CN111445046A - Hotel reservation information processing method and system, electronic equipment and storage medium - Google Patents

Hotel reservation information processing method and system, electronic equipment and storage medium Download PDF

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CN111445046A
CN111445046A CN202010191207.7A CN202010191207A CN111445046A CN 111445046 A CN111445046 A CN 111445046A CN 202010191207 A CN202010191207 A CN 202010191207A CN 111445046 A CN111445046 A CN 111445046A
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full
hotel
house
probability
reservation information
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黎建辉
吉聪睿
胡泓
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Ctrip Computer Technology Shanghai Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/12—Hotels or restaurants

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Abstract

The invention discloses a processing method, a system, electronic equipment and a storage medium of hotel reservation information, wherein the processing method comprises the following steps: s1, when a hotel orders a room type to be full, predicting the probability of the full room of other room types of the same parent hotel and the same check-in day with the order by using the XGboost model; s2, judging whether the probability that the other house types of the mother hotel are full is greater than a preset threshold value, if so, entering the step S3; and S3, closing the house according to the house type. The invention utilizes a machine learning method, when an order is full, the XGboost model is triggered in real time to predict the probability of full occurrence of other types of the selling rooms which have the same entering date with the mother liquor shop, and when the probability of full occurrence is larger than a preset threshold value, the business intervention is carried out on the type of the selling room of the corresponding entering date, so that the occurrence ratio of continuous full occurrence in hotel reservation is reduced, and the reservation experience of a user is improved.

