CN104759100A - Prediction method and device of game life cycle and game operator server - Google Patents

Abstract

The invention discloses a prediction method and prediction device of game life cycle and a game operator server. The method comprises the following steps: clustering game users according to user login information of a game user acquired in a first preset time segment; determining the liveness parameters of the game users; determining the current life cycle states of the games according to the liveness parameters of the game users, so as to prevent some initial qualities from being missed. However, the users can be quickly increased, the games with very high user payment potentials can be popularized and operated so as to improve the operating efficiency.

Description

The game Forecasting Methodology of life cycle, device and gaming operators server

Technical field

The disclosure relates to communication technical field, particularly relates to a kind of Forecasting Methodology of life cycle of playing, device and gaming operators server.

Background technology

The development of current mobile Internet is swift and violent, as one of the mainstream applications of mobile Internet, although mobile phone games time of occurrence is shorter, rely on the convenience of terminal, operation terseness, take the very rapid of the feature development such as fragmentation time.The operation of game and promoting as the key link in game life cycle, constantly comes out at high-quality game work, and under the overall situation that competition situation is day by day fierce, how low-cost high-efficiency operation is played the matter of utmost importance that Shi Ge operator needs to face.

In correlation technique, promote the strategy of game generally according to the quality of game, such as game picture, game, operability, Interface Options specification etc., operation personnel can determine that dropping into how many resources initially promotes.After initial popularization, the business revenue situation of operation personnel to this stage of game is added up, and according to popularization cost and business revenue situation, operation personnel determines that whether continuing additional resource promotes.

Summary of the invention

Disclosure embodiment provides a kind of Forecasting Methodology of life cycle of playing, device and gaming operators server.Described technical scheme is as follows:

According to the first aspect of disclosure embodiment, provide a kind of Forecasting Methodology of life cycle of playing, the method comprises:

Obtain the user login information of game user in the first preset time period;

According to described user login information, cluster is carried out to described game user, determine the liveness parameter of described game user;

According to the liveness parameter of described game user, determine current residing life cycle phase of playing;

According to the current residing life cycle phase of game, send corresponding game information to user terminal.

According to the second aspect of disclosure embodiment, provide a kind of prediction unit of life cycle of playing, this device comprises:

User profile acquisition module, is configured to the user login information of game user in acquisition first preset time period;

User's liveness determination module, is configured to carry out cluster according to described user login information to described game user, determines the liveness parameter of described game user;

Life cycle phase determination module, is configured to the liveness parameter according to described game user, determines current residing life cycle phase of playing;

Sending module, is configured to, according to the current residing life cycle phase of game, send corresponding game information to user terminal.

According to the third aspect of disclosure embodiment, provide a kind of gaming operators server, this server comprises:

Processing components;

For storing the memory of the executable instruction of described processor;

Wherein, described processing components is configured to:

Obtain the user login information of game user in the first preset time period;

According to described user login information, cluster is carried out to described game user, determine the liveness parameter of described game user;

According to the liveness parameter of described game user, determine current residing life cycle phase of playing;

According to the current residing life cycle phase of game, send corresponding game information to user terminal.

The technical scheme that disclosure embodiment provides can comprise following beneficial effect:

By obtaining the user login information of game user in the first preset time period, according to user login information, cluster is carried out to game user, determine the liveness parameter of game user, according to the liveness parameter of game user, determine current residing life cycle phase of playing, avoid missing some initial quality general, but user's rapid development, the very large game of user charges potentiality is carried out promoting and runing, thus improves efficiency of operation.

Should be understood that, it is only exemplary and explanatory that above general description and details hereinafter describe, and can not limit the disclosure.

Accompanying drawing explanation

Accompanying drawing to be herein merged in description and to form the part of this description, shows and meets embodiment of the present disclosure, and is used from description one and explains principle of the present disclosure.

Fig. 1 is the flow chart of the Forecasting Methodology of a kind of life cycle of playing according to an exemplary embodiment;

Fig. 2 is the flow chart of the Forecasting Methodology of a kind of life cycle of playing according to another exemplary embodiment;

Fig. 3 is the flow chart of the Forecasting Methodology of a kind of life cycle of playing according to another exemplary embodiment;

Fig. 4 is the flow chart of the Forecasting Methodology of a kind of life cycle of playing according to another exemplary embodiment;

Fig. 5 is the flow chart of the Forecasting Methodology of a kind of life cycle of playing according to another exemplary embodiment;

Fig. 6 is the flow chart of the Forecasting Methodology of a kind of life cycle of playing according to another exemplary embodiment;

Fig. 7 is the block diagram of the prediction unit of a kind of life cycle of playing according to an exemplary embodiment;

Fig. 8 is the block diagram of the prediction unit of a kind of life cycle of playing according to another exemplary embodiment;

Fig. 9 is the block diagram of the prediction unit of a kind of life cycle of playing according to another exemplary embodiment;

Figure 10 is the block diagram of the prediction unit of a kind of life cycle of playing according to another exemplary embodiment;

Figure 11 is the block diagram of the prediction unit of a kind of life cycle of playing according to another exemplary embodiment;

Figure 12 is the block diagram of a kind of gaming operators server according to an exemplary embodiment.

By above-mentioned accompanying drawing, illustrate the embodiment that the disclosure is clear and definite more detailed description will be had hereinafter.These accompanying drawings and text description be not in order to limited by any mode the disclosure design scope, but by reference to specific embodiment for those skilled in the art illustrate concept of the present disclosure.

Detailed description of the invention

Here will be described exemplary embodiment in detail, its sample table shows in the accompanying drawings.When description below relates to accompanying drawing, unless otherwise indicated, the same numbers in different accompanying drawing represents same or analogous key element.Embodiment described in following exemplary embodiment does not represent all embodiments consistent with the disclosure.On the contrary, they only with as in appended claims describe in detail, the example of apparatus and method that aspects more of the present disclosure are consistent.

Fig. 1 is the flow chart of the Forecasting Methodology of a kind of life cycle of playing according to an exemplary embodiment, the Forecasting Methodology of this game life cycle can be applied to the life cycle prediction of the game such as web game, online game or mobile phone games, can comprise following several step:

In a step 101, the user login information of game user in the first preset time period is obtained.

This first preset time period can be one day, one week, some skies or a month equal time section.First preset time period can according to the length of the life cycle of game, and game user log-on message pace of change etc. is carried out selection and determined.

In a step 102, according to user login information, cluster is carried out to game user, determine the liveness parameter of game user.

In step 103, according to the liveness parameter of game user, determine current residing life cycle phase of playing.

