Airline service method, device and storage medium

Abstract

The application discloses an airline service method and device and a storage medium. The method relates to the field of artificial intelligence and comprises the following steps: acquiring flight query information of a target customer under the condition that the target customer is authorized; determining a plurality of preset service combinations according to the target customer information and the flight information; outputting a preference value of the target customer for each preset service combination through a target model; determining a recommended weight of each preset service combination and service configuration information of an additional service in each preset service combination through a service configuration optimization model combined with a constraint condition; determining a target service combination according to the recommended weight of each preset service combination and the service configuration information of the additional service in each preset service combination, and providing a service to the target customer according to the service configuration information of a target additional service in the target service combination. Through the application, the problem that a customer experience is poor because a personalized service cannot be provided in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence, and more specifically, to a service method, apparatus, and storage medium for an airline. Background Technology

[0002] In the civil aviation transportation service sector, airlines and service providers face a common challenge: how to provide accurate and efficient service solutions in the context of increasingly personalized and changing passenger demands. Traditional service recommendation and resource allocation strategies often rely on static rules and fixed service combinations. While these methods simplify operational processes to some extent, they fail to fully consider the diverse preferences of passengers and the dynamic market environment, resulting in poor service matching and a subpar passenger experience.

[0003] Specifically, current airline service methods have the following limitations: First, they typically rely on historical data to pre-define and display service combinations, often neglecting the individual passenger's sensitivity to services at specific times. For example, passengers' needs for flexible refund and change policies differ drastically between peak and off-peak travel seasons, but traditional methods struggle to capture these subtle differences, leading to recommended service combinations that don't match actual passenger needs. Second, airlines and service platforms lack effective data analysis and predictive support when adjusting service configuration parameters, causing adjustment strategies to often lag behind market demand. For instance, baggage allowance settings may not match passengers' baggage-carrying habits, thus affecting passenger satisfaction and willingness to choose.

[0004] There is currently no effective solution to the problem that related technologies cannot provide personalized services to customers, resulting in a poor customer experience. Summary of the Invention

[0005] The main purpose of this application is to provide a service method, device and storage medium for airlines to solve the problem that the inability to provide personalized services to customers in related technologies leads to a poor customer experience.

[0006] To achieve the above objectives, according to one aspect of this application, an airline service method is provided. The method includes: obtaining flight query information of a target customer upon obtaining authorization from that customer, wherein the flight query information includes at least: target customer information and flight information, and the target customer is a customer who initiated a flight service query on an airline or target platform; determining multiple preset service combinations based on the target customer information and flight information, wherein each preset service combination includes at least one additional service; inputting the target customer information and each preset service combination into a target model, and outputting the target customer's preference value for each preset service combination through the target model; inputting the preference value of each preset service combination and the service configuration range of the additional services in each preset service combination into a service configuration optimization model, and determining the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination through the service configuration optimization model combined with constraints; determining a target service combination based on the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination, and providing services to the target customer based on the service configuration information of the target additional services in the target service combination.

[0007] Optionally, the training steps of the target model include: obtaining training samples, wherein the training samples include input information and validation results, the input information includes multiple alternative service combinations of the sample customers and sample customer information of the sample customers, and the validation results include the actual service combinations selected by the sample customers; inputting multiple alternative service combinations and sample customer information into an initial model, and outputting a predicted service combination through the initial model, wherein the predicted service combination is the predicted service combination output by the initial model based on the preference value of each alternative service combination according to the sample customer information; calculating the loss value of the predicted service combination and the actual selected service combination; adjusting the parameters of the initial model according to the loss value to obtain the adjusted model; repeating the steps of inputting multiple alternative service combinations and sample customer information into the adjusted model, outputting a new predicted service combination through the adjusted model, calculating a new loss value of the new predicted service combination and the actual selected service combination, and adjusting the parameters of the model according to the new loss value, until a preset stopping condition is reached to obtain the target model.

[0008] Optionally, the flight information includes flight schedule information and additional service information. With the authorization of the target customer, obtaining the target customer's flight query information includes: determining the time window for flight service query; obtaining the flight schedule information within the time window; determining the additional service information corresponding to the flight schedule information; obtaining the target customer information within a preset time period before initiating the flight service query; and determining the target customer's flight query information based on the flight schedule information, additional service information, and target customer information.

[0009] Optionally, determining multiple preset service combinations based on target customer information and flight information includes: determining candidate supplementary service information based on target customer information and flight information; converting candidate supplementary service information into candidate supplementary service features; preprocessing target customer information to obtain preprocessed customer information; extracting features from the preprocessed customer information to obtain customer features; inputting customer features and candidate supplementary service features into a deep learning model, and predicting the target customer's demand rating for each supplementary service through forward propagation of the deep learning model; and determining multiple preset service combinations based on the target customer's demand rating for each supplementary service.

[0010] Optionally, determining the optional supplementary service information based on target customer information and flight information includes: determining the target flight for the target customer based on target customer information and flight information; and determining the optional supplementary services corresponding to the target flight based on the target flight and supplementary service information.