Description

Hotel reservation information processing method and system, electronic equipment and storage medium
Technical Field
The invention relates to the field of online booking services, in particular to a processing method and system of hotel booking information, electronic equipment and a storage medium.
Background
In the process of booking the hotel, due to untimely room maintenance state of the hotel or a supplier, over-sale of the hotel and the like, the defect of full room service before order confirmation occurs, namely, the user sees that there is a room on the booking page, but the hotel actually has no room, and the state of no room can be fed back to the user before order confirmation. The generation of the full-room service defect not only seriously affects the single ordering experience of the user, but also gradually affects the enterprise image of the OTA platform in the long term. If the user places the order twice within two hours continuously, the primary liquor shops of the two orders have the same order and the same day of stay, the order placing twice meets the full room and can not be ordered successfully, namely the order placing twice is called as the continuous full room, and the adverse effect on the user caused by the continuous full room is more serious. At present, no good method for processing the service defects of continuous full rooms exists.
Disclosure of Invention
The invention aims to overcome the defect that the prior art cannot process continuous full rooms, and provides a processing method, a processing system, electronic equipment and a storage medium for reducing hotel reservation information.
The invention solves the technical problems through the following technical scheme:
the invention provides a processing method of hotel reservation information, which comprises the following steps:
s1, when a hotel orders a room type to be full, predicting the probability of the full room of other room types of the same parent hotel and the same check-in day with the order by using the XGboost model;
s2, judging whether the probability that the other house types of the mother hotel are full is greater than a preset threshold value, if so, entering the step S3;
and S3, closing the house according to the house type.
Preferably, the XGboost model is constructed based on a dimension of a mother liquor shop, a dimension of a house type, a dimension of a living day, a dimension of a tension degree of an urban business district and a dimension of a house state operation on the same day;
the XGboost model is obtained by training a plurality of historical order data, wherein the historical order data comprises the dimensions of the hotel, the submission date of the historical order, the check-in date of the historical order, the proportion of full rooms of orders in a first time window including the submission date of the historical order, and the proportion of full rooms of orders in a second time window including the check-in date of the historical order.
Preferably, the preset threshold range is 0.1 to 0.9.
Preferably, a timing task is set, the timing task uses an XGboost model to predict a target probability, the target probability is the probability of full room occurrence of all types of rooms of all the hotels in each day in a preset period, and the preset period comprises the current day and n future days;
and judging whether the target probability is greater than a preset threshold value, and if so, closing the house type of the corresponding day.
Preferably, n has a value of 60.
The invention also provides a processing system of the hotel reservation information, which comprises a prediction module, a judgment module and an operation module;
when a hotel reserves an order of one house type and is full, the prediction module is used for predicting the probability of full house of other house types on the same hotel and the same check-in date as the order by using the XGboost model;
the judging module is used for judging whether the probability of the other house types of the parent hotel being full is greater than a preset threshold value;
when the probability that the other house types of the mother hotel are full is larger than a preset threshold value, the operation module is used for carrying out house closing operation on the house types.
Preferably, the processing system of the hotel reservation information further comprises a training module; the XGboost model is constructed based on a dimension of a mother liquor shop, a dimension of a house type, a dimension of a living day, a dimension of tension of an urban business district and a dimension of house state operation in the day;
the training module is used for training the XGboost model by using a plurality of historical order data, and the historical order data comprises the dimensions of the hotel, the submission date of the historical order, the check-in date of the historical order, the proportion of full rooms of orders in a first time window including the submission date of the historical order, and the proportion of full rooms of orders in a second time window including the check-in date of the historical order.
Preferably, the preset threshold range is 0.1 to 0.9.
Preferably, the processing system of hotel reservation information further comprises a timing module;
the timing module is used for setting a timing task, the timing task uses an XGboost model to predict a target probability, the target probability is the probability of full room occurrence of all types of all the hotels in each day in a preset period, and the preset period comprises the current day and the next n days;
the timing module is further used for judging whether the target probability is larger than a preset threshold value or not, and if yes, the timing module carries out house closing operation on the house type of the corresponding day.
Preferably, n has a value of 60.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the hotel reservation information processing method.
The present invention also provides a computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, realizes the steps of the aforementioned method for processing hotel reservation information.
On the basis of the common knowledge in the field, the above preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows: by using a machine learning method, when an order is full, the XGboost model is triggered in real time to predict the probability of full of other selling house types on the same entering date with the order and the mother liquor shop, and when the probability of full of the order is greater than a preset threshold value, business intervention is carried out on the selling house type on the corresponding entering date, so that the proportion of continuous full of the order in hotel reservation is reduced, and the reservation experience of a user is improved. The probability that the hotel sells the house type and becomes full by regularly predicting the probability that the user reserves a plurality of hotels on the coming-in-date is high, and the house is closed when the probability is high, so that the probability that the user becomes full when ordering is effectively reduced, the occurrence of continuous full house is greatly reduced, and the booking experience of the user is further improved.
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Fig. 1 is a flowchart of a processing method of hotel reservation information according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of a processing method of hotel reservation information according to embodiment 2 of the present invention.
Fig. 3 is a schematic block diagram of a system for processing hotel reservation information according to embodiment 3 of the present invention.
Fig. 4 is a module schematic diagram of a hotel reservation information processing system according to embodiment 4 of the present invention.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a processing method of hotel reservation information according to embodiment 5 of the present invention.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.
Example 1
The embodiment provides a processing method of hotel reservation information, which is used for avoiding the hotel reservation information processing that a user encounters continuous full rooms in hotel reservation. As shown in fig. 1, the processing method of hotel reservation information of the present embodiment includes the following steps:
and S11, when the order of the hotel booking the house type is full, predicting the probability of the full house of other house types on the same hotel parent and the same check-in date as the order by using the XGboost model.
