatically identify and parse commercial data from different data sources, and automatically fill in the access configuration form;

[0016] For each item of data in the access configuration form, use a preset connector to automatically access it in the order of filling;

[0017] Perform preprocessing on the accessed commercial data including data cleaning, deduplication, and format conversion;

[0018] Classify and organize the preprocessed commercial data in the vertical dimension according to the hierarchical relationship determined by business attributes and business logic;

[0019] For the preprocessed commercial data or the commercial data processed in the vertical dimension, find a reference data dimension or common data element, and classify and organize it in the horizontal dimension based on the reference data dimension or common data element;

[0020] Integrate the data after vertical and / or horizontal dimension fusion processing onto a unified data platform to form a commercial data center.

[0021] Optionally, obtain the short-term sales data of commercial resources within a specific time period from the commercial data center, analyze the short-term sales data through a pre-constructed business dynamic warning model, and output the short-term sales results including:

[0022] Obtain the short-term sales data of specific commercial resources within a specific time period from the commercial data center;

[0023] Construct a business dynamic warning model and use the historical sales data obtained from the commercial data center to estimate parameters including dynamic average, dynamic standard deviation, and adaptive coefficient;

[0024] According to the dynamic average, dynamic standard deviation, and adaptive coefficient, dynamically calculate the upper track line and the lower track line as the adaptive warning threshold;

[0025] Input the short-term sales data within a specific time period into the business dynamic warning model to generate warning information including the commercial resources triggering the warning, the indicators triggering the warning, the warning direction, the amplitude exceeding the threshold, and the triggering time;

[0026] Generate short-term sales results based on the warning information through a visual interface or report form;

[0027] As new short-term sales data is continuously input into the business dynamic warning model, continuously iteratively update the store business dynamic warning model.

[0028] Optionally, the dynamic average is:

[0029]

[0030] In the formula, x i is the sales data at time point i, ω i is the weight of data point x i , and n is the moving average window size;

[0031] The dynamic standard deviation is:

[0032]

[0033] The adaptive coefficient is;

[0034] α = f(h d );

[0035] In the formula, α is the adaptive coefficient learned according to the historical sales data h d and is used to adjust the sensitivity of the warning threshold;

[0036] The warning threshold is:

[0037]

[0038] In the formula, UT , L T are the upper and lower track lines respectively. If the value of the lower track line is lower than 0, the lower track line is L T = max(0, D A - α·D S ).

[0039] Optionally, obtain the operation data of commercial resources within two adjacent specific periods from the commercial data middle platform, input at least one index value in the operation data of commercial resources into a pre-constructed three-dimensional matrix model, output the position of the commercial resources in the matrix, and by comparing the position changes of the same commercial resources in the matrix and the change trends of each index within two adjacent specific periods, the output periodic operation results include:

[0040] Obtain the operation data of commercial resources within two adjacent specific periods from the commercial data middle platform;

[0041] According to the characteristics of the operation data, select the key evaluation dimensions including sales ability, profitability, and customer satisfaction, construct a three-dimensional matrix model, and determine the value range and scale of each dimension;

[0042] Select at least one key index in the operation data of commercial resources, and input the value of the selected index into the three-dimensional matrix model;

[0043] Compare the direction, distance, and speed of the position migration of the same commercial resources in the matrix within two adjacent specific periods;

[0044] For each key index, analyze the change trends including growth rate and decline rate within two adjacent specific periods;

[0045] According to the position migration of commercial resources in the matrix and the change trends of the indexes, output the periodic operation results through a visualization interface or in the form of a report.

[0046] Optionally, the three-dimensional matrix model is:

[0047] f(S, P, C) = (g1(S), g2(P), g3(C));

[0048] g1(S) = a·S 2 + b·S + c;

[0049] g2(P) = d·e (f·P) ;

[0050] g3(C) = g·log(h·C + i);

[0051] Wherein, g1(S) is the position coordinate on the sales ability dimension S, g2(P) is the position coordinate on the profitability dimension P, g3(C) is the position coordinate on the customer satisfaction dimension C, a, b, and c are the coefficients of the polynomial, d and f are the parameters of the exponential function, and g, h, and i are the parameters of the logarithmic function.

[0052] Optionally, obtain the sales data and market attention data of the brands corresponding to the business resources within a specific time period from the business data center, analyze the sales data and market attention data of the brands through a pre-constructed brand fitness model, and output the brand fitness including;

[0053] Obtain the sales data and market attention data of the brands corresponding to the business resources within a specific time period from the business data center;

[0054] Fill in the missing time series data for the obtained sales data by interpolation or extrapolation;

[0055] For the market attention data, extract the sentiment tendency features related to the brand through sentiment analysis technology on the externally crawled text data, and add them to the market attention data;

[0056] Normalize the filled sales data and the market attention data with new data added to convert them to the same magnitude;

[0057] Establish a brand fitness model;

[0058] Input the normalized data into the brand fitness model for comparative analysis to calculate the fitness score of each brand.

[0059] Optionally, the normalization formula:

[0060] X n =(X - X min ) / (X max - X min );

[0061] Wherein, X is the original data, X min , X max are respectively the minimum and maximum values of the data, and X n is the normalized data;

[0062] The brand fitness model is:

[0063]

[0064] Wherein, represents sales, M represents market attention, S max and M maxThey are the maximum values of sales and market attention respectively. α, β, γ, δ, and ε are all model parameters, which are determined by regression analysis, machine learning algorithms, or expert scoring.

