Passenger lift-up processing method, electronic equipment and storage medium

Abstract

The invention provides a passenger lift-up processing method, electronic equipment and a storage medium. The method comprises the following steps: acquiring historical travel data and cabin rising rule data; performing feature extraction on the historical travel data to obtain portrait feature data; generating cabin rising recommendation information corresponding to the target user based on the portrait feature data, and sending the cabin rising recommendation information to the target user; in response to a received booking cabin raising request of a target user for the cabin raising recommendation information, generating a target cabin raising order corresponding to the booking cabin raising request; and analyzing and processing the target cabin ascending order by using the cabin ascending rule data to obtain an order processing result. The technical problems that according to a passenger cabin lifting processing method provided in the related technology, the cabin lifting purchase rate and the cabin space utilization rate are low, and the passenger shopping experience is poor are solved.

Description

Technical Field

[0001] The present disclosure relates to the field of aviation passenger services, and in particular to a passenger upgrade processing method, electronic equipment and storage medium. Background Art

[0002] In the field of aviation passenger services, paid upgrade services allow passengers to upgrade their purchased lower-level cabins to higher levels by paying additional fees, which not only improves the passenger's riding comfort, but also effectively improves the airline's seat utilization and economic benefits. The passenger upgrade process in the related art mainly adopts two modes: manual counter upgrade and online self-service upgrade. In the manual counter upgrade mode, the passenger's upgrade request is directly assisted by the on-site staff; in the online self-service upgrade mode, the passenger completes the upgrade operation independently through the self-service function of the airline's official website or mobile application. However, both upgrade modes in the related art have the problems of low upgrade purchase rate and cabin utilization rate, and poor passenger shopping experience.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present disclosure provide a passenger upgrade processing method, an electronic device, and a storage medium to at least solve the technical problems of low upgrade purchase rate and cabin space utilization rate, and poor passenger shopping experience in the passenger upgrade processing method provided in the related art.

[0005] According to one aspect of an embodiment of the present disclosure, a method for processing passenger upgrades is provided, comprising: acquiring historical travel data and upgrade rule data, wherein the historical travel data is used to represent passenger travel information within a historical time period, and the upgrade rule data includes at least one of the following: upgrade time rules, upgrade flight rules, and upgrade passenger rules; performing feature extraction on the historical travel data to obtain portrait feature data, wherein the portrait feature data includes: passenger portrait features and flight portrait features; generating upgrade recommendation information corresponding to a target user based on the portrait feature data, and sending the upgrade recommendation information to the target user; in response to receiving a reservation upgrade request from the target user for the upgrade recommendation information, generating a target upgrade order corresponding to the reservation upgrade request; analyzing and processing the target upgrade order using the upgrade rule data to obtain an order processing result, wherein the order processing result is used to determine whether the order status corresponding to the target upgrade order is in an upgrade success status.

[0006] Optionally, the passenger portrait features include at least one of the following: passenger static features, dynamic order features and passenger matching features, and the flight portrait features include at least one of the following: flight static features and flight statistical features.

[0007] Optionally, generating upgrade recommendation information corresponding to the target user based on the portrait feature data includes: using a target machine learning model to analyze and process the portrait feature data to obtain the upgrade recommendation information, wherein the target machine learning model is trained using upgrade log data, and the upgrade recommendation information includes: upgrade willingness prediction results and upgrade suggestion values.

[0008] Optionally, sending the upgrade recommendation information to the target user includes: in response to the upgrade willingness prediction result associated with the upgrade recommendation information satisfying a preset push condition, pushing the upgrade suggestion value to the target user, wherein the preset push condition is used to determine whether the target user has the willingness to upgrade.

[0009] Optionally, analyzing and processing the target upgrade order using the upgrade rule data to obtain the order processing result includes: calculating an order priority level corresponding to the target upgrade order using the upgrade rule data; and in response to the target upgrade order being in an initialized state, analyzing and processing the target upgrade order according to the order priority level to obtain the order processing result.

[0010] Optionally, calculating the order priority level corresponding to the target upgrade order using the upgrade rule data includes: acquiring order time information corresponding to the target upgrade order; in response to determining that the target upgrade order is a non-concurrent upgrade order based on the order time information, determining the order priority level of the target upgrade order using the target priority identifier; in response to determining that the target upgrade order is a concurrent upgrade order based on the order time information, determining the order priority level of the target upgrade order using the upgrade rule data and multiple priority determination rules, wherein the multiple priority determination rules include: passenger value rule, passenger attribute rule, ticket attribute rule and preset priority rule.

[0011] Optionally, the passenger upgrade processing method further includes: in response to receiving a cancellation request from the target user for the target upgrade order, setting the order status of the target upgrade order to a canceled status; in response to receiving a query request from the target user for the order processing result, sending order details information and the order processing result of the target upgrade order to the target user.

[0012] According to another aspect of an embodiment of the present disclosure, there is also provided a passenger upgrade processing device, comprising: an acquisition module, for acquiring historical travel data and upgrade rule data, wherein the historical travel data is used to represent passenger travel information within a historical time period, and the upgrade rule data includes at least one of the following: upgrade time rules, upgrade flight rules and upgrade passenger rules; a feature extraction module, for extracting features from the historical travel data to obtain portrait feature data, wherein the portrait feature data includes: passenger portrait features and flight portrait features; a first generation module, for generating upgrade recommendation information corresponding to a target user based on the portrait feature data, and sending the upgrade recommendation information to the target user; a second generation module, for generating a target upgrade order corresponding to the reservation upgrade request in response to receiving a reservation upgrade request from the target user for the upgrade recommendation information; a processing module, for analyzing and processing the target upgrade order using the upgrade rule data to obtain an order processing result, wherein the order processing result is used to determine whether the order status corresponding to the target upgrade order is in an upgrade success state.

