A hotel data update system based on multiple factors

Abstract

The present invention discloses a hotel data update system based on multiple factors. The data update system includes a first update time interval setting module, an update parameter acquisition module, an update parameter comparison module, a first update time interval judgment module, and a hotel room status update module. The first update time interval setting module is used to preset a first update time interval for updating the hotel room status. The update parameter acquisition module is used to acquire update parameters. The update parameter comparison module is used to compare the update parameters with an update threshold and determine whether to transmit information to the first update time interval judgment module or the hotel room status update module according to the comparison result. The first update time interval judgment module is used to judge whether the time interval from the current time to the last update of the hotel room status reaches the first update time interval.

Description

Technical Field

[0001] The present invention relates to the field of data update, and in particular to a hotel data update system based on multiple factors. Background Art

[0002] With the advent of the Internet era, the network has provided great convenience for people's life and work. When traveling on business or traveling to a new and unfamiliar city, people can search for some local hotel accommodation information on the Internet in advance, and online hotel reservation provides great convenience for people. Since hotel rooms are sold both offline and online at the same time, if the update period of hotel room status data is long, errors may occur during the room reservation process. If the hotel room status data is always kept in an updated state, it will occupy a large amount of data operation space. Summary of the Invention

[0003] The purpose of the present invention is to provide a hotel data update system and method based on multiple factors to solve the problems in the prior art.

[0004] To achieve the above purpose, the present invention provides the following technical solutions:

[0005] A hotel data update system based on multiple factors, characterized in that: the data update system includes a first update time interval setting module, an update parameter acquisition module, an update parameter comparison module, a first update time interval judgment module, and a hotel room status update module. The first update time interval setting module is used to preset a first update time interval for updating the hotel room status. The update parameter acquisition module is used to acquire update parameters. The update parameter comparison module is used to compare the update parameters with an update threshold and determine whether to transmit information to the first update time interval judgment module or the hotel room status update module according to the comparison result. The first update time interval judgment module is used to judge whether the time interval from the current time to the last update of the hotel room status reaches the first update time interval. The hotel room status update module is used to update the hotel room status;

[0006] Preferably, the update parameter acquisition module includes a first update reference factor acquisition module, a second update reference factor acquisition module, and an update parameter calculation module. The first update reference factor acquisition module is used to acquire a first update reference factor. The second update reference factor acquisition module is used to acquire a second update reference factor. The update parameter calculation module calculates update parameters according to the first update reference factor and the second update reference factor;

[0007] Preferably, the first update reference factor acquisition module includes a hotel data acquisition module for the day before the prediction date, a reference factor calculation module for the day before the prediction date, a hotel data acquisition module for the same day last year as the prediction date, a reference factor calculation module for the same day last year as the prediction date, a first prediction ratio calculation module, a second prediction ratio calculation module, a third prediction ratio calculation module, a festival reference factor acquisition module, and a first update reference factor calculation module. The hotel data acquisition module for the day before the prediction date is used to acquire the number of available rooms ncs1 on the day before the hotel starts accepting reservations on the day before the prediction date, the number of available rooms ncs2 on the off-peak days in the next week before the hotel starts accepting reservations on the day before the prediction date, the number of available rooms ncs3 on the peak days in the next week before the hotel starts accepting reservations on the day before the prediction date, the number of rooms nc1 reserved on the day of the hotel on the day before the prediction date, the number of rooms nc2 reserved on the off-peak days in the next week for the hotel reserved on the day before the prediction date, and the number of rooms nc3 reserved on the peak days in the next week for the hotel reserved on the day before the prediction date. The reference factor calculation module for the day before the prediction date calculates the first reference factor, the second reference factor, and the third reference factor respectively based on the quantity ncs1 and the quantity nc1, the quantity ncs2 and the quantity nc2, and the quantity ncs3 and the quantity nc3. The hotel data acquisition module for the same day last year as the prediction date is used to acquire the number of available rooms ncs4 on the day before the hotel starts accepting reservations on the same day last year as the prediction date, the number of available rooms ncs5 on the off-peak days in the next week before the hotel starts accepting reservations on the same day last year as the prediction date, the number of available rooms ncs6 on the peak days in the next week before the hotel starts accepting reservations on the same day last year as the prediction date, the number of rooms nc4 reserved on the day of the hotel on the same day last year as the prediction date, the number of rooms nc5 reserved on the off-peak days in the next week for the hotel reserved on the same day last year as the prediction date, and the number of rooms nc6 reserved on the peak days in the next week for the hotel reserved on the same day last year as the prediction date. The reference factor calculation module for the same day last year as the prediction date calculates the fourth reference factor, the fifth reference factor, and the sixth reference factor respectively based on the quantity ncs4 and the quantity nc4, the quantity ncs5 and the quantity nc5, and the quantity ncs6 and the quantity nc6. The first prediction ratio calculation module calculates the first prediction ratio based on the first reference factor and the fourth reference factor. The second prediction ratio calculation module calculates the second prediction ratio based on the second reference factor and the fifth reference factor. The third prediction ratio calculation module calculates the third prediction ratio based on the third reference factor and the sixth reference factor. The festival reference factor acquisition module outputs the festival reference factor according to whether the prediction date is an off-peak day or a peak day. The first update reference factor calculation module calculates the first update reference factor based on the first prediction ratio, the second prediction ratio, the third prediction ratio, and the festival reference factor;