[0065] Optionally, at least one measurement model is used to evaluate the rental value of commercial resources. Combining the short-term sales results, periodic operation results, and brand suitability of commercial resources, the output of the optimal allocation plan for each commercial resource includes:

[0066] Obtain the historical rental data of commercial resources from the commercial data center.

[0067] Select at least one measurement model suitable for the current commercial resource evaluation from the measurement models including the investment income method, rent comparison method, and turnover reverse deduction method.

[0068] Based on the selected measurement model, evaluate the rental value of commercial resources, and determine the rental value range of commercial resources according to the evaluation results.

[0069] According to the rental value range of commercial resources, the short-term sales results, periodic operation results, and brand suitability of commercial resources, formulate the optimal allocation plan for each commercial resource, including the rental pricing, lease term, and brand matching of each commercial resource.

[0070] In a second aspect, an embodiment of the present invention provides a commercial data intelligent analysis and processing system for a super-large international hub airport, including:

[0071] A commercial data center formation module, which is used to automatically identify and access airport commercial data from different data sources, perform fusion processing including vertical dimensions and / or horizontal dimensions on the accessed airport commercial data, and integrate it onto a data platform to form a commercial data center.

[0072] A short-term sales result output module, which is used to obtain the short-term sales data of commercial resources within a specific time period from the commercial data center, analyze the short-term sales data through a pre-constructed business dynamic warning model, and output the short-term sales result.

[0073] A periodic operation result output module, which is used to obtain the operation data of commercial resources within two adjacent specific periods from the commercial data center, input at least one index value in the operation data of commercial resources into a pre-constructed three-dimensional matrix model, output the position of the commercial resource in the matrix, and output the periodic operation result by comparing the position change of the same commercial resource in the matrix and the change trend of each index within two adjacent specific periods.

[0074] A brand adaptation degree output module, which is used to obtain the sales data and market attention data of the brands corresponding to commercial resources within a specific time period from the commercial data center, analyze the sales data and market attention data of the brands through a pre-constructed brand adaptation degree model, and output the brand adaptation degree;

[0075] A commercial resource allocation module, which is used to evaluate the rental value of commercial resources through at least one measurement model, and output the best allocation plan for each commercial resource in combination with the short-term sales results, cycle operation results, and brand adaptation degree of the commercial resources.

[0076] (III) Beneficial effects

[0077] The beneficial effects of the present invention are as follows:

[0078] The present invention can automatically identify and access airport commercial data from different data sources, and through efficient data fusion and processing technologies, integrate these data onto a unified data platform to form a commercial data center. This innovative data processing architecture not only simplifies the data access and integration process, but also greatly improves the efficiency and accuracy of data processing.

[0079] On this basis, the present invention further uses advanced mathematical models and algorithms to deeply analyze commercial data. Through the comprehensive application of multiple models such as an operation dynamic warning model, a three-dimensional matrix model, and a brand adaptation degree model, it is possible to comprehensively and deeply mine the value information in commercial data and provide scientific and accurate decision-making support for airport commercial operations.

[0080] Therefore, the present invention will greatly improve the airport's commercial data processing ability, decision-making efficiency, and resource allocation optimization level, and thus promote a significant improvement in the overall operation efficiency of the airport. Brief description of the drawings

[0081] Figure 1 It is a schematic flow chart of a method for intelligent analysis and processing of commercial data of a super-large international hub airport provided by an embodiment of the present invention;

[0082] Figure 2 It is a specific schematic flow chart of step S1 of a method for intelligent analysis and processing of commercial data of a super-large international hub airport provided by an embodiment of the present invention;

[0083] Figure 3 It is an example diagram of the longitudinal dimension of a method for intelligent analysis and processing of commercial data of a super-large international hub airport provided by an embodiment of the present invention;

[0084] Figure 4 It is a display interface of the data center of a method for intelligent analysis and processing of commercial data of a super-large international hub airport provided by an embodiment of the present invention;

[0085] Figure 5 Schematic diagram of the specific process of step S2 of a method for intelligent analysis and processing of commercial data of a super-large international hub airport provided by an embodiment of the present invention;

[0086] Figure 6 Schematic diagram of the specific process of step S3 of a method for intelligent analysis and processing of commercial data of a super-large international hub airport provided by an embodiment of the present invention;

[0087] Figure 7 Schematic diagram of the specific process of step S4 of a method for intelligent analysis and processing of commercial data of a super-large international hub airport provided by an embodiment of the present invention;

[0088] Figure 8 Schematic diagram of the specific process of step S5 of a method for intelligent analysis and processing of commercial data of a super-large international hub airport provided by an embodiment of the present invention;

[0089] Figure 9 Detailed information list diagram of rent value evaluation of a method for intelligent analysis and processing of commercial data of a super-large international hub airport provided by an embodiment of the present invention;

[0090] Figure 10 Thermal map and information summary table of the commercial resource rent value of a method for intelligent analysis and processing of commercial data of a super-large international hub airport provided by an embodiment of the present invention. Detailed implementation manners

[0091] In order to better explain the present invention for easy understanding, the present invention will be described in detail below with reference to the accompanying drawings through specific implementation manners.

