Travel information processing methods, systems, equipment and storage media

Abstract

This invention provides a travel information processing method, system, device, and storage medium. The method includes the following steps: acquiring a user's first location information at a first moment and second location information at a second moment; the second moment is after the first moment; determining whether the user's travel type matches a preset outbound travel type based on the first location information corresponding to the first moment and the second location information corresponding to the second moment; if the user's travel type matches the preset outbound travel type, acquiring the user's outbound travel information; determining matching return travel product information based on the outbound travel information; and pushing the return travel product information to the terminal device corresponding to the user. This invention accurately determines user churn tendency and helps reduce user churn rate.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a method, system, device, and storage medium for processing travel information. Background Technology

[0002] With the rapid development of OTA (Online Travel Agent) platforms, more and more users are conveniently and quickly booking airline or train tickets online, i.e., making travel reservations. For OTA platforms, in their daily operations, it is necessary to avoid user churn, that is, to have early warning systems in place for users to switch to competitors.

[0003] However, OTA platforms currently face two common problems in the process of churn warning: 1) they cannot accurately determine user churn; 2) after determining that users are prone to churn, they cannot provide effective measures to help reduce the user churn rate.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] To address the problems in the prior art, the present invention aims to provide a travel information processing method, system, device, and storage medium that can accurately determine user churn tendency and help reduce user churn rate.

[0006] To achieve the above objectives, the present invention provides a travel information processing method, the method comprising the following steps:

[0007] S110, acquire the user's first location information at a first moment and second location information at a second moment; the second moment is after the first moment.

[0008] S120, based on the first location information corresponding to the first time and the second location information corresponding to the second time, determine whether the user's travel type conforms to the preset outbound travel type;

[0009] S130, if the user's travel type matches the preset outbound travel type, then obtain the user's outbound travel information;

[0010] S140, Based on the outbound travel information, determine the matching return travel product information;

[0011] And S150, push the return trip product information to the terminal device corresponding to the user.

[0012] Optionally, step S120 includes:

[0013] Obtain the distance between the first location information and the second location information;

[0014] Based on the first time point, the second time point, and the distance, the user's average moving speed during the time period between the first time point and the second time point is calculated;

[0015] When the average moving speed is greater than a first preset threshold, it is determined that the user's travel type matches the preset outbound travel type;

[0016] Otherwise, it is determined that the user's travel type does not conform to the preset outbound travel type.

[0017] Optionally, step S120 includes:

[0018] Acquire multiple displacement segments where the user's movement distance between the first location information and the second location information exceeds a second preset threshold;

[0019] Based on the distance of each displacement segment and the corresponding movement time, calculate the initial movement speed corresponding to each displacement segment.

[0020] Based on the initial moving speed corresponding to each displacement segment, calculate the user's average moving speed during the time period between the first time and the second time.

[0021] When the average moving speed is greater than a first preset threshold, it is determined that the user's travel type matches the preset outbound travel type;

[0022] Otherwise, it is determined that the user's travel type does not conform to the preset outbound travel type.

[0023] Optionally, step S130 includes:

[0024] If the user's travel type matches the preset outbound travel type, and there are no travel booking orders related to the user's travel on the target network platform, then obtain the user's outbound travel information.

[0025] Optionally, step S130 includes:

[0026] If a user does not have any historical orders matching the city where the first location information is located and the city where the second location information is located within a preset time period on the target network platform, then it is determined that there are no travel booking orders related to the user's travel on the target network platform.

[0027] Optionally, the outbound travel information includes the departure city and the arrival city; step S140 includes:

[0028] Based on the departure city and the arrival city, return travel products with prices lower than a third preset threshold are used as the matched return travel product information; the third preset threshold is the average transportation cost expenditure of the user in their historical trips between the departure city and the arrival city.