[0008] Preferably, the second update reference factor obtaining module includes a predeterminer judgment module, a predeterminer number obtaining module, a predeterminer identity parameter obtaining module, a module for obtaining the number of hotel rooms reserved by a predeterminer, a module for obtaining the number of days of reservation for check-in by a predeterminer, and a second update reference factor calculation module. The predeterminer judgment module is used to judge whether there is a new predeterminer reserving a hotel room within the first update time interval, and transmit information to the second update reference factor calculation module when there is a new predeterminer. The predeterminer number obtaining module is used to obtain the number G of all new predeterminers within the first update time interval. The predeterminer identity parameter obtaining module includes a predeterminer identity obtaining module and an identity parameter obtaining module. The predeterminer identity obtaining module includes a hotel reservation check-in history obtaining module, a type parameter obtaining module, a check-in parameter obtaining module, an identity evaluation parameter calculation module, and an identity determination module. The hotel reservation check-in history obtaining module is used to obtain the hotel reservation history and check-in history of a predeterminer within the most recent three months. The type parameter obtaining module includes a hotel reservation times obtaining module, a hotel reservation days obtaining module, and a hotel type obtaining module. The hotel reservation times obtaining module obtains the number of hotel reservations made by a predeterminer according to the information obtained by the hotel reservation check-in history obtaining module. The hotel reservation days obtaining module obtains the number of days of reservation for each hotel reservation made by a predeterminer according to the information obtained by the hotel reservation check-in history obtaining module. The hotel type obtaining module obtains a type parameter according to the information obtained by the hotel reservation check-in history obtaining module. The check-in parameter obtaining module obtains a check-in parameter according to the information obtained by the hotel reservation check-in history obtaining module. The identity evaluation parameter calculation module calculates an identity evaluation parameter according to the number of hotel reservations, the number of reservation days, the type parameter, and the check-in parameter. The identity determination module compares the identity evaluation parameter with a first evaluation threshold and a second evaluation threshold to judge whether the predeterminer is a first-level predeterminer, a second-level predeterminer, or a third-level predeterminer. The identity parameter obtaining module obtains an identity parameter J according to whether the predeterminer is a first-level predeterminer, a second-level predeterminer, or a third-level predeterminer. The module for obtaining the number of hotel rooms reserved by a predeterminer is used to obtain the number K of hotel rooms reserved by all new predeterminers within the first update time interval. The module for obtaining the number of days of reservation for check-in by a predeterminer is used to obtain the number L of days of reservation for check-in by all new predeterminers within the first update time interval. The second update reference factor calculation module calculates a second update reference factor according to the number G of all new predeterminers, the identity parameter J of each predeterminer, the number K of hotel rooms reserved by each predeterminer, and the number L of days of reservation for check-in;

[0009] A hotel data update method based on multiple factors, the data update method comprising the following steps:

[0010] Preset a first update time interval for updating the status of hotel rooms;

[0011] Calculate the update parameter and compare the relationship between the update parameter and the update threshold.

[0012] If the update parameter is greater than the update threshold, immediately update the hotel room status.

[0013] If the update decimal is less than the update threshold, determine whether the time interval from the current time to the last update of the hotel room status reaches the first update time interval.