The life cycle of game refers to game and puts the whole process of finally withdrawing from the market on market.After a game is put on market, introductory phase, growth stage, maturity period, decline phase in several stage be experienced.For different game, the duration in each stage is different.Introductory phase refers to the initial period of playing and putting on market.Growth stage refers to the period after the paying player of game attraction some.Maturity period refers to game enrollment and tends towards stability, and game user enlivens and pays the period of also tending towards stability every day.Decline phase refers to game and enters the superseded stage.

At step 104, according to the current residing life cycle phase of game, corresponding game information is sent to user terminal.

If play, current residing life cycle phase is the introductory phase, then the user terminal to the first quantity sends game information; If play, current residing life cycle phase is the growth stage, then the user terminal to the second quantity sends game information; If play, current residing life cycle is the maturity period, then change game content, extends the active time of any active ues.

Wherein, if current residing life cycle phase of playing is the introductory phase, illustrate that game is just put on market, now need to carry out a good deal of propaganda, send game information to a large amount of user terminals, to enable user recognize this game, and then register the game user that this game becomes this game.

If play, current residing life cycle phase is the growth stage, and the user terminal to the second quantity sends game information.The game user number that the user terminal of the second quantity can increase newly according to the user terminal quantity and game accepting game information in the introductory phase is determined, or the game user number directly increased newly according to going game determines the user terminal of the second quantity, or determines according to the quantity forwarded of similar game in the growth stage in advance.Such as, the user terminal of the second quantity refers to the user terminal never receiving game information; Or the certain customers' terminal in the user terminal of the second quantity can be the user terminal never receiving game information, certain customers' terminal can be the user terminal receiving game information but do not register game.

In sum, the Forecasting Methodology of the game life cycle that the present embodiment provides, by obtaining the user login information of game user in the first preset time period, according to user login information, cluster is carried out to game user, determine the liveness parameter of game user, according to the liveness parameter of game user, determine current residing life cycle phase of playing, avoid missing some initial quality general, but user's rapid development, the very large game of user charges potentiality is carried out promoting and runing, thus improves efficiency of operation.

In one embodiment, according to user login information, cluster is carried out to game user, determines the process of the liveness parameter of game user, as shown in Figure 2, following step can be comprised:

In step 1021, according to user login information, determine the active users in game user, normal users number and risk subscribers number.

User login information can comprise: per day login times, accumulation login times, accumulation log in number of days, accumulation paying number of times and accumulation paying number of days.Corresponding, any active ues in a few days average login times, accumulation login times, accumulation can log in the game user that number of days, accumulation paying number of times or accumulation paying number of days exceed some.Normal users in a few days average login times, accumulation login times, accumulation can log in the game user that number of days, accumulation paying number of times or accumulation paying number of days are positioned at certain limit.Risk subscribers in a few days average login times, accumulation login times, accumulation can log in the game user that number of days, accumulation paying number of times or accumulation paying number of days are less than certain value.

Wherein, the average login times of per day login times every day that is game user in the first preset time period.Accumulation login times is the total login times of game user in the first preset time period.It is total number of days that game user logs in the first preset time period that accumulation logs in number of days.Accumulation paying number of times is the total degree that game user is paid in the first preset time period.Accumulation paying number of days is total number of days that game user is paid in the first preset time period.

In addition, game user also has life cycle, a user may continue to log in after have registered certain game inside a very long time, and the stage property bought in game of paying, after a period of time, login times reduces, and user reduces, after a period of time the interest of game, user is logging in game or no longer logging in game once in a while, and user disappears to the interest of game; Also, after a possibility user registers game, just directly run off, again do not occur in gaming.Wherein, any active ues can be the user continuing to log in inside a very long time.Normal users can reduce for login times, to the user that the interest of game reduces.Risk subscribers can be the user of logging in game or no longer logging in game once in a while.

In step 1022, according to the active users in game user, normal users number and risk subscribers number, determine that any active ues accounts for the ratio of total game user.

Such as, if the active users in game user is 400, normal users number is 200, and risk subscribers number is 150, then any active ues accounts for the ratio of total game user is 400/ (400+200+150)=53.3%.

Corresponding, in above-mentioned steps 103, the ratio of total game user can be accounted for according to any active ues, determine current residing life cycle phase of playing.

As a kind of example, in conjunction with reference to figure 3, in step 1021, specifically following step can be comprised:

In step 10211, log in number of days, accumulation paying number of times and accumulation paying number of days according to per day login times, accumulation login times, accumulation respectively, determine per day login times, accumulation login times, active users, normal users number and risk subscribers number that accumulation login number of days, accumulation paying number of times and accumulation paying number of days is corresponding.

Wherein, if per day login times corresponding to game user is greater than the first corresponding preset value, then game user is defined as any active ues corresponding to per day login times; If the per day login times that game user is corresponding is positioned at corresponding preset range, then game user is defined as normal users corresponding to per day login times; If the per day login times that game user is corresponding is less than the second corresponding preset value, then game user is defined as risk subscribers corresponding to per day login times.

If the accumulation login times that game user is corresponding is greater than the first corresponding preset value, then game user is defined as any active ues corresponding to accumulation login times; If the accumulation login times that game user is corresponding is positioned at corresponding preset range, then game user is defined as normal users corresponding to accumulation login times; If the accumulation login times that game user is corresponding is less than the second corresponding preset value, then game user is defined as risk subscribers corresponding to accumulation login times.

If the accumulation that game user is corresponding logs in number of days be greater than the first corresponding preset value, be then defined as game user accumulating logging in any active ues corresponding to number of days; If the accumulation that game user is corresponding logs in number of days be positioned at corresponding preset range, be then defined as game user accumulating logging in normal users corresponding to number of days; If the accumulation that game user is corresponding logs in number of days be less than the second corresponding preset value, be then defined as game user accumulating logging in risk subscribers corresponding to number of days.

If the accumulation paying number of times that game user is corresponding is greater than the first corresponding preset value, then game user is defined as any active ues corresponding to accumulation paying number of times; If the accumulation paying number of times that game user is corresponding is positioned at corresponding preset range, then game user is defined as normal users corresponding to accumulation paying number of times; If the accumulation paying number of times that game user is corresponding is less than the second corresponding preset value, then game user is defined as risk subscribers corresponding to accumulation paying number of times.

If the accumulation paying number of days that game user is corresponding is greater than the first corresponding preset value, then game user is defined as any active ues corresponding to accumulation paying number of days; If the accumulation paying number of days that game user is corresponding is positioned at corresponding preset range, then game user is defined as normal users corresponding to accumulation paying number of days; If the accumulation paying number of days that game user is corresponding is less than the second corresponding preset value, then game user is defined as risk subscribers corresponding to accumulation paying number of days.