[0029] Optionally, step S120 includes:

[0030] Obtain the distance between the first location information and the second location information;

[0031] Based on the first time point, the second time point, and the distance, the user's average moving speed during the time period between the first time point and the second time point is calculated;

[0032] The outbound travel information includes the departure city and the arrival city; step S140 includes:

[0033] Based on the average moving speed, a matching first mode of transportation is obtained;

[0034] Based on the first mode of transportation, the departure city, and the arrival city, determine the matching return trip product information.

[0035] Optionally, determining the matching return trip product information based on the first mode of transportation, the departure city, and the arrival city includes:

[0036] Obtain users' historical travel order data on the target network platform;

[0037] Extract target order data from the historical travel order data, which is the return trip from the arrival city to the departure city.

[0038] Based on the target order data, determine the preferred travel time period corresponding to the trip from the arrival city back to the departure city;

[0039] Based on the preferred travel time, the first mode of transportation, the departure city, and the arrival city, the matching return trip product information is determined.

[0040] Optionally, the outbound travel information further includes departure time and arrival time; determining the matching return travel product information based on the first mode of transportation, the departure city, and the arrival city includes:

[0041] When the first mode of transportation is an airplane, the outbound flight information is determined based on the outbound travel information;

[0042] Obtain users' historical travel order data on the target network platform;

[0043] Extract all historical round-trip flight combinations between the arrival city and the departure city from the historical travel order data;

[0044] Based on the historical round-trip flight combinations, determine the return flight information that matches the outbound flight information;

[0045] The matching return product information is determined based on the return flight information.

[0046] Optionally, the outbound travel information includes the departure city and the arrival city; step S140 includes:

[0047] Obtain users' historical travel order data on the target network platform;

[0048] Based on the historical travel order data, obtain the user's historical preferred mode of transportation between the departure city and the arrival city;

[0049] Based on the historical preferred modes of transportation, determine the return trip product information.

[0050] The present invention also provides a travel information processing system for implementing the above-described travel information processing method, the system comprising:

[0051] The location information acquisition module acquires the user's first location information at a first moment and second location information at a second moment; the second moment is after the first moment.

[0052] The trip type determination module determines whether the user's trip type matches the preset outbound trip type based on the first location information and the second location information;

[0053] The outbound travel information acquisition module acquires the user's outbound travel information if the user's travel type matches the preset outbound travel type.

[0054] The return trip product information matching module determines the matching return trip product information based on the outbound travel information;

[0055] And a return trip product recommendation module, which pushes the return trip product information to the terminal device corresponding to the user.

[0056] The present invention also provides a travel information processing device, comprising:

[0057] processor;

[0058] A memory in which an executable program of the processor is stored;

[0059] The processor is configured to perform the steps of any of the above-described travel information processing methods by executing the executable program.

[0060] The present invention also provides a computer-readable storage medium for storing a program, which, when executed by a processor, implements the steps of any of the above-described travel information processing methods.

[0061] Compared with the prior art, the present invention has the following advantages and outstanding effects:

[0062] The travel information processing method, system, device, and storage medium provided by this invention can accurately determine the user's churn tendency or identify churned users. Based on the outbound travel information of users using orders placed outside the target platform, the invention recommends corresponding return travel products within the target platform, which helps reduce the user churn rate on the target platform and enhances user stickiness. Attached Figure Description

[0063] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0064] Figure 1 This is a schematic diagram of a travel information processing method disclosed in an embodiment of the present invention;

[0065] Figure 2 This is a schematic diagram of a travel information processing method disclosed in another embodiment of the present invention;

[0066] Figure 3 This is a schematic diagram of a travel information processing method disclosed in another embodiment of the present invention;

[0067] Figure 4 This is a schematic diagram of a travel information processing method disclosed in another embodiment of the present invention;

[0068] Figure 5 This is a schematic diagram of step S146 in a travel information processing method disclosed in another embodiment of the present invention;

[0069] Figure 6 This is a schematic diagram of step S146 in a travel information processing method disclosed in another embodiment of the present invention;

[0070] Figure 7 This is a schematic diagram of the structure of a travel information processing system disclosed in an embodiment of the present invention;

[0071] Figure 8 This is a schematic diagram of the structure of a travel information processing device disclosed in an embodiment of the present invention;

[0072] Figure 9 This is a schematic diagram of the structure of a computer-readable storage medium disclosed in an embodiment of the present invention. Detailed Implementation

[0073] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that the invention will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore repeated descriptions of them will be omitted.