[0014] If the first update time interval is reached, update the hotel room status.

[0015] Preferably, the calculation of the update parameter includes the following steps:

[0016] Step S1: Calculate the first update reference factor Z1;

[0017] Step S2: Calculate the second update reference factor Z2;

[0018] Step S3: Calculate the update parameter Z0 = Z1 * Z2.

[0019] Preferably, the calculation of the first update reference factor in step S1 includes the following steps:

[0020] Step S11: Obtain the number of vacant rooms ncs1 on the day before the prediction day when the hotel started accepting reservations, the number of vacant rooms ncs2 on the off-peak days in the following week before the hotel started accepting reservations on the previous day, the number of vacant rooms ncs3 on the peak days in the following week before the hotel started accepting reservations on the previous day, the number of rooms nc1 reserved for the hotel on the day of the previous day's reservation, the number of rooms nc2 reserved for the hotel on the off-peak days in the following week of the previous day's reservation, and the number of rooms nc3 reserved for the hotel on the peak days in the following week of the previous day's reservation;

[0021] Then calculate the first reference factor a1 = nc1 / ncs1, the second reference factor a2 = nc2 / ncs2, and the third reference factor a3 = nc3 / ncs3;

[0022] Step S12: Obtain the number of vacant rooms ncs4 on the day before the same day of last year of the prediction day when the hotel started accepting reservations, the number of vacant rooms ncs5 on the off-peak days in the following week before the hotel started accepting reservations on the same day of last year, the number of vacant rooms ncs6 on the peak days in the following week before the hotel started accepting reservations on the same day of last year, the number of rooms nc4 reserved for the hotel on the day of the same day of last year's reservation, the number of rooms nc5 reserved for the hotel on the off-peak days in the following week of the same day of last year's reservation, and the number of rooms nc6 reserved for the hotel on the peak days in the following week of the same day of last year's reservation;

[0023] Then calculate the fourth reference factor a4 = nc4 / ncs4, the fifth reference factor a5 = nc5 / ncs5, and the sixth reference factor a6 = nc6 / ncs6.

[0024] Step S13: Calculate the first predicted occupancy rate of the hotel rooms reserved on the prediction day for the same day.

[0025] nw1 = 0.7a1 + 0.3a4,

[0026] The second predicted occupancy rate of the hotel rooms reserved on the prediction day for the off-peak days in the next week.

[0027] nw2 = 0.7a2 + 0.3a5,

[0028] The third predicted occupancy rate of the hotel rooms reserved on the prediction day for the peak days in the next week.

[0029] nw3 = 0.7a3 + 0.3a6;

[0030] Step S14: Determine the type of the prediction day respectively.

[0031] If the prediction day is an off-peak day, the festival reference factor f = 0.75; if the prediction day is a peak day, the festival reference factor f = 1, where the peak days are legal holidays and the day before legal holidays, and the off-peak days are the days other than peak days.

[0032] Step S15: Calculate the first updated reference factor Z1 = (nw1 + nw2 + nw3) / 3 * f.

[0033] Preferably, the step S2 of calculating the second updated reference factor includes the following steps:

[0034] When there are new bookers booking hotel rooms within the first update time interval, obtain the number G of all new bookers within the first update time interval, the identity parameter J of each booker, the number K of hotel rooms booked by each booker, and the booked stay days L.

[0035] When obtaining the identity parameter of the booker according to the identity of the booker, the identities of the bookers include first-level bookers, second-level bookers, and third-level bookers. When the booker is a first-level booker, the identity parameter J = 1; when the booker is a second-level booker, the identity parameter J = 2; when the booker is a third-level booker, the identity parameter J = 3.

[0036] Calculate the second updated reference factor Z2 = , where h represents the hth new booker within the first update time interval, J h represents the identity parameter of the hth booker, K h represents the number of hotel rooms booked by the hth booker, and L hIt represents the number of days of reservation for check-in by the h-th reservationist.

[0037] Preferably, the determination of the identity of the reservationist includes the following steps:

[0038] Obtain the hotel reservation history and check-in history of the reservationist in the most recent three months, and based on the hotel reservation history and check-in history, obtain the number of hotel reservations U, the type parameter V of the hotel reserved each time, the reserved number of days W for check-in at the reserved hotel each time, and the check-in parameter X after each hotel reservation.