Wherein, first preset value, preset range can log in number of days, accumulation paying number of times and accumulation paying number of days and determine according to the per day login times of the user of similar game, accumulation login times, accumulation with the second preset value, defining method can be statistical analysis etc., is no longer described in detail herein.

In step 10212, log in active users corresponding to number of days, accumulation paying number of times and accumulation paying number of days according to per day login times, accumulation login times, accumulation, determine the active users in game user.

In step 10213, log in normal users number corresponding to number of days, accumulation paying number of times and accumulation paying number of days according to per day login times, accumulation login times, accumulation, determine the normal users number in game user.

In step 10214, log in risk subscribers number corresponding to number of days, accumulation paying number of times and accumulation paying number of days according to per day login times, accumulation login times, accumulation, determine the risk subscribers number in game user.

Such as, the active users that Q1=a1 × A1+a2 × B1+a3 × C1+a4 × D1+a5 × E1 determines in game user can be adopted; Q1 is the active users in game user.

Wherein, a1 is the default weight of per day login times, and a2 is the default weight of accumulation login times, and a3 is the default weight that accumulation logs in number of days, and a4 is the default weight of accumulation paying number of times, and a5 is the default weight of accumulation paying number of days; A1 is the game user number that per day login times is greater than the first corresponding preset value; B1 is the game user number that accumulation login times is greater than the first corresponding preset value; C1 is the game user number that accumulation login number of days is greater than the first corresponding preset value; D1 is the game user number that accumulation paying number of times is greater than the first corresponding preset value; E1 is the game user number that accumulation paying number of days is greater than the first corresponding preset value.

Such as, the normal users number that Q2=a1 × A2+a2 × B2+a3 × C2+a4 × D2+a5 × E2 determines in game user can be adopted;

Wherein, A2 is the game user number that per day login times is positioned at corresponding preset range; B2 is the game user number that accumulation login times is positioned at corresponding preset range; C2 is the game user number that accumulation logs in preset range corresponding to number of days; D2 is the game user number that accumulation paying number of times is positioned at corresponding preset range; E2 is the game user number that accumulation paying number of days is positioned at corresponding preset range.The implication of a1, a2, a3, a4 and a5 is see foregoing description.

Such as, the risk subscribers number that Q3=a1 × A3+a2 × B3+a3 × C3+a4 × D3+a5 × E3 determines in game user can be adopted;

Wherein, A3 is the game user number that per day login times is less than the second corresponding preset value; B3 is the game user number that accumulation login times is less than the second corresponding preset value; C3 is the game user number that accumulation login number of days is less than the second corresponding preset value; D3 is the game user number that accumulation paying number of times is less than the second corresponding preset value; E3 is the game user number that accumulation paying number of days is less than the second corresponding preset value.

In addition, the prediction unit of life cycle of playing also according to any one in above-mentioned 5 parameters or many kinds of parameters, can determine the active users in game user, normal users number and risk subscribers number.Such as, active users, normal users number and the risk subscribers number in game user is determined according to per day login times, accumulation login times and accumulation paying number of times.Again such as, active users, normal users number and the risk subscribers number in game user is determined according to per day login times, accumulation login number of days and accumulation paying number of days.In addition, can also comprise in user login information: per day login duration, accumulation log in the parameters such as duration, per day paying value, accumulation paying value, corresponding, the prediction unit of game life cycle, when predicting game life cycle, also can determine active users, normal users number and the risk subscribers number in game user in conjunction with above-mentioned parameter.Defining method is similar to the above embodiments, is no longer described in detail herein.

In this example, in conjunction with per day login times, accumulation login times, accumulation logs in number of days, accumulation paying number of times and default weight corresponding to accumulation paying number of days and per day login times, accumulation login times, accumulation logs in number of days, accumulation paying number of times and active users corresponding to accumulation paying number of days, normal users number and risk subscribers number, determine the active users in game user, normal users number and risk subscribers number, the active users in game user can be improved, the accuracy that normal users number and risk subscribers number are determined, and then accurately determine current residing life cycle phase of playing.

As a kind of example, in conjunction with reference to figure 4, according to the liveness parameter of game user, determine current residing life cycle phase of playing, following step can be comprised:

In step 1031, if the ratio of any active ues is more than or equal to the 3rd preset value in game user, then current for game residing life cycle phase is defined as the introductory phase.

In step 1032, if the ratio of any active ues is less than the 3rd preset value and is more than or equal to the 4th preset value in game user, then current for game residing life cycle phase is defined as the growth stage.

In step 1033, if the ratio of any active ues is less than the 4th preset value and is more than or equal to the 5th preset value in game user, then current for game residing life cycle phase is defined as the maturity period.

In step 1034, if the ratio of any active ues is less than the 5th preset value in game user, then current for game residing life cycle phase is defined as the decline phase.

3rd preset value, the 4th preset value can be arranged according to the experience that arranges of similar game with the 5th preset value.

In sum, the Forecasting Methodology of the game life cycle that the present embodiment provides, by obtaining the user login information of game user in the first preset time period, according to user login information, determine the active users in game user, normal users number and risk subscribers number, according to the active users in game user, normal users number and risk subscribers number, determine that any active ues accounts for the ratio of total game user, the ratio of total game user is accounted for according to any active ues, determine current residing life cycle phase of playing, avoid missing some initial quality general, but user's rapid development, the very large game of user charges potentiality is carried out promoting and runing, thus raising efficiency of operation.

Fig. 5 is the flow chart of the Forecasting Methodology of a kind of life cycle of playing according to another exemplary embodiment, and can be applied in the life cycle prediction of web game, online game or mobile phone games, can comprise the following steps:

In step 501, the user login information of game user in the first preset time period is obtained.

In step 502, according to user login information, determine the type of each game user.

Wherein, user login information can comprise: per day login times, accumulation login times, accumulation log in number of days, accumulation paying number of times, accumulation paying number of days, per day login duration, accumulation log in any one or many kinds of parameters in duration, per day paying value or accumulation paying value etc.Such as, suppose that user login information comprises: per day login times, accumulation login times, accumulation logs in number of days, accumulation paying number of times and accumulation paying number of days five parameters, wherein a kind of optional defining method of the type of game user can be: if more than three parameters are greater than the first corresponding preset value in the above-mentioned parameter of game user, then determine that the type of game user is any active ues, if there are two parameters to be greater than the first corresponding preset value in the above-mentioned parameter of game user, then determine that the type of game user is normal users, if only have a parameter to be greater than the first corresponding preset value in the above-mentioned parameter of game user or all parameters are all less than or equal to the first corresponding preset value, then determine that the type of game user is risk subscribers.