[0074] like Figure 1 As shown, an embodiment of the present invention discloses a travel information processing method, which includes the following steps:

[0075] S110, acquire the user's first location information at a first moment and second location information at a second moment. The second moment is after the first moment. For example, the first and second moments may be on the same calendar day. For instance, the first moment may be 8:00 AM and the second moment may be 11:00 AM. The first and second location information may be located in different cities. In other embodiments, the first and second moments may be on different calendar days. The first and second location information may be collected based on the user's terminal device, such as a mobile phone. This application does not limit the collection method.

[0076] S120: Based on the first location information corresponding to the first moment and the second location information corresponding to the second moment, determine whether the user's travel type matches the preset outbound travel type. In specific implementation, the location at the first moment and the location at the second moment can be used to determine whether the user has undergone a long-distance displacement in a short period of time. For example, if the user's geographical location has moved 2,000 kilometers within 3 hours, and it is known that this movement could not have been achieved by sea transportation, that is, it must have been achieved by land transportation, then it indicates that the user has engaged in air travel, and it is impossible for them to have traveled by car or train.

[0077] That is, the user's travel type is determined based on the average speed per unit time. If the average speed is greater than a threshold, the user's travel type is determined to be compatible with the preset outbound travel type. If the average speed is less than or equal to the threshold, the user's travel type is determined to be inconsistent with the preset outbound travel type. For example, the preset outbound travel type can be air travel or train travel. If the user's travel type is inconsistent with the preset outbound travel type, the process ends. If the user's travel type is compatible with the preset outbound travel type, step S130 is executed.

[0078] S130, Obtain the user's outbound travel information. For example, the outbound travel information may include the departure city, arrival city, departure time, and / or arrival time. It should be noted that this embodiment of the invention only addresses the situation where the user has only undertaken a one-way trip and has not yet returned.

[0079] In specific implementation, step S130 may include:

[0080] If the user's travel type matches the preset outbound travel type, and there are no travel bookings associated with the user's trip on the target network platform, the user's outbound travel information is obtained. The target network platform can be an OTA (Online Travel Agency). The user is a user with historical consumption behavior on the OTA platform, or a user whose historical consumption frequency reaches a preset threshold. If the user's current trip is by air, and there are no associated travel bookings on the OTA platform (e.g., no bookings for a flight from city A to city B on the same day), then it is determined that the user has a tendency to churn, or that the user is a churned user. City A is the city corresponding to the first location information, i.e., the departure city. City B is the city corresponding to the second location information, i.e., the arrival city. The departure time is the last time the user's geographical location appears in city A. The arrival time is the time when the user's geographical location begins to appear in city B.

[0081] In some embodiments, step S130 may further include:

[0082] If a user has no historical orders matching the city where the first location information is located and the city where the second location information is located within a preset time period on the target network platform, then it is determined that there are no travel booking orders related to the user's trip on the target network platform. For example, if there are no orders for flights from the city where the first location information is located to the city where the second location information is located on the same day, then it is determined that there are no associated travel booking orders on the target network platform.

[0083] S140, Based on the aforementioned outbound travel information, determine the matching return travel product information. For example, if the outbound travel information is from city A to city B, then the return travel product information could be a flight from city B to city A. This flight could be on the day after the user arrives in city B, or on another day thereafter.

[0084] In some embodiments, the return date can be obtained from the user's email data. This scenario applies to users on business trips, and the return date corresponding to the return product information is then determined based on this data.