[0039] When the price of the standard room of the previously reserved hotel differs from the price of the standard room of this hotel by within twenty percent, the type parameter V of the previously reserved hotel = 2; when the price of the standard room of the previously reserved hotel is greater than or equal to twenty percent of the price of the standard room of this hotel, the type parameter V of the previously reserved hotel = 4; when the price of the standard room of the previously reserved hotel is less than or equal to twenty percent of the price of the standard room of this hotel, the type parameter V of the previously reserved hotel = 1.

[0040] When the reservationist cancels the check-in after this hotel reservation, the check-in parameter X = 0.2; when the reservationist checks in after this hotel reservation, the check-in parameter X = 1.

[0041] Then the reservationist identity evaluation parameter Y = , where i represents the i-th hotel reservation within the most recent three months, V i represents the type parameter of the hotel reserved for the i-th time, W i represents the reserved number of days for check-in at the hotel reserved for the i-th time, and X i represents the check-in parameter of the hotel reserved for the i-th time.

[0042] When the identity evaluation parameter is less than or equal to the first evaluation threshold, this reservationist is a first-level reservationist;

[0043] When the identity evaluation parameter is greater than the first evaluation threshold and less than the second evaluation threshold, this reservationist is a second-level reservationist;

[0044] When the identity evaluation parameter is greater than the second evaluation threshold, this reservationist is a third-level reservationist.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: By considering the reservation situation of hotel rooms on the day before the prediction date, the reservation situation of hotel rooms on the same day of last year of the prediction date, the identity of the reservationist, and by setting the first update time interval, the present invention can not only obtain the updated hotel room status within a reasonable time period to prevent errors in hotel room reservations caused by untimely update of hotel room status, but also avoid the room status being continuously updated and occupying a large amount of data operation space. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 Schematic diagram of a module of a hotel data update system based on multiple factors according to the present invention;

[0047] Figure 2 Schematic flow chart of a hotel data update method based on multiple factors according to the present invention. Specific embodiments

[0048] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0049] Please refer to Figures 1-2 , in an embodiment of the present invention, a hotel data update system based on multiple factors, characterized in that: the data update system includes a first update time interval setting module, an update parameter acquisition module, an update parameter comparison module, a first update time interval judgment module, and a hotel room status update module. The first update time interval setting module is used to preset a first update time interval for updating the hotel room status. The update parameter acquisition module is used to acquire update parameters. The update parameter comparison module is used to compare the update parameters with an update threshold and determine whether to transmit information to the first update time interval judgment module or the hotel room status update module according to the comparison result. The first update time interval judgment module is used to judge whether the time interval from the current time to the last update of the hotel room status reaches the first update time interval. The hotel room status update module is used to update the hotel room status.

[0050] The update parameter acquisition module includes a first update reference factor acquisition module, a second update reference factor acquisition module, and an update parameter calculation module. The first update reference factor acquisition module is used to acquire a first update reference factor. The second update reference factor acquisition module is used to acquire a second update reference factor. The update parameter calculation module calculates update parameters according to the first update reference factor and the second update reference factor.