[0011] Optionally, the preference values ​​of each preset service combination and the service configuration range of the additional services in each preset service combination are input into the service configuration optimization model. The service configuration optimization model, combined with constraints, determines the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination. This includes: inputting the preference values ​​of each preset service combination, the service configuration range of the additional services in each preset service combination, and the resource cost of each preset service combination into the service configuration optimization model; and using a nonlinear programming algorithm to solve for the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination, combined with constraints.

[0012] Optionally, after providing services to the target customer based on the service configuration information of the target additional services in the target service portfolio, the method further includes: collecting feedback results from the target customer on the target service portfolio; and adjusting the parameters of the target model based on the feedback results to obtain the adjusted target model.

[0013] To achieve the above objectives, according to another aspect of this application, an airline service device is provided. The device includes: a first acquisition unit, configured to acquire flight query information of a target customer upon obtaining authorization from the target customer, wherein the flight query information includes at least: target customer information and flight information, and the target customer is a customer who initiates a flight service query on an airline or target platform; a first determination unit, configured to determine multiple preset service combinations based on the target customer information and flight information, wherein each preset service combination includes at least one additional service; an output unit, configured to input the target customer information and each preset service combination into a target model, and output the target customer's preference value for each preset service combination through the target model; a second determination unit, configured to input the preference value of each preset service combination and the service configuration range of the additional services in each preset service combination into a service configuration optimization model, and determine the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination through the service configuration optimization model combined with constraints; and a third determination unit, configured to determine a target service combination based on the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination, and provide services to the target customer based on the service configuration information of the target additional services in the target service combination.

[0014] According to another aspect of this application, a computer-readable storage medium is provided, which includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any airline service method.

[0015] According to another aspect of this application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include methods for performing any type of airline service.

[0016] According to another aspect of this application, a computer program product is provided, including computer instructions that, when executed by a processor, implement the steps of an airline service method according to any one of the above.

[0017] In this embodiment, by obtaining flight query information of the target customer with their authorization, the flight query information includes at least: target customer information and flight information. The target customer is a customer who initiates a flight service query on an airline or target platform. Based on the target customer information and flight information, multiple preset service combinations are determined, each preset service combination including at least one additional service. The target customer information and each preset service combination are input into a target model, and the target model outputs the target customer's preference value for each preset service combination. The preference value of each preset service combination and the service configuration range of the additional services in each preset service combination are input into a service configuration optimization model. The service configuration optimization model, combined with constraints, determines the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination. Based on the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination, a target service combination is determined, and services are provided to the target customer based on the service configuration information of the target additional services in the target service combination. This solves the technical problem of being unable to provide personalized services to customers, resulting in a poor customer experience.

[0018] In this application, after obtaining authorization from the target customer, basic customer information and flight query preferences are collected. Based on the collected customer and flight information, multiple preset service combinations are generated, each containing additional services. The customer information and preset service combinations are input into the target model, and the trained model calculates the customer's preference value for each service combination. The preference value and the configuration range of the additional services in each service combination are input into the service configuration optimization model. The service configuration optimization model solves the recommendation weight of each service combination and the specific configuration parameters of the additional services through nonlinear programming. Finally, based on the service combination recommendation weight and additional service configuration information output by the configuration optimization model, the optimal target service combination is determined. Personalized services are then provided to the target customer based on the specific configuration parameters of the additional services in the target service combination, thereby achieving the technical effect of improving customer experience. Attached Figure Description

[0019] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0020] Figure 1 A hardware structure block diagram of a computer terminal for implementing an airline service method is shown.

[0021] Figure 2 This is a flowchart of an airline service method provided according to an embodiment of this application;

[0022] Figure 3This is a schematic diagram of an airline service device provided according to an embodiment of this application;

[0023] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] It should be noted that all relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties. For example, if there is an interface between this system and the relevant user or organization, before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain the relevant information after receiving consent information from the aforementioned user or organization.

[0027] Example 1

[0028] According to an embodiment of this application, a method embodiment for providing airline services is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal (or mobile device) for implementing an airline service method is shown. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0030] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0031] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the airline service method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned airline service method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0032] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0033] The display can be, for example, a touchscreen liquid crystal display (LCD), which allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0034] Under the aforementioned operating environment, this application provides the following: Figure 2 The airline's service methods are shown. Figure 2 This is a flowchart of an airline's service method according to Embodiment 1 of this application.

[0035] Step S201: With the authorization of the target customer, obtain the target customer's flight query information, wherein the flight query information includes at least: target customer information and flight information, and the target customer is a customer who initiates a flight service query on an airline or target platform.

[0036] Optionally, with the authorization of the target customer, target customer information may be collected, including basic customer information (such as name, age, gender, membership level, etc.) and travel preferences (such as frequent travel periods, preferred airlines, cabin class, etc.). Flight information includes flight schedule information (e.g., flight number, departure and arrival times, departure and arrival airports, etc.) and corresponding ancillary service information (such as baggage policy, meal options, refund and change rules, etc.).