In step 503, according to the type of each game user, determine the active users in game user, normal users number and risk subscribers number.

In step 504, according to the active users in game user, normal users number and risk subscribers number, determine that any active ues accounts for the ratio of total game user.

In step 505, account for the ratio of total game user according to any active ues, determine current residing life cycle phase of playing.

In step 506, according to the current residing life cycle phase of game, corresponding game information is sent to user terminal.

In sum, the Forecasting Methodology of the game life cycle that the present embodiment provides, by obtaining the user login information of game user in the first preset time period, the type of each game user is determined according to user login information, according to the type of each game user, determine the active users in game user, normal users number and risk subscribers number, according to the active users in game user, normal users number and risk subscribers number, determine that any active ues accounts for the ratio of total game user, the ratio of total game user is accounted for according to any active ues, determine current residing life cycle phase of playing, thus avoid missing some initial quality general, but user's rapid development, the very large game of user charges potentiality is carried out promoting and runing, thus raising efficiency of operation.

Fig. 6 is the flow chart of the Forecasting Methodology of a kind of life cycle of playing according to another exemplary embodiment, can be applied to web game, online game or mobile phone games life cycle prediction in, can comprise the following steps:

In step 601, the user login information of game user in the first preset time period is obtained.

In step 602, according to user login information, cluster is carried out to game user, determine the liveness parameter of game user.

In step 603, the user login information of similar game in the first preset time period and the user terminal respective amount of the similar game information of reception of game is obtained.

Optionally, similar game refers to the similar game of type of play, the game painting style or game play.Type of play can be: network game RPG, action gunbattle, leisure intention, cool run are made a breakthrough, the anti-labyrinth of tower etc.

In step 604, according to user login information, cluster is carried out to game user, determine the liveness parameter of the game user of similar game.

In step 605, the liveness parameter of the game user of game is compared to the liveness parameter of the game user of similar game, obtains comparative result.

In step 606, according to the liveness parameter of game user, determine current residing life cycle phase of playing.

In step 607, according to comparative result, the user terminal respective amount receiving similar game information and current residing life cycle phase of playing, the user terminal to respective amount sends game information.

Particularly, the prediction unit of game life cycle is according to the current residing life cycle phase of game, determine the user terminal quantity needing to send game information, and adjust needing the user terminal quantity sending game information according to comparative result, the user terminal respective amount that receives similar game information, user terminal quantity after being adjusted, sends game information to the user terminal quantity after adjustment.

In sum, the Forecasting Methodology of the game life cycle that the present embodiment provides, by obtaining the user login information of game user in the first preset time period, according to user login information, cluster is carried out to game user, determine the liveness parameter of game user, obtain the user login information of similar game in the first preset time period and the user terminal respective amount of the similar game information of reception of game, according to user login information, cluster is carried out to game user, determine the liveness parameter of the game user of similar game, the liveness parameter of the game user of game is compared to the liveness parameter of the game user of similar game, obtain comparative result, according to the liveness parameter of game user, determine current residing life cycle phase of playing, according to comparative result, receive user terminal respective amount and the current residing life cycle phase of game of similar game information, user terminal to respective amount sends game information, avoid missing some initial quality general, but user's rapid development, the very large game of user charges potentiality is carried out promoting and runing, thus raising efficiency of operation.

Following is disclosure device embodiment, may be used for performing disclosure embodiment of the method.For the details do not disclosed in disclosure device embodiment, please refer to disclosure embodiment of the method.

Fig. 7 is the block diagram of the prediction unit of a kind of life cycle of playing according to an exemplary embodiment, and the prediction unit of this game life cycle can realize said method by software, hardware or both combinations.The prediction unit of this game life cycle can comprise:

User profile acquisition module 701, is configured to the user login information of game user in acquisition first preset time period;

User's liveness determination module 702, is configured to carry out cluster according to user login information to game user, determines the liveness parameter of game user;

Life cycle phase determination module 703, is configured to the liveness parameter according to game user, determines current residing life cycle phase of playing;

Sending module 704, be configured to according to the current residing life cycle phase of game, the user terminal to respective amount sends game information.

The life cycle of game refers to game and puts the whole process of finally withdrawing from the market on market.After a game is put on market, introductory phase, growth stage, maturity period, decline phase in several stage be experienced.For different game, the duration in each stage is different.Introductory phase refers to the initial period of playing and putting on market.Growth stage refers to the period after the paying player of game attraction some.Maturity period refers to game enrollment and tends towards stability, and game user enlivens and pays the period of also tending towards stability every day.Decline phase refers to game and enters the superseded stage.

Optionally, sending module 704 is configured to, and when current residing life cycle phase of playing is the introductory phase, the user terminal to the first quantity sends game information;

When current residing life cycle phase of playing is the growth stage, the user terminal to the second quantity sends game information;

When current residing life cycle phase of playing is the maturity period, change game content, extends the active time of any active ues.

In sum, the prediction unit of the game life cycle that the present embodiment provides, by obtaining the user login information of game user in the first preset time period, according to user login information, cluster is carried out to game user, determine the liveness parameter of game user, according to the liveness parameter of game user, determine current residing life cycle phase of playing, avoid missing some initial quality general, but user's rapid development, the very large game of user charges potentiality is carried out promoting and runing, thus improves efficiency of operation.

Fig. 8 is the block diagram of the prediction unit of a kind of life cycle of playing according to an exemplary embodiment, on basis embodiment illustrated in fig. 7, described game user comprises: any active ues, normal users and risk subscribers, and liveness parameter comprises: any active ues accounts for the ratio of total game user.

Corresponding, user's liveness determination module 702 comprises: number of users determining unit 7021 and user's liveness determining unit 7022;

Number of users determining unit 7021, is configured to according to user login information, determines the active users in game user, normal users number and risk subscribers number;

User's liveness determining unit 7022, is configured to, according to the active users in game user, normal users number and risk subscribers number, determine that any active ues accounts for the ratio of total game user.

As a kind of example, in conjunction with reference to figure 9, user login information comprises: per day login times, accumulation login times, accumulation log in number of days, accumulation paying number of times and accumulation paying number of days.