[0013] Optionally, the first generating module is further used to: use a target machine learning model to analyze and process the portrait feature data to obtain upgrade recommendation information, wherein the target machine learning model is trained using upgrade log data, and the upgrade recommendation information includes: upgrade willingness prediction results and upgrade suggestion values.

[0014] Optionally, the first generating module is further used to: in response to the upgrade willingness prediction result associated with the upgrade recommendation information satisfying a preset push condition, push the upgrade recommendation value to the target user, wherein the preset push condition is used to determine whether the target user has the willingness to upgrade.

[0015] Optionally, the processing module is further used to: calculate the order priority level corresponding to the target upgrade order using the upgrade rule data; in response to the target upgrade order being in an initialized state, analyze and process the target upgrade order according to the order priority level to obtain an order processing result.

[0016] Optionally, the processing module is further used to: obtain order time information corresponding to the target upgrade order; in response to determining that the target upgrade order is a non-concurrent upgrade order based on the order time information, determine the order priority level of the target upgrade order using the target priority identifier; in response to determining that the target upgrade order is a concurrent upgrade order based on the order time information, determine the order priority level of the target upgrade order using upgrade rule data and multiple priority determination rules, wherein the multiple priority determination rules include: passenger value rules, passenger attribute rules, ticket attribute rules and preset priority rules.

[0017] According to another aspect of an embodiment of the present disclosure, an electronic device is also provided, including: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute instructions to implement the passenger upgrade processing method in the embodiment of the present disclosure.

[0018] According to another aspect of an embodiment of the present disclosure, a computer-readable storage medium is further provided, the computer-readable storage medium including a stored executable program, wherein when the executable program runs, the device where the storage medium is located is controlled to execute the passenger upgrade processing method in the embodiment of the present disclosure.

[0019] According to another aspect of an embodiment of the present disclosure, a computer program product is further provided. The computer program product includes computer instructions. When the computer instructions are executed by a processor, the passenger upgrade processing method in the embodiment of the present disclosure is implemented.

[0020] In the disclosed embodiment, historical travel data and upgrade rule data are obtained, feature extraction is performed on the historical travel data to obtain portrait feature data, and upgrade recommendation information corresponding to the target user is generated based on the portrait feature data, and the upgrade recommendation information is sent to the target user, and then in response to receiving the reservation upgrade request of the target user for the upgrade recommendation information, a target upgrade order corresponding to the reservation upgrade request is generated, and finally the target upgrade order is analyzed and processed using the upgrade rule data to obtain an order processing result, thereby achieving the purpose of providing personalized upgrade services to passengers and optimizing the inventory management and revenue of the airline, thereby achieving the technical effect of improving the upgrade purchase rate, cabin utilization rate and passenger shopping experience, and thus solving the technical problems of low upgrade purchase rate and cabin utilization rate and poor passenger shopping experience in the passenger upgrade processing method provided in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0022] Figure 1 is a flow chart of a passenger upgrade processing method according to one embodiment of the present disclosure;

[0023] Figure 2 is a schematic diagram of a rule control unit according to one embodiment of the present disclosure;

[0024] Figure 3 The present invention is a structural block diagram of a passenger upgrade processing device according to one embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present disclosure, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present disclosure.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0027] In the related art, passenger upgrades are mainly processed in two modes: manual counter upgrades and online self-service upgrades. In the manual counter upgrade mode, the passenger's upgrade request is directly assisted by the on-site staff; in the online self-service upgrade mode, the passenger completes the upgrade operation independently through the self-service function of the airline's official website or mobile application. However, both upgrade modes in the related art have the problems of low upgrade purchase rate and cabin utilization rate, and poor passenger shopping experience.

[0028] Specifically, the passive nature of the manual upgrade counter model often results in airlines being unable to provide passengers with personalized upgrade service options at the right time, thereby reducing potential upgrade sales opportunities. In the online self-service upgrade model, due to the lack of in-depth understanding and intelligent matching of passengers' personalized needs, coupled with the lack of friendliness of the operation interface and information transparency, passengers may encounter information asymmetry or operational complexity during the self-service operation process, thus affecting passengers' purchase intention and experience satisfaction.

[0029] In addition, during busy business hours, the manual counter model may have problems such as slow processing speed and reduced service quality, as on-site staff need to respond to the real-time needs of a large number of passengers, making it difficult to effectively cope with resource constraints. The online self-service model lacks an intelligent resource allocation algorithm and cannot achieve accurate and efficient allocation of seat resources, which not only affects the utilization rate of seat resources, but also may indirectly reduce the economic benefits and market competitiveness of airlines by failing to give priority to the upgrade expectations of high-value passengers.

[0030] According to an embodiment of the present disclosure, a method embodiment of a passenger upgrade processing method is provided. It should be noted that the steps shown in the flowchart of 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 can be executed in an order different from that shown here.

