CN111340541A - Early warning method, system, equipment and medium for hotel room type abnormal price - Google Patents
Early warning method, system, equipment and medium for hotel room type abnormal price Download PDFInfo
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- CN111340541A CN111340541A CN202010112141.8A CN202010112141A CN111340541A CN 111340541 A CN111340541 A CN 111340541A CN 202010112141 A CN202010112141 A CN 202010112141A CN 111340541 A CN111340541 A CN 111340541A
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- 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
- G06Q30/00—Commerce
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- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
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- 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
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
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- G—PHYSICS
- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
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Abstract
The invention discloses a method, a system, equipment and a medium for early warning hotel room type abnormal price, wherein the method for early warning the hotel room type abnormal price of an OTA platform comprises the following steps: modeling is carried out on the hotel historical data by adopting a GBDT algorithm, a room type price prediction model to be trained is obtained, a room type price threshold range corresponding to each room type of the hotel is obtained according to the prediction model, whether the room type price of each room type of the OTA platform is in the room type price threshold range corresponding to the room type is judged, if not, the room type price is determined to be abnormal, and early warning is output. The invention can improve the processing efficiency and avoid the loss of the claim price difference and the indirect loss of the brand image caused by the entering of abnormal prices into the system.
Description
Technical Field
The invention relates to the technical field of information processing of an OTA (on-line Travel Agency) platform, in particular to a method, a system, equipment and a medium for early warning of hotel room type abnormal prices.
Background
The OTA displays a selling price platform for the hotel, and can also obtain a part of commission when the hotel type is successfully sold. Because the prices of products pushed by hotels, suppliers and the like entering the OTA platform system are easy to be wrong, the prices of the products displayed by the OTA platform are abnormal, and the trouble is caused to the normal booking of users.
According to the existing identification method and system for hotel room type price abnormity of the OTA platform, the hotel room type price pushed to a user is inaccurate, and the phenomenon that the price is too high or too low often exists. When the price of the hotel room type is too high, the hotel room type cannot be sold; when hotel room type prices are too high, the hotel supplier is caused to override the previously confirmed order. And the hotel room type price abnormity identification method and system of the existing OTA platform have low processing efficiency on abnormal price investigation and are not processed in time. This may allow for the passage of unusual prices into the system and eventually out-of-the-net display on the OTA platform, resulting in loss of the paid price difference and indirect loss of brand image.
Disclosure of Invention
The invention provides a method, a system, equipment and a medium for early warning hotel room type abnormal price, aiming at overcoming the defects that the hotel room type price pushed by a user is inaccurate and the processing efficiency is low in the existing identification method and system for the hotel room type abnormal price of an OTA platform.
The invention solves the technical problems through the following technical scheme:
the invention provides a method for early warning abnormal hotel room price, which comprises the following steps:
obtaining hotel historical data;
modeling the hotel historical data by adopting a GBDT (Gradient Boosting Decision Tree) algorithm to obtain a room type price prediction model to be trained;
acquiring the historical data characteristics of the hotel;
inputting the hotel historical data characteristics into the house type price prediction model, and performing model training to obtain the house type price prediction model;
acquiring a hotel room type predicted price interval corresponding to each room type of the hotel according to the room type price prediction model, and acquiring a room type price threshold range corresponding to each room type according to the hotel room type predicted price interval;
and judging whether the house type price of each house type of the OTA platform is within the house type price threshold range of the corresponding house type, if not, determining that the house type price is abnormal, and outputting early warning.
Preferably, the hotel history data features comprise date information of the hotel check-in, house type attribute, history hotel house type price, competition circle house price, hotel tension information and existing check-in yield information.
Preferably, the first and second liquid crystal films are made of a polymer,
the step of obtaining the house type price threshold range corresponding to each house type of the hotel according to the house type price prediction model comprises the following steps:
acquiring hotel data characteristics to be predicted;
inputting the hotel data characteristics to be predicted into the room type price prediction model to obtain a hotel room type predicted price interval corresponding to each room type of the hotel to be predicted;
and obtaining a room type price threshold range corresponding to each room type to be predicted according to the quantile statistic and the service rule of the prediction model by combining the most value and the mean value of the historical price corresponding to each room type of the hotel to be predicted and based on the hotel room type prediction price interval corresponding to each room type to be predicted.