[0051] The first updated reference factor acquisition module includes a hotel data acquisition module on the day before the prediction date, a reference factor calculation module on the day before the prediction date, a hotel data acquisition module on the same day of last year as the prediction date, a reference factor calculation module on the same day of last year as the prediction date, a first prediction ratio calculation module, a second prediction ratio calculation module, a third prediction ratio calculation module, a festival reference factor acquisition module, and a first updated reference factor calculation module. The hotel data acquisition module on the day before the prediction date is used to acquire the number of vacant rooms ncs1 on the day before the hotel starts accepting reservations on the day before the prediction date, the number of vacant rooms ncs2 on the off-peak days in the next week before the hotel starts accepting reservations on the day before the prediction date, the number of vacant rooms ncs3 on the peak days in the next week before the hotel starts accepting reservations on the day before the prediction date, the number of rooms nc1 reserved on the day of the hotel on the day before the prediction date, the number of rooms nc2 reserved on the off-peak days in the next week of the hotel on the day before the prediction date, and the number of rooms nc3 reserved on the peak days in the next week of the hotel on the day before the prediction date. The reference factor calculation module on the day before the prediction date calculates the first reference factor, the second reference factor, and the third reference factor respectively according to the quantity ncs1 and the quantity nc1, the quantity ncs2 and the quantity nc2, and the quantity ncs3 and the quantity nc3. The hotel data acquisition module on the same day of last year as the prediction date is used to acquire the number of vacant rooms ncs4 on the day before the hotel starts accepting reservations on the same day of last year as the prediction date, the number of vacant rooms ncs5 on the off-peak days in the next week before the hotel starts accepting reservations on the same day of last year as the prediction date, the number of vacant rooms ncs6 on the peak days in the next week before the hotel starts accepting reservations on the same day of last year as the prediction date, the number of rooms nc4 reserved on the day of the hotel on the same day of last year as the prediction date, the number of rooms nc5 reserved on the off-peak days in the next week of the hotel on the same day of last year as the prediction date, and the number of rooms nc6 reserved on the peak days in the next week of the hotel on the same day of last year as the prediction date. The reference factor calculation module on the same day of last year as the prediction date calculates the fourth reference factor, the fifth reference factor, and the sixth reference factor respectively according to the quantity ncs4 and the quantity nc4, the quantity ncs5 and the quantity nc5, and the quantity ncs6 and the quantity nc6. The first prediction ratio calculation module calculates the first prediction ratio according to the first reference factor and the fourth reference factor. The second prediction ratio calculation module calculates the second prediction ratio according to the second reference factor and the fifth reference factor. The third prediction ratio calculation module calculates the third prediction ratio according to the third reference factor and the sixth reference factor. The festival reference factor acquisition module outputs the festival reference factor according to whether the prediction date is an off-peak day or a peak day. The first updated reference factor calculation module calculates the first updated reference factor according to the first prediction ratio, the second prediction ratio, the third prediction ratio, and the festival reference factor.

[0052] The second update reference factor acquisition module includes a predeterminer judgment module, a predeterminer number acquisition module, a predeterminer identity parameter acquisition module, a module for acquiring the number of hotel rooms reserved by a predeterminer, a module for acquiring the number of days of scheduled check-in by a predeterminer, and a second update reference factor calculation module. The predeterminer judgment module is used to judge whether there is a new predeterminer reserving a hotel room within the first update time interval, and transmit information to the second update reference factor calculation module when there is a new predeterminer. The predeterminer number acquisition module is used to acquire the number G of all new predeterminers within the first update time interval. The predeterminer identity parameter acquisition module includes a predeterminer identity acquisition module and an identity parameter acquisition module. The predeterminer identity acquisition module includes a hotel reservation check-in history acquisition module, a type parameter acquisition module, an occupancy parameter acquisition module, an identity evaluation parameter calculation module, and an identity determination module. The hotel reservation check-in history acquisition module is used to acquire the hotel reservation history and check-in history of a predeterminer in the most recent three months. The type parameter acquisition module includes a hotel reservation times acquisition module, a hotel reservation days acquisition module, and a hotel type acquisition module. The hotel reservation times acquisition module obtains the number of hotel reservations made by a predeterminer based on the information acquired by the hotel reservation check-in history acquisition module. The hotel reservation days acquisition module obtains the number of days of scheduled hotel check-in for each hotel reservation made by a predeterminer based on the information acquired by the hotel reservation check-in history acquisition module. The hotel type acquisition module obtains a type parameter based on the information acquired by the hotel reservation check-in history acquisition module. The occupancy parameter acquisition module obtains an occupancy parameter based on the information acquired by the hotel reservation check-in history acquisition module. The identity evaluation parameter calculation module calculates an identity evaluation parameter based on the number of hotel reservations, the number of reservation days, the type parameter, and the occupancy parameter. The identity determination module compares the identity evaluation parameter with a first evaluation threshold and a second evaluation threshold to judge whether the predeterminer is a first-level predeterminer, a second-level predeterminer, or a third-level predeterminer. The identity parameter acquisition module obtains an identity parameter J based on whether the predeterminer is a first-level predeterminer, a second-level predeterminer, or a third-level predeterminer. The module for acquiring the number of hotel rooms reserved by a predeterminer is used to acquire the number K of hotel rooms reserved by all new predeterminers within the first update time interval. The module for acquiring the number of days of scheduled check-in by a predeterminer is used to acquire the number L of days of scheduled check-in by all new predeterminers within the first update time interval. The second update reference factor calculation module calculates a second update reference factor based on the number G of all new predeterminers, the identity parameter J of each predeterminer, the number K of hotel rooms reserved by each predeterminer, and the number L of days of scheduled check-in.