[0085] And S150, pushes the aforementioned return trip product information to the terminal devices corresponding to the aforementioned users.

[0086] This embodiment accurately determines user churn tendency or identifies churned users. Based on the outbound travel information of users using orders placed outside the target platform, it recommends corresponding return travel products within the target platform, which helps reduce user churn rate on the target platform and enhances user stickiness.

[0087] like Figure 2 As shown, in some embodiments, in the above Figure 1 Based on the corresponding embodiment, the outbound travel information mentioned above includes the departure city and the arrival city.

[0088] In this embodiment, step S140 is replaced by step S141:

[0089] S141, based on the aforementioned departure and arrival cities, return travel products with prices lower than a third preset threshold are selected as matched return travel product information. The aforementioned third preset threshold is the average transportation cost incurred by the user during historical trips between the departure and arrival cities.

[0090] For example, by determining the recommended return flight products based on the average ticket price for users flying from city A to city B, it is beneficial to improve the conversion rate of return flight orders on the platform and reduce the churn rate of users on the target platform.

[0091] In some embodiments, in the above Figure 1 Based on the corresponding embodiment, the outbound travel information mentioned above includes the departure city and the arrival city. Step S140 includes:

[0092] S142, Obtain the user's historical travel order data on the target network platform.

[0093] S143, Based on the aforementioned historical travel order data, obtain the user's historical preferred mode of transportation between the aforementioned departure and arrival cities.

[0094] S144. Based on the above historical preferred modes of transportation, determine the return trip product information.

[0095] For example, if a user prefers to travel by air between city A and city B, then the recommended return travel product information would be flights. This implementation helps improve the conversion rate of return travel orders on the platform and reduce user churn on the target platform.

[0096] like Figure 3 As shown, in another embodiment of this application, another method for processing travel information is disclosed. This method is based on the above... Figure 1 Based on the corresponding embodiment, step S120 includes:

[0097] S121, Obtain the distance between the first location information and the second location information.

[0098] S122, based on the first moment, the second moment and the distance, calculate the user's average moving speed during the time period between the first moment and the second moment.

[0099] S123, determine whether the average moving speed is greater than the first preset threshold. If so, proceed to step S130. Otherwise, end the process.

[0100] That is, when the average moving speed is greater than a first preset threshold, the user's travel type is determined to conform to the preset outbound travel type. Otherwise, the user's travel type is determined to not conform to the preset outbound travel type. For example, the first preset threshold may be 250 km / h. This application does not limit this.

[0101] In another embodiment of this application, another method for processing travel information is disclosed. This method is described above... Figure 1 Based on the corresponding embodiment, step S120 includes:

[0102] S124, acquire multiple displacement segments where the user's movement distance between the first location information and the second location information is greater than the second preset threshold.

[0103] S125, calculate the user's average movement speed between the first and second moments based on the distance of each displacement segment and the corresponding movement time.

[0104] S126, determine whether the average moving speed is greater than the first preset threshold. If so, proceed to step S130. Otherwise, end the process.

[0105] That is, when the average moving speed is greater than a first preset threshold, the user's travel type is determined to conform to the preset outbound travel type. Otherwise, the user's travel type is determined to not conform to the preset outbound travel type. For example, the first preset threshold may be 250 km / h. This application does not limit this.

[0106] In specific implementation, step S125 can first calculate the initial moving speed corresponding to each of the aforementioned displacement segments, and then calculate the user's average moving speed between the first and second moments based on the initial moving speeds corresponding to each of the aforementioned displacement segments. For example, this average moving speed can be the average of the aforementioned initial moving speeds. The aforementioned second preset threshold can be, for example, 100km, such as when flying from city A to city B and transferring in city C. Then, when calculating the aforementioned average moving speed, it can be calculated based on the average of the two travel segments.