[0037] Step S202: Based on the target customer information and flight information, determine multiple preset service combinations, wherein each preset service combination includes at least one additional service.

[0038] Optionally, based on the acquired target customer information and flight information, a preset service plan containing different service attribute combinations can be generated. The service combination may include basic services (such as cabin class and flight time) and additional services (baggage allowance, extra meals, priority boarding, etc.).

[0039] Step S203: Input the target customer information and each set of preset service combinations into the target model, and output the target customer's preference value for each set of preset service combinations through the target model.

[0040] Optionally, target customer information and preset service combinations can be input into a trained target model. The target model can be a trained spiked-MNL model. By using historical data and prediction algorithms to estimate the target customer's preference value for each preset service combination, the strength of the target customer's preference for each preset service option can be quantified.

[0041] Step S204: Input the preference value of each preset service combination and the service configuration range of the additional services in each preset service combination into the service configuration optimization model. Then, determine the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination by combining the service configuration optimization model with the constraints.

[0042] Optionally, the preference values ​​of each set of preset service combinations and the configuration range of additional services in each set of preset service combinations (such as the adjustable range of baggage allowance) are input into the service configuration optimization model. Through nonlinear programming algorithms, under the constraints of resource inventory, cost budget and other conditions, the optimal service configuration parameters and the recommendation weights for different preset service combinations can be calculated. This information can guide the adjustment of specific service attributes and display order.

[0043] Step S205: Determine the target service combination based on the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination, and provide services to the target customer based on the service configuration information of the target additional services in the target service combination.

[0044] Optionally, based on the recommended weight of each set of preset service combinations and the service configuration information of the additional services in each set of preset service combinations, one or more sets of target service combinations can be determined, which include the specific configuration parameters of the target additional services (such as the size of the baggage allowance).

[0045] The airline service method provided in this application, after obtaining authorization from the target customer, collects the customer's basic information and flight query preferences. Based on the collected customer and flight information, multiple preset service combinations are generated, each combination including additional services. The customer information and preset service combinations are input into a target model, and the trained model calculates the customer's preference value for each service combination. The preference value and the configuration range of the additional services in each service combination are input into a service configuration optimization model. The service configuration optimization model solves the recommendation weight of each service combination and the specific configuration parameters of the additional services through nonlinear programming. Finally, based on the service combination recommendation weight and additional service configuration information output by the configuration optimization model, the optimal target service combination is determined, and personalized services are provided to the target customer according to the specific configuration parameters of the additional services in the target service combination. This solves the technical problem of poor customer experience due to the inability to provide personalized services, and achieves the technical effect of improving customer experience.

[0046] To improve the prediction accuracy of the target model, optionally, in the airline service method provided in this application embodiment, the training step of the target model includes:

[0047] The first step is to obtain training samples, which include input information and validation results. The input information includes multiple alternative service combinations for sample customers and sample customer information. The validation results include the actual service combinations selected by the sample customers.

[0048] Optionally, the training samples include input information and validation results. The input information consists of alternative service combinations for one or more sample customers in history, along with their basic customer information. The validation results are the service combinations that the sample customers ultimately choose when faced with these alternative service combinations.

[0049] The second step involves inputting multiple sets of alternative service combinations and sample customer information into the initial model, and then outputting a predicted service combination through the initial model. The predicted service combination is the predicted service combination output by the initial model based on the preference value of each set of alternative service combinations according to the sample customer information.

[0050] Optionally, the initial model uses sample customer information to analyze the preference values ​​of each alternative service combination (i.e., the probability that the sample customer will choose the alternative service combination) through a built-in algorithm. Based on the preference values ​​of each alternative service combination, the initial model selects the predicted service combination, i.e. the service combination most likely to be chosen.

[0051] The third step is to calculate the loss values ​​for the predicted service portfolio and the actual selected service portfolio.

[0052] Optionally, the loss value between the predicted service combination and the actual service combination selected by the sample customers can be calculated to measure the difference between the two. The smaller the loss value, the closer the model prediction is to the actual situation.

[0053] The fourth step is to adjust the parameters of the initial model based on the loss value to obtain the adjusted model.

[0054] Optionally, based on the calculated loss value, the model parameters can be adjusted using optimization algorithms such as backpropagation to reduce the loss value and improve the model's prediction accuracy.

[0055] The fifth step involves repeatedly inputting multiple sets of alternative service combinations and sample customer information into the adjusted model, outputting new predicted service combinations through the adjusted model, calculating new loss values ​​for the new predicted service combinations and the actual selected service combinations, and adjusting the model parameters based on the new loss values, until the preset stopping condition is met, thus obtaining the target model.

[0056] Optionally, the process from step two to step four is repeated, continuously training the model with training samples, calculating the loss value and adjusting the parameters until a preset stopping condition is reached. The preset stopping condition may be that the reduction in the loss value caused by parameter adjustment is less than a preset threshold or the maximum number of iterations is reached.