[0031] The method embodiment can be executed in an electronic device or a similar computing device including a memory and a processor. Taking running on a computer terminal as an example, the computer terminal may include one or more processors (processors may include but are not limited to central processing units (CPU), graphics processing units (GPU), digital signal processing (DSP) chips, microprocessors (MCU), programmable logic devices (Field Programmable Gate Array, FPGA), neural network processors (Neural-network Processor Unit, NPU), tensor processors (TensorProcessing Unit, TPU), artificial intelligence (Artificial Intelligence, AI) type processors and other processing devices) and a memory for storing data. Optionally, the above-mentioned computer terminal may also include a transmission device, an input and output device, and a display device for communication functions. It can be understood by those of ordinary skill in the art that the above-mentioned structural description is only for illustration, and it does not limit the structure of the above-mentioned computer terminal. For example, the computer terminal may also include more or fewer components than the above-mentioned structural description, or have a configuration different from the above-mentioned structural description.

[0032] The memory can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the passenger upgrade processing method in the embodiment of the present disclosure. The processor executes various functional applications and data processing by running the computer program stored in the memory, that is, the above-mentioned passenger upgrade processing method is realized. The memory may include a high-speed random access memory, and may also include a 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 a memory remotely arranged relative to the processor, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0033] The transmission device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0034] The display device may be, for example, a touch screen type liquid crystal display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display may enable a user to interact with a user interface of a mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), and a user may interact with the GUI by finger contacts and / or gestures on a touch-sensitive surface, wherein the human-computer interaction functions here may optionally include the following interactions: creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital videos, playing digital music and / or web browsing, etc. The executable instructions for executing the above human-computer interaction functions are configured / stored in a computer program product or readable storage medium executable by one or more processors.

[0035] Figure 1 is a flow chart of a passenger upgrade processing method according to one embodiment of the present disclosure, such as Figure 1 As shown, the method comprises the following steps:

[0036] Step S11, acquiring historical travel data and upgrade rule data, wherein the historical travel data is used to represent passenger travel information within a historical time period, and the upgrade rule data includes at least one of the following: upgrade time rules, upgrade flight rules, and upgrade passenger rules;

[0037] Step S12, extracting features from the historical travel data to obtain portrait feature data, wherein the portrait feature data includes: passenger portrait features and flight portrait features;

[0038] Step S13, generating upgrade recommendation information corresponding to the target user based on the portrait feature data, and sending the upgrade recommendation information to the target user;

[0039] Step S14, in response to receiving a reservation upgrade request from a target user for the upgrade recommendation information, generating a target upgrade order corresponding to the reservation upgrade request;

[0040] Step S15, analyzing and processing the target upgrade order using the upgrade rule data to obtain an order processing result, wherein the order processing result is used to determine whether the order status corresponding to the target upgrade order is in an upgrade success status.

[0041] The above-mentioned historical travel data refers to the travel details of passengers within a certain period of time in the past recorded in the airline database, including but not limited to the passenger's booking record, flight information, cabin class, fare, travel frequency, frequent flyer level, points or mileage information, departure and arrival time, flight route, whether it is a connecting flight, check-in method, and other service needs related to the passenger.

[0042] The above-mentioned upgrade time rules refer to the time windows set by airlines for upgrade services, including: the start time of reservation upgrade, the end time of reservation upgrade, the start time of automatic upgrade, and the end time of automatic upgrade. Among them, the start time of reservation upgrade refers to the earliest time point that the airline allows passengers to start reserving upgrade services, which is usually set a few days before the flight takes off, so that passengers have sufficient time to consider whether they need to upgrade, and it also reserves buffer time for the airline to process reservations; the end time of reservation upgrade refers to the deadline for the airline to receive upgrade reservation requests, ensuring that the airline can summarize and process all reservation requests in a timely manner to avoid processing confusion when the flight is about to depart; the start time of automatic upgrade refers to the earliest time point when the airline automatically processes the upgrade order based on the upgrade rules and the priority of the passenger's reservation; the end time of automatic upgrade refers to the final deadline of the automatic upgrade processing process set by the airline. After this time point, the system will no longer automatically process any reservation upgrade orders.

[0043] The above-mentioned upgrade flight rules refer to the conditions and restrictions for airlines to open upgrade services for specific flights or routes, including but not limited to prohibited flights and routes, upgrade space restrictions, upgrade inventory reservations, etc. Among them, prohibited flights and routes refer to flights or routes that airlines decide not to provide upgrade services based on operating strategies, seat management needs, market analysis or special conditions of specific flights; upgrade space restrictions refer to the conditions set by airlines for passengers to upgrade from the current cabin to a specific higher-level cabin, for example, stipulating that economy class passengers can only be upgraded to business class, but not directly to first class, or limiting the upgrade scope of specific passenger groups; upgrade inventory reservations refer to the number of cabin seats reserved in advance by airlines for upgrade services, which are usually dynamically adjusted based on flight bookings, historical upgrade data, seasonal demand changes and other factors. The goal is to ensure that there are enough empty seats in the upgrade service for passengers willing to pay for the upgrade, while avoiding excessive reservations that lead to waste of seat resources.