[0092] Such as Figure 1As shown in the figure, a method for intelligent analysis and processing of commercial data of a super-large international hub airport proposed by an embodiment of the present invention includes: automatically identifying and accessing airport commercial data from different data sources, performing fusion processing including vertical dimensions and / or horizontal dimensions on the accessed airport commercial data and integrating it onto a data platform to form a commercial data mid-platform; obtaining short-term sales data of commercial resources within a specific time period from the commercial data mid-platform, analyzing the short-term sales data through a pre-constructed operation dynamic warning model, and outputting short-term sales results; obtaining operation data of commercial resources within two adjacent specific cycles from the commercial data mid-platform, inputting at least one index value in the operation data of the commercial resources into a pre-constructed three-dimensional matrix model, outputting the position of the commercial resources in the matrix, and outputting cycle operation results by comparing the position changes of the same commercial resources in the matrix and the change trends of each index within two adjacent specific cycles; obtaining sales data and market attention data of the brands corresponding to commercial resources within a specific time period from the commercial data mid-platform, analyzing the sales data and market attention data of the brands through a pre-constructed brand fitness model, and outputting brand fitness; evaluating the rental value of commercial resources through at least one measurement model, and combining the short-term sales results, cycle operation results, and brand fitness of commercial resources to output the optimal allocation plan for each commercial resource.

[0093] The present invention can automatically identify and access airport commercial data from different data sources, and through efficient data fusion and processing technologies, integrate these data onto a unified data platform to form a commercial data mid-platform. This innovative data processing architecture not only simplifies the data access and integration process, but also greatly improves the efficiency and accuracy of data processing.

[0094] On this basis, the present invention further uses advanced mathematical models and algorithms to deeply analyze commercial data. Through the comprehensive application of multiple models such as operation dynamic warning models, three-dimensional matrix models, and brand fitness models, it is possible to comprehensively and deeply mine the value information in commercial data and provide scientific and accurate decision-making support for airport commercial operations.

[0095] Therefore, the present invention will greatly improve the airport's commercial data processing ability, decision-making efficiency, and resource allocation optimization level, and thus promote the significant improvement of the overall operation efficiency of the airport.

[0096] In order to better understand the above technical solutions, the exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more clear and thorough understanding of the present invention and to be able to convey the scope of the present invention completely to those skilled in the art.

[0097] Specifically, the embodiments of the present invention provide a method and system for intelligent analysis and processing of commercial data of ultra-large international hub airports, including:

[0098] S1. Automatically identify and access airport commercial data from different data sources, perform fusion processing including vertical dimensions and / or horizontal dimensions on the accessed airport commercial data, and integrate it onto a data platform to form a commercial data middle platform.

[0099] Further, as Figure 2 shown, step S1 includes:

[0100] S11. Introduce a pre-trained natural language processing model, automatically identify and parse commercial data from different data sources, and automatically fill in the access configuration form.

[0101] The present invention collects commercial data samples from different data sources, including CSV files, database export files, API responses, etc. Preprocess the data, such as cleaning and formatting, for subsequent model training. Among them, the commercial data samples collected from different data sources include CSV files, database export files, API responses, etc.

[0102] Then, preprocess the data, such as cleaning and formatting, for subsequent model training, and input the preprocessed data into the fine-tuned model. The model automatically identifies the structure and content of the data, such as the column names and data types of the table. The model further parses the data and extracts key information, such as store names, transaction amounts, dates, etc. Then, based on the key information parsed by the model, automatically fill in the data access configuration form. The form content includes data source type, access method, data format, field mapping, etc. Ensure that all necessary information is filled in correctly for subsequent data access and analysis work.

[0103] S12. For each item of data in the access configuration form, use a preset connector to automatically access it in the order of entry.

[0104] S13. Perform preprocessing on the accessed commercial data, including data cleaning, duplicate removal, and format conversion.

[0105] S14. Classify and organize the preprocessed commercial data according to the hierarchical relationship determined by business attributes and business logic in the vertical dimension.

[0106] Further, analyze the preprocessed commercial data to understand the business attributes and logic behind it. According to the business logic, divide the data into different vertical dimensions, referring to Figure 3, such as storefronts, orders, products, etc. Within each vertical dimension, further subdivide the data types to ensure clear hierarchical relationships of the data. Set clear rules and standards for each data type to ensure data consistency and accuracy.

[0107] S15. For the preprocessed commercial data or the commercial data processed by vertical dimensions, find a benchmark data dimension or common data element, and classify and organize the data in the horizontal dimension based on the benchmark data dimension or common data element.

[0108] In this step, identify the common benchmark data dimension or element from the preprocessed or vertically processed data. Based on the benchmark dimension, classify the data horizontally. For example, all time-related data can be grouped into one category, and all location-related data into another category. In each horizontal classification, further organize and improve the data types to ensure data integrity and consistency. Design data hooks, that is, establish relationships between different types of data to achieve vertical connectivity and interaction of the data.

[0109] S16. Integrate the data processed by vertical and / or horizontal dimension fusion into a unified data platform to form Figure 4 the shown commercial data middle platform. The commercial data middle platform is the core and foundation of the solution of the present invention, and is an integrated platform integrating commercial data management, governance, and application. It aims to provide stable and efficient data support for commercial operation and management. Its core is to uniformly integrate all data resources and data applications into one platform, realizing centralized management and application of commercial data, thereby avoiding repeated construction of various data centers, reducing resource waste and duplicate labor, and improving data management efficiency and quality.

[0110] S2. Obtain the short-term sales data of commercial resources within a specific time period from the commercial data middle platform, and analyze the short-term sales data through a pre-constructed business dynamic warning model to output short-term sales results.

[0111] Further, as Figure 5 shown, step S2 includes:

[0112] S21. Obtain the short-term sales data of specific commercial resources within a specific time period from the commercial data middle platform; wherein, the short-term sales data includes but is not limited to key business indicators such as sales amount, number of sales transactions, average customer price, sales quantity, and sales per unit area.

[0113] S22. Construct a business dynamic warning model, and use the historical sales data obtained from the commercial data middle platform to perform parameter estimation including dynamic average value, dynamic standard deviation, and adaptive coefficient.