[0057] In summary, through the above steps—from acquiring historical data samples to initial predictions of the initial model, calculating loss values, adjusting model parameters, and iterating repeatedly—a target model capable of accurately predicting customer choice behavior was finally obtained.

[0058] To obtain more comprehensive flight query information, optionally, in the airline service method provided in this application embodiment, flight information includes flight schedule information and additional service information. Obtaining the target customer's flight query information, with the target customer's authorization, includes:

[0059] The first step is to determine the time window for flight service inquiries.

[0060] Optionally, the time window can define the flight time periods that target customers are interested in, and filter flight service queries by setting a specific time range. This can be based on a specific number of days in the future (such as recent flight queries) or a specific time period (such as holidays, business peaks, etc.).

[0061] The second step is to obtain the shift information within the time window.

[0062] Optionally, flight information may include the specific flight identifier, departure and arrival times, aircraft type, departure and arrival airports, cabin class, etc.

[0063] The third step is to determine the additional service information corresponding to the schedule information.

[0064] Optionally, additional service information refers to information on value-added services provided by the airline beyond the basic ticket service, such as baggage allowance, meals, priority boarding, and extra mileage accrual.

[0065] The fourth step is to obtain target customer information within a preset time period before initiating a flight service query.

[0066] Optionally, in order to better understand the service preferences of target customers, relevant information of the target customers can be collected within a specific time period before the flight service inquiry. The selection of the preset time period can be based on experience or data statistics, thereby improving the relevance of the target customer information.

[0067] The fifth step is to determine the flight search information for the target customers based on the flight schedule information, additional service information, and target customer information.

[0068] Optionally, the flight information and additional service information collected in the first three steps can be combined with the target customer information obtained in the fourth step to form a structured flight query information.

[0069] In summary, by following the steps outlined above, determining the time window, collecting flight information, corresponding supplementary service information, and target customer information, and ultimately integrating them into flight search information, we can better provide more personalized services to target customers in the future.

[0070] To meet the personalized needs of target customers, optionally, in the airline service method provided in this application embodiment, determining multiple preset service combinations based on target customer information and flight information includes:

[0071] The first step is to determine the available supplementary services based on target customer information and flight information.

[0072] Optionally, the optional ancillary service information is determined based on flight information and the airline's service range, such as extra baggage allowance, priority boarding, in-flight Wi-Fi access, and extra meal ordering. Based on the basic service details of the flight queried by the target customer (such as departure and arrival airports, flight time, estimated arrival time, etc.), and according to this information and the airline's service catalog, optional ancillary service information applicable to the flight and potentially enhancing the customer experience can be filtered.

[0073] The second step is to convert the information of the optional additional services into the features of the optional additional services.

[0074] Optionally, converting the candidate additional service information obtained in the first step into a feature vector can transform the candidate additional service information into a machine-readable form.

[0075] The third step is to preprocess the target customer information to obtain preprocessed customer information.

[0076] Optionally, target customer information can be standardized using preprocessing techniques such as data cleaning.

[0077] The fourth step is to extract features from the preprocessed customer information to obtain customer features.

[0078] Optionally, key features that significantly influence service selection can be extracted from preprocessed customer information, such as customer age, gender, historical service selection patterns, and frequent flyer membership level.

[0079] The fifth step involves inputting customer characteristics and features of potential add-on services into a deep learning model, and then using the forward propagation of the deep learning model to predict the target customer's demand rating for each add-on service.

[0080] Optionally, the forward propagation process of a deep learning model can be used to input customer features and ancillary service features into the model. The model outputs a demand score for each ancillary service based on the trained parameters. The demand score can reflect the target customer's interest in a specific ancillary service.

[0081] The sixth step is to determine multiple preset service combinations based on the target customers' ratings of their needs for each additional service.

[0082] Optionally, based on the demand rating of each additional service, high-rated additional services can be combined with basic services to generate multiple preset service combinations.

[0083] In summary, by following the steps outlined above—from initial screening of supplementary services to deep learning model-predicted demand scores, and finally customizing multiple sets of preset service combinations—the personalization and accuracy of service recommendations can be improved.

[0084] In order to provide accurate services to target customers, optionally, in the airline service method provided in this application embodiment, determining the optional additional service information based on target customer information and flight information includes:

[0085] The first step is to determine the target schedule for the target customers based on their information and schedule details.

[0086] Optionally, by analyzing the target customer's query requests and historical preferences, it is possible to filter out flights that meet the target customer's current travel needs, i.e., target flights.

[0087] The second step is to determine the optional additional services corresponding to the target flight based on the target flight and additional service information.

[0088] Optionally, once a target flight is identified, information on all additional services available for that flight can be queried to create a list of potential services.

[0089] In summary, by taking the above steps, identifying the target flight that the target customer is most likely to choose, and determining all possible additional service options for that flight, the identified optional additional services can meet both customer needs and the actual operating conditions of the airline.