Corresponding, number of users determining unit 7021 comprises: first user number determination subelement 70211 and the second number of users determination subelement 70212;

First user number determination subelement 70211, be configured to log in number of days, accumulation paying number of times and accumulation paying number of days according to per day login times, accumulation login times, accumulation respectively, determine per day login times, accumulation login times, active users, normal users number and risk subscribers number that accumulation login number of days, accumulation paying number of times and accumulation paying number of days is corresponding;

Second number of users determination subelement 70212, is configured to log in active users corresponding to number of days, accumulation paying number of times and accumulation paying number of days according to per day login times, accumulation login times, accumulation, determines the active users in game user; Log in normal users number corresponding to number of days, accumulation paying number of times and accumulation paying number of days according to per day login times, accumulation login times, accumulation, determine the normal users number in game user; Log in risk subscribers number corresponding to number of days, accumulation paying number of times and accumulation paying number of days according to per day login times, accumulation login times, accumulation, determine the risk subscribers number in game user.

Optionally, first user number determination subelement 70211 is configured to, and when the per day login times that game user is corresponding is greater than the first corresponding preset value, game user is defined as any active ues corresponding to per day login times; When the per day login times that game user is corresponding is positioned at corresponding preset range, game user is defined as normal users corresponding to per day login times; When the per day login times that game user is corresponding is less than the second corresponding preset value, game user is defined as risk subscribers corresponding to per day login times.

Optionally, the second number of users determination subelement 70212, is configured to the active users adopting Q1=a1 × A1+a2 × B1+a3 × C1+a4 × D1+a5 × E1 to determine in game user;

Wherein, a1 is the default weight of per day login times, and a2 is the default weight of accumulation login times, and a3 is the default weight that accumulation logs in number of days, and a4 is the default weight of accumulation paying number of times, and a5 is the default weight of accumulation paying number of days; A1 is the game user number that per day login times is greater than the first corresponding preset value; B1 is the game user number that accumulation login times is greater than the first corresponding preset value; C1 is the game user number that accumulation login number of days is greater than the first corresponding preset value; D1 is the game user number that accumulation paying number of times is greater than the first corresponding preset value; E1 is the game user number that accumulation paying number of days is greater than the first corresponding preset value.

Optionally, the second number of users determination subelement 70212, is configured to the normal users number adopting Q2=a1 × A2+a2 × B2+a3 × C2+a4 × D2+a5 × E2 to determine in game user;

Wherein, A2 is the game user number that per day login times is positioned at corresponding preset range; B2 is the game user number that accumulation login times is positioned at corresponding preset range; C2 is the game user number that accumulation logs in preset range corresponding to number of days; D2 is the game user number that accumulation paying number of times is positioned at corresponding preset range; E2 is the game user number that accumulation paying number of days is positioned at corresponding preset range.

Optionally, the second number of users determination subelement 70212, is configured to the risk subscribers number adopting Q3=a1 × A3+a2 × B3+a3 × C3+a4 × D3+a5 × E3 to determine in game user;

Wherein, A3 is the game user number that per day login times is less than the second corresponding preset value; B3 is the game user number that accumulation login times is less than the second corresponding preset value; C3 is the game user number that accumulation login number of days is less than the second corresponding preset value; D3 is the game user number that accumulation paying number of times is less than the second corresponding preset value; E3 is the game user number that accumulation paying number of days is less than the second corresponding preset value.

Optionally, life cycle phase determination module 703 is configured to,

When the ratio of any active ues is more than or equal to the 3rd preset value in game user, current for game residing life cycle phase is defined as the introductory phase; In game user, the ratio of any active ues is less than the 3rd preset value when being more than or equal to the 4th preset value, and current for game residing life cycle phase is defined as the growth stage; In game user, the ratio of any active ues is less than the 4th preset value when being more than or equal to the 5th preset value, and current for game residing life cycle phase is defined as the maturity period; When the ratio of any active ues is less than the 5th preset value in game user, current for game residing life cycle phase is defined as the decline phase.

In sum, the prediction unit of the game life cycle that the present embodiment provides, by obtaining the user login information of game user in the first preset time period, according to user login information, determine the active users in game user, normal users number and risk subscribers number, according to the active users in game user, normal users number and risk subscribers number, determine that any active ues accounts for the ratio of total game user, the ratio of total game user is accounted for according to any active ues, determine current residing life cycle phase of playing, avoid missing some initial quality general, but user's rapid development, the very large game of user charges potentiality is carried out promoting and runing, thus raising efficiency of operation.

Figure 10 is the block diagram of the prediction unit of a kind of life cycle of playing according to another exemplary embodiment, on basis embodiment illustrated in fig. 7, user's liveness determination module 702 comprises: user type determining unit 7023, all types of number of users determining unit 7024 and any active ues ratio determining unit 7025.

Wherein, user type determining unit 7023, for according to user login information, determines the type of each game user.

Wherein, user login information can comprise: per day login times, accumulation login times, accumulation log in number of days, accumulation paying number of times, accumulation paying number of days, per day login duration, accumulation log in any one or many kinds of parameters in duration, per day paying value or accumulation paying value etc.Such as, suppose that user login information comprises: per day login times, accumulation login times, accumulation logs in number of days, accumulation paying number of times and accumulation paying number of days five parameters, wherein a kind of optional defining method of the type of game user can be: if more than three parameters are greater than the first corresponding preset value in the above-mentioned parameter of game user, then determine that the type of game user is any active ues, if there are two parameters to be greater than the first corresponding preset value in the above-mentioned parameter of game user, then determine that the type of game user is normal users, if only have a parameter to be greater than the first corresponding preset value in the above-mentioned parameter of game user or all parameters are all less than or equal to the first corresponding preset value, then determine that the type of game user is risk subscribers.

All types of number of users determining unit 7024, for the type according to each game user, determines the active users in game user, normal users number and risk subscribers number.

Any active ues ratio determining unit 7025, for according to the active users in game user, normal users number and risk subscribers number, determines that any active ues accounts for the ratio of total game user.

In sum, the Forecasting Methodology of the game life cycle that the present embodiment provides, by obtaining the user login information of game user in the first preset time period, the type of each game user is determined according to user login information, according to the type of each game user, determine the active users in game user, normal users number and risk subscribers number, according to the active users in game user, normal users number and risk subscribers number, determine that any active ues accounts for the ratio of total game user, the ratio of total game user is accounted for according to any active ues, determine current residing life cycle phase of playing, thus avoid missing some initial quality general, but user's rapid development, the very large game of user charges potentiality is carried out promoting and runing, thus raising efficiency of operation.