[0114] S23. Dynamically calculate the upper track line and the lower track line as adaptive warning thresholds according to the dynamic average value, the dynamic standard deviation, and the adaptive coefficient.

[0115] In a specific embodiment, the dynamic average value is:

[0116]

[0117] In the formula, x i is the sales data at time point i, ω i is the weight of the data point x i , and n is the moving average window size;

[0118] The dynamic standard deviation is:

[0119]

[0120] The adaptive coefficient is;

[0121] α = f(h d );

[0122] In the formula, α is the adaptive coefficient learned according to the historical sales data h d , and is used to adjust the sensitivity of the warning threshold;

[0123] The warning threshold is:

[0124]

[0125] In the formula, U T , L T are the upper and lower track lines respectively. If the value of the lower track line is lower than 0, then the lower track line is L T = max(0, D A - α·D S ).

[0126] Furthermore, the warning threshold satisfies: middle track line = 7-day moving average, upper track line = middle track line + 2 times the standard deviation, lower track line = middle track line - 2 times the standard deviation. If the lower track line is lower than 0, then the lower track line = middle track line - 1 times the standard deviation; if the lower track line is still less than 0, then the lower track line = 0.

[0127] S24. Input the short-term sales data within a specific time period into the business dynamic warning model to generate warning information including the business resources triggering the warning, the indicators triggering the warning, the warning direction, the amplitude exceeding the threshold, and the triggering time. Real-time monitor the key business indicators of each store. Once the warning upper or lower limit is triggered, the system immediately reacts, such as changing the color of the indicator line (from green to red)

[0128] S25. Generate short-term sales results through a visual interface or in the form of a report based on the warning information;

[0129] S26. As new short-term sales data is continuously input into the business dynamic warning model, the store business dynamic warning model is continuously iteratively updated.

[0130] S3. Obtain the business operation data of commercial resources within two adjacent specific periods from the business data center. Input at least one index value in the business operation data of commercial resources into a pre-constructed three-dimensional matrix model, output the position of the commercial resources in the matrix, and by comparing the position changes of the same commercial resources in the matrix and the change trends of each index within two adjacent specific periods, output the periodic operation results.

[0131] Further, as Figure 6 shown, step S3 includes:

[0132] S31. Obtain the business operation data of commercial resources within two adjacent specific periods from the business data center. Determine two adjacent analysis periods, such as the previous period and the current period, or the same period last year and the same period this year.

[0133] S32. According to the characteristics of the business operation data, select key evaluation dimensions including sales ability, profitability, and customer satisfaction, construct a three-dimensional matrix model, and determine the value range and scale of each dimension.

[0134] In a specific embodiment, the three-dimensional matrix model is:

[0135] f(S, P, C) = (g1(S), g2(P), g3(C));

[0136] g1(S) = a·S 2 + b·S + c;

[0137] g2(P) = d·e (f·P) ;

[0138] g3(C) = g·log(h·C + i);

[0139] In the formula, g1(S) is the position coordinate on the sales ability dimension S, g2(P) is the position coordinate on the profitability dimension P, and g3(C) is the position coordinate on the customer satisfaction C dimension.

[0140] It should be noted that a, b, and c are the coefficients of the polynomial and can be determined by fitting according to the data. For example, a = 0.01, b = 0.5, and c = 10 can be selected. In this way, when S = 0, g1(S) = 10; when S = 100, g1(S) = 110, assuming this is a reasonable range on the x-axis.

[0141] d and f are parameters of the exponential function. Assuming that the numerical range of the profitability P is also between 0 and 100, d = 1 and f = 0.01 can be selected. In this way, when P = 0, g2(P) = 1; when P = 100, g2(P) ≈ 2.72, assuming this is a reasonable range on the y-axis.

[0142] g, h, and i are parameters of the logarithmic function. For the customer satisfaction C, a logarithmic function can be used to map to the position coordinates on the z-axis, and g, h, and i are parameters of the logarithmic function. Similarly, assuming that the numerical range of the customer satisfaction C is between 0 and 100, g = 10, h = 0.1, and i = 1 can be selected. In this way, when C = 0, g3(C) = 0 (the domain problem of the logarithmic function needs to be processed, and here it is assumed that C will not be 0); when C = 100, g3(C) = 10 * log(11) ≈ 10.45, assuming this is a reasonable range on the z-axis.

[0143] In addition, for the logarithmic function g3(C), there is a domain problem when C = 0. In practical applications, this situation of log(0) can be avoided by adding a small positive number, or other methods can be used to handle this special situation.

[0144] S33. Select at least one key indicator from the business operation data of the commercial resources, and input the numerical value of the selected indicator into the three-dimensional matrix model. Generally, 9 key composite indicators are selected, and these indicators should cover multiple aspects such as sales ability, operation efficiency, financial status, and customer satisfaction.

[0145] S34. Compare the direction, distance, and speed of the position migration of the same commercial resource in the matrix within two adjacent specific periods.

[0146] Compare the positions of the same commercial resource in the matrix within two adjacent specific periods to observe whether it has migrated. Record the direction, distance, and speed of the migration to reflect the change in the business operation efficiency of the commercial resources.

[0147] The migration direction can be determined by calculating the difference between the position vectors in the two periods. For example, if the position of the commercial resource A in the first period is (X1, Y1, Z1) and in the second period is (X2, Y2, Z2), then the migration vector is (ΔX, ΔY, ΔZ) = (X2 - X1, Y2 - Y1, Z2 - Z1).

[0148] The migration distance can be determined by calculating the modulus of the migration vector, that is The migration speed can be defined as the ratio of the migration distance to the time interval between the two periods.