[0090] To improve the operational efficiency of airlines, optionally, in the airline service method provided in this application embodiment, the preference value of each preset service combination and the service configuration range of the additional services in each preset service combination are input into a service configuration optimization model. The service configuration optimization model, combined with constraints, determines the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination, including:

[0091] The first step is to input the preference value of each preset service combination, the service configuration range of the additional services in each preset service combination, and the resource cost of each preset service combination into the service configuration optimization model.

[0092] Optionally, the preference values, service configuration ranges, and resource costs of preset service combinations can be input into the service configuration optimization model as input parameters, comprehensively considering customer preferences, service provision costs, and resource constraints. The service configuration range refers to the adjustable parameter range for additional services within each preset service combination; for example, extra baggage allowance can range from 10kg to 30kg, and priority boarding rights can be set from 1 hour to 3 hours in advance. Resource costs are the costs incurred by the airline's operational resources when each preset service combination is provided to customers, including but not limited to seat resources, service item inventory costs, and operational manpower.

[0093] The second step involves using a service configuration optimization model combined with constraints to solve for the recommended weights of each set of preset service combinations and the service configuration information of additional services in each set of preset service combinations using a nonlinear programming algorithm.

[0094] Optionally, the service configuration optimization model utilizes a nonlinear programming algorithm, combined with constraints on airline operations (such as seat inventory, service cost limits, etc.), to solve for a set of optimal solutions, including the recommended weights for each set of preset service combinations and the most suitable configuration information for each additional service, thus providing a solution that satisfies both customer preferences and resource constraints.

[0095] In summary, by following the steps above, integrating customer preferences, service costs, and resource constraints, and using nonlinear programming algorithms, the most suitable configuration scheme can be found, thereby optimizing resource allocation and improving operational efficiency.

[0096] To continuously optimize the prediction accuracy of the target model, optionally, in the airline service method provided in the embodiments of this application, after providing services to the target customer according to the service configuration information of the target additional services in the target service package, the method further includes:

[0097] The first step is to collect feedback from target customers regarding the target service combination.

[0098] Optionally, the above feedback results are feedback from target customers on their satisfaction, user experience, or subsequent behavior after accepting the target service package offered by the airline, and can be collected through various means such as questionnaires, social media comments, and repeat booking behavior.

[0099] The second step is to adjust the parameters of the target model based on the feedback results to obtain the adjusted target model.

[0100] Alternatively, the collected feedback dataset can be re-input into the target model, and the target model parameters can be adjusted through the retraining process.

[0101] In summary, by following the steps outlined above, collecting and analyzing genuine feedback from target customers, and dynamically adjusting the target model parameters, we can continuously optimize the model's predictive capabilities and refine its service recommendation strategy.

[0102] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0103] Example 2

[0104] This application also provides a service device for an airline. It should be noted that the airline service device of this application can be used to execute the service method for airlines provided in this application. The airline service device provided in this application is described below.

[0105] According to embodiments of this application, an apparatus for implementing the above-described airline service method is also provided, such as... Figure 3 As shown, the device includes: a first acquisition unit 301, a first determination unit 302, an output unit 303, a second determination unit 304, and a third determination unit 305.

[0106] Specifically, the first acquisition unit 301 is used to acquire the flight query information of the target customer when the target customer's authorization is obtained. The flight query information includes at least: target customer information and flight information. The target customer is a customer who initiates a flight service query on an airline or target platform.

[0107] The first determining unit 302 is used to determine multiple sets of preset service combinations based on target customer information and flight information, wherein each set of preset service combinations includes at least one additional service.

[0108] The output unit 303 is used to input the target customer information and each set of preset service combinations into the target model, and output the target customer's preference value for each set of preset service combinations through the target model;

[0109] The second determining unit 304 is used to input the preference value of each group of preset service combinations and the service configuration range of the additional services in each group of preset service combinations into the service configuration optimization model, and determine the recommendation weight of each group of preset service combinations and the service configuration information of the additional services in each group of preset service combinations through the service configuration optimization model combined with the constraints.

[0110] The third determining unit 305 is used to determine the target service combination based on the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination, and to provide services to the target customer based on the service configuration information of the target additional services in the target service combination.

[0111] The airline service device provided in this application embodiment includes a first acquisition unit 301 that, upon obtaining authorization from a target customer, acquires the target customer's flight query information, wherein the flight query information includes at least: target customer information and flight information, and the target customer is a customer who initiated a flight service query on an airline or target platform; a first determination unit 302 that, based on the target customer information and flight information, determines multiple sets of preset service combinations, wherein each set of preset service combinations includes at least one additional service; an output unit 303 that inputs the target customer information and each set of preset service combinations into a target model, and outputs the target customer's preference value for each set of preset service combinations through the target model; and a second determination unit 303. 04. The preference values ​​of each preset service combination and the service configuration range of the additional services in each preset service combination are input into the service configuration optimization model. The service configuration optimization model, combined with the constraints, determines the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination. The third determining unit 305 determines the target service combination based on the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination, and provides services to the target customer based on the service configuration information of the target additional services in the target service combination. This solves the technical problem of being unable to provide personalized services to customers, resulting in a poor customer experience, and achieves the technical effect of improving the customer experience.