[0053] A hotel data update method based on multiple factors, the data update method comprising the following steps:

[0054] Pre-set a first update time interval for updating the status of hotel rooms;

[0055] Calculate update parameters:

[0056] Step S1: Calculate the first update reference factor Z1;

[0057] Step S11: Obtain the number of vacant rooms ncs1 on the day before the prediction day when the hotel started accepting reservations, the number of vacant rooms ncs2 on the off-peak days in the following week before the hotel started accepting reservations on the previous day, the number of vacant rooms ncs3 on the peak days in the following week before the hotel started accepting reservations on the previous day, the number of rooms nc1 reserved on the day of the hotel on the previous day, the number of rooms nc2 reserved on the off-peak days in the following week for the hotel on the previous day, and the number of rooms nc3 reserved on the peak days in the following week for the hotel on the previous day;

[0058] Then calculate the first reference factor a1 = nc1 / ncs1, the second reference factor a2 = nc2 / ncs2, and the third reference factor a3 = nc3 / ncs3;

[0059] Step S12: Obtain the number of vacant rooms ncs4 on the day before the same day of last year when the hotel started accepting reservations, the number of vacant rooms ncs5 on the off-peak days in the following week before the hotel started accepting reservations on the same day of last year, the number of vacant rooms ncs6 on the peak days in the following week before the hotel started accepting reservations on the same day of last year, the number of rooms nc4 reserved on the day of the hotel on the same day of last year, the number of rooms nc5 reserved on the off-peak days in the following week for the hotel on the same day of last year, and the number of rooms nc6 reserved on the peak days in the following week for the hotel on the same day of last year;

[0060] Then calculate the fourth reference factor a4 = nc4 / ncs4, the fifth reference factor a5 = nc5 / ncs5, and the sixth reference factor a6 = nc6 / ncs6.

[0061] Step S13: Calculate the first predicted proportion of rooms reserved on the day of the prediction day

[0062] nw1 = 0.7a1 + 0.3a4,

[0063] The second predicted proportion of rooms reserved on the off-peak days in the following week for the hotel on the prediction day

[0064] nw2 = 0.7a2 + 0.3a5,

[0065] The third predicted proportion of rooms reserved on the peak days in the following week for the hotel on the prediction day

[0066] nw3 = 0.7a3 + 0.3a6;

[0067] Step S14: Determine the type of the prediction day respectively.

[0068] If the prediction day is a flat peak day, the festival reference factor f = 0.75. If the prediction day is a peak day, the festival reference factor f = 1, where the peak day is a legal holiday and the day before a legal holiday, and the flat peak day is the days other than the peak days.

[0069] Step S15: Calculate the first update reference factor Z1 = (nw1 + nw2 + nw3) / 3 * f.

[0070] Step S2: Calculate the second update reference factor Z2;

[0071] When there are new bookers booking hotel rooms within the first update time interval, obtain the number G of all new bookers within the first update time interval, the identity parameter J of each booker, the number K of hotel rooms booked by each booker, and the scheduled check-in days L.

[0072] When obtaining the identity parameter of the booker according to the identity of the booker, the identity of the booker includes first-level bookers, second-level bookers, and third-level bookers. When the booker is a first-level booker, the identity parameter J = 1. When the booker is a second-level booker, the identity parameter J = 2. When the booker is a third-level booker, the identity parameter J = 3.

[0073] The identity determination of the booker includes the following steps:

[0074] Obtain the hotel booking history and check-in history of the booker in the recent three months, and obtain the number U of hotel bookings, the type parameter V of the hotel booked each time, the scheduled check-in days W of each hotel booking, and the check-in parameter X after each hotel booking according to the hotel booking history and check-in history.

[0075] When the price of the standard room of the hotel previously booked is within 20% of the price of the standard room of this hotel, the type parameter V of the previously booked hotel = 2. When the price of the standard room of the previously booked hotel is greater than or equal to 20% of the price of the standard room of this hotel, the type parameter V of the previously booked hotel = 4. When the price of the standard room of the previously booked hotel is less than or equal to 20% of the price of the standard room of this hotel, the type parameter V of the previously booked hotel = 1.