[0107] like Figure 4As shown, in another embodiment of this application, another method for processing travel information is disclosed. This method is based on the above... Figure 3 Based on the corresponding embodiment, the outbound travel information mentioned above includes the departure city and the arrival city. Step S140 includes:

[0108] S145, Based on the above average moving speed, obtain the first matching mode of transportation.

[0109] S146. Based on the aforementioned first mode of transportation, departure city, and arrival city, determine the matching return product information.

[0110] For example, if a user's geographical location has moved 2100 kilometers within 3 hours, with an average speed of 700 km / h, and the primary mode of transportation is an airplane, then when recommending return travel products to the user, the platform should directly recommend the same mode of transportation as the outbound journey, i.e., the same primary mode of transportation, such as recommending a flight. This implementation helps improve the conversion rate of return travel orders on the platform and reduces user churn on the target platform.

[0111] The matching between average moving speed and the first mode of transportation can be achieved according to a preset mapping table.

[0112] like Figure 5 As shown, in another embodiment of this application, another method for processing travel information is disclosed. This method is based on the above... Figure 4 Based on the corresponding embodiment, step S146 includes:

[0113] S1461, Obtain the user's historical travel order data on the target network platform.

[0114] S1462, Extract target order data from the above-mentioned historical travel order data, which is the return trip from the above-mentioned arrival city to the above-mentioned departure city.

[0115] S1463, Based on the target order data mentioned above, determine the preferred travel time period corresponding to the trip from the arrival city back to the departure city.

[0116] S1464. Based on the above-mentioned preferred travel time, primary mode of transportation, departure city, and arrival city, determine the matching return product information.

[0117] For example, if the departure city is city A and the arrival city is city B, then the target order data mentioned above would be travel orders from city B to city A, such as flight order data from city B to city A. In step S1463, the time period with the highest frequency among travel orders from city B to city A can be taken as the corresponding preferred travel time period. Then, in step S1464, return products for flights or other means of transportation from city B to city A during that time period can be used as the matched return product information.

[0118] This implementation method helps to improve the conversion rate of return orders on the platform and reduce the churn rate of users on the target platform.

[0119] like Figure 6 As shown, in another embodiment of this application, another method for processing travel information is disclosed. This method is based on the above... Figure 4 Based on the corresponding embodiment, the aforementioned outbound travel information also includes departure time and arrival time. Step S146 includes:

[0120] S1465, when the first mode of transportation is an airplane, the outbound flight information is determined based on the outbound travel information.

[0121] S1466, Obtain the user's historical travel order data on the target network platform.

[0122] S1467, Extract all historical round-trip flight combinations between the above-mentioned arrival city and departure city from the above-mentioned historical travel order data.

[0123] S1468, Based on the above historical round-trip flight combinations, determine the return flight information that matches the above outbound flight information.

[0124] S1469, based on the above return flight information, determine the matching return product information.

[0125] Specifically, this embodiment determines the outbound flight information based on the departure and arrival times in the outbound travel information, for example, flight D, traveling from city A to city B. Then, it extracts round-trip flight combinations that include flight D and where flight D corresponds to an outbound flight from all historical travel order data. For example, this round-trip flight combination could be flight D+E or flight D+F. This indicates that when a user returns from city B to city A, they prefer to take flight E or flight F for the return trip. Therefore, the return flight information will recommend flight E or flight F accordingly.

[0126] Alternatively, in some embodiments, the return flight information can also be matched with the most frequently used flight among all historical return flights, such as flight E.

[0127] This implementation method helps to improve the conversion rate of return orders on the platform and reduce the churn rate of users on the target platform.

[0128] In some embodiments, in the above Figure 6 Based on the corresponding embodiment, in step S1469, the return flight information with the lowest current price is determined as the return product information.

[0129] In some embodiments, in the above Figure 1 Based on the corresponding embodiment, when the first location information and the second location information are detected to each correspond to an airport, the means of transportation corresponding to the return product information in step S140 is directly determined to be a flight.