[0149] S35. For each key indicator, analyze the change trend including the growth rate and decline rate within two adjacent specific periods.

[0150] For each key metric, analyze its trend of change over two adjacent specific periods. Calculate the growth rate, decline rate, or other relevant statistics of the metric to quantify the change in the metric.

[0151] For example, if the sales of business resource A increase from S1 in the first period to S2 in the second period, the sales growth rate is (S2 - S1) / S1 * 100%. Similarly, the trend of change of other metrics such as the profit margin growth rate can be calculated.

[0152] S36. Output the periodic business results in the form of a visual interface or report based on the position migration of business resources in the matrix and the trend of change of metrics.

[0153] S4. Obtain the sales data and market attention data of the brands corresponding to business resources within a specific time period from the business data center, and analyze the sales data and market attention data of the brands through a pre-constructed brand fitness model to output the brand fitness.

[0154] Furthermore, as Figure 7 shown, step S4 includes:

[0155] S41. Obtain the sales data and market attention data of the brands corresponding to business resources within a specific time period from the business data center.

[0156] Selectively extract the brand sales data corresponding to business resources within a specific time period from the business data center. This step requires ensuring the accuracy and integrity of the data while filtering out any invalid or incorrect data points.

[0157] At the same time, obtain the market attention data of these brands within the same time period from the business data center or other relevant data sources. The market attention data can be collected through multiple channels, including but not limited to social media interactions, search engine query volumes, news reporting frequencies, etc.

[0158] S42. Fill in the missing time series data of the obtained sales data through interpolation or extrapolation methods.

[0159] S43. Through sentiment analysis technology on the text data crawled externally for the market attention data, extract the sentiment tendency characteristics related to the brands and add them to the market attention data.

[0160] S44. Normalize the filled sales data and the market attention data with new data added to convert them to the same magnitude.

[0161] Among them, the normalization processing formula:

[0162] X n=(X - X min ) / (X max - X min );

[0163] Wherein, X is the original data, and X min , X max are the minimum and maximum values of the data respectively, and X n is the data after normalization.

[0164] S45. Establish a brand fitness model.

[0165] The brand fitness model is:

[0166]

[0167] Wherein, represents the sales amount, M represents the market attention, and S max and M max are the maximum values of the sales amount and the market attention respectively, and α, β, γ, δ, ε are all model parameters, which are determined by regression analysis, machine learning algorithms or expert scoring.

[0168] The first part is the weighted average of the sales amount and the market attention, where α determines the relative importance between the two. The second part introduces an interaction term to consider the synergy effect between the sales amount and the market attention. When both are relatively high, this interaction term will increase the score of the brand fitness.

[0169] S46. Input the normalized data into the brand fitness model for comparative analysis, and calculate the fitness score of each brand.

[0170] S5. Evaluate the rental value of business resources through at least one measurement model, and combine the short-term sales results, cycle operation results and brand fitness of the business resources to output the optimal allocation plan for each business resource.

[0171] Furthermore, as Figure 8 shown, step S5 includes:

[0172] S51. Obtain the historical rental data of business resources from the business data center.

[0173] S52. Select at least one measurement model suitable for the current business resource evaluation from the measurement models including the investment income method, the rent comparison method and the turnover reverse deduction method.

[0174] In a specific embodiment, the rent comparison method is as follows: find cases that are similar to the target commercial resource and have recent lease transactions. Compare the similarities and differences between the comparison cases and the target commercial resource in terms of geographical location, area, facilities, etc. Estimate the rent value of the target commercial resource based on the rent levels of similar cases and the difference adjustment factors; the investment income method is as follows: predict the future income of the commercial resource, considering factors such as rent income, expense expenditure, capitalization rate, etc. Use an appropriate discount rate to discount the future income to the present value to obtain the rent value of the commercial resource. And, the turnover reverse deduction method: this is a method to reverse deduce the rent value of a commercial resource through its turnover. First, it is necessary to determine the turnover data of the target commercial resource, which is obtained through financial statements, sales records, or market research. Then, analyze the potential relationship between turnover and rent, which may need to consider factors such as industry average level, profit margin of the commercial resource, market competition situation, etc.

[0175] Evaluate the market rent value of commercial resources within a certain time period (1 month, 1 quarter, half a year, 1 year), providing decision-making references for resource optimization and adjustment and the determination of the minimum price for investment promotion. At the same time, as Figure 9 shown, provide two presentation methods such as intuitive visualization of the resource rent value and detailed information list. Again, as Figure 10 shown, calculate and evaluate the rent value of commercial resources through three models and compare it with the actual rent level of the resource.

[0176] S53. Based on the selected calculation model, evaluate the rent value of commercial resources, and determine the rent value range of commercial resources according to the evaluation results.

[0177] S54. According to the rent value range of commercial resources, the short-term sales results, cycle operation results, and brand suitability of commercial resources, formulate the best allocation plan for each commercial resource, including rent pricing, lease term, and brand matching of each commercial resource.

[0178] In a specific embodiment, a multi-objective genetic algorithm is used to output the best allocation plan:

[0179] (1) Randomly generate an initial population, where each individual represents a possible allocation plan, including information on rent pricing, lease term, and brand matching. Set parameters such as the number of iterations, crossover rate, and mutation rate of the algorithm.

[0180] (2) For each individual (allocation plan), calculate its fitness value. The fitness function can comprehensively consider multiple objectives such as rent income, brand suitability, and resource utilization efficiency. Rent income is calculated by multiplying the rent pricing by the lease term. Brand suitability can be obtained through market research or expert scoring. Resource utilization efficiency can be evaluated through the utilization situation of commercial resources (such as area utilization rate, sales volume, etc.).