The invention also provides a system for early warning the abnormal price of the hotel room type, which comprises the following components:
the first acquisition module is used for acquiring hotel historical data;
the modeling module is used for modeling the hotel historical data by adopting a GBDT algorithm to obtain a room type price prediction model to be trained;
the second acquisition module is used for acquiring the hotel historical data characteristics;
the training module is used for inputting the hotel historical data characteristics into the house type price prediction model and carrying out model training to obtain the house type price prediction model;
the prediction module is used for acquiring a room type price threshold range corresponding to each room type of the hotel according to the room type price prediction model;
the judgment module is used for judging whether the house type price of each house type of the OTA platform is in the house type price threshold range corresponding to the house type, if not, the house type price is determined to be abnormal, and early warning is output.
Preferably, the hotel history data features comprise date information of the hotel check-in, house type attribute, history hotel house type price, competition circle house price, hotel tension information and existing check-in yield information.
Preferably, the prediction module comprises:
the system comprises an acquisition unit, a prediction unit and a prediction unit, wherein the acquisition unit is used for acquiring hotel data characteristics to be predicted;
the input unit is used for inputting the hotel data characteristics to be predicted into the room type price prediction model so as to obtain a hotel room type predicted price interval corresponding to each room type of the hotel to be predicted;
and the prediction unit is used for obtaining the house type price threshold range corresponding to each house type to be predicted according to the quantile statistic and the service rule of the prediction model based on the hotel house type prediction price interval corresponding to each house type of the hotel to be predicted and by combining the most value and the mean value of the historical price corresponding to each house type of the hotel to be predicted.
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the computer program to realize the early warning method for the hotel room type abnormal price.
The present invention also provides a computer readable storage medium having stored thereon a computer program which, when being executed by a processor, implements the steps of the aforementioned hotel room type abnormal price early warning method.
The positive progress effects of the invention are as follows:
according to the hotel model price forecasting method and system, modeling processing is carried out on historical hotel data by adopting a GBDT algorithm, a to-be-trained room type price forecasting model is obtained, a hotel room type forecasting room type price threshold range corresponding to each room type of a hotel is obtained according to the forecasting model, whether the room type price of each room type of the OTA platform is in the room type price threshold range corresponding to the room type is judged, if the room type price is not in the room type price threshold range, the room type price is determined to be abnormal, early warning is output, and compared with the existing hotel room type price abnormal recognition method and system of the OTA platform, the hotel room type price pushed to a user is not accurate, and the processing efficiency is low.
Drawings
Fig. 1 is a flowchart of an early warning method for hotel room type abnormal prices in embodiment 1 of the present invention;
FIG. 2 is a flowchart of step S105 according to embodiment 1 of the present invention;
fig. 3 is a schematic block diagram of an early warning system for hotel room type abnormal prices according to embodiment 2 of the present invention;
FIG. 4 is a block diagram of the prediction module 4 according to embodiment 2 of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to embodiment 3 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
As shown in fig. 1, the embodiment discloses a method for early warning of abnormal hotel room price, which comprises the following steps:
step S101, obtaining hotel historical data;
step S102, modeling the hotel historical data by adopting a GBDT algorithm to obtain a room type price prediction model to be trained;
step S103, acquiring historical data characteristics of the hotel;
step S104, inputting the historical data characteristics of the hotel into the house type price prediction model, and performing model training to obtain the house type price prediction model;
in this embodiment, the hotel history data features include date information of the hotel check-in, house type attribute, history hotel house type price, competition circle house price, hotel tension information, and existing check-in yield information. The date information includes whether the check-in is on holidays, weekends and stars. The house type attribute comprises whether the house type is a big bed house, a standard room, a corresponding hotel star level, a point score, a house type area, whether the house type contains an early stage or not and a payment mode. The historical prices comprise the mean, median, maximum and minimum of the final rate of the transaction of the last 30 days and 60 days. The competition circle rate comprises historical rate information of the same-star trade circle hotels. The tension information includes the total number of rooms, the number of full rooms, and the like. The existing stay-in daily output information includes night time information of a single future stay-in day for a period of time in the past.
Step S105, obtaining a hotel room type prediction price interval corresponding to each room type of the hotel according to the room type price prediction model, and obtaining a room type price threshold range corresponding to each room type according to the hotel room type prediction price interval;
step S106, judging whether the house type price of each house type of the OTA platform is within the house type price threshold range of the corresponding house type, if not, executing step S107, if so, determining that the house type price is normal, and not outputting early warning;
s107, determining that the house type price is abnormal, and outputting high-price early warning or low-price early warning to an early warning platform;
and S108, the service personnel contact the hotel to change the price according to the early warning list displayed on the early warning platform.