[0112] Optionally, in the airline service device provided in this application embodiment, the device includes: a second acquisition unit, used to acquire training samples, wherein the training samples include input information and verification results, the input information includes multiple alternative service combinations of sample customers and sample customer information of sample customers, and the verification results include the actual selected service combinations of sample customers; a prediction unit, used to input multiple alternative service combinations and sample customer information into an initial model, and output a predicted service combination through the initial model, wherein the predicted service combination is a predicted service combination output by the initial model based on the preference value of each alternative service combination according to the sample customer information; a calculation unit, used to calculate the loss value of the predicted service combination and the actual selected service combination; a first adjustment unit, used to adjust the parameters of the initial model according to the loss value to obtain an adjusted model; and a second adjustment unit, used to repeatedly execute the steps of inputting multiple alternative service combinations and sample customer information into the adjusted model, outputting a new predicted service combination through the adjusted model, calculating a new loss value of the new predicted service combination and the actual selected service combination, and adjusting the parameters of the model according to the new loss value, until a preset stopping condition is reached to obtain a target model.

[0113] Optionally, in the airline service device provided in this application embodiment, the first acquisition unit 301 includes: a first determining module, used to determine a time window for flight service query based on flight information including flight number information and additional service information; a first acquisition module, used to acquire flight number information within the time window; a second determining module, used to determine the additional service information corresponding to the flight number information; a second acquisition module, used to acquire target customer information within a preset time period before initiating a flight service query; and a third determining module, used to determine the flight query information of the target customer based on the flight number information, additional service information, and target customer information.

[0114] Optionally, in the airline service device provided in this application embodiment, the first determining unit 302 includes: a fourth determining module, used to determine candidate supplementary service information based on target customer information and flight information; a conversion module, used to convert the candidate supplementary service information into candidate supplementary service features; a preprocessing module, used to preprocess the target customer information to obtain preprocessed customer information; an extraction module, used to extract features from the preprocessed customer information to obtain customer features; a prediction module, used to input the customer features and candidate supplementary service features into a deep learning model, and predict the target customer's demand rating for each supplementary service through the forward propagation of the deep learning model; and a fifth determining module, used to determine multiple sets of preset service combinations based on the target customer's demand rating for each supplementary service.

[0115] Optionally, in the airline service device provided in this application embodiment, the fourth determining module includes: a first determining submodule, used to determine the target flight of the target customer based on the target customer information and flight information; and a second determining submodule, used to determine the alternative additional services corresponding to the target flight based on the target flight and additional service information.

[0116] Optionally, in the airline service device provided in this application embodiment, the second determining unit 304 includes: an input module, used to input the preference value of each preset service combination, the service configuration range of the additional services in each preset service combination, and the resource cost of each preset service combination into the service configuration optimization model; and a solving module, used to solve the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination by using a nonlinear programming algorithm in combination with the constraints of the service configuration optimization model.

[0117] Optionally, in the airline service device provided in the embodiments of this application, the device further includes: a collection unit, used to collect feedback results from the target customer on the target service combination after providing services to the target customer according to the service configuration information of the target additional service in the target service combination; and a third adjustment unit, used to adjust the parameters of the target model according to the feedback results to obtain the adjusted target model.

[0118] It should be noted that the first acquisition unit 301, the first determination unit 302, the output unit 303, the second determination unit 304, and the third determination unit 305 mentioned above correspond to steps S201 to S205 in Embodiment 1. The five units and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above modules can also be part of a device and run in the computer terminal 10 provided in Embodiment 1.

[0119] Example 3

[0120] Embodiments of this application may provide a computer terminal, which may be any computer terminal device in a group of computer terminals. Optionally, in this embodiment, the aforementioned computer terminal may also be replaced with a mobile terminal or an electronic device, etc.

[0121] Optionally, in this embodiment, the computer terminal may be located in at least one of a plurality of network devices in a computer network.

[0122] In this embodiment, the computer terminal described above can execute the program code for the following steps in the airline's service method: Upon obtaining authorization from the target customer, acquire the target customer's flight query information, wherein the flight query information includes at least: target customer information and flight information, and the target customer is a customer who initiated a flight service query on an airline or target platform; Based on the target customer information and flight information, determine multiple sets of preset service combinations, wherein each set of preset service combinations includes at least one additional service; Input the target customer information and each set of preset service combinations into a target model, and output the target customer's preference value for each set of preset service combinations through the target model; Input the preference value of each set of preset service combinations and the service configuration range of the additional services in each set of preset service combinations into a service configuration optimization model, and determine the recommendation weight of each set of preset service combinations and the service configuration information of the additional services in each set of preset service combinations through the service configuration optimization model combined with constraints; Determine a target service combination based on the recommendation weight of each set of preset service combinations and the service configuration information of the additional services in each set of preset service combinations, and provide services to the target customer based on the service configuration information of the target additional services in the target service combination.