[0181] (3) Use the non - dominated sorting and crowding distance comparison methods in multi - objective optimization to select excellent individuals to enter the next generation. Non - dominated sorting is used to determine the superiority and inferiority relationship between individuals, while crowding distance comparison is used to maintain the diversity of the population.

[0182] (4) Randomly select two individuals for crossover operation to generate new individuals. The crossover operation can be achieved by exchanging some genes (such as rent pricing, lease term, etc.). Mutate the newly generated individuals to increase the diversity of the population. The mutation operation can randomly change the values of some genes.

[0183] (5) When the preset number of iterations is reached or other termination conditions are met, the algorithm stops. Output the optimal allocation plan, that is, the individual with the highest fitness.

[0184] In addition, the embodiment of the present invention provides an intelligent analysis and processing system for commercial data of a super - large international hub airport, including:

[0185] A commercial data mid - platform formation module, which is used to automatically identify and access airport commercial data from different data sources, perform fusion processing including vertical dimension and / or horizontal dimension on the accessed airport commercial data and integrate it onto a data platform to form a commercial data mid - platform.

[0186] A short - term sales result output module, which is used to obtain the short - term sales data of commercial resources within a specific time period from the commercial data mid - platform, analyze the short - term sales data through a pre - constructed business dynamic warning model, and output the short - term sales result.

[0187] A periodic operation result output module, which is used to obtain the operation data of commercial resources within two adjacent specific periods from the commercial data mid - platform, input at least one index value in the operation data of commercial resources into a pre - constructed three - dimensional matrix model, output the position of the commercial resources in the matrix, and output the periodic operation result by comparing the position changes of the same commercial resources in the matrix and the change trends of each index within two adjacent specific periods.

[0188] A brand fitness output module, which is used to obtain the sales data and market attention data of the brands corresponding to commercial resources within a specific time period from the commercial data mid - platform, analyze the sales data and market attention data of the brands through a pre - constructed brand fitness model, and output the brand fitness.

[0189] A commercial resource allocation module, which is used to evaluate the rent value of commercial resources through at least one measurement model, and output the best allocation plan for each commercial resource in combination with the short - term sales result, periodic operation result, and brand fitness of the commercial resources.

[0190] Furthermore, an embodiment of the present invention provides a computer-readable medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method for intelligent analysis and processing of commercial data of a super-large international hub airport. Such a computer-readable medium carries a complete set of instruction sets that can guide the processor to complete complex intelligent analysis tasks of airport commercial data, thereby significantly improving the operating efficiency of super-large international hub airports.

[0191] In summary, an embodiment of the present invention provides a method and system for intelligent analysis and processing of commercial data of a super-large international hub airport, which have multiple advantageous functions:

[0192] First of all, the present invention can automatically identify and access airport commercial data from different data sources, effectively solving the problem of complex data source access. After fusion processing and integration onto a data platform, a commercial data middle platform is formed, thereby realizing unified management and efficient utilization of data. This step not only simplifies the data processing process but also improves the efficiency and accuracy of data processing.

[0193] Secondly, by obtaining short-term sales data from the commercial data middle platform and analyzing it using an operation dynamic warning model, the method and system can quickly output short-term sales results. This provides timely feedback on sales situations for airport managers, helping them make quick and accurate business decisions.

[0194] In addition, the present invention can also obtain operation data from the commercial data middle platform, input the key index values in the operation data into a three-dimensional matrix model, and then output the position of commercial resources in the matrix. By comparing the changes in the position of the same commercial resource in the matrix and the change trends of each index in two adjacent specific periods, the operating conditions of commercial resources can be clearly understood, and the periodic operation results can be output accordingly. This function helps airport managers comprehensively grasp the operation of commercial resources and provides strong support for future resource allocation.

[0195] At the same time, the present invention can also analyze the sales data and market attention data of brands through a brand adaptation model and output the brand adaptation degree. This index helps airport managers understand the performance of each brand in the market and provides a scientific basis for brand cooperation and selection.

[0196] Finally, through a measurement model to evaluate the rental value of commercial resources and combining the short-term sales results, periodic operation results, and brand adaptation degree of commercial resources, the method and system can output the best allocation plan for each commercial resource. This not only optimizes resource allocation, improves resource utilization efficiency, but also brings greater economic benefits to the airport.

[0197] Therefore, through intelligent data processing and analysis, the present invention provides comprehensive, accurate, and timely operation information for airport managers, helping them make more informed decisions, thereby enhancing the commercial operation efficiency and overall competitiveness of the airport.

[0198] Since the system / device described in the above embodiments of the present invention is the system / device adopted for implementing the method of the above embodiments of the present invention, based on the method described in the above embodiments of the present invention, those skilled in the art can understand the specific structure and variations of the system / device, and thus will not be elaborated herein. Any system / device adopted by the method of the above embodiments of the present invention falls within the scope of protection of the present invention.

[0199] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented 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] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can also be implemented by computer program instructions.

[0201] It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present invention can be implemented by means of hardware including several different components and by means of a suitably programmed computer. In the claims listing several apparatuses, several of these apparatuses can be embodied by the same hardware. The use of the words first, second, third, etc. is only for convenience of expression and does not denote any order. These words can be understood as part of the component name.

[0202] In addition, it should be noted that in the description of this specification, the descriptions of terms such as "one embodiment", "some embodiments", "embodiment", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0203] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications after learning the basic creative concepts. Therefore, the claims should be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0204] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention should also include these modifications and variations.