Figure 11 is the block diagram of the prediction unit of a kind of life cycle of playing according to another exemplary embodiment, and on basis embodiment illustrated in fig. 7, described device can also comprise: comparison module 705;

User's liveness determination module 702 carries out cluster according to user login information to game user, after determining the liveness parameter of game user, user profile acquisition module 701 is also configured to, and obtains the user login information of similar game in the first preset time period and the user terminal respective amount of the similar game information of reception of game;

User's liveness determination module 702 is also configured to, and carries out cluster according to user login information to game user, determines the liveness parameter of the game user of similar game;

Comparison module 705, is configured to the liveness parameter of the game user of game to compare to the liveness parameter of the game user of similar game, obtains comparative result;

Sending module 704 is configured to, and according to comparative result, the user terminal respective amount receiving similar game information and current residing life cycle phase of playing, the user terminal to respective amount sends game information.

In sum, the prediction unit of the game life cycle that the present embodiment provides, by obtaining the user login information of game user in the first preset time period, according to user login information, cluster is carried out to game user, determine the liveness parameter of game user, obtain the user login information of similar game in the first preset time period and the user terminal respective amount of the similar game information of reception of game, according to user login information, cluster is carried out to game user, determine the liveness parameter of the game user of similar game, the liveness parameter of the game user of game is compared to the liveness parameter of the game user of similar game, obtain comparative result, according to the liveness parameter of game user, determine current residing life cycle phase of playing, according to comparative result, receive user terminal respective amount and the current residing life cycle phase of game of similar game information, user terminal to respective amount sends game information, avoid missing some initial quality general, but user's rapid development, the very large game of user charges potentiality is carried out promoting and runing, thus raising efficiency of operation.

About the device in above-described embodiment, wherein the concrete mode of modules executable operations has been described in detail in about the embodiment of the method, will not elaborate explanation herein.

Figure 12 is the block diagram of a kind of gaming operators server 1000 according to an exemplary embodiment.

With reference to Figure 12, gaming operators server 1000 can comprise following one or more assembly: processing components 1002, memory 1004, power supply module 1006, the interface 1008 of I/O (I/O), and communications component 1010.

Processing components 1002 usually controls the integrated operation of gaming operators server 1000, and such as with acquisition user login information, user clustering, data communication, life cycle phase is determined and the operation such as transmit operation.Processing components 1002 specifically can be configured to the user login information of game user in acquisition first preset time period; According to user login information, cluster is carried out to game user, determine the liveness parameter of game user; According to the liveness parameter of game user, determine current residing life cycle phase of playing; According to the current residing life cycle phase of game, send corresponding game information to user terminal.

Processing components 1002 can comprise one or more processor 1020 to perform instruction, to complete all or part of step of above-mentioned method.In addition, processing components 1002 can comprise one or more module, and what be convenient between processing components 1002 and other assemblies is mutual.Such as, processing components 1002 can comprise communication module, mutual with what facilitate between communications component 1010 and processing components 1002.

Memory 1004 is configured to store the executable instruction of various types of data and processing components 1002 to be supported in the operation of gaming operators server 1000.The example of these data comprises for any application program of operation on gaming operators server 1000 or the instruction of method, message, data etc.Memory 1004 can be realized by the volatibility of any type or non-volatile memory device or their combination, as static RAM (SRAM), Electrically Erasable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory EPROM (EPROM), programmable read only memory (PROM), read-only storage (ROM), magnetic memory, flash memory, disk or CD.

The various assemblies that power supply module 1006 is gaming operators server 1000 provide electric power.Power supply module 1006 can comprise power-supply management system, one or more power supply, and other and the assembly generating, manage and distribute electric power for gaming operators server 1000 and be associated.

I/O interface 1008 is for providing interface between processing components 1002 and peripheral interface module, and above-mentioned peripheral interface module can be keyboard, some striking wheel, button etc.These buttons can include but not limited to: home button, volume button, start button and locking press button.

Communications component 1010 is configured to the communication being convenient to wired or wireless mode between gaming operators server 1000 and other equipment.Gaming operators server 1000 can access the wireless network based on communication standard, as WiFi, 2G or 3G, or their combination.In one exemplary embodiment, communications component 1010 receives from the broadcast singal of external broadcasting management system or broadcast related information via broadcast channel.In one exemplary embodiment, described communications component 1010 also comprises near-field communication (NFC) module, to promote junction service.Such as, can based on RF identification (RFID) technology in NFC module, Infrared Data Association (IrDA) technology, ultra broadband (UWB) technology, bluetooth (BT) technology and other technologies realize.

In the exemplary embodiment, gaming operators server 1000 can be realized, for performing the Forecasting Methodology of above-mentioned game life cycle by one or more application specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing appts (DSPD), PLD (PLD), field programmable gate array (FPGA), controller, microcontroller, microprocessor or other electronic components.

In the exemplary embodiment, additionally provide a kind of non-transitory computer-readable recording medium comprising instruction, such as, comprise the memory 1004 of instruction, above-mentioned instruction can perform said method by the processor 1020 of gaming operators server 1000.Such as, described non-transitory computer-readable recording medium can be ROM, random access memory (RAM), CD-ROM, tape, floppy disk and optical data storage devices etc.

A kind of non-transitory computer-readable recording medium, when the instruction in described storage medium is performed by the processor of gaming operators server 1000, makes gaming operators server 1000 can perform the Forecasting Methodology of above-mentioned game life cycle.

Those skilled in the art, at consideration description and after putting into practice invention disclosed herein, will easily expect other embodiment of the present disclosure.The application is intended to contain any modification of the present disclosure, purposes or adaptations, and these modification, purposes or adaptations are followed general principle of the present disclosure and comprised the undocumented common practise in the art of the disclosure or conventional techniques means.Description and embodiment are only regarded as exemplary, and true scope of the present disclosure and spirit are pointed out by claim below.

Should be understood that, the disclosure is not limited to precision architecture described above and illustrated in the accompanying drawings, and can carry out various amendment and change not departing from its scope.The scope of the present disclosure is only limited by appended claim.

Claims (25)

1. a Forecasting Methodology for life cycle of playing, is characterized in that, comprising:

Obtain the user login information of game user in the first preset time period;

According to described user login information, cluster is carried out to described game user, determine the liveness parameter of described game user;

According to the liveness parameter of described game user, determine current residing life cycle phase of playing;

According to the current residing life cycle phase of game, the user terminal to respective amount sends game information.

2. method according to claim 1, is characterized in that, described game user comprises: any active ues, normal users and risk subscribers, and described liveness parameter comprises: described any active ues accounts for the ratio of total game user.

3. method according to claim 2, is characterized in that, describedly carries out cluster according to described user login information to described game user, determines the liveness parameter of described game user, comprising:

According to described user login information, determine the active users in described game user, normal users number and risk subscribers number;

According to the active users in described game user, normal users number and risk subscribers number, determine that described any active ues accounts for the ratio of total game user.