[0044] The above-mentioned rules for upgraded passengers refer to the policies formulated by airlines based on the different attributes and needs of passengers, which are used to control and adjust which passenger groups can participate in the upgrade, as well as the specific conditions and restrictions that may be encountered when participating in the upgrade. The rules for upgraded passengers include but are not limited to passenger special service restrictions, infant and child restrictions, group restrictions, etc. Among them, passenger special service restrictions refer to the specific rules implemented when upgrading for passenger groups that need special services, for example, it is stipulated that disabled people can only choose seats near emergency exits or with wheelchair access when upgrading; infant and child restrictions refer to the additional conditions set in the upgrade service for passengers traveling with infants or children, for example, it is stipulated that infants must travel with adult passengers and be upgraded to the same cabin; group restrictions refer to the specific rules set by airlines for group passengers, such as tour groups, business groups, etc. in the upgrade service, for example, requiring all members of the group to be upgraded to the same cabin at the same time, or limiting the total number or proportion of group upgrades to avoid excessive occupation of flight seat resources.

[0045] Exemplarily, the historical travel data, upgrade time rules, upgrade flight rules, and upgrade passenger rules of all passengers in the past year are extracted from a database of an airline. Subsequently, the passenger portrait features and flight portrait features in the historical travel data are extracted, and the upgrade recommendation information of the target object is generated based on the above-mentioned portrait feature data, and the upgrade recommendation information is pushed to the target user via SMS or official website message. When a reservation upgrade request issued by the target user in response to the upgrade recommendation information is received, a target upgrade order is generated, and the target upgrade order is analyzed and processed using the above-mentioned upgrade time rules, upgrade flight rules, and upgrade passenger rules to determine whether the order status corresponding to the target upgrade order is in the upgrade success state.

[0046] Based on the above steps S11 to S15, by acquiring historical travel data and upgrade rule data, feature extraction is performed on the historical travel data to obtain portrait feature data, and upgrade recommendation information corresponding to the target user is generated based on the portrait feature data, and the upgrade recommendation information is sent to the target user, and then in response to receiving the reservation upgrade request of the target user for the upgrade recommendation information, a target upgrade order corresponding to the reservation upgrade request is generated, and finally the target upgrade order is analyzed and processed using the upgrade rule data to obtain an order processing result, thereby achieving the purpose of providing personalized upgrade services to passengers and optimizing the inventory management and revenue of airlines, thereby achieving the technical effect of improving the upgrade purchase rate, cabin utilization rate and passenger shopping experience, and thus solving the technical problems of low upgrade purchase rate and cabin utilization rate and poor passenger shopping experience in the passenger upgrade processing method provided in the related art.

[0047] Optionally, the passenger portrait features include at least one of the following: passenger static features, dynamic order features and passenger matching features, and the flight portrait features include at least one of the following: flight static features and flight statistical features.

[0048] The above-mentioned passenger static characteristics refer to the passenger's personal attributes in the passenger's personal profile that are relatively fixed and do not change with time or specific situations, including but not limited to the passenger's age range, gender, frequent flyer card airline, frequent flyer card level, etc.

[0049] The above-mentioned dynamic order features refer to the specific behaviors and preferences of passengers during their past travels extracted from their historical orders, including but not limited to airlines, flight numbers, departure stations, arrival stations, departure time, arrival time, check-in methods, fares, discounts, purchase channels, etc.

[0050] The above passenger matching characteristics refer to the correlation between passengers or the relationship between a flight and a specific passenger group, including but not limited to family passengers, group passengers, passengers on the same flight, etc.

[0051] The above-mentioned static flight characteristics refer to the inherent attributes of the flight, which will not change with the flight booking situation or the passage of time, including but not limited to the airline, flight number, departure station, arrival station, aircraft model, etc.

[0052] The above-mentioned flight statistical features include but are not limited to real-time statistical features, such as the number of people who have booked seats, the number of people who have checked in, the number of remaining available seats, timestamps, etc.

[0053] Based on the above optional embodiments, by comprehensively analyzing the characteristics of passenger portraits and flight portraits, it is helpful for airlines to accurately locate the target group for upgrade services and provide upgrade recommendations that better meet their needs, thereby improving the sales rate of upgrade products and passenger satisfaction.

[0054] Optionally, in step S13, generating upgrade recommendation information corresponding to the target user based on the portrait feature data includes: using a target machine learning model to analyze and process the portrait feature data to obtain upgrade recommendation information, wherein the target machine learning model is trained using upgrade log data, and the upgrade recommendation information includes: upgrade willingness prediction results and upgrade suggestion values.

[0055] The target machine learning model mentioned above refers to a prediction model trained with historical upgrade log data, which can predict the passenger's demand for upgrade services and recommend the class of cabin to be upgraded based on the input passenger profile features and flight profile features. The target machine learning model usually uses supervised learning methods such as logistic regression, random forest, neural network, etc., combined with a large amount of historical upgrade records and passenger behavior data, to learn the patterns and predictors of passengers' willingness to upgrade, so that when new requests come in, upgrade recommendations can be given quickly and accurately.

[0056] The above-mentioned upgrade log data refers to the detailed historical data recorded by the airline on the passenger's upgrade behavior, including but not limited to successful upgrade cases, the time of upgrade request, cabin space before and after the upgrade, upgrade fee, whether the passenger accepts the recommendation, passenger satisfaction feedback after the upgrade, etc.

[0057] For example, first, the collected passenger profile feature data and flight profile feature data are preprocessed to ensure data quality and the effectiveness of model training. Then, the upgrade log data is used as a training set and trained through a neural network. After the model training is completed, the prediction results of passenger upgrade willingness and upgrade recommendation values ​​can be output by inputting real-time passenger profile features and flight profile features.