In the hotel booking process, in order to avoid continuous full rooms encountered by a user, when the order of booking one room type is full, the model prediction is triggered in real time: the XGboost model is used for predicting the probability of full rooms of other house types on the same hotel and the same stay date as the order.
The parent liquor shop corresponds to a hotel which actually exists, and the sub-hotel corresponds to the concept of the sub-hotel after the parent liquor shop distributes to each supplier. When a certain house type of a hotel is full, a user may change to reserve other house types of the hotel, at this time, the probability that the other house types of the same hotel and the same check-in day are full needs to be predicted, if the probability that the other house types are full is predicted to be high, and the other house types display rooms due to reasons such as house state maintenance, the user can be subjected to continuous full at the moment with high probability when ordering again, and extremely poor use experience is brought to the user.
The XGboost model adopts a machine learning method, and constructs a prediction model from multiple dimensions such as dimensions of a selling room type (such as room type price, commission rate, whether the sale of the room type is promoted or not), dimensions of a child hotel, a mother hotel (such as the number of rooms, grading information, star level, whether the room is in a business district or not), a living day time dimension (whether holidays are saved, how many days are reserved in advance, days of the week and the like), the tension of the mother hotel in a city business district, historical order information of the hotel, room state operation information of the day and the like, so as to predict the probability of full room when a user reserves orders for a plurality of living days in the future of the room type. The output data dimensionality of the XGboost model is as follows: the hotel sells the house type ID, date of check-in, probability value of full house.
The XGboost model is trained by a plurality of historical order data, wherein the historical order data comprises the dimensions of the hotel, the submission date of the historical order, the entry date of the historical order, the proportion of full rooms of the order in a first time window including the submission date of the historical order, and the proportion of full rooms of the order in a second time window including the entry date of the historical order. The XGboost model is trained through a large amount of historical order data, and the accuracy of model prediction can be gradually improved.
S12, judging whether the probability of the other house types of the mother hotel being full is larger than a preset threshold value, and if yes, entering the step S13.
If the probability that other types of the mother hotel output by the XGboost model are full is larger than a preset threshold value, the probability that other types of the mother hotel are full is high, namely the probability that the other types of the mother hotel are continuously full is high, intervention is needed, and the step S13 is continued; if the probability that other house types of the parent hotel output by the XGboost model are full is not larger than the preset threshold value, the probability that the other house types are reserved to be full is small, and the condition that the house types are continuously full can be provided for a user to choose to place an order. The preset threshold is not constant and can be adjusted according to the business effect, and the range is 0.1 to 0.9.
And S13, closing the house according to the house type.
When the probability that the other house types of the parent hotel output by the XGboost model are full is larger than a preset threshold value, house closing operation needs to be carried out on the house type of the order which is firstly placed by the user, so that the situation that the subsequent user places the order and continues to meet the full house condition is avoided.
The method of the embodiment is used for respectively constructing models for domestic and overseas hotel services, and is applied to a scene of predicting service intervention by an order full real-time trigger model, so that the proportion of the domestic supplier continuous full-house orders is reduced by 47%, the proportion of the domestic total continuous full-house orders is reduced by 6.23%, the proportion of the overseas supplier continuous full-house orders is reduced by 35%, and the proportion of the overseas total continuous full-house orders is reduced by 10%.
In the embodiment, by using a machine learning method, when an order is full, the XGboost model is triggered in real time to predict the probability of full occurrence of other types of selling rooms on the same date with the order and the mother liquor shop, and when the probability of full occurrence is greater than a preset threshold value, business intervention is performed on the type of selling rooms on the corresponding date of living, so that the proportion of continuous full occurrence in hotel booking is reduced, and the booking experience of a user is greatly improved.
Example 2
As shown in fig. 2, the processing method of hotel reservation information of this embodiment is a further improvement of embodiment 1, and specifically includes:
before step S11, step S10 is further included: setting a timing task, periodically predicting the probability of full houses of all house types of all the female hotels in the system in a plurality of coming-in days, and performing house closing operation on the house type on a certain date when the probability of full houses of the certain house type on the certain date is predicted to be greater than a preset threshold value so as to reduce the probability of full houses in the booking process of a user. The timing task can set a period as required, and can be once a day or multiple times a day.
The timing task predicts a target probability by using an XGboost model, wherein the target probability refers to the probability of full room occurrence of all types of all the female hotels in each day in a preset period, and the preset period comprises the current day and n days in the future; n has a value of 60.
And then judging whether the target probability of each day is greater than a preset threshold value, and if so, closing the house of the house type of the day.
The probability that all hotels and all house types in the system are full in a plurality of coming-in days is predicted by setting the timing task, and the house closing operation is carried out when the probability that the house is full is larger than a preset threshold value, so that the probability that the house is full when a user places orders can be effectively reduced, the continuous full house occurrence is greatly reduced, and the booking experience of the user is further improved.
Example 3
The embodiment provides a processing system of hotel reservation information, which is used for avoiding the hotel reservation information processing that a user encounters continuous full rooms in hotel reservation. As shown in fig. 3, the processing system of hotel reservation information of the present embodiment includes a prediction module 1, a judgment module 2, and an operation module 3.
In the hotel booking process, in order to avoid continuous full rooms encountered by a user, when the order of booking one room type is full, the model prediction is triggered in real time: the XGboost model is used for predicting the probability of full rooms of other house types on the same hotel and the same stay date as the order.
The parent liquor shop corresponds to a hotel which actually exists, and the sub-hotel corresponds to the concept of the sub-hotel after the parent liquor shop distributes to each supplier. When a certain house type of a hotel is full, a user may change to reserve other house types of the hotel, at this time, the probability that the other house types of the same hotel and the same check-in day are full needs to be predicted, if the probability that the other house types are full is predicted to be high, and the other house types display rooms due to reasons such as house state maintenance, the user can be subjected to continuous full at the moment with high probability when ordering again, and extremely poor use experience is brought to the user.