Claims

1. A method for intelligent analysis and processing of commercial data of a super-large international hub airport, characterized in that, Including: Automatically identify and access airport commercial data from different data sources, perform fusion processing including vertical dimension and / or horizontal dimension on the accessed airport commercial data, and integrate it onto a data platform to form a commercial data center. This includes: introducing a pre-trained natural language processing model to automatically identify and parse heterogeneous data formats from different data sources, generating an access configuration form containing data source types, access methods, and field mappings, and automating data access based on the access configuration form through a preset connector; Obtain the short-term sales data of commercial resources within a specific period from the commercial data center, dynamically calculate the warning threshold including dynamic average, dynamic standard deviation, and an adaptive coefficient for adjusting the warning threshold sensitivity through a pre-constructed business dynamics warning model, analyze the short-term sales data, output the short-term sales results, and continuously iterate and update the model parameters with the input of new data; Obtain the business operation data of commercial resources within two adjacent specific periods from the commercial data center, input at least one index value in the business operation data of commercial resources into a pre-constructed three-dimensional matrix model, output the position of the commercial resources in the matrix, and output the periodic operation results by comparing the position changes of the same commercial resources in the matrix and the change trends of each index within two adjacent specific periods; The three-dimensional matrix model is generated by combining a polynomial function in the sales ability dimension, an exponential function in the profitability dimension, and a logarithmic function in the customer satisfaction dimension to form a multi-dimensional mapping coordinate; Obtain the sales data and market attention data of the brands corresponding to commercial resources within a specific period from the commercial data center, analyze the sales data and market attention data of the brands through a pre-constructed brand suitability model, and output the brand suitability; The brand suitability model introduces sentiment analysis technology to extract the sentiment tendency characteristics of external text data, and quantifies the synergy effect between sales and market attention through interaction terms. The model formula is: In the formula, represents the sales volume, M represents the market attention, and S max and M max are respectively the maximum values of the sales volume and the market attention. α, β, γ, δ, and ε are all model parameters, which are determined by regression analysis, machine learning algorithms, or expert scoring; Evaluate the rental value of commercial resources through at least one measurement model, combine the short-term sales results, periodic operation results, and brand suitability of commercial resources, adopt a multi-objective genetic algorithm, set the fitness function with rental income, brand suitability score, and resource utilization efficiency as objectives, and output the optimal allocation plan for each commercial resource including rental pricing, lease term, and brand combination through non-dominated sorting and crowding degree.

2. The intelligent analysis and processing method for commercial data of an ultra-large international hub airport according to claim 1, characterized in that Automatically identify and access airport commercial data from different data sources, perform fusion processing including vertical dimension and / or horizontal dimension on the accessed airport commercial data, and integrate it onto a data platform to form a commercial data center, including: Introduce a pre-trained natural language processing model to automatically identify and parse commercial data from different data sources, and automatically fill in the access configuration form; For each item of data in the access configuration form, use a preset connector for automatic access in the order of entry; Perform preprocessing on the accessed commercial data including data cleaning, deduplication, and format conversion; Classify and organize the preprocessed commercial data in the vertical dimension according to the hierarchical relationship determined by business attributes and business logic; For pre-processed commercial data or commercially data processed by vertical dimension, find a benchmark data dimension or common data elements, and classify and organize the horizontal dimension based on the benchmark data dimension or common data elements; Integrate the data processed by vertical and / or horizontal dimension fusion into a unified data platform to form a commercial data middle platform.

3. The intelligent analysis and processing method for commercial data of a super-large international hub airport according to claim 1, characterized in that, Obtain the short-term sales data of commercial resources within a specific time period from the commercial data middle platform, analyze the short-term sales data through a pre-constructed business dynamic warning model, and output the short-term sales results including: Obtain the short-term sales data of specific commercial resources within a specific time period from the commercial data middle platform; Construct a business dynamic warning model, and use the historical sales data obtained from the commercial data middle platform to estimate parameters including dynamic average value, dynamic standard deviation, and adaptive coefficient; According to the dynamic average value, dynamic standard deviation, and adaptive coefficient, dynamically calculate the upper track line and the lower track line as the adaptive warning threshold; Input the short-term sales data within a specific time period into the business dynamic warning model, and generate warning information including the commercial resources triggering the warning, the indicators triggering the warning, the warning direction, the amplitude exceeding the threshold, and the triggering time; Generate short-term sales results in the form of a visual interface or report based on the warning information; As new short-term sales data is continuously input into the business dynamic warning model, continuously iterate and update the store business dynamic warning model.

4. The intelligent analysis and processing method for commercial data of a super-large international hub airport according to claim 3, characterized in that The dynamic average value is: where x i is the sales data at time point i, ω i is the weight of data point x i , and n is the moving average window size; The dynamic standard deviation is: The adaptive coefficient is; α = f(h d ); where α is an adaptive coefficient learned based on historical sales data h d and is used to adjust the sensitivity of the early warning threshold; The warning threshold is: where U T and L T are the upper and lower track lines respectively. If the value of the lower track line is less than 0, the lower track line is L T = max(0, D A - α·D S ).

5. The intelligent analysis and processing method for commercial data of a super-large international hub airport according to claim 1, wherein Obtain the business operation data of commercial resources within two adjacent specific periods from the commercial data middle platform, input at least one index value in the business operation data of commercial resources into a pre-constructed three-dimensional matrix model, output the position of the commercial resources in the matrix, and by comparing the position change of the same commercial resource in the matrix and the change trend of each index within two adjacent specific periods, output the periodic operation results including: Obtain the business operation data of commercial resources within two adjacent specific periods from the commercial data middle platform; According to the characteristics of the business operation data, select key evaluation dimensions including sales ability, profitability, and customer satisfaction, construct a three-dimensional matrix model, and determine the value range and scale of each dimension; Select at least one key index in the business operation data of commercial resources, and input the value of the selected index into the three-dimensional matrix model; Compare the direction, distance, and speed of the position migration of the same commercial resource in the matrix within two adjacent specific periods; For each key index, analyze the change trend including growth rate and decline rate within two adjacent specific periods; According to the position migration of the commercial resources in the matrix and the change trend of the index, output the periodic operation results in the form of a visual interface or report.