[0058] For example, when a Platinum Card member passenger purchases an economy class ticket and is about to take an international flight, the passenger's age range, gender, frequent flyer level, historical flight records, remaining seats on the flight, ticket prices, flight time, and other data are input into the trained neural network model. After the neural network model analyzes the above features, the output passenger upgrade willingness prediction result indicates that the passenger is willing to upgrade, and the upgrade recommendation value is to upgrade to business class.

[0059] Based on the above optional embodiment, the target machine learning model is used to analyze and process the portrait feature data to obtain the upgrade willingness prediction result and the upgrade recommendation value, which can achieve accurate prediction of passengers' upgrade needs, thereby improving the upgrade purchase rate.

[0060] Optionally, in step S13, sending the upgrade recommendation information to the target user includes: in response to the upgrade willingness prediction result associated with the upgrade recommendation information satisfying a preset push condition, pushing the upgrade suggestion value to the target user, wherein the preset push condition is used to determine whether the target user has an upgrade willingness.

[0061] For example, assuming that the preset push condition is that the target user has the willingness to upgrade when the predicted result of the willingness to upgrade is greater than or equal to 60%. If the predicted result of a passenger's willingness to upgrade is 70%, which exceeds the preset push condition, the upgrade recommendation value will be pushed to the passenger; on the contrary, if the predicted probability of a passenger's willingness to upgrade is only 55%, which is lower than the preset 60%, the system will not push any upgrade recommendations to the passenger to avoid unnecessary information interference and save system processing resources.

[0062] Based on the above optional embodiments, by presetting push conditions, the airline can intelligently screen out passengers who really have the need and willingness to upgrade, and formulate and push upgrade suggestions at the best time, thereby improving passenger experience and airline revenue.

[0063] Optionally, in step S15, the target upgrade order is analyzed and processed using the upgrade rule data, and the order processing result obtained includes:

[0064] Step S151, calculating the order priority level corresponding to the target upgrade order using the upgrade rule data;

[0065] Step S152, in response to the target upgrade order being in the initialized state, analyzing and processing the target upgrade order according to the order priority level to obtain an order processing result.

[0066] The above order priority level refers to a numerical identifier assigned to each target upgrade order, which reflects the priority order of the order in the upgrade processing flow. Generally speaking, the smaller the numerical value, the higher the priority.

[0067] The initialization state mentioned above refers to the state of the upgrade order when it is just created in the system, usually marked as "init". In the life cycle of an order, the initialization state means that the order has not yet been processed by the system. At this time, the order information is complete, the passenger's intention is clear, but the upgrade operation has not yet started. The system will upgrade the orders in the "init" state in order of priority based on the priority level of the order, at the start time of the automatic upgrade and thereafter.

[0068] Furthermore, during subsequent processing, an order in the initialization state may be transformed into one of the following states depending on the upgrade result:

[0069] (1) Successful status: The upgrade operation is successfully completed and the order status is set to "success";

[0070] (2) Failed status: The upgrade operation is not completed due to insufficient seats, system errors or other reasons, and the order status is set to "fail";

[0071] (3) Cancel status: If the passenger actively cancels the upgrade request before the automatic upgrade starts, the order status will be set to "cancel".

[0072] Exemplarily, the order priority level corresponding to the target upgrade order is calculated using the upgrade time rule, the upgrade flight rule, and the upgrade passenger rule. When the target upgrade order is in "init", the target upgrade order is upgraded according to the order priority level.

[0073] Based on the above optional embodiment, by using the upgrade rule data to calculate the order priority level corresponding to the target upgrade order, and when the order is in the initialization state, the upgrade process is performed according to the priority order, thereby achieving efficient and orderly management and execution of the upgrade request.

[0074] Optionally, in step S151, calculating the order priority level corresponding to the target upgrade order by using the upgrade rule data includes:

[0075] Step S1511, obtaining order time information corresponding to the target upgrade order;

[0076] Step S1512, in response to determining that the target upgrade order is a non-concurrent upgrade order based on the order time information, determining the order priority level of the target upgrade order using the target priority identifier;

[0077] Step S1513, in response to determining that the target upgrade order is a concurrent upgrade order based on the order time information, the order priority level of the target upgrade order is determined using the upgrade rule data and multiple priority determination rules, wherein the multiple priority determination rules include: passenger value rules, passenger attribute rules, ticket attribute rules and preset priority rules.

[0078] The above-mentioned concurrent upgrade orders refer to multiple upgrade requests received by the system at the same or similar time points. In the case of concurrent upgrade orders, the order submission time alone is not sufficient to determine the priority of each order.

[0079] Table 1 Passenger value rules

[0080]

[0081] The above passenger value rules are shown in Table 1, including the passenger's frequent flyer card level, flight times and total flight kilometres. Among them, the priority of the frequent flyer card level is 0, and its categories include platinum card, gold card, silver card and ordinary card. The sub-level of platinum card is 0, the sub-level of gold card is 1, the sub-level of silver card is 2, and the sub-level of ordinary card is 3; the priority of the flight times is 1, the priority of the flight times greater than 200 is 0, the priority of the flight times greater than 150 and less than or equal to 200 is 1, and the priority of the flight times less than 150 is 2; the priority of the total flight kilometres is 3, the priority of the total flight kilometres greater than or equal to 300,000 is 0, the priority of the total flight kilometres greater than or equal to 200,000 and less than 300,000 is 1, and the priority of the total flight kilometres less than 200,000 is 0,

[0082] Table 2 Passenger attribute rules

[0083]

[0084] The above passenger attribute rules are shown in Table 2, including whether the passenger is carrying an infant and whether the passenger is a pregnant woman. Among them, the priority of whether the passenger is carrying an infant is 1, the sub-level of the passenger carrying an infant is 0, and the sub-level of the passenger not carrying an infant is 1; the priority of whether the passenger is a pregnant woman is 1, the sub-level of the passenger pregnant woman is 0, and the sub-level of the passenger not pregnant woman is 1. In the priority of judgment, the smaller the number, the greater the priority.