As shown in fig. 2, in this embodiment, step S105 specifically includes the following steps:
step S1051, obtaining hotel data characteristics to be predicted;
step S1052, inputting the hotel data characteristics to be predicted into the room type price prediction model to obtain a hotel room type predicted price interval corresponding to each room type of the hotel to be predicted;
in the embodiment, the price interval of 30 coming-in-live days of each hotel room type corresponding to each hotel room type is predicted;
and S1053, based on the hotel room type prediction price interval corresponding to each room type of the hotel to be predicted, and in combination with the most value and the mean value of the historical price corresponding to each room type of the hotel to be predicted, obtaining a room type price threshold range corresponding to each room type to be predicted according to the quantile statistic and the business rule of the prediction model.
In this example, the quantile value is 95%. The price threshold range of the house type corresponding to each house type is 30 price threshold ranges of the incoming date in the future.
According to the early warning method for the abnormal hotel room price, a GBDT algorithm is adopted to perform modeling processing on hotel historical data, a room price threshold range corresponding to each room of a hotel is obtained according to a prediction model, whether the room price of each room of an OTA platform is within the room price threshold range corresponding to the room is judged, if not, the room price is determined to be abnormal, early warning is output, and compared with the prior art, the problem that the product price of the OTA platform is abnormal is solved through a manual checking mode.
Example 2
As shown in fig. 3, the embodiment discloses an early warning system for hotel room type abnormal prices of an OTA platform, which includes a first obtaining module 1, a modeling module 2, a second obtaining module 3, a training module 4, a prediction module 5, and a judgment module 6.
The first acquisition module 1 is used for acquiring hotel historical data;
the modeling module 2 is used for modeling the hotel historical data by adopting a GBDT algorithm to obtain a room type price prediction model to be trained;
the second obtaining module 3 is used for obtaining the hotel historical data characteristics;
the training module 4 is used for inputting the hotel historical data characteristics into the house type price prediction model and performing model training to obtain the house type price prediction model;
the prediction module 5 is used for acquiring a hotel room type prediction price interval corresponding to each room type of the hotel according to the room type price prediction model, and acquiring a room type price threshold range corresponding to each room type according to the hotel room type prediction price interval;
the judging module 6 is used for judging whether the house type price of each house type of the OTA platform is in the house type price threshold range corresponding to the house type, if not, the house type price is determined to be abnormal, and early warning is output.
As shown in fig. 4, the prediction module 5 in this embodiment includes:
an obtaining unit 51, configured to obtain hotel data characteristics to be predicted;
an input unit 52, configured to input the hotel data characteristics to be predicted into the room type price prediction model, so as to obtain a hotel room type predicted price interval corresponding to each room type of the hotel to be predicted;
and the predicting unit 53 is used for obtaining a room type price threshold range corresponding to each room type to be predicted according to the quantile statistic and the service rule of the prediction model based on the hotel room type predicted price interval corresponding to each room type of the hotel to be predicted and by combining the most value and the mean value of the historical price corresponding to each room type of the hotel to be predicted.
According to the early warning system for the hotel room type abnormal price of the OTA platform, the GBDT algorithm is adopted to carry out modeling processing on the hotel historical data, the room type price threshold range corresponding to each room type of the hotel is obtained according to the prediction model, whether the room type price of each room type of the OTA platform is in the room type price threshold range corresponding to the room type is judged, if not, the room type price is determined to be abnormal, early warning is output, and compared with the prior art, the problem that the product price of the OTA platform is abnormal is solved in a manual checking mode.
Example 3
Fig. 5 is a schematic structural diagram of an electronic device according to embodiment 3 of the present invention. The electronic device 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 program to realize the early warning method of hotel room type abnormal price of the OTA platform provided by the embodiment 1. The electronic device 30 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 30 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 30 may include, but are not limited to: the at least one processor 31, the at least one memory 32, and a bus 33 connecting the various system components (including the memory 32 and the processor 31).
The bus 33 includes a data bus, an address bus, and a control bus.
The memory 32 may include volatile memory, such as Random Access Memory (RAM)321 and/or cache memory 322, and may further include Read Only Memory (ROM) 323.
The processor 31 executes various functional applications and data processing, such as a hotel room type abnormal price early warning method of the OTA platform provided in embodiment 1 of the present invention, by running the computer program stored in the memory 32.
The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). Such communication may be through input/output (I/O) interfaces 35. Also, model-generating device 30 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via network adapter 36. As shown, network adapter 36 communicates with the other modules of model-generating device 30 via bus 33. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the model-generating device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
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 4
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 of the hotel room type abnormal price early warning method of the OTA platform provided in embodiment 1.