[0123] Optionally, the aforementioned computer terminal can execute program code for the following steps in the airline's service method: The training steps of the target model include: acquiring training samples, wherein the training samples include input information and verification results, the input information includes multiple alternative service combinations of sample customers and sample customer information of sample customers, and the verification results include the actual service combinations selected by sample customers; inputting multiple alternative service combinations and sample customer information into an initial model, and outputting a predicted service combination through the initial model, wherein the predicted service combination is the predicted service combination output by the initial model based on the preference value of each alternative service combination according to the sample customer information; calculating the loss value of the predicted service combination and the actual selected service combination; adjusting the parameters of the initial model according to the loss value to obtain an adjusted model; repeatedly executing the steps of inputting multiple alternative service combinations and sample customer information into the adjusted model, outputting a new predicted service combination through the adjusted model, calculating a new loss value of the new predicted service combination and the actual selected service combination, and adjusting the parameters of the model according to the new loss value, until a preset stopping condition is reached to obtain the target model.

[0124] Optionally, the aforementioned computer terminal may execute program code for the following steps in the airline's service method: flight information includes flight number information and supplementary service information; with the authorization of the target customer, obtaining the target customer's flight query information includes: determining the time window for flight service query; obtaining flight number information within the time window; determining the supplementary service information corresponding to the flight number information; obtaining the target customer information within a preset time period before initiating the flight service query; and determining the target customer's flight query information based on the flight number information, supplementary service information, and target customer information.

[0125] Optionally, the aforementioned computer terminal can execute program code for the following steps in the airline's service method: determining multiple sets of preset service combinations based on target customer information and flight information, including: determining candidate supplementary service information based on target customer information and flight information; converting candidate supplementary service information into candidate supplementary service features; preprocessing target customer information to obtain preprocessed customer information; extracting features from the preprocessed customer information to obtain customer features; inputting customer features and candidate supplementary service features into a deep learning model, and predicting the target customer's demand rating for each supplementary service through forward propagation of the deep learning model; and determining multiple sets of preset service combinations based on the target customer's demand rating for each supplementary service.

[0126] Optionally, the aforementioned computer terminal may execute program code for the following steps in the airline's service method: determining the optional supplementary service information based on target customer information and flight information, including: determining the target flight for the target customer based on the target customer information and flight information; and determining the optional supplementary services corresponding to the target flight based on the target flight and supplementary service information.

[0127] Optionally, the aforementioned computer terminal can execute program code for the following steps in the airline's service method: inputting the preference value of each preset service combination and the service configuration range of the additional services in each preset service combination into the service configuration optimization model; determining the recommended weight of each preset service combination and the service configuration information of the additional services in each preset service combination through the service configuration optimization model in combination with constraints, including: inputting the preference value of each preset service combination, the service configuration range of the additional services in each preset service combination, and the resource cost of each preset service combination into the service configuration optimization model; and solving the recommended weight of each preset service combination and the service configuration information of the additional services in each preset service combination using a nonlinear programming algorithm through the service configuration optimization model in combination with constraints.

[0128] Optionally, the aforementioned computer terminal may execute program code for the following steps in the airline's service method: after providing services to the target customer based on the service configuration information of the target supplementary services in the target service package, the method further includes: collecting feedback results from the target customer on the target service package; adjusting the parameters of the target model based on the feedback results to obtain the adjusted target model.

[0129] Optionally, Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 4 As shown, the electronic device may include: one or more ( Figure 4 (Only one is shown) Processor 402, memory 04, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0130] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the airline service method and apparatus in this embodiment. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned airline service method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0131] The processor can access the information and application programs stored in the memory via the transmission device to execute the steps described above in the airline's service method.

[0132] This application provides a service solution for airlines. By obtaining authorization from the target customer, flight query information of the target customer is acquired. This flight query information includes at least: target customer information and flight information. The target customer is a customer who initiated a flight service query on an airline or target platform. Based on the target customer information and flight information, multiple preset service combinations are determined, each including at least one additional service. The target customer information and each preset service combination are input into a target model, which outputs the target customer's preference value for each preset service combination. The preference value of each preset service combination and the service configuration range of the additional services in each preset service combination are input into a service configuration optimization model. The service configuration optimization model, combined with constraints, determines the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination. Based on the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination, a target service combination is determined. Services are then provided to the target customer based on the service configuration information of the target additional services in the target service combination. This solves the technical problem of being unable to provide personalized services, resulting in a poor customer experience.

[0133] Those skilled in the art will understand that Figure 4 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 4 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 4 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 4 The different configurations shown.

[0134] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0135] Example 4

[0136] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the airline service method provided in Embodiment 1.

[0137] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0138] This application also provides a computer program product that, when executed on a data processing device, is suitable for performing airline service method steps.