[0085] Table 3 Ticket attribute rules

[0086]

[0087] The above ticket attribute rules are shown in Table 3, including ticket prices and discounts. Among them, the ticket price judgment priority is 1, the ticket price higher than 10,000 is 0, the ticket price between 5,000 and 10,000 is 1, the ticket price between 2,000 and 5,000 is 2, and the ticket price lower than 2,000 is 3. In the judgment priority, the smaller the number, the greater the priority.

[0088] Exemplarily, when the system receives a new upgrade order request, it will determine whether the order is a non-concurrent upgrade order based on the time information of the request. If the time interval between the order submission time and the previous order is long enough to avoid order processing conflicts, it is determined to be a non-concurrent upgrade order. For non-concurrent upgrade orders, the system queries the existing upgrade orders in the current database and records the order with the highest priority identification value. The priority identification value of the newly received non-concurrent upgrade order is usually based on the highest identification value found in the previous step plus 1 to ensure that the priority of the new order is lower than the unprocessed reserved upgrade orders for the current flight.

[0089] For example, for concurrent upgrade orders, first, it is determined to be a concurrent upgrade order based on the order time information. Then, the corresponding discrimination priority and sub-level are calculated according to the passenger value rule, passenger attribute rule and ticket attribute rule. Finally, based on the above discrimination priorities and sub-levels, the final priority identification is set for the concurrent upgrade order in a progressive manner, starting from 0, and the order with a smaller number will be processed with higher priority.

[0090] For example, assume that the system receives upgrade request orders from passenger A and passenger B at the same time. Passenger A's frequent flyer card level is gold card, the number of flights is 180, the total flight mileage is 250,000, and she is not carrying an infant and is not a pregnant woman. The ticket price is 8,000 yuan, and the ticket discount is 10%; Passenger B's frequent flyer card level is platinum card, the number of flights is 280, the total flight mileage is 350,000, she is carrying an infant and is not a pregnant woman. The ticket price is 10,000 yuan, and the ticket discount is full price.

[0091] Furthermore, based on the above priority determination rules, the order priorities of passenger A and passenger B are calculated:

[0092] Passenger A's upgrade order priority = 1 (frequent flyer card level) + 1 (number of flights) + 2 (total flight kilometres) + 1 (whether carrying an infant) + 1 (whether pregnant) + 1 (fare) + 1 (discount) = 8.

[0093] The priority of passenger B's upgrade order is: 0 (frequent flyer card level) + 0 (number of flights) + 0 (total flight kilometres) + 0 (whether carrying an infant) + 1 (whether pregnant) + 0 (fare) + 0 (discount) = 1.

[0094] From the above calculation results, it can be seen that the order priority of passenger B is 1, and the order priority of passenger A is 8. Since in the priority determination rule, the smaller the value, the higher the priority, the order of passenger B will be processed first.

[0095] For example, when persisting, the system queries the current database for the maximum priority value X of the existing upgrade orders for the flight, and records the order priority of passenger B as X+1 and the order priority of passenger A as X+2, to ensure that passenger B's order is given priority.

[0096] Based on the above optional embodiments, by implementing a multi-dimensional priority determination mechanism for passenger upgrade reservations, not only the passenger's personal value, attributes and face value are taken into consideration, but also time factors and considerations of concurrent order processing are incorporated, ensuring that in the case of limited resources, priority can be given to satisfying the upgrade requests of passengers who have made great contributions to the airline, have special needs or have higher face values.

[0097] Optionally, the passenger upgrade processing method further includes: in response to receiving a cancellation request from the target user for the target upgrade order, setting the order status of the target upgrade order to a canceled status; in response to receiving a query request from the target user for the order processing result, sending order details information and the order processing result of the target upgrade order to the target user.

[0098] For example, when the system detects that the target user has issued a cancellation request for a target upgrade order, it will immediately respond and execute the corresponding cancellation process, find the record of the upgrade order by accessing the database, and then update the status of the order to "cancelled".

[0099] For example, when the target user initiates a query command for the processing result of the upgrade order, the system will respond quickly and retrieve the complete information of the upgrade order from the database, including the basic details of the order (such as order ID, passenger information, flight information, etc.) and the final processing result of the order (upgrade successful, upgrade failed or cancelled). Subsequently, the system will send the sorted order details and processing results to the target user to ensure that the user can obtain the processing status of his upgrade request in real time.

[0100] Based on the above optional embodiments, in response to the target user's cancellation request for the target upgrade order, the order status is immediately updated to the cancellation status, and in response to the target user's query request for the processing result, the order details and processing results are promptly fed back, which can enhance the user's interactive experience and thus improve the passenger's shopping experience.