[0130] In some embodiments, in the above Figure 1 Based on the corresponding implementation example, for customers with churn warnings, a churn model is tested using the BI algorithm (ab), and the churn probability is output. Data retrieval logic: Data source: BI-PLTF-014-Platform Customer Churn Prediction. The retrieved data field is, for example:

Today's Churn Probability

Number of Times Daily Churn Probability ≥0.8 in 45 Days

Total Number of Users

Can It Be Followed Up

Company Status

Account Status

[0131] In practice, the user performance table is retrieved, and users are tagged according to the following logic: Complete Churn, Churning, Active, and New User. Each node is identified in the table dim_corpbi_mldb.customer_operation_platformchurningprediction_corp_status, using the field corp_churning_status_flow. The actual value of this field is in the format {status}_{level}_{column}. In status, new represents a new user, churning represents a churning user, churned represents a complete churn, and active represents an active user. level represents the tier depth (coded from 0). column represents the column index from left to right (coded from 0). A user is defined as a complete churn if one of the following conditions is met: 1) Registration time is greater than 180 days and there is no consumption. 2) Registration time is greater than 180 days, there are consumption records, but the inactivity period exceeds a specified threshold.

[0132] The active duration is the number of days between the first and last purchase. The inactive duration is the number of days since the last purchase. Thresholds are set as follows: 30 days for major clients, and the maximum historical purchase interval for platform clients, limited to 60-180 days (if the value is less than 60, the threshold is set to 60; if the value is greater than 180, the threshold is set to 180). Filtering criteria are: within the past eight weeks, the number of weeks with a week-on-week decrease in GMV does not exceed 6, and the total GMV over the past eight weeks decreases by no more than 50% compared to the previous eight weeks, and the total GMV over the past eight weeks decreases by no more than 50% compared to the same period last year (adjustments based on the pandemic factor are acceptable; 50%, eight weeks, etc., can be further refined and optimized).

[0133] The decision date serves as the right boundary for data collection in the model; data older than or equal to the decision date is excluded from the model. (In the model data `performance_weekly_df`, the column `week_before_decision` illustrates the impact of different decision dates on the data range before feature engineering.) For completely churned users: the decision date is the last consumption date. For active users: the decision date is yesterday (T-1) of the current date. For users currently churning: the decision date is yesterday (T-1) of the current date.

[0134] The churn model is based on the data structure Array to extract features. First, a consumption array is extracted for each company (e.g., array = [10, 20, 10, 30, 20, ...], which means that the company spent 10 in the first week before the decision date, 20 in the second week before that date, and so on). Then, the following features are extracted based on the consumption array (some features are first averaged over 4 weeks on the array).

[0135] For completely churned customers and active customers, no churn probability is output; the churn probability is set to 1 and 0, respectively. For new users, no churn probability is output; the churn probability is set to -1. For customers "currently churning," a prediction is made, and a churn probability (between 0 and 1) is output. Customers with a churn probability less than 0.5 are labeled as "active customers." For customers with a churn probability greater than 0.5, the churn probability is normalized: new_prob = 2 * prob - 1. For companies with account managers, a further sorting percentage is performed. Therefore, the final churn probability returned is the probability of transforming [0.5, 1) into (0, 1). Finally, combining the customer value score and the churn probability, the final_value (comprehensive churn probability score) is output. The determination of the churn probability score is: final_value (comprehensive churn probability score) = customer value score * churn probability. This score is only output for customers with a churn probability greater than 0.5; all other customers receive -1.

[0136] It should be noted that all the embodiments disclosed in this application can be freely combined, and the resulting technical solutions are also within the protection scope of this application.

[0137] like Figure 7 As shown, an embodiment of the present invention also discloses a travel information processing system 7, which includes:

[0138] The location information acquisition module 71 acquires the user's first location information at a first moment and second location information at a second moment. The second moment is located after the first moment.

[0139] The trip type determination module 72 determines whether the user's trip type matches the preset outbound trip type based on the first location information and the second location information.