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 manner, the present invention can also be implemented in a form of a program product, which includes program code for causing a terminal device to execute the steps in the method for early warning of hotel room type abnormal prices of an OTA platform provided in embodiment 1 when the program product runs 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 (8)
1. The early warning method for the abnormal hotel room price is characterized by comprising the following steps:
obtaining hotel historical data;
modeling the hotel historical data by adopting a GBDT algorithm to obtain a room type price prediction model to be trained;
acquiring the historical data characteristics of the hotel;
inputting the hotel historical data characteristics into the house type price prediction model, and performing model training to obtain the house type price prediction model;
acquiring a room type price threshold range corresponding to each room type of the hotel according to the room type price prediction model;
and judging whether the house type price of each house type of the OTA platform is within the house type price threshold range of the corresponding house type, if not, determining that the house type price is abnormal, and outputting early warning.
2. The method for early warning of hotel room type abnormal price as claimed in claim 1, wherein the hotel history data characteristics comprise date information of hotel check-in, room type attribute, history hotel room type price, competition circle room price, hotel tension information, and existing check-in yield information.
3. The early warning method for the abnormal hotel room type price according to claim 1, wherein the step of obtaining the room type price threshold range corresponding to each hotel room type according to the room type price prediction model comprises the following steps:
acquiring hotel data characteristics to be predicted;
inputting the hotel data characteristics to be predicted into the room type price prediction model to obtain a hotel room type predicted price interval corresponding to each room type of the hotel to be predicted;
and obtaining a room type price threshold range corresponding to each room type to be predicted according to the quantile statistic and the service rule of the prediction model by combining the most value and the mean value of the historical price corresponding to each room type of the hotel to be predicted and based on the hotel room type prediction price interval corresponding to each room type to be predicted.
4. The early warning system for the hotel room type abnormal price is characterized by comprising a first acquisition module, a modeling module, a second acquisition module, a training module, a prediction module and a judgment module;
the first acquisition module is used for acquiring hotel historical data;
the modeling module is used for modeling the hotel historical data by adopting a GBDT algorithm to obtain a room type price prediction model to be trained;
the second acquisition module is used for acquiring the hotel historical data characteristics;
the training module is used for inputting the hotel historical data characteristics into the house type price prediction model and carrying out model training to obtain the house type price prediction model;
the prediction module is used for acquiring a room type price threshold range corresponding to each room type of the hotel according to the room type price prediction model;
the judgment module is used for judging whether the house type price of each house type of the OTA platform is in the house type price threshold range corresponding to the house type, if not, the house type price is determined to be abnormal, and early warning is output.
5. The system of claim 4, wherein the hotel history data characteristics comprise date information of hotel check-in, house type attribute, history hotel house type price, competition circle house price, hotel tension information, and existing check-in yield information.
6. The system of claim 4, wherein the prediction module comprises:
the system comprises an acquisition unit, a prediction unit and a prediction unit, wherein the acquisition unit is used for acquiring hotel data characteristics to be predicted;
the input unit is used for inputting the hotel data characteristics to be predicted into the room type price prediction model so as to obtain a hotel room type predicted price interval corresponding to each room type of the hotel to be predicted;
and the prediction unit is used for obtaining the house type price threshold range corresponding to each house type to be predicted according to the quantile statistic and the service rule of the prediction model based on the hotel house type prediction price interval corresponding to each house type of the hotel to be predicted and by combining the most value and the mean value of the historical price corresponding to each house type of the hotel to be predicted.
7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor,
the processor, when executing the computer program, implements a method of pre-warning of hotel room-type abnormal prices as set forth in any one of claims 1 to 3.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for early warning of hotel room type abnormal prices according to any one of claims 1 to 3.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111932298A (en) * | 2020-07-24 | 2020-11-13 | 深圳市道旅旅游科技股份有限公司 | Hotel price control method, system, computer equipment and storage medium |
| CN116166889A (en) * | 2023-02-21 | 2023-05-26 | 深圳市天下房仓科技有限公司 | Hotel product screening method, device, equipment and storage medium |
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| CN108230120A (en) * | 2018-02-07 | 2018-06-29 | 上海携程商务有限公司 | Method, system, equipment and the storage medium of order price abnormal monitoring |
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| CN111932298A (en) * | 2020-07-24 | 2020-11-13 | 深圳市道旅旅游科技股份有限公司 | Hotel price control method, system, computer equipment and storage medium |
| CN116166889A (en) * | 2023-02-21 | 2023-05-26 | 深圳市天下房仓科技有限公司 | Hotel product screening method, device, equipment and storage medium |
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