[0101] Figure 2 is a schematic diagram of a rule control unit according to one embodiment of the present disclosure, such as Figure 2 As shown, the unit includes: rule control unit, upgrade recommendation unit, reservation cancellation unit, automatic upgrade unit, and query result unit. Among them, the rule control unit refers to the reservation upgrade rules that airlines use to maintain themselves in the management tool; the upgrade recommendation unit is used to build an algorithm model based on passenger travel history data through machine learning technology and give recommendations; the reservation upgrade unit is used to receive user requests, establish a priority algorithm mechanism, and create reservation orders; the reservation cancellation unit is used to receive user requests and cancel reservation orders; the automatic upgrade unit is used to process reservation orders according to the priority according to the reservation upgrade rules and update the order results; the query result unit is used to receive user query requests and return reservation order details.

[0102] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse. All content involving sensitive information of passengers has been strictly encrypted.

[0103] In the embodiments of the present disclosure, a passenger upgrade processing device is also provided, which is used to implement the above embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0104] Figure 3 is a structural block diagram of a passenger upgrade processing device according to one embodiment of the present disclosure, such as Figure 3 As shown, the device comprises:

[0105] The acquisition module 301 is used to acquire historical travel data and upgrade rule data, wherein the historical travel data is used to represent the travel information of passengers in a historical time period, and the upgrade rule data includes at least one of the following: upgrade time rules, upgrade flight rules, and upgrade passenger rules;

[0106] The feature extraction module 302 is used to extract features from the historical travel data to obtain portrait feature data, wherein the portrait feature data includes: passenger portrait features and flight portrait features;

[0107] The first generating module 303 is used to generate the recommended upgrade information corresponding to the target user based on the portrait feature data, and send the recommended upgrade information to the target user;

[0108] The second generating module 304 is configured to generate a target upgrade order corresponding to the reservation upgrade request in response to receiving a reservation upgrade request from a target user for the upgrade recommendation information;

[0109] The processing module 305 is used to analyze and process the target upgrade order using the upgrade rule data to obtain an order processing result, wherein the order processing result is used to determine whether the order status corresponding to the target upgrade order is in an upgrade success status.

[0110] Optionally, the first generating module 303 is further used to: use a target machine learning model to analyze and process the portrait feature data to obtain upgrade recommendation information, wherein the target machine learning model is trained using upgrade log data, and the upgrade recommendation information includes: upgrade willingness prediction results and upgrade suggestion values.

[0111] Optionally, the first generating module 303 is further used for: in response to the upgrade willingness prediction result associated with the upgrade recommendation information satisfying a preset push condition, pushing the upgrade recommendation value to the target user, wherein the preset push condition is used to determine whether the target user has an upgrade willingness.

[0112] Optionally, the processing module 305 is further used to: calculate the order priority level corresponding to the target upgrade order using the upgrade rule data; in response to the target upgrade order being in an initialized state, analyze and process the target upgrade order according to the order priority level to obtain an order processing result.

[0113] Optionally, the processing module 305 is further used to: obtain order time information corresponding to the target upgrade order; in response to determining that the target upgrade order is a non-concurrent upgrade order based on the order time information, determine the order priority level of the target upgrade order using the target priority identifier; in response to determining that the target upgrade order is a concurrent upgrade order based on the order time information, determine the order priority level of the target upgrade order using the upgrade rule data and multiple priority determination rules, wherein the multiple priority determination rules include: passenger value rules, passenger attribute rules, ticket attribute rules and preset priority rules.

[0114] According to another aspect of an embodiment of the present disclosure, an electronic device is also provided, including: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute instructions to implement the passenger upgrade processing method in the embodiment of the present disclosure.

[0115] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0116] S1, acquiring historical travel data and upgrade rule data, wherein the historical travel data is used to represent passenger travel information within a historical time period, and the upgrade rule data includes at least one of the following: upgrade time rules, upgrade flight rules, and upgrade passenger rules;

[0117] S2, extracting features from historical travel data to obtain portrait feature data, wherein the portrait feature data includes: passenger portrait features and flight portrait features;

[0118] S3, generating upgrade recommendation information corresponding to the target user based on the portrait feature data, and sending the upgrade recommendation information to the target user;

[0119] S4, in response to receiving a reservation upgrade request from a target user for the upgrade recommendation information, generating a target upgrade order corresponding to the reservation upgrade request;

[0120] S5, analyzing and processing the target upgrade order using the upgrade rule data to obtain an order processing result, wherein the order processing result is used to determine whether the order status corresponding to the target upgrade order is in an upgrade success status.

[0121] According to another aspect of an embodiment of the present disclosure, a computer-readable storage medium is further provided, the computer-readable storage medium including a stored executable program, wherein when the executable program runs, the device where the storage medium is located is controlled to execute the passenger upgrade processing method in the embodiment of the present disclosure.

[0122] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0123] S1, acquiring historical travel data and upgrade rule data, wherein the historical travel data is used to represent passenger travel information within a historical time period, and the upgrade rule data includes at least one of the following: upgrade time rules, upgrade flight rules, and upgrade passenger rules;

[0124] S2, extracting features from historical travel data to obtain portrait feature data, wherein the portrait feature data includes: passenger portrait features and flight portrait features;

[0125] S3, generating upgrade recommendation information corresponding to the target user based on the portrait feature data, and sending the upgrade recommendation information to the target user;

[0126] S4, in response to receiving a reservation upgrade request from a target user for the upgrade recommendation information, generating a target upgrade order corresponding to the reservation upgrade request;

[0127] S5, analyzing and processing the target upgrade order using the upgrade rule data to obtain an order processing result, wherein the order processing result is used to determine whether the order status corresponding to the target upgrade order is in an upgrade success status.