[0140] The outbound travel information acquisition module 73 acquires the user's outbound travel information if the user's travel type matches the preset outbound travel type.

[0141] The return trip product information matching module 74 determines the matching return trip product information based on the aforementioned outbound travel information.

[0142] And the return trip product recommendation module 75 pushes the aforementioned return trip product information to the terminal devices corresponding to the aforementioned users.

[0143] It is understood that the travel information processing system of the present invention also includes other existing functional modules that support the operation of the travel information processing system. Figure 7 The travel information processing system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0144] The travel information processing system in this embodiment is used to implement the travel information processing method described above. Therefore, the specific implementation steps of the travel information processing system can be referred to the description of the travel information processing method described above, and will not be repeated here.

[0145] An embodiment of the present invention also discloses a travel information processing device, including a processor and a memory, wherein the memory stores an executable program of the processor; the processor is configured to perform the steps in the above-described travel information processing method by executing the executable program. Figure 8 This is a schematic diagram of the travel information processing device disclosed in this invention. See below for reference. Figure 8 To describe an electronic device 600 according to this embodiment of the present invention. Figure 8 The electronic device 600 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0146] like Figure 8As shown, the electronic device 600 is presented in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0147] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the travel information processing method section of this specification, based on various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0148] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0149] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0150] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0151] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0152] The present invention also discloses a computer-readable storage medium for storing a program that, when executed, implements the steps of the travel information processing method described above. In some possible embodiments, various aspects of the present invention can also be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps of the travel information processing method described in this specification according to various exemplary embodiments of the present invention.

[0153] As shown above, when the program of the computer-readable storage medium of this embodiment is executed, it can accurately determine the user's churn tendency or identify churned users, and recommend corresponding return product information within the target platform based on the outbound travel information of the user using orders from outside the target platform. This helps to reduce the churn rate of users on the target platform and enhance the stickiness of platform users.

[0154] Figure 9 This is a schematic diagram of the structure of the computer-readable storage medium of the present invention. (Reference) Figure 9 As shown, a program product 800 for implementing the above-described method according to an embodiment of the present invention is described. This product may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0155] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0156] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0157] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0158] The travel information processing method, system, device, and storage medium provided in this invention can accurately determine user churn tendency or identify churned users. Based on the outbound travel information of users using orders placed outside the target platform, the system recommends corresponding return travel products within the target platform, which helps reduce user churn rate on the target platform and enhances user stickiness.

[0159] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for processing travel information, characterized in that, Includes the following steps: S110, acquire the user's first location information at a first moment and second location information at a second moment; the second moment is after the first moment. S120, based on the first location information corresponding to the first time and the second location information corresponding to the second time, determine whether the user's travel type conforms to a preset outbound travel type, including: Obtain the distance between the first location information and the second location information; Based on the first time point, the second time point, and the distance, the user's average moving speed during the time period between the first time point and the second time point is calculated; When the average moving speed is greater than a first preset threshold, it is determined that the user's travel type matches the preset outbound travel type; Otherwise, it is determined that the user's travel type does not conform to the preset outbound travel type; S130, if the user's travel type matches the preset outbound travel type, and there is no travel booking order related to the user's travel on the target network platform, then obtain the user's outbound travel information; S140, Based on the outbound travel information, determine the matching return travel product information; And S150, push the return trip product information to the terminal device corresponding to the user.

2. The travel information processing method as described in claim 1, characterized in that, Step S120 includes: Acquire multiple displacement segments where the user's movement distance between the first location information and the second location information exceeds a second preset threshold; Based on the distance of each displacement segment and the corresponding movement time, calculate the initial movement speed corresponding to each displacement segment. Based on the initial moving speed corresponding to each displacement segment, calculate the user's average moving speed during the time period between the first time and the second time. When the average moving speed is greater than a first preset threshold, it is determined that the user's travel type matches the preset outbound travel type; Otherwise, it is determined that the user's travel type does not conform to the preset outbound travel type.