[0139] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0140] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0141] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0142] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0143] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0144] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0145] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A service method for an airline, characterized in that, include: With the authorization of the target customer, the flight query information of the target customer is obtained, wherein the flight query information includes at least: target customer information and flight information, and the target customer is a customer who initiates a flight service query on an airline or target platform; Based on the target customer information and the flight information, multiple preset service combinations are determined, wherein each preset service combination includes at least one additional service; The target customer information and each preset service combination are input into the target model, and the target model outputs the target customer's preference value for each preset service combination. The preference value of each preset service combination and the service configuration range of the additional services in each preset service combination are input into the service configuration optimization model. The recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination are determined by the service configuration optimization model in combination with the constraints. The target service combination is determined based on the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination, and the service is provided to the target customer based on the service configuration information of the target additional services in the target service combination.

2. The method according to claim 1, characterized in that, The training steps for the target model include: Obtain training samples, wherein the training samples include input information and verification results, the input information includes multiple alternative service combinations of the sample customer and sample customer information of the sample customer, and the verification results include the actual service combination selected by the sample customer; The multiple sets of alternative service combinations and the sample customer information are input into the initial model, and the initial model outputs a predicted service combination. The predicted service combination is the predicted service combination output by the initial model based on the preference value of each set of alternative service combinations according to the sample customer information. Calculate the loss values ​​for the predicted service combination and the actual selected service combination; The parameters of the initial model are adjusted based on the loss value to obtain the adjusted model; Repeat the steps of inputting the multiple sets of alternative service combinations and the sample customer information into the adjusted model, outputting a new predicted service combination through the adjusted model, calculating a new loss value for the new predicted service combination and the actual selected service combination, and adjusting the model parameters according to the new loss value, until a preset stopping condition is reached to obtain the target model.

3. The method according to claim 1, characterized in that, The flight information includes flight schedule information and additional service information. With the authorization of the target customer, obtaining the target customer's flight query information includes: Determine the time window for the flight service query; Obtain the shift information within the time window; Determine the additional service information corresponding to the aforementioned schedule information; Obtain the target customer information within a preset time period prior to initiating the flight service query; Based on the flight information, the additional service information, and the target customer information, the flight search information for the target customer is determined.

4. The method according to claim 3, characterized in that, Based on the target customer information and the flight information, multiple preset service combinations are determined, including: Based on the target customer information and the flight information, determine the optional supplementary services; The information on the candidate additional services is converted into features of the candidate additional services; The target customer information is preprocessed to obtain preprocessed customer information; Feature extraction is performed on the preprocessed customer information to obtain customer features; The customer characteristics and the candidate additional service characteristics are input into a deep learning model, and the forward propagation of the deep learning model is used to predict the target customer’s demand rating for each additional service. Based on the target customer's rating of their demand for each additional service, multiple preset service combinations are determined.

5. The method according to claim 4, characterized in that, Based on the target customer information and the flight information, the following optional supplementary services are determined: Based on the target customer information and the shift information, determine the target shift for the target customer; Based on the target flight and the additional service information, determine the alternative additional services corresponding to the target flight.

6. The method according to claim 1, characterized in that, The preference value of each preset service combination and the service configuration range of the additional services in each preset service combination are input into the service configuration optimization model. The service configuration optimization model, combined with constraints, determines the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination, including: The preference value of each preset service combination, the service configuration range of the additional services in each preset service combination, and the resource cost of each preset service combination are input into the service configuration optimization model. By combining the service configuration optimization model with constraints, a nonlinear programming algorithm is used to solve for the recommended weights of each preset service combination and the service configuration information of the additional services in each preset service combination.

7. The method according to claim 1, characterized in that, After providing services to the target customer based on the service configuration information of the target additional services in the target service portfolio, the method further includes: Collect feedback from the target customers regarding the target service combination; The parameters of the target model are adjusted based on the feedback results to obtain the adjusted target model.

8. A service device for an airline, characterized in that, include: The first acquisition unit is used to acquire the flight query information of the target customer when the target customer's authorization is obtained, wherein the flight query information includes at least: target customer information and flight information, and the target customer is a customer who initiates a flight service query on an airline or target platform; The first determining unit is configured to determine multiple preset service combinations based on the target customer information and the flight information, wherein each preset service combination includes at least one additional service. The output unit is used to input the target customer information and each set of preset service combinations into the target model, and output the target customer's preference value for each set of preset service combinations through the target model; The second determining unit is used to input the preference value of each preset service combination and the service configuration range of the additional services in each preset service combination into the service configuration optimization model, and determine the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination through the service configuration optimization model combined with the constraints. The third determining unit is used to determine the target service combination based on the recommendation weight of each preset service combination and the service configuration information of the additional services in each preset service combination, and to provide services to the target customer based on the service configuration information of the target additional services in the target service combination.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the computer-readable storage medium is located to perform the airline service method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the airline service method according to any one of claims 1 to 7.

11. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the airline service method according to any one of claims 1 to 7.

Images