[0128] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0129] According to another aspect of an embodiment of the present disclosure, a computer program product is further provided. The computer program product includes computer instructions. When the computer instructions are executed by a processor, the passenger upgrade processing method in the embodiment of the present disclosure is implemented.

[0130] Optionally, in this embodiment, the computer program product may be configured as a computer program for executing the following steps:

[0131] S1, acquiring historical travel data and upgrade rule data, wherein the historical travel data is used to represent passenger travel information within a historical time period, and the upgrade rule data includes at least one of the following: upgrade time rules, upgrade flight rules, and upgrade passenger rules;

[0132] S2, extracting features from historical travel data to obtain portrait feature data, wherein the portrait feature data includes: passenger portrait features and flight portrait features;

[0133] S3, generating upgrade recommendation information corresponding to the target user based on the portrait feature data, and sending the upgrade recommendation information to the target user;

[0134] S4, in response to receiving a reservation upgrade request from a target user for the upgrade recommendation information, generating a target upgrade order corresponding to the reservation upgrade request;

[0135] S5, analyzing and processing the target upgrade order using the upgrade rule data to obtain an order processing result, wherein the order processing result is used to determine whether the order status corresponding to the target upgrade order is in an upgrade success status.

[0136] The serial numbers of the above-mentioned embodiments of the present disclosure are only for description and do not represent the advantages or disadvantages of the embodiments.

[0137] In the above embodiments of the present disclosure, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0138] In the several embodiments provided in the present disclosure, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0139] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0140] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0141] If the integrated unit is implemented in the form of 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 the present disclosure is essentially 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, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the methods described in each embodiment of the present disclosure. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0142] The above is only a preferred embodiment of the present disclosure. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present disclosure. These improvements and modifications should also be regarded as the scope of protection of the present disclosure.

Claims

1. A passenger upgrade processing method, characterized in that: include: Acquire historical travel data and upgrade rule data, wherein the historical travel data is used to represent passenger travel information within a historical time period, and the upgrade rule data includes at least one of the following: upgrade time rules, upgrade flight rules, and upgrade passenger rules; Extracting features from the historical travel data to obtain portrait feature data, wherein the portrait feature data includes: passenger portrait features and flight portrait features; generating upgrade recommendation information corresponding to the target user based on the portrait feature data, and sending the upgrade recommendation information to the target user; In response to receiving a reservation upgrade request from the target user for the upgrade recommendation information, generating a target upgrade order corresponding to the reservation upgrade request; The target upgrade order is analyzed and processed using the upgrade rule data to obtain an order processing result, wherein the order processing result is used to determine whether an order status corresponding to the target upgrade order is in an upgrade success status.

2. The passenger upgrade processing method according to claim 1, characterized in that: The passenger portrait features include at least one of the following: passenger static features, dynamic order features and passenger matching features; the flight portrait features include at least one of the following: flight static features and flight statistical features.

3. The passenger upgrade processing method according to claim 1, characterized in that: Generating the upgrade recommendation information corresponding to the target user based on the portrait feature data includes: The portrait feature data is analyzed and processed by using a target machine learning model to obtain the upgrade recommendation information, wherein the target machine learning model is trained using the upgrade log data, and the upgrade recommendation information includes: an upgrade willingness prediction result and an upgrade suggestion value.

4. The passenger upgrade processing method according to claim 3, characterized in that: Sending the upgrade recommendation information to the target user includes: In response to the upgrade willingness prediction result associated with the upgrade recommendation information satisfying a preset push condition, the upgrade recommendation value is pushed to the target user, wherein the preset push condition is used to determine whether the target user has an upgrade willingness.

5. The passenger upgrade processing method according to claim 1, characterized in that: Analyzing and processing the target upgrade order by using the upgrade rule data to obtain the order processing result includes: Calculating the order priority level corresponding to the target upgrade order by using the upgrade rule data; In response to the target upgrade order being in an initialized state, the target upgrade order is analyzed and processed according to the order priority level to obtain the order processing result.

6. The passenger upgrade processing method according to claim 5, characterized in that: Calculating the order priority level corresponding to the target upgrade order by using the upgrade rule data includes: Obtaining order time information corresponding to the target upgrade order; In response to determining that the target upgrade order is a non-concurrent upgrade order based on the order time information, determining the order priority level of the target upgrade order by using a target priority identifier; In response to determining that the target upgrade order is a concurrent upgrade order based on the order time information, the order priority level of the target upgrade order is determined by using the upgrade rule data and a plurality of priority determination rules, wherein the plurality of priority determination rules include: a passenger value rule, a passenger attribute rule, a ticket attribute rule, and a preset priority rule.

7. The passenger upgrade processing method according to claim 1, characterized in that: The method further comprises: In response to receiving a cancellation request from the target user for the target upgrade order, setting the order status of the target upgrade order to a canceled status; In response to receiving a query request from the target user regarding the order processing result, the order details information of the target upgrade order and the order processing result are sent to the target user.

8. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the passenger upgrade processing method according to any one of claims 1 to 7.

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, the device where the storage medium is located is controlled to execute the passenger upgrade processing method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The computer program product includes computer instructions, which, when executed by a processor, implement the passenger upgrade processing method described in any one of claims 1 to 7.

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