The XGboost model adopts a machine learning method, and constructs a prediction model from multiple dimensions such as dimensions of a selling room type (such as room type price, commission rate, whether the sale of the room type is promoted or not), dimensions of a child hotel, a mother hotel (such as the number of rooms, grading information, star level, whether the room is in a business district or not), a living day time dimension (whether holidays are saved, how many days are reserved in advance, days of the week and the like), the tension of the mother hotel in a city business district, historical order information of the hotel, room state operation information of the day and the like, so as to predict the probability of full room when a user reserves orders for a plurality of living days in the future of the room type. The output data dimensionality of the XGboost model is as follows: the hotel sells the house type ID, date of check-in, probability value of full house.
The XGboost model is trained by a plurality of historical order data, wherein the historical order data comprises the dimensions of the hotel, the submission date of the historical order, the entry date of the historical order, the proportion of full rooms of the order in a first time window including the submission date of the historical order, and the proportion of full rooms of the order in a second time window including the entry date of the historical order. The XGboost model is trained through a large amount of historical order data, and the accuracy of model prediction can be gradually improved.
When a hotel reserves an order of one house type and is full, the prediction module 1 is used for predicting the probability of full of other house types of the same hotel and the same check-in day as the order by using the XGboost model.
The judging module 2 is used for judging whether the probability of the other houses of the same mother liquor shop as the order form being full is larger than a preset threshold value. If the judgment module 2 judges that the probability that other types of rooms of the mother wine shop output by the XGboost model are full is larger than a preset threshold value, the probability that other types of rooms of the mother wine shop are full is high, namely the probability that continuous full rooms occur is high, and intervention is needed; if the judgment module 2 judges that the probability that the other house types of the parent hotel output by the XGboost model are full is not greater than the preset threshold, the probability that the other house types are reserved to be full is small, and the condition that the house types are continuously full can be provided for the user to choose to place orders. The preset threshold is not constant and can be adjusted according to the business effect, and the range is 0.1 to 0.9.
When the judgment module 2 judges that the probability that the other house types of the mother hotel output by the XGboost model are full is greater than the preset threshold value, the operation module 3 is used for closing the house type of the order which is firstly placed by the user so as to avoid the situation that the subsequent user places the order and continues to meet the full house.
The method of the embodiment is used for respectively constructing models for domestic and overseas hotel services, and is applied to a scene of predicting service intervention by an order full real-time trigger model, so that the proportion of the domestic supplier continuous full-house orders is reduced by 47%, the proportion of the domestic total continuous full-house orders is reduced by 6.23%, the proportion of the overseas supplier continuous full-house orders is reduced by 35%, and the proportion of the overseas total continuous full-house orders is reduced by 10%.
In the embodiment, by using a machine learning method, when an order is full, the XGboost model is triggered in real time to predict the probability of full occurrence of other types of selling rooms on the same date with the order and the mother liquor shop, and when the probability of full occurrence is greater than a preset threshold value, business intervention is performed on the type of selling rooms on the corresponding date of living, so that the proportion of continuous full occurrence in hotel booking is reduced, and the booking experience of a user is greatly improved.
Example 4
The processing system of hotel reservation information of the present embodiment is a further improvement of embodiment 3, specifically:
as shown in fig. 4, the processing system of hotel reservation information of the present embodiment further includes a timing module 4. The timing module 4 is used for setting a timing task, periodically predicting the probability of full house occurrence of all house types of all the parent hotels in the system on a plurality of coming-in days, and performing house closing operation on the house type on a certain date when the probability of full house occurrence of the certain house type on the certain date is predicted to be larger than a preset threshold value so as to reduce the probability of full house occurrence in the user booking process. The timing task can set a period as required, and can be once a day or multiple times a day.
The timing module 4 sets a timing task and predicts a target probability by using an XGboost model, wherein the target probability refers to the probability of full room occurrence of all types of all the hotels in each day in a preset period, and the preset period comprises the current day and n future days; n has a value of 60.
And then the timing module 4 judges whether the target probability of each day is greater than a preset threshold value, if so, the timing module 4 closes the house of the house type on the day.
The probability that all hotels and all house types in the system are full in a plurality of coming-in days is predicted by setting the timing task, and the house closing operation is carried out when the probability that the house is full is larger than a preset threshold value, so that the probability that the house is full when a user places orders can be effectively reduced, the continuous full house occurrence is greatly reduced, and the booking experience of the user is further improved.
Example 5
Fig. 5 is a schematic structural diagram of an electronic device according to embodiment 5 of the present invention. The electronic device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the processing method of hotel reservation information of embodiment 1 or 2 when executing the program. The electronic device 50 shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 5, the electronic device 50 may be embodied in the form of a general purpose computing device, which may be, for example, a server device. The components of the electronic device 50 may include, but are not limited to: the at least one processor 51, the at least one memory 52, and a bus 53 connecting the various system components (including the memory 52 and the processor 51).
The bus 53 includes a data bus, an address bus, and a control bus.
The memory 52 may include volatile memory, such as Random Access Memory (RAM)521 and/or cache memory 522, and may further include Read Only Memory (ROM) 523.
Memory 52 may also include a program/utility 525 having a set (at least one) of program modules 524, such program modules 524 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The processor 51 executes various functional applications and data processing, such as the processing method of hotel reservation information of embodiment 1 or 2 of the present invention, by running the computer program stored in the memory 52.
The electronic device 50 may also communicate with one or more external devices 54 (e.g., keyboard, pointing device, etc.) such communication may be through AN input/output (I/O) interface 55, and the model-generated device 30 may also communicate with one or more networks (e.g., a local area network (L AN), a Wide Area Network (WAN) and/or a public network, such as the Internet) through a network adapter 56. As shown in FIG. 5, the network adapter 56 communicates with other modules of the model-generated device 50 through a bus 53. it should be understood that, although not shown, other hardware and/or software modules may be used in connection with the model-generated device 50, including, but not limited to, microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 6
The present embodiment provides a computer-readable storage medium on which a computer program is stored, which when executed by a processor, implements the steps in the processing method of hotel reservation information of embodiment 1 or 2.
More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible implementation, the present invention can also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps in the processing method for hotel reservation information implementing embodiment 1 or 2 when the program product is run on the terminal device.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may be executed entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.