[0076] When the booker cancels the check-in after this hotel booking, the check-in parameter X = 0.2. When the booker checks in after this hotel booking, the check-in parameter X = 1.

[0077] Then the booker identity evaluation parameter Y = , where i represents the i-th hotel booking in the recent three months, V i represents the type parameter of the hotel booked for the i-th time, W i represents the scheduled check-in days of the i-th hotel booking, X iRepresents the check-in parameters for the i-th reserved hotel.

[0078] When the identity evaluation parameter is less than or equal to the first evaluation threshold, the reserving person is a first-level reserving person;

[0079] When the identity evaluation parameter is greater than the first evaluation threshold and less than the second evaluation threshold, the reserving person is a second-level reserving person;

[0080] When the identity evaluation parameter is greater than the second evaluation threshold, the reserving person is a third-level reserving person;

[0081] Calculate the second update reference factor Z2 = , where h represents the h-th new reserving person within the first update time interval, J h represents the identity parameter of the h-th reserving person, K h represents the number of hotel rooms reserved by the h-th reserving person, L h represents the number of days of check-in reserved by the h-th reserving person.

[0082] Compare the relationship between the update parameter and the update threshold,

[0083] If the update parameter is greater than the update threshold, immediately update the hotel room status,

[0084] If the update decimal is less than the update threshold, determine whether the time interval from the current time to the last time the hotel room status was updated reaches the first update time interval,

[0085] If it reaches the first update time interval, update the hotel room status.