6. The intelligent analysis and processing method for commercial data of a super-large international hub airport according to claim 5, characterized in that The three-dimensional matrix model is: f(S,P,C)=(g1(S),g2(P),g3(C)); g1(S) = a·S 2 + b·S + c; g2(P) = d·e (f·P) ; g3(C)=g·log(h·C + i); Wherein, g1(S) is the position coordinate on the dimension of sales ability S, g2(P) is the position coordinate on the dimension of profitability P, g3(C) is the position coordinate on the dimension of customer satisfaction C, a, b, and c are the coefficients of the polynomial, d and f are the parameters of the exponential function, and g, h, and i are the parameters of the logarithmic function.

7. The intelligent analysis and processing method of commercial data for a super-large international hub airport according to claim 1, characterized in that Obtain the sales data and market attention data of the brand corresponding to the business resources within a specific time period from the business data center, and analyze the sales data and market attention data of the brand through a pre-constructed brand fitness model, and output the brand fitness including; Obtain the sales data and market attention data of the brand corresponding to the business resources within a specific time period from the business data center; Fill in the missing time series data for the obtained sales data by interpolation or extrapolation; Perform sentiment analysis on the text data crawled externally for the market attention data, extract the sentiment tendency features related to the brand, and add them to the market attention data; Normalize the filled sales data and the market attention data with new data added, and convert them to the same magnitude; Establish a brand fitness model; Input the normalized data into the brand fitness model for comparative analysis, and calculate the fitness score of each brand.

8. The intelligent analysis and processing method for airport commercial data of a super-large international hub airport according to claim 7, wherein Normalization formula: X n = (X - X min ) / (X max - X min ); where X is the original data, X min , X max are the minimum and maximum values of the data respectively, and X n is the data after normalization.

9. The intelligent analysis and processing method for commercial data of a super-large international hub airport according to claim 1, characterized in that Evaluate the rental value of business resources through at least one measurement model, and combine the short-term sales results, periodic operation results, and brand fitness of the business resources to output the optimal allocation plan for each business resource, including: Obtain the historical rental data of business resources from the business data center; Select at least one measurement model suitable for the current business resource evaluation from the measurement models including the investment income method, the rent comparison method, and the turnover reverse deduction method; Based on the selected measurement model, evaluate the rental value of business resources, and determine the rental value range of business resources according to the evaluation results; According to the rental value range of business resources, the short-term sales results, periodic operation results, and brand fitness of business resources, formulate the optimal allocation plan for each business resource including the rental pricing, lease term, and brand matching of each business resource.

10. A commercial data intelligent analysis and processing system for a super-large international hub airport, characterized in that, Including: The business data center formation module is used to automatically identify and access airport commercial data from different data sources, perform fusion processing including vertical dimension and / or horizontal dimension on the accessed airport commercial data and integrate it onto a data platform to form a business data center, including: introducing a pre-trained natural language processing model, automatically identifying and parsing heterogeneous data formats from different data sources, generating an access configuration form including data source type, access method, and field mapping, and automatically accessing data based on the access configuration form through a preset connector; Short-term sales result output module, which is used to obtain short-term sales data of commercial resources within a specific time period from the commercial data middle platform, dynamically calculate the warning threshold including the dynamic average value, dynamic standard deviation and the adaptive coefficient for adjusting the warning threshold through a pre-constructed business dynamic warning model, analyze the short-term sales data, output the short-term sales result, and continuously iterate and update the model parameters with the input of new data; Periodic business result output module, which is used to obtain the business data of commercial resources within two adjacent specific periods from the commercial data middle platform, input at least one index value in the business data of commercial resources into a pre-constructed three-dimensional matrix model, output the position of the commercial resources in the matrix, and output the periodic business result by comparing the position changes of the same commercial resources in the matrix and the change trends of each index within two adjacent specific periods; the three-dimensional matrix model is generated by combining a polynomial function in the sales ability dimension, an exponential function in the profitability dimension and a logarithmic function in the customer satisfaction dimension to form a multi-dimensional mapping coordinate; Brand fitness output module, which is used to obtain the sales data and market attention data of the brand corresponding to the commercial resources within a specific time period from the commercial data middle platform, analyze the sales data and market attention data of the brand through a pre-constructed brand fitness model, and output the brand fitness; the brand fitness model introduces sentiment analysis technology to extract the sentiment tendency characteristics of external text data, and quantifies the synergy effect of sales and market attention through interaction terms. The model formula is: In the formula, represents sales, M represents market attention, and S max and M max are the maximum values of sales and market attention respectively. α, β, γ, δ, and ε are all model parameters, which are determined by regression analysis, machine learning algorithms, or expert scoring; Commercial resource allocation module, which is used to evaluate the rental value of commercial resources through at least one measurement model, combine the short-term sales result, periodic business result and brand fitness of commercial resources, adopt a multi-objective genetic algorithm, set the fitness function with the rental income, brand fitness score and resource utilization efficiency as the objectives, and output the best allocation plan including the rental price, lease term and brand combination of each commercial resource through non-dominated sorting and crowding degree.

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