Claims (12)

1. A processing method of hotel reservation information is characterized by comprising the following steps:
s1, when a hotel orders a room type to be full, predicting the probability of the full room of other room types of the same parent hotel and the same check-in day with the order by using the XGboost model;
s2, judging whether the probability that the other house types of the mother hotel are full is greater than a preset threshold value, if so, entering the step S3;
and S3, closing the house according to the house type.
2. The method for processing hotel reservation information according to claim 1, wherein the XGBoost model is constructed based on a dimension of a mother liquor shop, a dimension of a house type, a dimension of a living-in day, a dimension of a city business district tension and a dimension of a current day house state operation;
the XGboost model is obtained by training a plurality of historical order data, wherein the historical order data comprises the dimensions of the hotel, the submission date of the historical order, the check-in date of the historical order, the proportion of full rooms of orders in a first time window including the submission date of the historical order, and the proportion of full rooms of orders in a second time window including the check-in date of the historical order.
3. The method of processing hotel reservation information as recited in claim 1, wherein the preset threshold range is 0.1 to 0.9.
4. The hotel reservation information processing method according to claim 1, wherein a timing task is set, the timing task uses the XGBoost model to predict a target probability, the target probability is a probability of full room occurrence of all types of rooms of all the parent hotels on each day within a preset period, and the preset period comprises the current day and n days in the future;
and judging whether the target probability is greater than a preset threshold value, and if so, closing the house type of the corresponding day.
5. The method of processing hotel reservation information as recited in claim 4, wherein n has a value of 60.
6. The system for processing the hotel reservation information is characterized by comprising a prediction module, a judgment module and an operation module;
when a hotel reserves an order of one house type and is full, the prediction module is used for predicting the probability of full house of other house types on the same hotel and the same check-in date as the order by using the XGboost model;
the judging module is used for judging whether the probability of the other house types of the parent hotel being full is greater than a preset threshold value;
when the probability that the other house types of the mother hotel are full is larger than a preset threshold value, the operation module is used for carrying out house closing operation on the house types.
7. The system for processing hotel reservation information of claim 6, further comprising a training module; the XGboost model is constructed based on a dimension of a mother liquor shop, a dimension of a house type, a dimension of a living day, a dimension of tension of an urban business district and a dimension of house state operation in the day;
the training module is used for training the XGboost model by using a plurality of historical order data, and the historical order data comprises the dimensions of the hotel, the submission date of the historical order, the check-in date of the historical order, the proportion of full rooms of orders in a first time window including the submission date of the historical order, and the proportion of full rooms of orders in a second time window including the check-in date of the historical order.
8. The system for processing hotel reservation information of claim 6, wherein the preset threshold range is 0.1 to 0.9.
9. The system for processing hotel reservation information of claim 6, further comprising a timing module;
the timing module is used for setting a timing task, the timing task uses an XGboost model to predict a target probability, the target probability is the probability of full room occurrence of all types of all the hotels in each day in a preset period, and the preset period comprises the current day and the next n days;
the timing module is further used for judging whether the target probability is larger than a preset threshold value or not, and if yes, the timing module carries out house closing operation on the house type of the corresponding day.
10. The system for processing hotel reservation information of claim 9, wherein n has a value of 60.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the method of processing hotel reservation information of any of claims 1-5.
12. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the method of processing hotel reservation information of any of claims 1-5.
CN202010191207.7A 2020-03-18 2020-03-18 Hotel reservation information processing method and system, electronic equipment and storage medium Pending CN111445046A (en)

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Application publication date: 20200724