[0086] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed within the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. A hotel data update system based on multiple factors Characterized in that: The data update system includes a first update time interval setting module, an update parameter acquisition module, an update parameter comparison module, a first update time interval judgment module, and a hotel room status update module. The first update time interval setting module is used to preset a first update time interval for updating the hotel room status. The update parameter acquisition module is used to acquire update parameters. The update parameter comparison module is used to compare the update parameters with an update threshold and determine whether to transmit information to the first update time interval judgment module or the hotel room status update module according to the comparison result. The first update time interval judgment module is used to judge whether the time interval from the current time to the last update of the hotel room status reaches the first update time interval. The hotel room status update module is used to update the hotel room status; The update parameter acquisition module includes a first update reference factor acquisition module, a second update reference factor acquisition module, and an update parameter calculation module. The first update reference factor acquisition module is used to acquire a first update reference factor. The second update reference factor acquisition module is used to acquire a second update reference factor. The update parameter calculation module calculates update parameters according to the first update reference factor and the second update reference factor; The first updated reference factor acquisition module includes a hotel data acquisition module for the day before the prediction date, a reference factor calculation module for the day before the prediction date, a hotel data acquisition module for the same day of last year as the prediction date, a reference factor calculation module for the same day of last year as the prediction date, a first prediction ratio calculation module, a second prediction ratio calculation module, a third prediction ratio calculation module, a festival reference factor acquisition module, and a first updated reference factor calculation module. The hotel data acquisition module for the day before the prediction date is used to acquire the number of vacant rooms ncs1 on the day before the hotel starts accepting reservations on the day before the prediction date, the number of vacant rooms ncs2 on the off-peak days in the next week before the hotel starts accepting reservations on the day before the prediction date, the number of vacant rooms ncs3 on the peak days in the next week before the hotel starts accepting reservations on the day before the prediction date, the number of rooms nc1 reserved on the day of the hotel on the day before the prediction date, the number of rooms nc2 reserved on the off-peak days in the next week for the hotel on the day before the prediction date, and the number of rooms nc3 reserved on the peak days in the next week for the hotel on the day before the prediction date. The reference factor calculation module for the day before the prediction date calculates the first reference factor, the second reference factor, and the third reference factor based on the quantity ncs1 and the quantity nc1, the quantity ncs2 and the quantity nc2, and the quantity ncs3 and the quantity nc3 respectively. The hotel data acquisition module for the same day of last year as the prediction date is used to acquire the number of vacant rooms ncs4 on the day before the hotel starts accepting reservations on the same day of last year as the prediction date, the number of vacant rooms ncs5 on the off-peak days in the next week before the hotel starts accepting reservations on the same day of last year as the prediction date, the number of vacant rooms ncs6 on the peak days in the next week before the hotel starts accepting reservations on the same day of last year as the prediction date, the number of rooms nc4 reserved on the day of the hotel on the same day of last year as the prediction date, the number of rooms nc5 reserved on the off-peak days in the next week for the hotel on the same day of last year as the prediction date, and the number of rooms nc6 reserved on the peak days in the next week for the hotel on the same day of last year as the prediction date. The reference factor calculation module for the same day of last year as the prediction date calculates the fourth reference factor, the fifth reference factor, and the sixth reference factor based on the quantity ncs4 and the quantity nc4, the quantity ncs5 and the quantity nc5, and the quantity ncs6 and the quantity nc6 respectively. The first prediction ratio calculation module calculates the first prediction ratio based on the first reference factor and the fourth reference factor. The second prediction ratio calculation module calculates the second prediction ratio based on the second reference factor and the fifth reference factor. The third prediction ratio calculation module calculates the third prediction ratio based on the third reference factor and the sixth reference factor. The festival reference factor acquisition module outputs the festival reference factor according to whether the prediction date is an off-peak day or a peak day. The first updated reference factor calculation module calculates the first updated reference factor based on the first prediction ratio, the second prediction ratio, the third prediction ratio, and the festival reference factor; The second update reference factor acquisition module includes a predeterminer judgment module, a predeterminer number acquisition module, a predeterminer identity parameter acquisition module, a module for acquiring the number of hotel rooms reserved by a predeterminer, a module for acquiring the reserved check-in days of a predeterminer, and a second update reference factor calculation module. The predeterminer judgment module is used to judge whether there is a new predeterminer reserving a hotel room within the first update time interval, and transmit information to the second update reference factor calculation module when there is a new predeterminer. The predeterminer number acquisition module is used to acquire the number G of all new predeterminers within the first update time interval. The predeterminer identity parameter acquisition module includes a predeterminer identity acquisition module and an identity parameter acquisition module. The predeterminer identity acquisition module includes a hotel reservation check-in history acquisition module, a type parameter acquisition module, a check-in parameter acquisition module, an identity evaluation parameter calculation module, and an identity determination module. The hotel reservation check-in history acquisition module is used to acquire the hotel reservation history and check-in history of a predeterminer within the most recent three months. The type parameter acquisition module includes a hotel reservation times acquisition module, a hotel reservation days acquisition module, and a hotel type acquisition module. The hotel reservation times acquisition module obtains the number of hotel reservations made by a predeterminer based on the information acquired by the hotel reservation check-in history acquisition module. The hotel reservation days acquisition module obtains the reserved days for each hotel reservation made by a predeterminer based on the information acquired by the hotel reservation check-in history acquisition module. The hotel type acquisition module obtains a type parameter based on the information acquired by the hotel reservation check-in history acquisition module. The check-in parameter acquisition module obtains a check-in parameter based on the information acquired by the hotel reservation check-in history acquisition module. The identity evaluation parameter calculation module calculates an identity evaluation parameter based on the number of hotel reservations, the reserved days, the type parameter, and the check-in parameter. The identity determination module compares the identity evaluation parameter with a first evaluation threshold and a second evaluation threshold to judge whether the predeterminer is a first-level predeterminer, a second-level predeterminer, or a third-level predeterminer. The identity parameter acquisition module obtains an identity parameter J based on whether the predeterminer is a first-level predeterminer, a second-level predeterminer, or a third-level predeterminer. The module for acquiring the number of hotel rooms reserved by a predeterminer is used to acquire the number K of hotel rooms reserved by all new predeterminers within the first update time interval. The module for acquiring the reserved check-in days of a predeterminer is used to acquire the reserved check-in days L of all new predeterminers within the first update time interval. The second update reference factor calculation module calculates a second update reference factor based on the number G of all new predeterminers, the identity parameter J of each predeterminer, the number K of hotel rooms reserved by each predeterminer, and the reserved check-in days L; The data update method of the hotel data update system includes the following steps: Pre-set a first update time interval for updating the hotel room status; Calculate an update parameter and compare the relationship between the update parameter and an update threshold; If the update parameter is greater than the update threshold, immediately update the hotel room status; If the update decimal is less than the update threshold, judge whether the time interval from the current time to the last time the hotel room status was updated reaches the first update time interval; If the first update time interval is reached, update the hotel room status.

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