4. method according to claim 3, is characterized in that, described user login information comprises: per day login times, accumulation login times, accumulation log in number of days, accumulation paying number of times and accumulation paying number of days.

5. method according to claim 4, is characterized in that, described according to described user login information, determines the active users in described game user, normal users number and risk subscribers number, comprising:

Log in number of days, described accumulation paying number of times and described accumulation paying number of days according to described per day login times, described accumulation login times, described accumulation respectively, determine described per day login times, described accumulation login times, active users, normal users number and risk subscribers number that described accumulation login number of days, described accumulation paying number of times and described accumulation paying number of days is corresponding;

Log in active users corresponding to number of days, described accumulation paying number of times and described accumulation paying number of days according to described per day login times, described accumulation login times, described accumulation, determine the active users in described game user;

Log in normal users number corresponding to number of days, described accumulation paying number of times and described accumulation paying number of days according to described per day login times, described accumulation login times, described accumulation, determine the normal users number in described game user;

Log in risk subscribers number corresponding to number of days, described accumulation paying number of times and described accumulation paying number of days according to described per day login times, described accumulation login times, described accumulation, determine the risk subscribers number in described game user.

6. method according to claim 5, is characterized in that, describedly determines according to described per day login times active users, normal users number and the risk subscribers number that described per day login times is corresponding, comprising:

If the per day login times that described game user is corresponding is greater than the first corresponding preset value, then described game user is defined as any active ues corresponding to described per day login times;

If the per day login times that described game user is corresponding is positioned at corresponding preset range, then described game user is defined as normal users corresponding to described per day login times;

If the per day login times that described game user is corresponding is less than the second corresponding preset value, then described game user is defined as risk subscribers corresponding to described per day login times.

7. method according to claim 5, it is characterized in that, describedly log in active users corresponding to number of days, described accumulation paying number of times and described accumulation paying number of days according to described per day login times, described accumulation login times, described accumulation, determine the active users in described game user, comprising:

Employing Q1=a1 × A1+a2 × B1+a3 × C1+a4 × D1+a5 × E1 determines the active users in described game user;

Wherein, a1 is the default weight of described per day login times, and a2 is the default weight of described accumulation login times, and a3 is the default weight that described accumulation logs in number of days, and a4 is the default weight of accumulation paying number of times, and a5 is the default weight of described accumulation paying number of days; A1 is the game user number that described per day login times is greater than the first corresponding preset value; B1 is the game user number that described accumulation login times is greater than the first corresponding preset value; C1 is the game user number that described accumulation login number of days is greater than the first corresponding preset value; D1 is the game user number that described accumulation paying number of times is greater than the first corresponding preset value; E1 is the game user number that described accumulation paying number of days is greater than the first corresponding preset value.

8. method according to claim 5, it is characterized in that, describedly log in normal users number corresponding to number of days, described accumulation paying number of times and described accumulation paying number of days according to described per day login times, described accumulation login times, described accumulation, determine the normal users number in described game user, comprising:

Employing Q2=a1 × A2+a2 × B2+a3 × C2+a4 × D2+a5 × E2 determines the normal users number in described game user;

Wherein, A2 is the game user number that described per day login times is positioned at corresponding preset range; B2 is the game user number that described accumulation login times is positioned at corresponding preset range; C2 is the game user number that described accumulation logs in preset range corresponding to number of days; D2 is the game user number that described accumulation paying number of times is positioned at corresponding preset range; E2 is the game user number that described accumulation paying number of days is positioned at corresponding preset range.

9. method according to claim 5, it is characterized in that, describedly log in risk subscribers number corresponding to number of days, described accumulation paying number of times and described accumulation paying number of days according to described per day login times, described accumulation login times, described accumulation, determine the risk subscribers number in described game user, comprising:

Employing Q3=a1 × A3+a2 × B3+a3 × C3+a4 × D3+a5 × E3 determines the risk subscribers number in described game user;

Wherein, A3 is the game user number that described per day login times is less than the second corresponding preset value; B3 is the game user number that described accumulation login times is less than the second corresponding preset value; C3 is the game user number that described accumulation login number of days is less than the second corresponding preset value; D3 is the game user number that described accumulation paying number of times is less than the second corresponding preset value; E3 is the game user number that described accumulation paying number of days is less than the second corresponding preset value.

10. method according to claim 2, is characterized in that, the described liveness parameter according to described game user, determines current residing life cycle phase of playing, comprising:

If the ratio of any active ues is more than or equal to the 3rd preset value in described game user, then current for game residing life cycle phase is defined as the introductory phase;

If the ratio of any active ues is less than the 3rd preset value and is more than or equal to the 4th preset value in described game user, then current for game residing life cycle phase is defined as the growth stage;

If the ratio of any active ues is less than the 4th preset value and is more than or equal to the 5th preset value in described game user, then current for game residing life cycle phase is defined as the maturity period;

If the ratio of any active ues is less than the 5th preset value in described game user, then current for game residing life cycle phase is defined as the decline phase.

11. methods according to claim 10, is characterized in that, described according to the current residing life cycle phase of game, the user terminal to respective amount sends game information, comprising:

If the current residing life cycle phase of described game is the introductory phase, then the user terminal to the first quantity sends game information;

If the current residing life cycle phase of described game is the growth stage, then the user terminal to the second quantity sends game information;

If the current residing life cycle phase of described game is the maturity period, then changes game content, extend the active time of any active ues.

12. methods according to claim 1, is characterized in that, describedly carry out cluster according to described user login information to described game user, after determining the liveness parameter of described game user, also comprise:

Obtain the described user login information of similar game in described first preset time period and the user terminal respective amount of the similar game information of reception of described game;

According to described user login information, cluster is carried out to described game user, determine the liveness parameter of the game user of described similar game;

The liveness parameter of the liveness parameter of the game user of described game to the game user of described similar game is compared, obtains comparative result;

According to the liveness parameter of described game user, determine current residing life cycle phase of playing, comprising:

According to user terminal respective amount and the current residing life cycle phase of described game of described comparative result, the similar game information of described reception, the user terminal to respective amount sends game information.

The prediction unit of 13. 1 kinds of life cycles of playing, is characterized in that, comprising:

User profile acquisition module, is configured to the user login information of game user in acquisition first preset time period;

User's liveness determination module, is configured to carry out cluster according to described user login information to described game user, determines the liveness parameter of described game user;

Life cycle phase determination module, is configured to the liveness parameter according to described game user, determines current residing life cycle phase of playing;

Sending module, be configured to according to the current residing life cycle phase of game, the user terminal to respective amount sends game information.