3. The travel information processing method as described in claim 1, characterized in that, Step S130 includes: If a user does not have any historical orders matching the city where the first location information is located and the city where the second location information is located within a preset time period on the target network platform, then it is determined that there are no travel booking orders related to the user's travel on the target network platform.

4. The travel information processing method as described in claim 1, characterized in that, The outbound travel information includes the departure city and the arrival city; step S140 includes: Based on the departure city and the arrival city, return travel products with prices lower than a third preset threshold are used as the matched return travel product information; the third preset threshold is the average transportation cost expenditure of the user in their historical trips between the departure city and the arrival city.

5. The travel information processing method as described in claim 1, characterized in that, Step S120 includes: Obtain the distance between the first location information and the second location information; Based on the first time point, the second time point, and the distance, the user's average moving speed during the time period between the first time point and the second time point is calculated; The outbound travel information includes the departure city and the arrival city; step S140 includes: Based on the average moving speed, a matching first mode of transportation is obtained; Based on the first mode of transportation, the departure city, and the arrival city, determine the matching return trip product information.

6. The travel information processing method as described in claim 5, characterized in that, The step of determining the matching return trip product information based on the first mode of transportation, the departure city, and the arrival city includes: Obtain users' historical travel order data on the target network platform; Extract target order data from the historical travel order data, which is the return trip from the arrival city to the departure city. Based on the target order data, determine the preferred travel time period corresponding to the trip from the arrival city back to the departure city; Based on the preferred travel time, the first mode of transportation, the departure city, and the arrival city, the matching return trip product information is determined.

7. The travel information processing method as described in claim 5, characterized in that, The outbound travel information also includes departure time and arrival time; the step of determining the matching return travel product information based on the first mode of transportation, the departure city, and the arrival city includes: When the first mode of transportation is an airplane, the outbound flight information is determined based on the outbound travel information; Obtain users' historical travel order data on the target network platform; Extract all historical round-trip flight combinations between the arrival city and the departure city from the historical travel order data; Based on the historical round-trip flight combinations, determine the return flight information that matches the outbound flight information; The matching return product information is determined based on the return flight information.

8. The travel information processing method as described in claim 1, characterized in that, The outbound travel information includes the departure city and the arrival city; step S140 includes: Obtain users' historical travel order data on the target network platform; Based on the historical travel order data, obtain the user's historical preferred mode of transportation between the departure city and the arrival city; Based on the historical preferred modes of transportation, determine the return trip product information.

9. A travel information processing system for implementing the travel information processing method as described in claim 1, characterized in that, The system includes: The location information acquisition module acquires the user's first location information at a first moment and second location information at a second moment; the second moment is after the first moment. The trip type determination module determines whether the user's trip type matches a preset outbound trip type based on the first location information and the second location information, including: Obtain the distance between the first location information and the second location information; Based on the first time point, the second time point, and the distance, the user's average moving speed during the time period between the first time point and the second time point is calculated; When the average moving speed is greater than a first preset threshold, it is determined that the user's travel type matches the preset outbound travel type; Otherwise, it is determined that the user's travel type does not conform to the preset outbound travel type; The outbound travel information acquisition module acquires the user's outbound travel information if the user's travel type matches the preset outbound travel type and there are no travel booking orders related to the user's travel on the target network platform. The return trip product information matching module determines the matching return trip product information based on the outbound travel information; And a return trip product recommendation module, which pushes the return trip product information to the terminal device corresponding to the user.

10. A travel information processing device, characterized in that, include: processor; A memory in which an executable program of the processor is stored; The processor is configured to perform the steps of the travel information processing method according to any one of claims 1 to 8 by executing the executable program.

11. A computer-readable storage medium for storing a program, characterized in that, When the program is executed by the processor, it implements the steps of the travel information processing method according to any one of claims 1 to 8.

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