14. devices according to claim 13, is characterized in that, described game user comprises: any active ues, normal users and risk subscribers, and described liveness parameter comprises: described any active ues accounts for the ratio of total game user.

15. devices according to claim 14, is characterized in that, described user's liveness determination module comprises: number of users determining unit and user's liveness determining unit;

Described number of users determining unit, is configured to according to described user login information, determines the active users in described game user, normal users number and risk subscribers number;

Described user's liveness determining unit, is configured to, according to the active users in described game user, normal users number and risk subscribers number, determine that described any active ues accounts for the ratio of total game user.

16. devices according to claim 15, is characterized in that, described user login information comprises: per day login times, accumulation login times, accumulation log in number of days, accumulation paying number of times and accumulation paying number of days.

17. devices according to claim 16, is characterized in that, described number of users determining unit comprises: first user number determination subelement and the second number of users determination subelement;

Described first user number determination subelement, be configured to log in number of days, described accumulation paying number of times and described accumulation paying number of days according to described per day login times, described accumulation login times, described accumulation respectively, determine described per day login times, described accumulation login times, active users, normal users number and risk subscribers number that described accumulation login number of days, described accumulation paying number of times and described accumulation paying number of days is corresponding;

Described second number of users determination subelement, be configured to log in active users corresponding to number of days, described accumulation paying number of times and described accumulation paying number of days according to described per day login times, described accumulation login times, described accumulation, determine the active users in described game user;

Described second number of users determination subelement, also be configured to log in normal users number corresponding to number of days, described accumulation paying number of times and described accumulation paying number of days according to described per day login times, described accumulation login times, described accumulation, determine the normal users number in described game user;

Described second number of users determination subelement, also be configured to log in risk subscribers number corresponding to number of days, described accumulation paying number of times and described accumulation paying number of days according to described per day login times, described accumulation login times, described accumulation, determine the risk subscribers number in described game user.

18. devices according to claim 17, is characterized in that, described first user number determination subelement is configured to,

When the per day login times that described game user is corresponding is greater than the first corresponding preset value, described game user is defined as any active ues corresponding to described per day login times;

When the per day login times that described game user is corresponding is positioned at corresponding preset range, described game user is defined as normal users corresponding to described per day login times;

When the per day login times that described game user is corresponding is less than the second corresponding preset value, described game user is defined as risk subscribers corresponding to described per day login times.

19. devices according to claim 17, is characterized in that, described second number of users determination subelement, are configured to the active users adopting Q1=a1 × A1+a2 × B1+a3 × C1+a4 × D1+a5 × E1 to determine in described game user;

Wherein, a1 is the default weight of described per day login times, and a2 is the default weight of described accumulation login times, and a3 is the default weight that described accumulation logs in number of days, and a4 is the default weight of accumulation paying number of times, and a5 is the default weight of described accumulation paying number of days; A1 is the game user number that described per day login times is greater than the first corresponding preset value; B1 is the game user number that described accumulation login times is greater than the first corresponding preset value; C1 is the game user number that described accumulation login number of days is greater than the first corresponding preset value; D1 is the game user number that described accumulation paying number of times is greater than the first corresponding preset value; E1 is the game user number that described accumulation paying number of days is greater than the first corresponding preset value.

20. devices according to claim 17, is characterized in that, described second number of users determination subelement, are configured to the normal users number adopting Q2=a1 × A2+a2 × B2+a3 × C2+a4 × D2+a5 × E2 to determine in described game user;

Wherein, A2 is the game user number that described per day login times is positioned at corresponding preset range; B2 is the game user number that described accumulation login times is positioned at corresponding preset range; C2 is the game user number that described accumulation logs in preset range corresponding to number of days; D2 is the game user number that described accumulation paying number of times is positioned at corresponding preset range; E2 is the game user number that described accumulation paying number of days is positioned at corresponding preset range.

21. devices according to claim 17, is characterized in that, described second number of users determination subelement, are configured to the risk subscribers number adopting Q3=a1 × A3+a2 × B3+a3 × C3+a4 × D3+a5 × E3 to determine in described game user;

Wherein, A3 is the game user number that described per day login times is less than the second corresponding preset value; B3 is the game user number that described accumulation login times is less than the second corresponding preset value; C3 is the game user number that described accumulation login number of days is less than the second corresponding preset value; D3 is the game user number that described accumulation paying number of times is less than the second corresponding preset value; E3 is the game user number that described accumulation paying number of days is less than the second corresponding preset value.

22. devices according to claim 14, is characterized in that, described life cycle phase determination module is configured to,

When the ratio of any active ues is more than or equal to the 3rd preset value in described game user, current for game residing life cycle phase is defined as the introductory phase;

In described game user, the ratio of any active ues is less than the 3rd preset value when being more than or equal to the 4th preset value, and current for game residing life cycle phase is defined as the growth stage;

In described game user, the ratio of any active ues is less than the 4th preset value when being more than or equal to the 5th preset value, and current for game residing life cycle phase is defined as the maturity period;

When the ratio of any active ues is less than the 5th preset value in described game user, current for game residing life cycle phase is defined as the decline phase.

23. devices according to claim 22, is characterized in that, described sending module is configured to,

When the current residing life cycle phase of described game is the introductory phase, the user terminal to the first quantity sends game information;

When the current residing life cycle phase of described game is the growth stage, the user terminal to the second quantity sends game information;

When the current residing life cycle phase of described game is the maturity period, change game content, extends the active time of any active ues.

24. devices according to claim 13, is characterized in that, described device also comprises: comparison module;

Described user's liveness determination module carries out cluster according to described user login information to described game user, after determining the liveness parameter of described game user, described user profile acquisition module is also configured to, and obtains the described user login information of similar game in described first preset time period and the user terminal respective amount of the similar game information of reception of described game;

Described user's liveness determination module is also configured to, and carries out cluster according to described user login information to described game user, determines the liveness parameter of the game user of described similar game;

Described comparison module, is configured to the liveness parameter of the liveness parameter of the game user of described game to the game user of described similar game to compare, obtains comparative result;

Described life cycle phase determination module is configured to, and according to user terminal respective amount and the current residing life cycle phase of described game of described comparative result, the similar game information of described reception, the user terminal to respective amount sends game information.

25. 1 kinds of gaming operators servers, is characterized in that, comprising:

Processing components;

For storing the memory of the executable instruction of described processor;

Wherein, described processing components is configured to:

Obtain the user login information of game user in the first preset time period;

According to described user login information, cluster is carried out to described game user, determine the liveness parameter of described game user;

According to the liveness parameter of described game user, determine current residing life cycle phase of playing;

According to the current residing life cycle phase of game, send corresponding game information to user terminal.